Reviewerly/exHarmony huggingface dataset:
Spherical data clustering and feature selection through nonparametric Bayesian mixture models with von Mises distributions

In this work, we tackle the problem of clustering spherical (i.e. L2 normalized) data vectors using nonparametric Bayesian mixture models with von Mises distributions. Our model is formulated by employing a nonparametric Bayesian framework known as the Pitman–Yor process mixture model. Different from finite mixture models in which the determination of the number of clusters is a crucial problem and often requires extra effort (e.g. by inspecting information criteria), the proposed model is nonparametric such that the number of clusters in the model is assumed to be infinite at the initial stage and will be inferred automatically based on the data. Moreover, an unsupervised feature selection scheme is incorporated into the proposed model to remove features that do not contribute significantly to the clustering process. We develop a stochastic variational inference algorithm to estimate model parameters, model complexity and feature saliencies simultaneously and effectively through the method of stochastic gradient ascent. We demonstrate the merits of the proposed nonparametric Bayesian mixture model on clustering spherical data vectors by conducting experiments on both synthetic datasets and two real-world applications namely topic novelty detection and flower images categorization.
A Modified Grey Wolf Optimizer for Optimum Parameters of Multilayer Type-2 Asymmetric Fuzzy Controller

This study presents a modified algorithm of the grey wolf optimizer to solve the problem of learning rate selection in the multilayer type-2 asymmetric fuzzy controller (MT2AFC). The improvements of our modified optimizer are: the best position of the swarm is memorized, thus making the alpha wolves only update when a better position appears in the next iteration; search performance is enhanced by giving more freedom to update the grey wolf position. The proposed optimizer algorithm is then applied to optimize the suitable learning rates for the proposed controller. The multilayer type-2 asymmetric membership function is used in the fuzzy control network to enhance the learning ability and flexibility of the designed network architecture. The gradient descent method is used to adjust the parameters of the proposed MT2AFC controller online. The stability of the system is guaranteed using the Lyapunov approach. Besides, the self-evolving algorithm is used to construct the network structure autonomously. Ultimately, the numerical simulations of the chaotic synchronization systems are carried out to verify the effectiveness of our proposed method.
RGB-D-Based Framework to Acquire, Visualize and Measure the Human Body for Dietetic Treatments

This research aims to improve dietetic-nutritional treatment using state-of-the-art RGB-D sensors and virtual reality (VR) technology. Recent studies show that adherence to treatment can be improved using multimedia technologies. However, there are few studies using 3D data and VR technologies for this purpose. On the other hand, obtaining 3D measurements of the human body and analyzing them over time (4D) in patients undergoing dietary treatment is a challenging field. The main contribution of the work is to provide a framework to study the effect of 4D body model visualization on adherence to obesity treatment. The system can obtain a complete 3D model of a body using low-cost technology, allowing future straightforward transference with sufficient accuracy and realistic visualization, enabling the analysis of the evolution (4D) of the shape during the treatment of obesity. The 3D body models will be used for studying the effect of visualization on adherence to obesity treatment using 2D and VR devices. Moreover, we will use the acquired 3D models to obtain measurements of the body. An analysis of the accuracy of the proposed methods for obtaining measurements with both synthetic and real objects has been carried out.
Annotador: a temporal tagger for Spanish

Temporal information is crucial in knowledge extraction. Being able to locate events in a timeline is necessary to understand the narrative behind every text. To this aim, several temporal taggers have been proposed in literature –nevertheless, not all languages received the same attention. Most taggers work only for English texts, and not many have been developed for other languages. Also the scarcity of annotated corpora in other languages notably hinders the task. In this paper we present a new rule-based tagger called Annotador (Añotador in Spanish) able to process texts both in Spanish and English. Furthermore, a new corpus with more than 300 short texts containing common temporal expressions, called the HourGlass corpus, has been built in order to test it and to facilitate the development of new resources and tools. Professionals from different domains intervened in the gathering of the text, making it heterogeneous and easy to use thanks to the tags added to each entry. Finally, we analyzed main challenges in the time expression extraction task.
Qubit-environment-entanglement generation and the spin echo

We analyze the relationship between qubit-environment entanglement that can be created during the pure dephasing of the qubit and the effectiveness of the spin-echo protocol. We focus here on mixed states of the environment. We show that whereas the echo protocol can obviously counteract classical environmental noise, it can also undo dephasing associated with qubit-environment entanglement, and there is no obvious difference in its efficiency in these two cases. Additionally, we show that qubit-environment entanglement can be generated at the end of the echo protocol even when it is absent at the time of application of the local operation on the qubit (the $\ensuremath{\pi}$ pulse). We prove that this can occur only at isolated points in time, after fine-tuning of the echo protocol duration. Finally, we discuss the conditions under which the observation of specific features of the echo signal can serve as a witness of the entangling nature of the joint qubit-environment evolution.
Fractional revival and association schemes

Fractional revival occurs between two vertices in a graph if a continuous-time quantum walk unitarily maps the characteristic vector of one vertex to a superposition of the characteristic vectors of the two vertices. This phenomenon is relevant in quantum information in particular for entanglement generation in spin networks. We study fractional revival in graphs whose adjacency matrices belong to the Bose–Mesner algebra of association schemes. A specific focus is a characterization of balanced fractional revival (which corresponds to maximal entanglement) in graphs that belong to the Hamming scheme. Our proofs exploit the intimate connections between algebraic combinatorics and orthogonal polynomials.
A General Multi-method Approach to Design-Loop Adaptivity in Intelligent Tutoring Systems

Design-loop adaptivity, which involves data-driven redesign of an instructional system based on student learning data, has shown promise in improving student learning. We present a general, systematic approach that combines new and existing data mining and instructional design methods to redesign intelligent tutors. Our approach is driven by the main goal of identifying knowledge components that are demonstrably difficult for students to learn and to optimize effective and efficient practice of them. We applied this approach to redesigning an algebraic symbolization tutor. Our classroom study with 76 high school freshmen shows that, compared to the original tutor, the redesigned tutor led to higher learning efficiency on more difficult skills, higher learning gain on unscaffolded whole tasks, and more robust transfer to less practiced tasks. Our work provides general guidance for performing design-loop adaptations for continuous improvement of intelligent tutors.
Between Collections and Connections: Analyzing Public Library Programs in Terms of Format, Content, and Role and Function

In recent years, the programs offered by Danish public libraries have increased in both number and variety, leading to difficulties defining and assessing such programs in a way that encompasses their complexity. To enrich future research and assessments of public library programs, this article presents an empirically based and textually grounded analytical model. Building on prior work collecting more than 2,500 Facebook events announcing public library programs, this article presents the analysis of these textual data. Inspired by grounded theory coding, the content of the Facebook events is categorized in terms of format, content, and role and function. As a result, an analytical model is developed. In light of the analysis, it is argued that public library programs exist between the library collections and the connections they foster. As such, they are an intriguing part of understanding the role and function of public libraries today.
Research Journeys

We argue for a new way of using pictorial publications to communicate the social, cultural, and material contexts in which "in the wild" research is carried out. Such research often allows for partial researcher perspectives, as the researchers travel to, encounter, and leave those places. However, in HCI research, the journeys and interactions in and around those places are rarely reported directly in archival papers. We argue that those journeys and interactions directly inform how we make sense of the project, and thus should be recorded and shared appropriately. We argue that pictorials can be a format that breaks the boundary between "supplementary materials" and archival publications, and allows us to do that sharing function. We illustrate this argument through reporting of our Research Journey to a number of islands off the west coast of Ireland as part of a project that is developing technology to support rural community radio.
Performance evaluation of enterprises’ innovation capacity based on fuzzy system model and convolutional neural network

Influenced by national policies and macro-economic environment, large domestic enterprises is actively promoting strategic transformation to enhance their core competitiveness, and performance evaluation of enterprises’ innovation capacity has become a hot topic in recent years. This paper proposes a performance evaluation method of enterprises’ innovation capacity based on deep learning fuzzy system model and convolutional neural network analysis of innovation network. First of all, on account of the characteristics of breakthrough innovation and drawing on the traditional innovation performance evaluation model, this paper constructs a breakthrough innovation performance evaluation index system for enterprises from the six dimensions of main resource input, technology out-turn, process management, product performance, social value and commercial Value. Secondly, the introduction of machine learning of fuzzy convolutional neural network to assess the advancement execution of enterprises is of great significance for enterprise managers to find out the problems and causes of enterprises’ innovation, optimize the allocation of enterprises’ resources and further improve the innovation performance of enterprises. The experimental results show to verify the adequacy of the algorithm.
Using Trajectory Features for Tai Chi Action Recognition

Action recognition plays an important role in many areas. In this paper, we propose a Tai Chi action recognition algorithm, using node trajectory features. We use wearable device based on inertial sensors to collect data about Tai Chi player’s actions. The desired node trajectories can be obtained by solving the data. In order to make full use of the information hidden in the trajectories, we segment the trajectories that have been preprocessed, which includes smoothing and rotating. Finally, we propose two methods for action recognition. The first method uses only one trajectory of a node. Applying the mixed Gaussian model and Fisher Vector to cluster the segmented trajectory, and then using SVM as a classifier for identification. The second method uses the trajectories of multiple nodes. DTW algorithm is utilized to calculate the minimum distance between two trajectories. Then the category of action is determined according to the similarity of trajectories. The experimental results show that both methods can classify Tai Chi actions effectively and obtain a high accuracy.
Diversifying with Cryptocurrencies during COVID-19

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Energy Efficient, Resource-Aware, Prediction Based VM Provisioning Approach for Cloud Environment

Over the past few decades, computing environments have progressed from a single-user milieu to highly parallel supercomputing environments, network of workstations (NoWs) and distributed systems, to more recently popular systems like grids and clouds. Due to its great advantage of providing large computational capacity at low costs, cloud infrastructures can be employed as a very effective tool, but due to its dynamic nature and heterogeneity, cloud resources consuming enormous amount of electrical power and energy consumption control becomes a major issue in cloud datacenters. This article proposes a comprehensive prediction-based virtual machine management approach that aims to reduce energy consumption by reducing active physical servers in cloud data centers. The proposed model focuses on three key aspects of resource management namely, prediction-based delay provisioning; prediction-based migration, and resource-aware live migration. The comprehensive model minimizes energy consumption without violating the service level agreement and provides the required quality of service. The experiments to validate the efficacy of the proposed model are carried out on a simulated environment, with varying server and user applications and parameter sizes.
Analysis of Decision Support Systems on Recommended Sales of the Best Ornamental Plants by Type

Abstract The research objective is to recommend the sale of the best ornamental plant species to lovers of ornamental plants by using decision support system techniques. Ornamental plants are a type of ornamental plants that can survive in various conditions. In this case ornamental plants are also very vulnerable to disease such as root rot, shoot rot and others. Many types of ornamental plant sellers do not know how to handle ornamental plants that have diseases or symptoms when they occur. The study was conducted in Simalungun District with a sample of thirteen traders selling ornamental plants using observation and questionnaire distribution techniques. Selection of ornamental plants based on the types in this study include: Orchid Plants (A1), Tulips (A2), Bougenvil (A3) and Jasmine (A4). The assessment criteria used include: Plant height (C1), Plant quality (C2), Plant type (C3) and Plant age (C4). The method used in this research is Simple Additive Weighting. The results showed that Orchid Plant (A1) was the best alternative for selling ornamental plant species (V1: 0.91) and Jasmine (A4) as a second alternative (V2: 0.8).
"You are asking me to pay for my legs"

Smart technologies have recently come under scrutiny for automating inequality. Given the current push towards developing and implementing smart cities policies that affect transportation systems in places like Kampala and Kigali, it is important to examine how the different modes of transportation meet the needs of diverse passengers and identify opportunities for technology to address any inequities. Prior studies have focused on the impact of informal public transportation on government policy and examined drivers' perspectives, but they largely overlooked the experiences of passengers and other industry stakeholders. In this study, we conducted interviews and surveys with public transportation riders with different disabilities as well as other stakeholders, including transport and financial technology creators. Our findings illuminate inequities in the transportation system surrounding discrimination and harassment, influence of ability on preferred transportation modes despite inaccessible interfaces, and influence of perceived social hierarchical structures on innovation. We present insights into how passengers appropriate technology to overcome challenges, and we uncover opportunities for technology to fill additional gaps. Lastly, we discuss how these findings support emergent frameworks such as aspiration-based design, and we present potential envisioned futures of technology for informal public transportation.
VOLUNTARY IMAGINATION: A FINE-GRAINED ANALYSIS

Abstract We study imagination as reality-oriented mental simulation (ROMS): the activity of simulating nonactual scenarios in one’s mind, to investigate what would happen if they were realized. Three connected questions concerning ROMS are: What is the logic, if there is one, of such an activity? How can we gain new knowledge via it? What is voluntary in it and what is not? We address them by building a list of core features of imagination as ROMS, drawing on research in cognitive psychology and the philosophy of mind. We then provide a logic of imagination as ROMS which models such features, combining techniques from epistemic logic, action logic, and subject matter semantics. Our logic comprises a modal propositional language with non-monotonic imagination operators, a formal semantics, and an axiomatization.
Wishart and random density matrices: Analytical results for the mean-square Hilbert-Schmidt distance

Hilbert-Schmidt distance is one of the prominent distance measures in quantum information theory which finds applications in diverse problems, such as construction of entanglement witnesses, quantum algorithms in machine learning, and quantum state tomography. In this work, we calculate exact and compact results for the mean square Hilbert-Schmidt distance between a random density matrix and a fixed density matrix, and also between two random density matrices. In the course of derivation, we also obtain corresponding exact results for the distance between a Wishart matrix and a fixed Hermitian matrix, and two Wishart matrices. We verify all our analytical results using Monte Carlo simulations. Finally, we apply our results to investigate the Hilbert-Schmidt distance between reduced density matrices generated using coupled kicked tops.
Somatic Sonification in Dance Performances. From the Artistic to the Perceptual and Back

Since the end of the 1980s, interactive musical systems have played an increasingly relevant role in dance performances. More recently, the use of interactive auditory feedback for sensorimotor learning such as movement sonification has gained currency and scientific attention in a variety of fields ranging from rehabilitation to sport training, neuroscience and product design. This paper investigates the convergence between interactive music/dance systems and movement sonification in the field of dance. The main question we address is whether the emergence of the notion of sonification can foster new perspectives for practice-based artistic research. In this context, we highlight a fundamental shift of perspective from musical interactivity per se to the somatic knowledge provided by the real time sonification of movement, which can be considered as a major somatic-sonification turn.
Do We Need a Coronavirus (Safeguards) Act 2020? Proposed Legal Safeguards for Digital Contact Tracing and Other Apps in the COVID-19 Crisis

The focus of this Preview is "The Coronavirus (Safeguards) Bill 2020: Proposed protections for digital interventions and in relation to immunity certificates," published by Lilian Edwards, professor of law, innovation and society at Newcastle Law School and a member of the NHSX Ethical Advisory Board (CV19 App).
Towards an Analysis of Network Partitioning Prevention for Distributed Ledgers and Blockchains

Distributed Ledgers and Blockchains involve decentralized networking technologies such as peer-to-peer networks. Both permissioned and unpermissioned blockchains are designed to tolerate and to overcome the failure of individual nodes, which need to update themselves once they reconnect to the blockchain network. At the same time, it is important to avoid accidental network partitioning, since partitioning can lead to inconsistent "multiple truths" and violate the shared consensus about the global state, which must be maintained across all active and re-emerging nodes. In general, reconciling inconsistent "multiple truths" would require the deletion of information which was persisted to the blockchain (in one of the network partitions), and such removal violates the core blockchain principle of audit-proof and tamper-proof "write once read many" access (WORM). However, current mainstream implementations such as Ethereum do not protect against network partitioning (either by accident or caused by an attack), and there is no research on how the consensus implementations behave in the presence of network partitioning. Given the differences in design and implementation across ledgers and blockchains, a systematic analytic approach must be established before partitioning prevention in specific products is studied. The contribution of this paper is a foundational analysis of enterprise-grade consensus protocols, including design recommendations for partitioning avoidance for Proof-of-Authority in Hyperledger Fabric. We also survey related work and lay out next research steps.
Deterministic Sparse Suffix Sorting in the Restore Model

Given a text T of length n , we propose a deterministic online algorithm computing the sparse suffix array and the sparse longest common prefix array of T in O( c √ lg n + m lg m lg n lg * n ) time with O( m ) words of space under the premise that the space of T is rewritable, where m ≤ n is the number of suffixes to be sorted (provided online and arbitrarily), and c is the number of characters with m ≤ c ≤ n that must be compared for distinguishing the designated suffixes.
Underwater 3D Doppler-Angle Target Tracking with Signal Time Delay

The traditional target tracking is a process of estimating the state of a moving target using measurement information obtained by sensors. However, underwater passive acoustic target tracking will confront further challenges, among which the system incomplete observability and time delay caused by the signal propagation create a great impact on tracking performance. Passive acoustic sensors cannot accurately obtain the target range information. The introduction of Doppler frequency measurement can improve the system observability performance; signal time delay cannot be ignored in underwater environments. It varies with time, which has a continuous negative impact on the tracking accuracy. In this paper, the Gauss-Helmert model is introduced to solve this problem by expanding the unknown signal emission time as an unknown variable to the state. This model allows the existence of the previous state and current state at the same time, while handling the implicit equations. To improve the algorithm accuracy, this paper further takes advantage of the estimated state and covariance for the second stage iteration and propose the Gauss-Helmert iterated Unscented Kalman filter under a three-dimensional environment. The simulation shows that the proposed method in this paper shows superior estimation accuracy and more stable performance compared with other filtering algorithms in underwater environments.