Reviewerly/exHarmony huggingface dataset:
Quantifying superpositions of quantum evolutions

Quantum mechanics allows coherent superposition of different states of matter. This quality is responsible for major nonclassical phenomena that occur in quantum systems. Beyond states, coherent superpositions are also possible among quantum evolutions. We characterize such superpositions here. A resource theoretic framework is developed to quantify superposition present in an arbitrary quantum evolution. In addition to characterization, the framework considers superposition as a quantum resource. This resource can be exploited to perform certain quantum tasks that are otherwise impossible. We identify maximally resourceful evolutions and demonstrate how these could enable one to implement arbitrary quantum operations and superoperations. We also discuss the roles of superposition to exhibit nonclassical behaviors present in evolutions; for example, acausality, temporal Bell correlations, and indefinite temporal and causal orders.
The Effect of Augmented Reality on Spatial Visualization Ability of Elementary School Student

<p class='IJASEITAbtract'>The invention of Augmented Reality (AR) has provided a platform for individuals to see virtual and real objects simultaneously in a real-life setting. As compared to other technologies being implemented in education setting, it may contribute a new method to educate. This study investigated the possible effect of AR technology on students’ spatial visualization ability. AR-Science Magic Book Learning System (AR-SMB) is a learning tool based on the AR technology which was developed in this study to facilitate students in learning science concept, hence enhancing their spatial visualization ability. The type of data collection methodology used is the quantitative approach in which pre-test and post-test design were implemented and the participant comprised of 34 fifth-grade students. According to the results, it was found that the scores of the Mental Rotation Test (MRT) post-test were significantly greater than the pre-test, thus AR technology can be considered to be beneficial in enhancing students’ spatial visualization ability. There is a huge difference in terms of the scores of MRT between the pre-test and post-test which concludes that the treatment given can significantly improve and contribute towards their spatial visualization ability. The findings of this research enable the educators to incorporate AR learning into their teaching process in order for students to understand the rotation and transformation of objects that are related to each other. As a result, this study has shown a significant implication for the understanding of AR’s effect in enhancing spatial visualization ability among primary school students.
What Are UX and UX Design?

In this chapter, we start with how desktops, graphical user interfaces, and the web are still here and growing. We describe how UX applies to all kinds of interaction, including localized, activity-based, and system-spanning interaction. We talk about the traditional concept of usability, and the evolution of the overall user experience to include usefulness, emotional impact, and meaningfulness. We discuss emotional impact, which includes joy-of-use, aesthetics, and fun. We explain how user experience is felt internally by the user; one cannot design user experience, but can design for a good user experience.
How quantum is the speedup in adiabatic unstructured search?

In classical computing, analog approaches have sometimes appeared to be more powerful than they really are. This occurs when resources, particularly precision, are not appropriately taken into account. While the same should also hold for analog quantum computing, precision issues are often neglected from the analysis. In this work, we present a classical analog algorithm for unstructured search that can be viewed as analogous to the quantum adiabatic unstructured search algorithm devised by Roland and Cerf (Phys Rev A 65:042308, 2002). We show that similarly to its quantum counterpart, the classical construction may also provide a quadratic speedup over standard digital unstructured search. We discuss the meaning and the possible implications of this result in the context of adiabatic quantum computing.
Optical detection and storage of entanglement in plasmonically coupled quantum-dot qubits

Recent proposals and advances in quantum simulations, quantum cryptography, and quantum communications substantially rely on quantum entanglement formation. Contrary to the conventional wisdom that dissipation destroys quantum coherence, coupling with a dissipative environment can also generate entanglement. We consider a system composed of two quantum-dot qubits coupled with a common, damped surface plasmon mode; each quantum dot is also coupled to a separate photonic cavity mode. Cavity quantum electrodynamics calculations show that upon optical excitation by a femtosecond laser pulse, entanglement of the quantum-dot excitons occurs, and the time evolution of the ${g}^{(2)}$ pair correlation function of the cavity photons is an indicator of the entanglement. We also show that the degree of entanglement is conserved during the time evolution of the system. Furthermore, if coupling of the photonic cavity and quantum-dot modes is large enough, the quantum-dot entanglement can be transferred to the cavity modes to increase the overall entanglement lifetime. This latter phenomenon can be viewed as a signature of entangled, long-lived quantum-dot exciton-polariton formation. The preservation of total entanglement in the strong-coupling limit of the cavity--quantum-dot interactions suggests a novel means of entanglement storage and manipulation in high-quality optical cavities.
Model-Free IRL Using Maximum Likelihood Estimation

The problem of learning an expert’s unknown reward function using a limited number of demonstrations recorded from the expert’s behavior is investigated in the area of inverse reinforcement learning (IRL). To gain traction in this challenging and underconstrained problem, IRL methods predominantly represent the reward function of the expert as a linear combination of known features. Most of the existing IRL algorithms either assume the availability of a transition function or provide a complex and inefficient approach to learn it. In this paper, we present a model-free approach to IRL, which casts IRL in the maximum likelihood framework. We present modifications of the model-free Q-learning that replace its maximization to allow computing the gradient of the Q-function. We use gradient ascent to update the feature weights to maximize the likelihood of expert’s trajectories. We demonstrate on two problem domains that our approach improves the likelihood compared to previous methods.
Human-in-the-Loop Feature Selection

Feature selection is a crucial step in the conception of Machine Learning models, which is often performed via datadriven approaches that overlook the possibility of tapping into the human decision-making of the model’s designers and users. We present a human-in-the-loop framework that interacts with domain experts by collecting their feedback regarding the variables (of few samples) they evaluate as the most relevant for the task at hand. Such information can be modeled via Reinforcement Learning to derive a per-example feature selection method that tries to minimize the model’s loss function by focusing on the most pertinent variables from a human perspective. We report results on a proof-of-concept image classification dataset and on a real-world risk classification task in which the model successfully incorporated feedback from experts to improve its accuracy.
A novel automatic device to measure deformation inside transparent soil based on digital image correlation technology

In geotechnical engineering, the measurement of soil deformation is one of the most important foundations of soil mechanics research. Transparent soil is a desired choice to obtain information on spatial deformation inside the soil. Based on this, a novel measuring device was developed to automatically record laser speckle images at different laser cross-sections, and the displacement deformation of transparent soil at arbitrary cross-section positions was determined by the planar digital image correlation (DIC) technique. The measuring system was built with two high-precision motorized linear stages. An industrial camera was used in the system to capture the laser speckle images, and synchronization control methods of both the laser transmitter and industrial camera were introduced. The planar DIC technique employed in this paper has been described in detail. The high-precision motorized linear stages proved to have excellent repeatability and the displacement field of a certain laser cross-section in transparent soil can be obtained. Theoretically, the spatial deformation inside the transparent soil can be built based on the displacement fields of multiple laser cross-sections. Finally, the design scheme was confirmed by the model pile experiment. The experiment results show that the automatic measuring system has stable operation, and the deformation of arbitrary laser cross-sections inside transparent soil can be obtained easily. This automatic measurement method can be regarded as an innovation in geotechnical physical model experiments.
Side Scan Sonar Image Super Resolution via Region-Selective Sparse Coding

Side scan sonar using low frequency can quickly search a wide range, but the images acquired are of low quality. The image super resolution (SR) method can mitigate this problem. The SR method typically uses sparse coding, but accurately estimating sparse coefficients incurs substantial computational costs. To reduce processing time, we propose a region-selective sparse coding based SR system that emphasizes object regions. In particular, the region that contains interesting objects is detected for side scan sonar based underwater images so that the subsequent sparse coding based SR process can be selectively applied. Effectiveness of the proposed method is verified by the reduced processing time required for image reconstruction yet preserving the same level of visual quality as conventional methods.
Acoustic Scene Identification for Audio Authentication

This paper discusses acoustic scene identification which is a part of audio authentication. The work focuses on extracting different acoustical parameters embedded in the recording that can aid in identifying the claimed environment in a legal investigation. Four different feature sets have been used. Out of these, two feature sets have been extracted from the original speech recording and the other two are based on the reverberation component embedded in the recording. Also, the methodology has been tested on two different classifiers. The methodology has been tested on synthetically created speech recordings in seven different environments. The paper gives a comparative study of classification accuracy obtained with different feature sets and different classifiers.
Brain Image Segmentation Based on Multi-Weight Probability Map

Due to the complexity of the brain image itself, the brain image segmentation technology has become a bottle for further application and development of the system. Considering the inconsistency of intensity, partial volume effect, and noise in medical images, this paper studies the brain image segmentation technology based on the multi-weight probability. The multi-weight probability method mainly models the data set with outliers and non-Gaussian noise. First, the probabilistic local ELM model is established. Based on this, the Parzen window method is used to establish the probability distribution of the local model, and then, the probability distribution is used as the weight to fuse. All local models are used to build a global robustness model. The method successfully applied the brain and UCI examples and compared with traditional ELM, regularized ELM, and robust ELM. The results show that the probability weight ELM shows better modeling performance.
An Argument Against Cross-Platform Development: Lessons From an Augmented Reality App Prototype for Rural Emergency Responders

Mobile augmented reality (MAR) apps offer potential support for emergency responders in rural areas.In this report, we described lessons learned from the development process of augmented reality (AR) Farm Mapping to Assist, Protect and Prepare Emergency Responders (MAPPER), a MAR app that provides emergency responders onsite information about the agricultural operation they enter.Cross-platform frameworks were used to create AR MAPPER to accommodate budget constraints and overcome issues with markerless MAR technologies. Although the single codebase and Web technologies streamlined development, cross-device hardware limitations impacted location accuracy, lengthened the development cycle, and required regular updates to third-party libraries.A hybrid development approach of using Web-based technologies with native tie-ins for specialized components and enhanced performance cut time and costs. This also led to consistency across multiple platforms and ensured that there is only a single set of source files to modify for Android and iPhone operating systems. Meanwhile, active development was delayed by some major hurdles. Apple and Google both released new versions of their operating systems, and the Wikitude framework issued four major updates, each of which brought with it some important enhancements and also led to some new issues.Developers should consider single platform native development to benefit from platform-specific MAR implementations and to avoid development, testing, and maintenance costs associated with cross-platform implementation. Emergency response organizations may be more likely to utilize a single platform across the devices used by their command staff. This also reduces the benefits of cross-platform development. Furthermore, providing map-based, non-AR cross-platform apps for landowners, farmers, and ranchers would help improve and maintain data quality, which is crucial for the utility and user experience of MAR apps.
Large-scale Collaborative Filtering with Product Embeddings

The application of machine learning techniques to large-scale personalized recommendation problems is a challenging task. Such systems must make sense of enormous amounts of implicit feedback in order to understand user preferences across numerous product categories. This paper presents a deep learning based solution to this problem within the collaborative filtering with implicit feedback framework. Our approach combines neural attention mechanisms, which allow for context dependent weighting of past behavioral signals, with representation learning techniques to produce models which obtain extremely high coverage, can easily incorporate new information as it becomes available, and are computationally efficient. Offline experiments demonstrate significant performance improvements when compared to several alternative methods from the literature. Results from an online setting show that the approach compares favorably with current production techniques used to produce personalized product recommendations.
Multi-modal Ensemble Classification for Generalized Zero Shot Learning

Generalized zero shot learning (GZSL) is defined by a training process containing a set of visual samples from seen classes and a set of semantic samples from seen and unseen classes, while the testing process consists of the classification of visual samples from seen and unseen classes. Current approaches are based on testing processes that focus on only one of the modalities (visual or semantic), even when the training uses both modalities (mostly for regularizing the training process). This under-utilization of modalities, particularly during testing, can hinder the classification accuracy of the method. In addition, we note a scarce attention to the development of learning methods that explicitly optimize a balanced performance of seen and unseen classes. Such issue is one of the reasons behind the vastly superior classification accuracy of seen classes in GZSL methods. In this paper, we mitigate these issues by proposing a new GZSL method based on multi-modal training and testing processes, where the optimization explicitly promotes a balanced classification accuracy between seen and unseen classes. Furthermore, we explore Bayesian inference for the visual and semantic classifiers, which is another novelty of our work in the GZSL framework. Experiments show that our method holds the state of the art (SOTA) results in terms of harmonic mean (H-mean) classification between seen and unseen classes and area under the seen and unseen curve (AUSUC) on several public GZSL benchmarks.
Daily global solar radiation estimation based on air temperature: case of study south of Algeria

Global solar radiation is needed for the analysis and scaling of solar conversion systems; however, global measurements of solar radiation are not available in all Algerian cities. The use of empirical models using an accessible parameter is a solution to this problem. In this study, seven empirical Models namely Hargreaves and Samani, Chen, M.F. Li, H.Li, Bristow and Campbell, Okonkwo and Abraha Savage have been employed to estimate daily average global solar radiation on the horizontal surface. These models use extreme temperatures (minimum and maximum). They were applied to three south Algerian sites (Biskra, Ghardaia, and Tamanrasset). The analyzed data were provided by the NASA site and cover four years (2001-2004). The validation of the models for predicting daily global solar radiation was done using four statistical parameters (R 2 , MBE, RMSE, and RPE). The results show that Bristow and Compbell Model shows better performance than the other models in all sites. A new model is proposed for each site. The results show that this later is the best one compared with the seven models analyzed. Therefore, the developed model can be suggested to estimate daily global solar radiation using only extreme air temperatures in south Algeria.
Fair Algorithms for Clustering

We study the problem of finding low-cost Fair Clusterings in data where each data point may belong to many protected groups. Our work significantly generalizes the seminal work of Chierichetti et.al. (NIPS 2017) as follows. - We allow the user to specify the parameters that define fair representation. More precisely, these parameters define the maximum over- and minimum under-representation of any group in any cluster. - Our clustering algorithm works on any $\ell_p$-norm objective (e.g. $k$-means, $k$-median, and $k$-center). Indeed, our algorithm transforms any vanilla clustering solution into a fair one incurring only a slight loss in quality. - Our algorithm also allows individuals to lie in multiple protected groups. In other words, we do not need the protected groups to partition the data and we can maintain fairness across different groups simultaneously. Our experiments show that on established data sets, our algorithm performs much better in practice than what our theoretical results suggest.
A cost-aware algorithm for placement of enterprise applications in federated cloud data center

Nowadays, many enterprise applications with a global coverage, huge volume and dense communication are deployed in the cloud. Federated data center (DC), where multiple cloud providers (CPs) share their resources, presents a scalable platform to serve such applications [1]. The main goal CPs is minimizing the operating cost. Energy consumption contributes a major fraction of operating cost; up to 50% [2]. Further, enterprise applications contain a large number of virtual machines (VMs) with huge data exchange up to 500 TB per day [3]. Subsequently, energy and bandwidth must be considered during VM placement to minimize the operating cost.
Text Mining for Evaluating Authors' Birth and Death Years

This article presents a unique method in text and data mining for finding the era, i.e., mining temporal data, in which an anonymous author was living. Finding this era can assist in the examination of a fake document or extracting the time period in which a writer lived. The study and the experiments concern Hebrew, and in some parts, Aramaic and Yiddish rabbinic texts. The rabbinic texts are undated and contain no bibliographic sections, posing an interesting challenge. This work proposes algorithms using key phrases and key words that allow the temporal organization of citations together with linguistic patterns. Based on these key phrases, key words, and the references, we established several types of “Iron-clad,” Heuristic and Greedy rules for estimating the years of birth and death of a writer in an interesting classification task. Experiments were conducted on corpora, including documents authored by 12, 24, and 36 rabbinic writers and demonstrated promising results.
Factual Instance Tweet Summarization and Opinion Analysis of Sport Competition

Spilling information study now factual instance remains fetching the best ever then the majority well-organized method to obtain useful knowledge from what is happening now, letting group to respond rapidly once problematic originate hooked on opinion or to classify newest tendencies portion to recuperate their performance. The problem we try to solve is that cutting-edge absence of existence living cutting-edge obverse of TV usual, shape information dispensation scheme that make informative information concerning contest competitions then relate view of admirers toward competition production. By means of tweet information, we discover sub-events cutting-edge willing and then view of admirers position Twitter associated toward game. We endorse a scheme aimed at factual instance summarization of arranged sub-events aimed at sporting race by means of tweet information. We too suggest a method that examines spirits of persons placement Twitter. We focused on summarizing sporting events, specifically FIFA World Cup 2017 and IPL 2017. For a system using social media like twitter toward retain path of belongings trendy about, we appearance on behalf of next qualities: (I) gratitude of bursty subject by way of rapidly as the situation arises; (II) summarization of linked bursty theme; (III) examining viewpoint of followers then relating view toward ready.
Converness: Ontology‐driven conversational awareness and context understanding in multimodal dialogue systems

Abstract Dialogue‐based systems often consist of several components, such as communication analysis, dialogue management, domain reasoning, and language generation. In this paper, we present Converness, an ontology‐driven, rule‐based framework to facilitate domain reasoning for conversational awareness in multimodal dialogue‐based agents. Converness uses Web Ontology Language 2 (OWL 2) ontologies to capture and combine the conversational modalities of the domain, for example, deictic gestures and spoken utterances, fuelling conversational topic understanding, and interpretation using description logics and rules. At the same time, defeasible rules are used to couple domain and user‐centred knowledge to further assist the interaction with end users, facilitating advanced conflict resolution and personalised context disambiguation. We illustrate the capabilities of the framework through its integration into a multimodal dialogue‐based agent that serves as an intelligent interface between users (elderly, caregivers, and health experts) and an ambient assistive living platform in real home settings.