Reviewerly/exHarmony huggingface dataset:
On the derivation of exact eigenstates of the generalized squeezing operator

Abstract We construct the states that are invariant under the action of the generalized squeezing operator <?CDATA $\exp ({{za}}^{\dagger k}-{z}^{* }{a}^{k})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>exp</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msup> <mml:mrow> <mml:mi mathvariant="italic">za</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>†</mml:mo> <mml:mi>k</mml:mi> </mml:mrow> </mml:msup> <mml:mo>−</mml:mo> <mml:msup> <mml:mrow> <mml:mi>z</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>*</mml:mo> </mml:mrow> </mml:msup> <mml:msup> <mml:mrow> <mml:mi>a</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>k</mml:mi> </mml:mrow> </mml:msup> <mml:mo stretchy="false">)</mml:mo> </mml:math> for arbitrary positive integer k . The states are given explicitly in the number representation. We find that for a given value of k there are k such states. We show that the states behave as n − k /4 when occupation number n → ∞ . This implies that for any k ≥ 3 the states are normalizable. For a given k , the expectation values of operators of the form <?CDATA ${\left({a}^{\dagger }a\right)}^{j}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mfenced close=")" open="("> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>a</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>†</mml:mo> </mml:mrow> </mml:msup> <mml:mi>a</mml:mi> </mml:mrow> </mml:mfenced> </mml:mrow> <mml:mrow> <mml:mi>j</mml:mi> </mml:mrow> </mml:msup> </mml:math> are finite for positive integer j &lt; ( k /2 − 1) but diverge for integer j ≥ ( k /2 − 1). For k = 3 we also give an explicit form of these states in the momentum representation in terms of Bessel functions.
2D-VPC: An Efficient Coverage Algorithm for Multiple Autonomous Vehicles

In this paper, we address a problem of multi-robotic coverage, where an area of interest is covered by multiple sensors, each mounted on an autonomous vehicle such as an aerial or a ground mobile robot. The area of interest is first decomposed into grids of equal size and then partitioned into Voronoi cells. Each robot/sensor is assigned the task of covering the corresponding Voronoi cell. We propose an optimal gridding size and partitioning methodology that eliminate the coverage inefficiencies induced by the partitioning process. We carried out experiments using multiple quadcopters and mobile robots to demonstrate and validate the proposed multi-sensor coverage strategy.
Synthesis of mobile applications using AgileUML

In this paper we describe a method to apply the AgileUML toolset to synthesise mobile apps from UML models. This is a lightweight model-driven engineering (MDE) approach suitable for app developers who need to rapidly produce native apps for either or both Android or iOS platforms.
Comparison Result of Hourly Solar Radiation Under The Clear Sky Condition Based on of Solar Radiation Model and Measured Data Experiment

Indonesia is a country on the equator, and a tropical country with two conditions, rainy and sunny. Indonesia has a long cycle of solar radiation intensity which began from 8:00 until 16:00, the effective time being 5 hours on peak solar time. To know the efficiency power applicable to solar power technology, calculation of the solar radiation intensity is required. The purpose of this paper is to compare the hourly solar radiation under the clear sky condition based on the solar radiation model and measured data experiment. The solar radiation model proposed by ASHRAE has been compared and evaluated with measured data using statistical analysis. The measurement data used as validation data on the estimation of solar radiation based on the solar radiation model suggested by ASHRAE with the used coefficient A is 1000, B is 0.4 and C is 0.136. The results of the statistical analysis show good agreement between measurement and empirical models with the peak of solar radiation is 641 W/m2 on 19 June 2019.
Glimpse: A Gaze-Based Measure of Temporal Salience

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. Glimpse could serve as the basis for several downstream tasks such as segmentation or summarization of videos. Glimpse’s software and data are publicly available.
A Framework for Service Semantic Description Based on Knowledge Graph

To construct a large-scale service knowledge graph is necessary. We propose a method, namely semantic information extension, for service knowledge graphs. We insist on the information of services described by Web Services Description Language (WSDL) and we design the ontology layer of web service knowledge graph and construct the service graph, and using the WSDL document data set, the generated service knowledge graph contains 3738 service entities. In particular, our method can give a full performance to its effect in service discovery. To evaluate our approach, we conducted two sets of experiments to explore the relationship between services and classify services that develop by service descriptions. We constructed two experimental data sets, then designed and trained two different deep neural networks for the two tasks to extract the semantics of the natural language used in the service discovery task. In the prediction task of exploring the relationship between services, the prediction accuracy rate reached 95.1%, and in the service classification experiment, the accuracy rate of TOP5 reached 60.8%. Our experience shows that the service knowledge graph has additional advantages over traditional file storage when managing additional semantic information is effective and the new service representation method is helpful for service discovery and composition tasks.
A decentralized document management system using blockchain and secret sharing

The use of digital documents is exploding around the world due to high productivity and convenience. However, the digital documents have some security issues. For example, when a user who has the permission behaves maliciously for his/her benefit, the digital document can be easily falsified. In this paper, we propose a decentralized document management system (DDMS) to improve security of a digital document. DDMS first distributes access permission to multiple users for higher security of the document. To do this, DDMS encrypts a document via a symmetric key and splits the key via a Shamir's secret sharing scheme. And, DDMS manages each split keys using blockchian and reconstructs the complete symmetric key when the document is retrieved via a devised smart contract. We implement DDMS based on Ethereum blockchain. The experimental results show that the proposed DDMS can provide higher security with a reasonable performance overhead compared with other three types of DMSs.
HardCompress: A Novel Hardware-based Low-power Compression Scheme for DNN Accelerators

The ever-increasing computing requirements of Deep Neural Networks (DNNs) have accentuated the deployment of such networks on hardware accelerators. Inference execution of large DNNs often manifests as an energy bottleneck in such accelerators, especially when used in resource-constrained Internet-of-Things (IoT) edge devices. This can be primarily attributed to the massive energy incurred in accessing millions of trained parameters stored in the on-chip memory, as demonstrated in existing research. To address this challenge, we propose HardCompress, which, to the best of our knowledge, is the first compression solution pertaining to commercial DNN accelerators. The three-step approach involves hardware-based post-quantization trimming of weights, followed by dictionary-based compression of the weights and subsequent decompression by a low-power hardware engine during inference in the accelerator. The efficiency of our proposed approach is evaluated on both lightweight networks trained on MNIST dataset and large DNNs trained on ImageNet dataset. Our results demonstrate that HardCompress, without any loss in accuracy on large DNNs, furnishes a maximum compression of 99.27%, equivalent to 137 * reduction in memory footprint in the systolic array-based DNN accelerator.
On the Robustness of Domain Constraints

Machine learning is vulnerable to adversarial examples-inputs designed to cause models to perform poorly. However, it is unclear if adversarial examples represent realistic inputs in the modeled domains. Diverse domains such as networks and phishing have domain constraints-complex relationships between features that an adversary must satisfy for an attack to be realized (in addition to any adversary-specific goals). In this paper, we explore how domain constraints limit adversarial capabilities and how adversaries can adapt their strategies to create realistic (constraint-compliant) examples. In this, we develop techniques to learn domain constraints from data, and show how the learned constraints can be integrated into the adversarial crafting process. We evaluate the efficacy of our approach in network intrusion and phishing datasets and find: (1) up to 82% of adversarial examples produced by state-of-the-art crafting algorithms violate domain constraints, (2) domain constraints are robust to adversarial examples; enforcing constraints yields an increase in model accuracy by up to 34%. We observe not only that adversaries must alter inputs to satisfy domain constraints, but that these constraints make the generation of valid adversarial examples far more challenging.
Feature Selection and Knapsack Problem Resolution Based on a Discrete Backtracking Optimization Algorithm

Backtracking search optimization algorithm is a recent stochastic-based global search algorithm for solving real-valued numerical optimization problems. In this paper, a binary version of backtracking algorithm is proposed to deal with 0-1 optimization problems such as feature selection and knapsack problems. Feature selection is the process of selecting a subset of relevant features for use in model construction. Irrelevant features can negatively impact model performances. On the other hand, knapsack problem is a well-known optimization problem used to assess discrete algorithms. The objective of this research is to evaluate the discrete version of backtracking algorithm on the two mentioned problems and compare obtained results with other binary optimization algorithms using four usual classifiers: logistic regression, decision tree, random forest, and support vector machine. Empirical study on biological microarray data and experiments on 0-1 knapsack problems show the effectiveness of the binary algorithm and its ability to achieve good quality solutions for both problems.
RegPattern2Vec: Link Prediction in Knowledge Graphs

Link prediction is an important task in many domains, including health sciences, biology, recommender systems, social networks, and many more. It is one of the problems residing within the intersection of knowledge graphs and machine learning. Link prediction aims to discover unknown links between entities in a graph using various techniques. However, due to the size of knowledge graphs today and their complexity, it is a challenging and time-consuming task. In this work, we present RegPattern2Vec, a method to effectively sample a large knowledge graph to learn node embeddings, while capturing the semantic relationships between graph nodes with minimum prior knowledge and human involvement. Our results show that the link prediction using RegPattern2Vec outperforms related graph embedding approaches on large-scale and complex knowledge graphs.
Six Reasons to Consider Using R in Psychological Research

The R Statistical Environment is becoming increasingly accessible to non-statistician researchers. This provides an opportunity for many researchers in psychology to consider adopting R in their work. Herein is presented six reasons for why R should not be overlooked by researchers in psychology: 1) R is free and open-source; 2) R has many packages built for common descriptive and statistical analyses in the psychological sciences; 3) the R community is supportive and have freely provided many useful resources, for beginners and advanced users alike; 4) R is becoming increasingly popular in psychology already, making it easier to collaborate using the software; 5) analyses in R can be highly reproducible with features like R Markdown and knitr, simplifying the process to provide open science; and 6) the R language has improved in diverse ways, making common data wrangling tasks fast, dependable, and highly replicable. These reasons are presented with their corresponding limitations.
A Comparative Study between Simulation of Machine Learning and Extreme Learning Techniques on Breast Cancer Diagnosis

Breast Cancer is a developing and most normal disease among ladies around the globe. Breast malignancy is an uncontrolled and exorbitant development of abnormal cells in the Breast because of hereditary, hormonal, and way of life factors. During the starting stages, the tumor is restricted to the Breast, and in the latter part, it can spread to lymph hubs in the armpit and different organs like the liver, bones, lungs, and cerebrum. At the point when the bosom disease spreads too different pieces of the body, it is going to metastasize. The sickness is repairable in the beginning periods, yet it is identified in later stages, which is the fundamental driver for the passing of such a large number of ladies in this entire world. Clinical tests led in medical clinics for deciding the malady are a lot of costly, just as tedious as well. The answer to counter this is by directing early and exact findings for quicker treatment, and accomplishing such exactness in a limited capacity to focus time demonstrates troublesome with existing techniques. In this paper, we look at changed AI and neural system calculations to foresee malignant growth in beginning times, intending to save the patient's life. Wisconsin Breast Cancer (WBC) data set from the UCI AI vault has been utilized. Various calculations were looked in particular Support Vector Machine Classification (SVM), K-Nearest Neighbor Classification (KNN), Decision tree Classification (DT), Random Forest Classification (RF) and Extreme Learning Machine (ELM) and they thought about based on precision and handling time taken by each. The outcomes show that an extreme learning machine gives the best outcome for both the ideal models.
Visualizing Web Users’ Attention to Text with Selection Heatmaps

Web analytics tools provide useful information about the interaction of users with websites, and particularly, on what captures the attention of web visitors on websites. User attention to areas of web pages can be visualized using heatmaps. Two types of attention indicators are commonly used in web analytics heatmaps: visibility duration of page sections in the browser’s viewport and mouse activity on areas and elements of web pages. This work introduces a new type of user attention heatmap, which visualizes the frequency of text selection operations on websites. Selection is the first step in the process of copying text to the clipboard, but it is also used to highlight important points while reading, similarly to highlighting words on a notebook with a marker pen. As demonstrated and discussed in this paper, selection heatmaps provide interesting perspectives on user attention to paragraphs, sentences, and words on websites, and this could be useful in web analytics.
COVID-19-driven sudden shift to remote teaching: the case of the Languages for the Community Program at the Universidad Nacional del Litoral

The Languages for the Community Program (LCP), one of the units that make up the Language Center at Universidad Nacional del Litoral (UNL), Santa Fe, República Argentina, is an extension service provided to people 17 years of age and older that live in Santa Fe or in neighboring towns, and to UNL students wishing to take language courses other than those offered in their own study programs. Since 1999 when they started, these language courses have been taught in a traditional face-to-face modality. The purpose of this chapter is to describe the way in which the first term in 2020 had to suddenly shift to remote teaching and how this shift was devised, coordinated, delivered, and evaluated in response to the unexpected context provoked by the outbreak of COVID-19. The chapter also reports the changes implemented during the second term according to the results of a satisfaction survey administered to students. Collaborative work, previous expertise, and methodological principles allowed the staff at LCP to offer a quality educational response to this paradigm shift in foreign language teaching and learning.
Self-Learning Mechanism for Mobile Game Adjustment towards a Player

Mobile app markets have faced huge expansion during the last decade. Among different apps, games represent a large portion with a wide range of game categories having consumers in all age groups. To make a mobile game suitable for different age categories, it is necessary to adjust difficulty levels in such a way to keep the game challenging for different players with different playing skills. The mobile app puzzle game Wonderful Animals has been developed consisting of puzzles, find pairs and find differences game (available on the Google Play Store). The game testing was conducted on a group of 40 players by recording game level completion time and conducting a survey of their subjective evaluation of completed level difficulty. The study aimed to find a mechanism to adjust game level difficulty to the individual player taking into account the player’s achievements on previously played games. A pseudo-algorithm for self-learning mechanism is presented, enabling level difficulty adaptation to the player. Furthermore, player classification into three classes using neural networks is suggested in order to offer a user-specific playing environment. The experimental results show that the average recognition rate of the player class was 96.1%.
Construction of composite mode of sports education professional football teaching based on sports video recognition technology

Sports video recognition technology is a set of high-tech products, science and technology and football sports combined with a special equipment system. The main purpose of sports video recognition technology is to assist football teaching. With the help of modern science and technology, the accuracy of football teaching can be improved by replays in slow motion, motion capture, analysis of results and so on. This paper proposes a more characteristics and neural network combining sports video recognition model (MF-RBFNN), extracted respectively reflect the static and dynamic characteristics of sports video, and then the RBF neural network is adopted to static and dynamic characteristics of classification, respectively, The basic probability assignment is constructed for the preliminary identification results, and the identification results are fused by using relevant theories, The results show that it can improve the accuracy of sports video identification. The research results can be applied to physical education and football teaching, and achieved good results.
Sig2Sig: Signal Translation Networks to Take the Remains of the Past

In these days, more and more deep learning models are applied to various tasks in an industrial area. Because sensors are changed rapidly, if we want to use the model then we need to collect new sensor data and train with them again. Moreover, sometimes we need to tune the model for the task and adjust the hyperparameters. In this paper, we propose a signal translation networks, Sig2Sig, that converts from the new sensor signals to old ones in order to reuse the past model, which was trained on plenty of old sensor signals. We only need a small paired-dataset for training Sig2Sig and then translated signals from a new sensor can be classified by the past classification model almost without performance degradation. To do such a robust translation, our model is based on a modified u-net with squeeze and excitation networks, instance normalization, and with attention parts for robust outputs. We also use various loss functions which include uncertainty loss for ignoring noise parts and focusing on important parts of signal images. Our results show that not only translated signal is realistic and similar to target data but also the past classification model can be reused on the new sensor domain with almost similar performance as the old sensor.
An Algebraic Framework for Out-of-Core Hierarchical Segmentation Algorithms

Binary partition hierarchies and minimum spanning trees are key structures for numerous hierarchical analysis methods, as those involved in computer vision and mathematical morphology. In this article, we consider the problem of their computation in an out-of-core manner, i.e., by minimizing the size of the data structures that are simultaneously needed at the different computation steps. Out-of-core algorithms are necessary when the data are too large to fit entirely in the main memory of the computer, which can be the case with very large images in 2-, 3-, or higher dimension space. We propose a new algebraic framework composed of four main operations on hierarchies: edge-addition, select, insert, and join. Based on this framework, we propose and establish the correctness of an out-of-core calculus for binary partition hierarchies and for minimum spanning trees. First applications to image processing suggest the practical efficiency of this calculus.
A Performance Comparison of Supervised Classifiers and Deep-learning Approaches for Predicting Toxicity in Thai Tweets

There are numerous tweeter user accounts in Thailand and many toxic comments are being generated every day on this platform. Sentimental Analysis can be used as a tool to identify toxic comments. In this study, two feature extraction techniques, including Bag of Words (BOW) and Term frequency-inverse document (TF-IDF), were investigated. Additionally, the performance of ten well-known traditional classifiers, along with three deep-learning approaches including Convolutional Neural Network (CNN), Long-short-term memory (LSTM) and pretrained Bidirectional Encoder Representations (BERT), were compared with the public Toxicity Thai tweeter corpus. The experiments reveal that by combining Bag of Words (BOW) with the Extra-Tree classifier, researchers were able to archive the highest F1-score of 0.72, classification accuracy rate of 72.27% and AUC value of 0.77 using the test set in contrast to other classifiers and other deep-learning techniques. Feature importance, correlation and impacts were also investigated through the use of SHapley Additive exPlanations (SHAP) diagram.