Reviewerly/exHarmony huggingface dataset:
The Concealed Weaknesses of Strong Early Warning Systems. The Case of Mexico

Abstract In September 2017, in only 17 days, two enormous earthquakes triggered Mexico’s earthquake early warning system (EEWS) in a unique sequence of events that tested its capabilities. Through a series of unforeseen circumstances, including a test and an accident, during those two and a half weeks, the EEWS was also activated three additional times. The EEWS presents several remarkable strengths. Mexico’s entire emergency management system is relatively well resourced and has helped produce a more resilient culture that appreciates the alert system. Public agencies in all levels of government work in close coordination. However, the system has not been able to overcome continual political expediency and general public distrust of some of its components. The consequence is an alert system that is relatively strong in Mexico City but leaves much of the rest of the country unprotected. But even in Mexico City, the system suffers from extensive concealed vulnerabilities that put the population and the city’s infrastructure at risk. In this paper, I analyze two specific weaknesses. The first is a result of weak regulations and inappropriate business models, which impacts the country as a whole. The second is essentially a local complication resulting from the increased risk generated by the excessive trust of the population in the sirens that are so characteristic of the system. The data were collected during a reconnaissance trip organized by the Earthquake Engineering Research Institute (EERI) in October 2017.
Rational points on complete symmetric hypersurfaces over finite fields

For any affine hypersurface defined by a complete symmetric polynomial in k≥3 variables of degree m over the finite field Fq of q elements, a special case of our theorem says that this hypersurface has at least 6qk−3 rational points over Fq if 1≤m≤q−3 and q is odd. A key ingredient in our proof is Segre’s classical theorem on ovals in finite projective planes.
Textual Feature Ensemble-Based Sarcasm Detection in Twitter Data

Sarcasm is one of the most popular ways of expressing one’s opinion on a specific subject. Sarcasm is an act of conveying a viewpoint with an opposite emotion. Identifying such statements among the textual information is an important challenge present in sentiment analysis. Often, sarcastic statements are misleading. They mislead the whole context of the sentence; hence, detecting sarcasm is important in order to understand the actual meaning involved. The main objective of this paper is to propose a feature ensemble-based learning algorithm for sarcasm detection. Most of the existing works focused on using lexical features for identifying sarcasm. In the proposed work apart from the traditional lexical features, hyperbolic and pragmatic features have been extracted. After extracting the features, a model for detecting sarcasm has been proposed based on stacking-based feature ensemble algorithm. Experimental results show that the feature-based approaches when considered individually, Internet slang-based features attain an accuracy of 62%; interjection features obtained an accuracy of 49%, and emoticon-based features attained 59%, respectively. However, the proposed ensemble learning algorithm attains an overall accuracy of 83%, which is much better than the feature set-based approaches.
Employee Advocacy as a Marketing Strategy to Power Brand Promotion: an Employee Perspective

Employee Advocacy is an emerging and cost-effective concept of employee amplification to market products and services by enabling employees to build and share the company's brand contents through social media platforms. The paper summarizes the available research-based contents as well as articles and blogs of experts in the field of digital and social media marketing. The study primarily intended to assess employees’ awareness and perception of various aspects of employee advocacy. It has identified forms and types of contents shared and social media platforms now used by the employees in the automotive sector for leveraging brand promotion and communicating with the target market. The study has been designed as a descriptive research paper based on the empirical data collected from a sample of 102 employees from automobile showrooms in the southern districts of Kerala using a snowball sampling technique. A questionnaire was drafted with the help of google forms and administered among the respondents via email and WhatsApp. Two-dimensional plot of correspondence analysis, Descriptive statistics, Mann-Whitney U test, and Kruskal-Wallis H test was applied to present and describe the empirical data and to draw inference about the significance of the difference in the awareness and perception of employees about the aspects of employee advocacy on brand promotion in terms of demographic factors. Awareness level and understanding of the elements of employee advocacy is found very low and significantly different among employees according to the designation. This paper also indicates that the social media policies and guidelines of the companies were not rightly imparted to the employees. That’s why the involvement and creative use of the potentials of social media platforms by the employees were not up to a remarkable level. Employees perceived that employee advocacy as a cost-effective brand promotion strategy for the future, and it demands the employer’s motivation, recognition, and rewards for inspiring employees to be brand ambassadors. This research addressed a relatively new concept in which there has been no extensive empirical researches and calls on the attention of the market as well as academic researchers for contributing the unexplored areas of the novel and powerful idea for utilizing human resources in a platform where they are interested and involved. Keywords: content marketing, digital marketing, employee amplification, employee engagement, social media marketing.
Packet Length Spectral Analysis for IoT Flow Classification Using Ensemble Learning

With the proliferation of ubiquitous and autonomous devices for sensing, control, monitoring and conditioning, the Internet of Things (IoT) holds a great potential for the development of innovative applications.At the same time, network operators must support these devices with differentiated services, which rely on the ability to automatically recognize and classify the nature of the communication flows.In this paper, we present a supervised learning approach to discriminate between IoT and non-IoT traffic, and to determine the class of the device originating the packet flows.We make use of a reduced set of features based on the spectral analysis of the packet lengths of a flow, and evaluate an ensemble learning algorithm that uses a Random Forest classifier.We first discuss the datasets and the procedure that we use to extract the features, with examples from different devices.The evaluation is performed using both 10-fold cross validation and a split between training, validation and test-set.The latter is used for hyperparameter tuning.The results show that for reasonably large datasets the classifier achieves very high accuracy, as well as Precision and Recall rates.We further improve the performance on individual devices by selectively replicating the flows in the dataset, to achieve a better balance.We then evaluate a real-time implementation, and propose a runtime procedure to evaluate the model confidence level and trigger a retraining phase to adapt to a changing environment.A detailed analysis of the performance shows that the algorithm can support networks up to 100 Gbps on standard computing platforms.
Driving-induced resonance narrowing in a strongly coupled cavity-qubit system

We study a system consisting of a superconducting flux qubit strongly coupled to a microwave cavity. Externally applied qubit driving is employed in order to manipulate the spectrum of dressed states. We observe resonance narrowing in the region where the splitting between the two dressed fundamental resonances is tuned to zero. The narrowing in this region of overlapping resonances can be exploited for long-time storage of quantum states. In addition, we measure the response to strong cavity mode driving, and find a qualitative deviation between the experimental results and the predictions of a semiclassical model. On the other hand, good agreement is obtained using theoretical predictions obtained by numerically integrating the master equation governing the system's dynamics. The observed response demonstrates a process of a coherent cancellation of two meta-stable dressed states.
Hybrid quantum variational algorithm for simulating open quantum systems with near-term devices

Hybrid quantum-classical (HQC) algorithms make it possible to use near-term quantum devices supported by classical computational resources by useful control schemes. In this paper, we develop an HQC algorithm using an efficient variational optimization approach to simulate open system dynamics under the Noisy-Intermediate Scale Quantum computer. Using the time-dependent variational principle (TDVP) method and extending it to McLachlan TDVP for density matrix which involves minimization of Frobenius norm of the error, we apply the unitary quantum circuit to obtain the time evolution of the open quantum system in the Lindblad formalism. Finally, we illustrate the use of our methods with detailed examples which are in good agreement with analytical calculations.
Methods of mobilities design research

The turn towards design within mobilities research is maturing and a number of studies connecting mobilities theory and design-orientated approaches indicates an emerging field of mobilities design. The methods cover architectural mappings, design interventions, ethnographic observations, qualitative interviews, eye-tracking detection and thermal (heat-sensitive) cameras. We illustrate the methods with research conducted in two cases: an airport and a metro. The chapter illustrates that a situational-mobilities approach to analysis and design brings together important novel techniques, methods and ways of understanding mobilities.
Experimental violations of Leggett-Garg inequalities up to the algebraic maximum for a photonic qubit

Leggett and Garg defined macrorealism by positing that a macroscopic system will exist in a well-defined state at all times, and that this state can be measured without disturbing it. They also formulated a class of inequalities which hold under macrorealism but can be violated by quantum mechanics. The degree to which quantum systems can violate these inequalities is restricted by the temporal Tsirelson bounds. Recently variants of Leggett-Garg inequalities which can be violated to the algebraic maximum 3 for a sufficient large number of measurement times have been proposed. In this work we report an experimental test of quantum violation of the variants of Leggett-Garg inequalities beyond the temporal Tsirelson bounds, up to $2.248\ifmmode\pm\else\textpm\fi{}0.039$ in five-time measurement scenarios for a photonic qubit. Our results clearly show the advantages of the modified measurement scenarios.
Ensemble Learning Based on GBDT and CNN for Adoptability Prediction

By efficiently and accurately predicting the adoptability of pets, shelters and rescuers can be positively guided on improving attraction of pet profiles, reducing animal suffering and euthanization. Previous prediction m... | Find, read and cite all the research you need on Tech Science Press
Validation of a database of mean uranium, thorium and potassium concentrations in rock samples of Portuguese geological units, generated of literature data

The European Atlas of Natural Radiation, recently published, contains a collection of maps of Europe showing the levels of natural sources of radiation. Among the lacunae of the Atlas are maps of U, Th and K concentrations in rocks due to lack of European-wide geochemical surveys of bedrock units. The objective of this paper is to investigate the usability of scattered geochemical data of rock samples for large-scale mapping of U, Th and K concentrations in geological units. For this purpose, geochemical data were compiled from literature sources to produce a geochemical database (LIT database) that includes 2817 entries of U, Th and K concentrations measured in rock samples of geological units outcropping in Portugal. Given the methodical heterogeneity within LIT database, the influence of the geochemical analysis techniques was assessed through a three-way analysis of variance (ANOVA) using geological units, geochemical analysis techniques and loss on ignition (LOI) as categorical variables. The percentage of variation explained by geological factors was large (>35%), while the percentage of variation explained by the geochemical analysis techniques and LOI was generally lower than 5%. The geological factors were the main source of variability in the data, followed by the error component which can be assumed to represent the true spatial variability of geochemical concentrations. The pairwise comparison of the least square (LS) means computed through the ANOVA for each geochemical analysis technique indicates that LIT database can be considered consistent within itself, thus, reliable. In order to validate the usability of literature data the terrestrial gamma dose rate (TGDR) calculated from LIT database (TGDRcalc) was compared to the TGDR displayed in the Radiometric Map of Portugal (TGDRobs). The correlation between TGDRcalc and TGDRobs was highly significant (p < 0.001) and the results of a paired sample t-test and Wilcoxon median tests indicate that the differences between the arithmetic means of TGDRcalc and TGDRobs were not statistically significant (p = 0.126 and p = 0.14, respectively). Distributions of TGDRcalc and TGDRobs were seemingly equal according to the Kolmogorov-Smirnov and Anderson-Darling tests. Although, systematic discrepancies between TGDRcalc and TGDRobs were observed for sedimentary rocks, the compatibility of the RMP and LIT databases can be considered acceptable, which implies that the estimation of the contents of terrestrial radionuclides using literature data for large-scale mapping of U, Th and K contents in geological units is reasonable.
Towards an Inductive Logic Programming Approach for Explaining Black-Box Preference Learning Systems

In this paper we advocate the use of Inductive Logic Programming as a device for explaining black-box models, e.g. Support Vector Machines (SVMs), when they are used to learn user preferences. We present a case study where we use the ILP system ILASP to explain the output of SVM classifiers trained on preference datasets. Explanations are produced in terms of weak constraints, which can be easily understood by humans. We use ILASP both as a global and a local approximator for SVMs, score its fidelity, and discuss how its output can prove useful e.g. for interactive learning tasks and for identifying unwanted biases when the original dataset is not available. Finally, we highlight directions for further work and discuss relevant application areas.
Eyewitness identifications after witnessing threatening and non-threatening scenes in 360-degree virtual reality (or 2D) from first and third person perspectives

In eyewitness research the frequent use of video playback presented on a computer screen (i.e., 2D videos) in laboratory-based research is problematic due to the low realism of this method when presenting, for example, threatening (and non-threatening) first-person (and third-person) scenarios. However, in contrast to 2D videos, 360-degree videos presented in virtual reality (VR) presents the opportunity of achieving more realistic and immersive scenarios that might be better suited to mimic real-life incidents, as for example, in the case of a threatening first-person robbery. In the present study, we asked 37 participants to watch eight pre-recorded threatening or non-threatening 2D and VR videos, viewed from either a first- or third-person perspective. After each video, participants assessed the observed target’s appearance and were then presented with either a target present (TP) or target absent (TA) six-person photograph line-up. We expected that VR would result in higher degrees of accuracy in both TP and TA line-ups compared with 2D and that the differences between manipulations would be more pronounced within VR compared with 2D. We found that TP (but not TA) accuracy was higher in 2D compared with VR videos (91 vs. 66%), that there was no main effect of perspective, and that threatening scenes increased TP (but not TA) accuracy compared to non-threatening scenes (86 vs. 70%). Furthermore, in VR (but not in 2D), threatening scenes increased TP (but not TA) accuracy compared with non-threatening scenes (85 vs. 40%). The results go against the expected increased accuracy in VR (vs. 2D) videos but support the notion that threatening (vs. non-threatening) scenes can increase identification accuracy in VR but not necessarily in 2D.
DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems.

Learning effective feature crosses is the key behind building recommender systems. However, the sparse and large feature space requires exhaustive search to identify effective crosses. Deep & Cross Network (DCN) was proposed to automatically and efficiently learn bounded-degree predictive feature interactions. Unfortunately, in models that serve web-scale traffic with billions of training examples, DCN showed limited expressiveness in its cross network at learning more predictive feature interactions. Despite significant research progress made, many deep learning models in production still rely on traditional feed-forward neural networks to learn feature crosses inefficiently. In light of the pros/cons of DCN and existing feature interaction learning approaches, we propose an improved framework DCN-M to make DCN more practical in large-scale industrial settings. In a comprehensive experimental study with extensive hyper-parameter search and model tuning, we observed that DCN-M approaches outperform all the state-of-the-art algorithms on popular benchmark datasets. The improved DCN-M is more expressive yet remains cost efficient at feature interaction learning, especially when coupled with a mixture of low-rank architecture. DCN-M is simple, can be easily adopted as building blocks, and has delivered significant offline accuracy and online business metrics gains across many web-scale learning to rank systems.
A Novel Pathological Images and Genomic Data Fusion Framework for Breast Cancer Survival Prediction

Survival analysis is a valid solution for cancer treatments and outcome evaluations. Due to the wide application of medical imaging and genome technology, computer-aided survival analysis has become a popular and promising area, from which we can get relatively satisfactory results. Although there are already some impressive technologies in this field, most of them make some recommendations using single-source medical data and have not combined multi-level and multi-source data efficiently. In this paper, we propose a novel pathological images and gene expression data fusion framework to perform the survival prediction. Different from previous methods, our framework can extract correlated multi-scale deep features from whole slide images (WSIs) and dimensionality reduced gene expression data respectively for jointly survival analysis. The experiment results demonstrate that the integrated multi-level image and genome features can achieve higher prediction accuracy compared with single-source features.
A Study for ANN Model for Spam Classification

The classical way of detecting spam emails based on the signature is not very effective in recent days due to the huge uses of emails in various activities. The online recommendation and push emails make the spam detection job very complex and tedious. Machine learning happens to be a widely used approach for automated email spam detection. Among various machine learning algorithms, Artificial Neural Network (ANN) is gaining popularity due to its powerful approximation and generalization characteristic. The effectiveness of the email spam classifier is heavily dependent on the learning capability of ANN. In our work, we have developed a BP and a BP+M model to do the spam classification and find the accuracy of classification. We have compared the two models so that we can conclude that the BP+M model gives the same or better result than the BP model using fewer epochs. Though state-of-the-art and classical learning algorithms like backpropagation (BP) and backpropagation with momentum (BP+M) are very popular and well researched, it is understood that often it gets trapped in local optima. In our future work, we can use recent optimization techniques like SGO which can elevate the results and can eradicate the drawbacks of BP and BP+M model. After thorough simulations and results analysis, we conclude that backpropagation + momentum optimized ANN provides superior classification results than BP optimized ANN.
Characterization of Quantum States Based on Creation Complexity

Abstract The creation complexity of a quantum state is the minimum number of elementary gates required to create it from a basic initial state. The creation complexity of quantum states is closely related to the complexity of quantum circuits, which is crucial in developing efficient quantum algorithms that can outperform classical algorithms. A major question unanswered so far is what quantum states can be created with a number of elementary gates that scales polynomially with the number of qubits. In this work, it is first shown that for an entirely general quantum state it is exponentially hard (requires a number of steps that scales exponentially with the number of qubits) to determine if the creation complexity is polynomial. Then, it is shown that it is possible for a large class of quantum states with polynomial creation complexity to have common coefficient features such that, given any candidate quantum state, an efficient coefficient sampling procedure can be designed to determine if the state belongs to the class or not with arbitrarily high success probability. Consequently, partial knowledge of a quantum state's creation complexity is obtained, which can be useful for designing quantum circuits and algorithms involving such a state.
Exponential speedup in measuring out-of-time-ordered correlators and gate fidelity with a single bit of quantum information

Out-of-time-ordered correlators (OTOC) are a quantifier of quantum information scrambling and quantum chaos. We propose an efficient quantum algorithm to measure OTOCs that provides an exponential speed-up over the best known classical algorithm provided the OTOC operator to be estimated admits an efficient gate decomposition. We also discuss a scheme to obtain information about the eigenvalue spectrum and the spectral density of OTOCs.