Reviewerly/exHarmony huggingface dataset:
TCLBM: A task chain-based load balancing algorithm for microservices

The microservices architecture has been proposed to overcome the drawbacks of the traditional monolithic architecture.Scalability is one of the most attractive features of microservices.Scaling in the microservices architecture requires the scaling of specified services only, rather than the entire application.Scaling services can be achieved by deploying the same service multiple times on different physical machines.However, problems with load balancing may arise.Most existing solutions of microservices load balancing focus on individual tasks and ignore dependencies between these tasks.In the present paper, we propose TCLBM, a task chainbased load balancing algorithm for microservices.When an Application Programming Interface (API) request is received, TCLBM chooses target services for all tasks of this API call and achieves load balancing by evaluating the system resource usage of each service instance.TCLBM reduces the API response time by reducing data transmissions between physical machines.We use three heuristic algorithms, namely, Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Genetic Algorithm (GA), to implement TCLBM, and comparison results reveal that GA performs best.Our findings show that TCLBM achieves load balancing among service instances and reduces API response times by up to 10% compared with existing methods.
Generating photon-added states without adding a photon

We show that a continuous range of nonclassical states of light can be generated using conditional measurements on the idler mode of an optical parametric amplifier. The output state is prepared by introducing a coherent state in the signal mode of the amplifier with a single photon in the idler mode, followed by a conditional measurement of a single photon in the output idler mode. By varying the gain of the amplifier, this approach can produce a coherent state, a photon-added state, a displaced number state, or a continuous range of other nonclassical states with intermediate properties. We note that this approach can generate a photon-added state even though the postselected amplifier does not add any photons to the signal or idler modes. The ability to generate a continuous range of nonclassical states may have practical applications in quantum information processing.
Backdoor Attacks in Neural Networks – A Systematic Evaluation on Multiple Traffic Sign Datasets

Machine learning, and deep learning in particular, has seen tremendous advances and surpassed human-level performance on a number of tasks. Currently, machine learning is increasingly integrated in many applications and thereby, becomes part of everyday life, and automates decisions based on predictions. In certain domains, such as medical diagnosis, security, autonomous driving, and financial trading, wrong predictions can have a significant influence on individuals and groups. While advances in prediction accuracy have been impressive, machine learning systems still can make rather unexpected mistakes on relatively easy examples, and the robustness of algorithms has become a reason for concern before deploying such systems in real-world applications. Recent research has shown that especially deep neural networks are susceptible to adversarial attacks that can trigger such wrong predictions. For image analysis tasks, these attacks are in the form of small perturbations that remain (almost) imperceptible to human vision. Such attacks can cause a neural network classifier to completely change its prediction about an image, with the model even reporting a high confidence about the wrong prediction. Of particular interest for an attacker are so-called backdoor attacks, where a specific key is embedded into a data sample, to trigger a pre-defined class prediction. In this paper, we systematically evaluate the effectiveness of poisoning (backdoor) attacks on a number of benchmark datasets from the domain of autonomous driving.
Improving students’ learning with a mobile augmented reality approach – the EduPARK game

Purpose The purpose of this paper is to present a survey study that analyzes mobile learning through students’ opinion regarding the use of mobile devices for learning, including their advantages and difficulties, as well as the educational value of a specific mobile learning strategy, reified in the EduPARK game, after an experience of exploring it in formal and non-formal educational contexts. Design/methodology/approach This paper comprises a survey study where mobile learning is analyzed through the eyes of 244 students attending the second or third cycles of basic education. The acknowledged advantages and difficulties of the use of mobile devices for learning, as well as the educational value and usability of a specific mobile learning strategy, using the EduPARK app and game, were analyzed. Findings Results revealed that most students owned a mobile device and were able to use them to learn. They had a positive perspective regarding mobile learning and valued the advantages of being easy to find up-to-date information, motivating for learning and easy to carry along. Difficulties are related to the use of mobile devices, such as requiring an internet connection, its slowness and prohibition of mobile devices in schools. The EduPARK game achieved an average educational value scale of 83.8 and an average system usability scale of 80.2, indicating its high educational value and usability for students. Originality/value This paper presents empirical evidence regarding the effectiveness of the integration of mobile game-based augmented reality approaches in basic education to promote students’ learning. It also includes an example of excellent cross-subjects educational materials comprising a very useful tool for teachers and students to explore scientific knowledge by accessing appealing information on biological and historical references of a local urban park.
Automatically Inferring Gender Associations from Language

Serina Chang, Kathy McKeown. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Attribute-aware Sequence Network for Review Summarization

Review summarization aims to generate a condensed summary for a review or multiple reviews.Existing review summarization systems mainly generate summary only based on review content and neglect the authors' attributes (e.g., gender, age, and occupation).In fact, when summarizing a review, users with different attributes usually pay attention to specific aspects and have their own word-using habits or writing styles.Therefore, we propose an Attribute-aware Sequence Network (ASN) to take the aforementioned users' characteristics into account, which includes three modules: an attribute encoder encodes the attribute preferences over the words; an attribute-aware review encoder adopts an attribute-based selective mechanism to select the important information of a review; and an attribute-aware summary decoder incorporates attribute embedding and attribute-specific word-using habits into word prediction.To validate our model, we collect a new dataset TripAtt, comprising 495,440 attribute-review-summary triplets with three kinds of attribute information: gender, age, and travel status.Extensive experiments show that ASN achieves state-of-the-art performance on review summarization in both auto-metric ROUGE and human evaluation.
Leveraging 2-hop Distant Supervision from Table Entity Pairs for Relation Extraction

Distant supervision (DS) has been widely used to automatically construct (noisy) labeled data for relation extraction (RE). Given two entities, distant supervision exploits sentences that directly mention them for predicting their semantic relation. We refer to this strategy as 1-hop DS, which unfortunately may not work well for long-tail entities with few supporting sentences. In this paper, we introduce a new strategy named 2-hop DS to enhance distantly supervised RE, based on the observation that there exist a large number of relational tables on the Web which contain entity pairs that share common relations. We refer to such entity pairs as anchors for each other, and collect all sentences that mention the anchor entity pairs of a given target entity pair to help relation prediction. We develop a new neural RE method REDS2 in the multi-instance learning paradigm, which adopts a hierarchical model structure to fuse information respectively from 1-hop DS and 2-hop DS. Extensive experimental results on a benchmark dataset show that REDS2 can consistently outperform various baselines across different settings by a substantial margin.
Constructions of unextendible entangled bases

We provide several constructions of special unextendible entangled bases with fixed Schmidt number k ( $$\hbox {SUEB}k$$ ) in $${\mathbb {C}}^{d}\otimes {\mathbb {C}}^{d'}$$ for $$2\le k\le d\le d'$$ . We generalize the space decomposition method in Guo (Phys Rev A 94:052302, 2016), by proposing a systematic way of constructing new $$\hbox {SUEB}k\hbox {s}$$ in $${\mathbb {C}}^{d}\otimes {\mathbb {C}}^{d'}$$ for $$2\le k < d \le d'$$ or $$2\le k=d< d'$$ . In addition, we give a construction of a $$(pqdd'-p(dd'-N))$$ -number SUEBpk in $${\mathbb {C}}^{pd}\otimes {\mathbb {C}}^{qd'}$$ from an N-number $$\hbox {SUEB}k$$ in $${\mathbb {C}}^{d}\otimes {\mathbb {C}}^{d'}$$ for $$p\le q$$ by using permutation matrices. We also connect a $$(d(d'-1)+m)$$ -number UMEB in $${\mathbb {C}}^{d}\otimes {\mathbb {C}}^{d'}$$ with an unextendible partial Hadamard matrix $$H_{m\times d}$$ with $$m<d$$ , which extends the result in Wang et al. (Quantum Inf Process 16:84, 2017).
Enhancing social exergames through idle game design

This paper recognizes idle games as a promising direction for exergames and other games designed for behavioral change. Based on a survey of 11 popular idle games, we extend existing literature by identifying the common core gameplay loop (active participation, inactive progress, and return reward) as well as the design patters used to support the loop. Furthermore, we propose an initial approach to extending idle game patterns to social exergames, focusing on improving player adherence.
Reservoir computing based on quenched chaos

Reservoir computing(RC) is a brain-inspired computing framework that employs a transient dynamical system whose reaction to an input signal is transformed to a target output. One of the central problems in RC is to find a reliable reservoir with a large criticality, since computing performance of a reservoir is maximized near the phase transition. In this work, we propose a continuous reservoir that utilizes transient dynamics of coupled chaotic oscillators in a critical regime where sudden amplitude death occurs. This "explosive death" not only brings the system a large criticality which provides a variety of orbits for computing, but also stabilizes them which otherwise diverge soon in chaotic units. The proposed framework shows better results in tasks for signal reconstructions than RC based on explosive synchronization of regular phase oscillators. We also show that the information capacity of the reservoirs can be used as a predictive measure for computational capability of a reservoir at a critical point.
Nonlinear mathematical model for monitoring and predicting the process of transfer and diffusion of fine-dispersed aerosol particles in the atmosphere

Abstract Mathematical model for predicting, monitoring and assessing the ecological state of the atmosphere and underlying surface with passive and active impurities is developed in this article; it takes into account the varying rate of particle displacements in the atmosphere. To determine the rate of displacements of fine-dispersed particles in the atmosphere, a system of nonlinear equations has been obtained, which took into account such important factors as: the basic physical-mechanical properties of the particles and the rate of air mass in the atmosphere.
STC Speaker Recognition Systems for the VOiCES from a Distance Challenge

This paper presents the Speech Technology Center (STC) speaker recognition (SR) systems submitted to the VOiCES From a Distance challenge 2019. The challenge's SR task is focused on the problem of speaker recognition in single channel distant/far-field audio under noisy conditions. In this work we investigate different deep neural networks architectures for speaker embedding extraction to solve the task. We show that deep networks with residual frame level connections outperform more shallow architectures. Simple energy based speech activity detector (SAD) and automatic speech recognition (ASR) based SAD are investigated in this work. We also address the problem of data preparation for robust embedding extractors training. The reverberation for the data augmentation was performed using automatic room impulse response generator. In our systems we used discriminatively trained cosine similarity metric learning model as embedding backend. Scores normalization procedure was applied for each individual subsystem we used. Our final submitted systems were based on the fusion of different subsystems. The results obtained on the VOiCES development and evaluation sets demonstrate effectiveness and robustness of the proposed systems when dealing with distant/far-field audio under noisy conditions.
parallel: A command for parallel computing

The parallel package allows parallel processing of tasks that are not interdependent. This allows all flavors of Stata to take advantage of multiprocessor machines. Even Stata/MP users can benefit because many community-contributed programs are not automatically parallelized but could be under our framework.
Teachers’ Perception toward the Obstacles of E-Learning Classes

This study aims to describe the teacher's perception of the obstacles in the implementation of e-learning in the learning process and the recommendation to solve the problems. This research is a qualitative descriptive study. The research subjects were two teachers who taught at a University in Yogyakarta. The research data collected through in-depth interviews, which were analyzed descriptively by using interactive techniques. The Interactive analysis is done by collecting data, reducing data, presenting data, and drawing conclusions. The results of the data analysis show that two teachers were not too familiar with e-learning, so they still have many obstacles in the teaching process by using e-learning. There are several obstacles, such as obstacles in teaching by using e-learning as devices, teachers' knowledge, time management, and students ’motivation. In order to run e-learning entirely, then these obstacles should be solved first. There are two teachers suggest overcoming the obstacles, such as training for the teachers, and repairing the facilities of the institution.
What Is Deep Learning?

Deep learning is a class of machine learning algorithms called neural networks. Neural networks are mathematical models inspired by the structure of the brain. Deep learning enables the neural network algorithm to perform very well in building prediction models around complex problems such as computer vision and language modeling. Self-driving cars and automatic speech translation, to mention just a few, are examples of technologies that have resulted from advances in deep learning.
The Perpetuation of Online Hate: A Criminological Analysis of Factors Associated with Participating in an Online Attack

Online extremism, or the use of information technology to profess attitudes devaluing others based on a characteristic such as race, religion, gender, or sexuality, is a growing problem. This has led to myriad harmful effects for some who are exposed to online hate. A critical first step toward stemming the tide of online hate is understanding factors associated with its creation and spread. To that end, this analysis examines factors associated with joining an ongoing attack against a targeted group online. We use insights from four leading criminological theories – routine activity theory, social control theory, general strain theory, and social learning/differential association theory - to investigate who is likely to join an attack on a targeted group when they view such an attack occurring. Using data from a national sample of 15 – 36-year-old Internet users, we conduct an ordinal logistic regression analysis. Results show support for social control theory and strain theory, as low levels of self-control and online strain are both positively correlated with joining an online attack. Similarly, we find support for the applicability of social learning theory; close engagement with online friends and groups is related to an increased likelihood of joining in online hate. Routine activity theory, however, is less relevant for understanding our outcome. Taken together, our findings shed light on factors associated with the perpetuation of online hate, and, in doing so, offer avenues for reducing its growth.
Hybridization of the imperialist competitive algorithm and local search with application to ship design optimization

In the wide scenario of the optimization techniques, a large number of algorithms are inspired by natural processes, in many different ways. One of the latest is the Imperialist Competitive Algorithm (ICA) Atashpaz-Gargari and Lucas (2007), judged by their authors as very efficient and competitive with other popular optimization algorithms. However, its diffusion is still limited, so that it has not yet been adequately studied. In this paper, we have investigated the convergence properties of the ICA algorithm, observing the effect of the various coefficients and their role in the global convergence. Some modifications, including the coupling with a local search method, have been listed/suggested and then tested on a suite of standard algebraic test functions, verifying the improvements on the speed of convergence of the original algorithm. An application to naval design has been also included, in order to check the ability to solve realistic problems.
Quantum walk neural networks with feature dependent coins

Abstract Recent neural networks designed to operate on graph-structured data have proven effective in many domains. These graph neural networks often diffuse information using the spatial structure of the graph. We propose a quantum walk neural network that learns a diffusion operation that is not only dependent on the geometry of the graph but also on the features of the nodes and the learning task. A quantum walk neural network is based on learning the coin operators that determine the behavior of quantum random walks, the quantum parallel to classical random walks. We demonstrate the effectiveness of our method on multiple classification and regression tasks at both node and graph levels.
Unsupervised selective rank fusion for image retrieval tasks

Several visual features have been developed for content-based image retrieval in the last decades, including global, local and deep learning-based approaches. However, despite the huge advances in features development and mid-level representations, a single visual descriptor is often insufficient to achieve effective retrieval results in several scenarios. Mainly due to the diverse aspects involved in human visual perception, the combination of different features has been establishing as a relevant trend in image retrieval. An intrinsic difficulty consists in the task of selecting the features to combine, which is often supported by supervised learning approaches. Therefore, in the absence of labeled data, selecting features in an unsupervised way is a very challenging, although essential task. In this paper, an unsupervised framework is proposed to select and fuse visual features in order to improve the effectiveness of image retrieval tasks. The framework estimates the effectiveness and correlation among features through a rank-based analysis and uses a list of ranker pairs to determine the selected features combinations. High-effective retrieval results were achieved through a comprehensive experimental evaluation conducted on 5 public datasets, involving 41 different features and comparison with other methods. Relative gains up to +55% were obtained in relation to the highest effective isolated feature.
Comixify: Transform Video Into Comics

In this paper, we propose a solution to transform a video into a comics. We approach this task using a neural style algorithm based on Generative Adversarial Networks (GANs). Several recent works in the field of Neural Style Transfer showed that producing an image in the style of another image is f easible. In this paper, we build up on these works and extend the existing set of style transfer use cases with a working application of video comixification. To that end, we train an end-to-end solution that transforms input video into a comics in two stages. In the first stage, we propose a state-of-the-art keyframes extraction algorithm that selects a subset of frames from the video to provide the most comprehensive video context and we filter those frames using image aesthetic estimation engine. In the second stage, the style of selected keyframes is transferred into a comics. To provide the most aesthetically compelling results, we selected the most state-of-the art style transfer solution and based on that implement our own ComixGAN framework. The final contribution of our work is a Web-based working application of video comixification available at http://comixify.ii.pw.edu.pl.
Structured Object-Aware Physics Prediction for Video Modeling and Planning

When humans observe a physical system, they can easily locate objects, understand their interactions, and anticipate future behavior, even in settings with complicated and previously unseen interactions. For computers, however, learning such models from videos in an unsupervised fashion is an unsolved research problem. In this paper, we present STOVE, a novel state-space model for videos, which explicitly reasons about objects and their positions, velocities, and interactions. It is constructed by combining an image model and a dynamics model in compositional manner and improves on previous work by reusing the dynamics model for inference, accelerating and regularizing training. STOVE predicts videos with convincing physical behavior over hundreds of timesteps, outperforms previous unsupervised models, and even approaches the performance of supervised baselines. We further demonstrate the strength of our model as a simulator for sample efficient model-based control in a task with heavily interacting objects.
A Hybrid Cascade Neuro–Fuzzy Network with Pools of Extended Neo–Fuzzy Neurons and its Deep Learning

Abstract This research contribution instantiates a framework of a hybrid cascade neural network based on the application of a specific sort of neo-fuzzy elements and a new peculiar adaptive training rule. The main trait of the offered system is its competence to continue intensifying its cascades until the required accuracy is gained. A distinctive rapid training procedure is also covered for this case that offers the possibility to operate with non-stationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined for handling non-stationary objects. Additionally, there is always an occasion to set up (increase) the inference order and the number of membership relations inside the extended neo-fuzzy neuron.