Reviewerly/exHarmony huggingface dataset:
Application of Bat Algorithm in Carbon Nanotubes Growing Process Parameters Optimization

Since their discovery, carbon nanotubes (CNTs) have become a subject of intense research for their potential use in various applications. Chemical vapor deposition (CVD) process is the most common method used to grow CNTs. However, the growing process suffers many difficulties in finding the optimal process parameters. Applying computational intelligence methods is a possible solution for optimization problems to reduce using conventional methods and experimental runs. In this work, a combination between bat algorithm (BA) and response surface methodology (RSM) is proposed to solve CNTs process parameters optimization problem. The study aims to maximize the CNTs yield percentage for mass production in two different datasets. BA search process is based on the objective function developed by RSM which represents the prediction mathematical model of growing process parameters. The optimized parameters from datasets are reaction temperature, reaction time, catalyst weight, and methane partial pressure. The algorithm search process was conducted with parameters tuning at different setting values to improve the algorithm’s performance and CNTs yield value. Different evaluation metrics were applied to compare the experimental results. The results have shown that BA has an efficient search performance and obtained better CNTs yield result than RSM in one of the datasets with 21% improvement of CNTs yield value. Besides, BA has shown a fast and stable convergence. Finally, the result was validated and found reliable to be used in real laboratory experiments.
Distributed Key-Value Storage for Edge Computing and its Explicit Data Distribution Method

In this article, we provide a data framework for edge computing where developers can easily attain efficient data transfer between devices or users. We propose a distributed key-value storage platform for edge computing and its explicit data distribution management method. In this platform, edge servers organize the distributed key-value storage in a uniform namespace. To enable fast data access to a record in edge computing, the allocation strategy of the record and its cache on the edge servers is important. Our platform offers a distributed objects that can dynamically change home server and allocate cache objects following user defined rules. A rule is defined in a declarative manner and specifies where to place cache objects depending on the status of the target record and its associated records. We also integrate a push notification system using WebSocket to immediately notify events on a specified table. We evaluate the performance of our system using a messaging service application between mobile appliances.
Outlier Detection Based on Local Density of Vector Dot Product in Data Stream

Outlier detection in data stream is an increasingly important research in many fields. To deal with the data stream with the properties of high dimension, rapid arrival in order, high cost of storing all data in memory and so on, an outlier detection algorithm based on local density of vector dot product in data stream (LDVP-OD) is proposed. LDVP-OD uses the model based on sliding window and multiple validations to decrease the false alarm rate, which divides the data stream into uniform-sized blocks. Local density of vector dot product (LDVP) is described in order to precisely evaluate the outlierness of data in data stream. Furthermore, an outlier judgment criterion based on supreme slope is introduced, which can determine the exact outliers without requiring the number of outliers or other parameters beforehand. Comparison experiments with existing algorithms on synthetic and real datasets prove the high detection rate, good stability, strong adaptability of LDVP-OD.
Conditional Generative Adversarial Networks for Regression

In recent years, impressive progress has been made in the design of implicit probabilistic models via Generative Adversarial Networks (GAN) and its extension, the Conditional GAN (CGAN). Excellent solutions have been demonstrated mostly in image processing applications which involve large, continuous output spaces. There is almost no application of these powerful tools to problems having small dimensional output spaces. Regression problems involving the inductive learning of a map, $y=f(x,z)$, $z$ denoting noise, $f:\mathbb{R}^n\times \mathbb{R}^k \rightarrow \mathbb{R}^m$, with $m$ small (e.g., $m=1$ or just a few) is one good case in point. The standard approach to solve regression problems is to probabilistically model the output $y$ as the sum of a mean function $m(x)$ and a noise term $z$; it is also usual to take the noise to be a Gaussian. These are done for convenience sake so that the likelihood of observed data is expressible in closed form. In the real world, on the other hand, stochasticity of the output is usually caused by missing or noisy input variables. Such a real world situation is best represented using an implicit model in which an extra noise vector, $z$ is included with $x$ as input. CGAN is naturally suited to design such implicit models. This paper makes the first step in this direction and compares the existing regression methods with CGAN. We notice however, that the existing methods like mixture density networks (MDN) and XGBoost do quite well compared to CGAN in terms of likelihood and mean absolute error, respectively. Both these methods are comparatively easier to train than CGANs. CGANs need more innovation to have a comparable modeling and ease-of-training with respect to the existing regression solvers. In summary, for modeling uncertainty MDNs are better while XGBoost is better for the cases where accurate prediction is more important.
Using Temporal Correlation to Optimize Stereo Matching in Video Sequences

The large computational complexity makes stereo matching a big challenge in real-time application scenario. The problem of stereo matching in a video sequence is slightly different with that in a still image because there exists temporal correlation among video frames. However, no existing method considered temporal consistency of disparity for algorithm acceleration. In this work, we proposed a scheme called the dynamic disparity range (DDR) to optimize matching cost calculation and cost aggregation steps by narrowing disparity searching range, and a scheme called temporal cost aggregation path to optimize the cost aggregation step. Based on the schemes, we proposed the DDR-SGM and the DDR-MCCNN algorithms for the stereo matching in video sequences. Evaluation results showed that the proposed algorithms significantly reduced the computational complexity with only very slight loss of accuracy. We proved that the proposed optimizations for the stereo matching are effective and the temporal consistency in stereo video is highly useful for either improving accuracy or reducing computational complexity.
Automatic Microseismic Event Detection Using Deep Learning: a Classification is Detection Method

Summary Automatic event detection is of vital importance for real-time microseismic or passive seismic monitoring in the oil and gas industry. With the recent advances in artificial intelligence and computing power, deep learning has now become a reliable tool for automatic microseismic event detection and other problems. We present a novel “classification is detection” strategy by leveraging convolutional neural networks (CNN) based deep learning method for automatic microseismic event detection in low signal-to-noise ration environment. The CNN model is trained using thousands of manually picked and labelled genuine event and noise segments. Validation on additional field data demonstrates the model&apos;s capability in recognising microseismic event patterns and distinguishing them from various noises. The trained model can also be deployed in real-time monitoring scenario to provide automated real-time event detection capability.
INFaaS: Managed & Model-less Inference Serving

The number of applications relying on inference from machine learning models is already large and expected to keep growing. For instance, Facebook applications issue tens-of-trillions of inference queries per day with varying performance, accuracy, and cost constraints. Unfortunately, existing inference serving systems are neither easy to use nor cost effective. Developers must manually match the performance, accuracy, and cost constraints of their applications to a large design space that includes decisions such as selecting the right model and model optimizations, selecting the right hardware architecture, selecting the right scale-out factor, and avoiding cold-start effects. These interacting decisions are difficult to make, especially when the application load varies over time, applications evolve over time, and the available resources vary over time. We present INFaaS, an inference-as-a-service system that abstracts resource management and model selection. Users simply specify their inference task along with any performance and accuracy requirements for queries. Given the currently available resources, INFaaS automatically selects and serves inference queries using a specific model that satisfies these requirements. INFaaS autoscales resources as model load changes both within and across inference workers. It also shares workers across users and models to increase utilization. We evaluate INFaaS using 44 model architectures and their 270 model variants against serving systems that rely on users for model selection and pre-load models, fix the scale policy, or use dedicated hardware resources. Our evaluation on realistic workloads shows that INFaaS achieves 2$\times$ higher throughput and violates latency SLO goals 3$\times$ less frequently, while maintaining high utilization and having overheads that are less than 12% of millisecond-scale queries.
Revisiting the behavior of quantum correlations under nondissipative decoherence by means of the correlation matrix

The main aim of this work is to investigate the relation between the freezing of Quantum Discord and the behavior of the actual quantum correlations present in bipartite quantum states. In order to accomplish this objective, we use first the Fano representation of two-qubit states from which we can identify a correlation matrix containing the information about the classical and quantum correlations present in the bipartite quantum state. From the behavior of the elements of the correlation matrix before and after making measurements on one of the subsystems, we identify the classical and quantum correlations present in these bipartite states. Then, we use this correlation matrix as a tool to analyze the phenomenon of non-dissipative decoherence in two-qubit states with maximally mixed marginals in typical dynamic scenarios where freezing of standard Quantum Discord takes place. We find that under some initial conditions where freezing of quantum discord occurs, some quantum correlations instead may not remain constant. In order to further explore into these results we also compute for the first time a recently introduced non-commutativity measure of quantum correlations (NCMQC) to analyze the behavior of quantum correlations under the same scenarios of non-dissipative decoherence. In complete agreement with the results obtained by means of the correlation matrix, our results also show that quantum correlations, as measured by NCMQC, do not freeze. Thus, our results put at stake the usual interpretation that the freezing of Quantum Discord is equivalent to the freezing of the physical quantum correlations themselves. We conclude from our study that freezing of quantum discord may not always be identified as equivalent to the freezing of the actual quantum correlations. Thus, the identification of freezing of quantum discord as a useful resource for tasks of quantum information processing is called into question.
Lexical Sense Alignment using Weighted Bipartite b-Matching.

This project has received funding from the European Union’s Horizon 2020 research andinnovation programme under grant agreement No 731015.
Mono and multi-modal biometric systems assessment by a common black box testing framework

As a trending method for the authentication, biometrics tends to be integrated in various devices, and in particular in smartphones. If the evaluation is performed on operational device, the biometric sample and algorithm are not reachable by the assessors. So, these latter have to perform an evaluation on a system considered as a black box. This kind of evaluation implies numerous manual comparison. This paper proposes a methodology to perform an evaluation of biometric black boxes. In order to obtain this methodology, two experiments were performed in order to determine an optimized conduct. Nevertheless, these experiments were realized with small test population, in order to assess the methodology a full scale evaluation was performed. This paper describes the used methodology to perform evaluation on black boxes systems, and the results obtained on the systems under test.
The risk of node re-identification in labeled social graphs

Real network datasets provide significant benefits for understanding phenomena such as information diffusion or network evolution. Yet the privacy risks raised from sharing real graph datasets, even when stripped of user identity information, are significant. When nodes have associated attributes, the privacy risks increase. In this paper we quantitatively study the impact of binary node attributes on node privacy by employing machine-learning-based re-identification attacks and exploring the interplay between graph topology and attribute placement. Our experiments show that the population's diversity on the binary attribute consistently degrades anonymity.
Echo State Network prediction based on Backtracking Search optimization Algorithm

Echo state network is one of the latest research results of artificial neural network, it's most notable feature is the simple network structure. Only the connection weight matrix of the reservoir (hidden layer) to the output layer needs to be calculated. In this paper, we aim to provide a kind of the echo state network prediction method based on adaptive backtracking search optimization. The adaptive back search algorithm is introduced into the echo state network to optimize the connection weights matrix of the echo state network reservoir and the output layer, then to improve the echo state network prediction accuracy.
Using Recurrent Neural Networks to Build a Stopping Algorithm for an Adaptive Assessment

ALEKS (“Assessment and LEarning in Knowledge Spaces”) is an adaptive learning and assessment system based on knowledge space theory. In this work, our goal is to improve the overall efficiency of the ALEKS assessment by developing an algorithm that can accurately predict when the assessment should be stopped. Using data from more than 1.4 million assessments, we first build recurrent neural network classifiers that attempt to predict the final result of each assessment. We then use these classifiers to develop our stopping algorithm, with the test results indicating that the length of the assessment can potentially be reduced by a large amount while maintaining a high level of accuracy.
A Low Power Binary Square rooter using Reversible Logic

Calculating square root is an important mathematical operation which has wide applications. The design of square rooter in hardware needs to achieve low power, low area and high speed. Often there can be a trade-off among the three metrics. As the current technology aims for low power, designs require major architectural modification. This paper presents a low power binary square rooter using reversible logic. It uses reversible logic to achieve low power. The binary square rooter is designed and implemented using RCSM (Reversible Controlled Subtract Multiplexer).For further development such as number of quantum cost, garbage outputs and the constant inputs , binary square rooter is implemented using SRG (Samiur Rahman Gate).Binary square rooter using non-restoring algorithm is designed using both SRG and conventional approach. Simulations are carried out using ModelSim software and the power is obtained using Synopsys Design Compiler The power obtained for SRG and conventional technique are compared. The gate count has been reduced to 35 from 75. Power improvement of 20% is obtained.
Visualizing Temperaments: Squaring the Circle?

Since our hearing system is accustomed to the equal temperament system, best applied to modern western musical instruments, we perceive the other tuning systems as unfamiliar, without being able to describe exactly how and what sounds weird and seems dissonant. This paper describes the construction of new mathemusical models and visualization concepts: Two dimensional static and dynamic models, 3D and 4D animated objects. These tools are designed to graphically compare temperaments and generate visualizations that reinforce/augment the auditory impression. We discuss limits and applications for our models and their different applications. All videos will be available on www.mathemusic.net .
Response awareness and instructional self-efficacy: influences on intent

Purpose This paper aims to examine the influence of response awareness on behavioral intent, and introduces instructional self-efficacy, a construct rarely examined within the context of information security (ISec). Design/methodology/approach A Web-based survey was conducted and a total of 211 valid responses were analyzed. The relationships among response awareness, instructional self-efficacy and behavioral intent were examined through a three-phase structural equation modeling analysis. Findings The results indicate that even at low levels, response awareness has a strong influential effect on the behavioral intent to perform the secure response and on the self-efficacy to instruct others to perform the response. Instructional self-efficacy was also found to be a significant predictor of behavioral intent to perform the response. Finally, evidence was found indicating instructional self-efficacy fully mediates the response awareness to the behavioral intent relationship. Research limitations/implications Because of the characteristics of the population, the focus on a single ISec response and the dependent variable of behavioral intent rather than actual behavior, the generalizability of the findings is impacted. Practical implications The results contribute to practice by confirming the importance of response awareness and of instructional self-efficacy within an ISec context. Specific implications include the indication that informal communications about ISec issues among peers should be encouraged and that instructional self-efficacy should be targeted within ISec awareness training programs. Originality/value This paper’s parsimonious model defined response awareness as vicarious experience with a response and presented instructional self-efficacy, a construct novel to ISec studies that was found to be a significant influence within the relationship between response awareness and behavioral intent.
Sampling of Graph Signals with Blue Noise Dithering

This paper discusses the generalization of the concept of blue noise sampling from traditional halftoning to signal processing on graphs. Making use of the spatial properties of blue noise, we generate sampling patterns that provide reconstruction errors that are similar to the ones obtained with state of the art approaches. This sampling scheme presents an alternative to those techniques that require spectral decompositions.
The consumer intention to use digital membership cards

The study focuses on the consumer perception regarding digital membership card, an trendy instrument in the customer relationship management (CRM).It adopts the Unified Theory of Acceptance of the Use of Technology 2, which takes into account the following determinants: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit.The population of the study is consumers having any membership cards.The data are collected from a random sample by using questionnaires on the Likert scale.The collected empirical data in conjunction with a multivariate regression model suggest the followings.The price value aspect is the key factor influencing the continuous intention of use of the instrument.The aspects of social influence, performance expectation, and effort expectation are more important than the hedonic motivation aspect.The findings imply that for companies to succeed with the digital CRM instrument, offering strong competitive advantages at a lower price is still important as much as the user friendliness of the device.
KSU at SemEval-2019 Task 3: Hybrid Features for Emotion Recognition in Textual Conversation

In this paper, we present the model submitted to the SemEval-2019 Task 3 competition: contextual emotion detection in text "EmoContext".We propose a model that hybridizes automatically extracted features and human engineered features to capture the representation of a textual conversation from different perspectives.The proposed model utilizes a fast gated-recurrent-unit backed by CuDNN (CuDNNGRU), and a convolutional neural network (CNN) to automatically extract features.The human engineered features take the term frequency-inverse document frequency (TF-IDF) of semantic meaning and mood tags extracted from SinticNet.For the classification, a dense neural network (DNN) is used with a sigmoid activation function.The model achieved a micro-F1 score of 0.6717 on the test dataset.
Proximity Full-Text Search by Means of Additional Indexes with Multi-component Keys: In Pursuit of Optimal Performance

Full-text search engines are important tools for information retrieval. In a proximity full-text search, a document is relevant if it contains query terms near each other, especially if the query terms are frequently occurring words. For each word in a text, we use additional indexes to store information about nearby words that are at distances from the given word of less than or equal to the MaxDistance parameter. We showed that additional indexes with three-component keys can be used to improve the average query execution time by up to 94.7 times if the queries consist of high-frequency occurring words. In this paper, we present a new search algorithm with even more performance gains. We consider several strategies for selecting multi-component key indexes for a specific query and compare these strategies with the optimal strategy. We also present the results of search experiments, which show that three-component key indexes enable much faster searches in comparison with two-component key indexes. This is a pre-print of a contribution "Veretennikov A.B. (2019) Proximity Full-Text Search by Means of Additional Indexes with Multi-component Keys: In Pursuit of Optimal Performance." published in "Manolopoulos Y., Stupnikov S. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2018. Communications in Computer and Information Science, vol 1003" published by Springer, Cham. This book constitutes the refereed proceedings of the 20th International Conference on Data Analytics and Management in Data Intensive Domains, DAMDID/RCDL 2018, held in Moscow, Russia, in October 2018. The 9 revised full papers presented together with three invited papers were carefully reviewed and selected from 54 submissions. The final authenticated version is available online at https://doi.org/10.1007/978-3-030-23584-0_7.