Reviewerly/exHarmony huggingface dataset:
A novel parameter estimation for hyperbolic frequency modulated signals using group delay

A two-stage technique for estimating hyperbolic frequency modulated (HFM) signal parameters is proposed. In the first stage, coarse parameter estimations are performed through a regression of the group delay estimate obtained from an unwrapped spectrum phase due to its robustness to noise. In the second stage, the coarse estimates are refined by a combination of the Newton-Raphson strategy and O'Shea refinement method. The proposed technique reaches the Cramér-Rao lower bound for HFM signal parameters, and it has better performance and lower computational complexity with respect to current state-of-the-art algorithms.
Defending Privacy Against More Knowledgeable Membership Inference Attackers

Membership Inference Attack (MIA) in deep learning is a common form of privacy attack which aims to infer whether a data sample is in a target classifier's training dataset or not. Previous studies of MIA typically tackle either a black-box or a white-box adversary model, assuming an attacker not knowing (or knowing) the structure and parameters of the target classifier while having access to the confidence vector of the query output. With the popularity of privacy protection methods such as differential privacy, it is increasingly easier for an attacker to obtain the defense method adopted by the target classifier, which poses extra challenge to privacy protection. In this paper, we name such attacker a crystal-box adversary. We present definitions for utility and privacy of target classifier, and formulate the design goal of the defense method as an optimization problem. We also conduct theoretical analysis on the respective forms of the optimization for three adversary models, namely black-box, white-box, and crystal-box, and prove that the optimization problem is NP-hard. Thereby we solve a surrogate problem and propose three defense methods, which, if used together, can make trade-off between utility and privacy. A notable advantage of our approach is that it can be used to resist attacks from three adversary models, namely black-box, white-box, and crystal-box, simultaneously. Evaluation results show effectiveness of our proposed approach for defending privacy against MIA and better performance compared to previous defense methods.
Direct and indirect reinforcement learning

Reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision-making and control tasks. In this paper, we classify RL into direct and indirect RL according to how they seek the optimal policy of the Markov decision process problem. The former solves the optimal policy by directly maximizing an objective function using gradient descent methods, in which the objective function is usually the expectation of accumulative future rewards. The latter indirectly finds the optimal policy by solving the Bellman equation, which is the sufficient and necessary condition from Bellman's principle of optimality. We study policy gradient (PG) forms of direct and indirect RL and show that both of them can derive the actor–critic architecture and can be unified into a PG with the approximate value function and the stationary state distribution, revealing the equivalence of direct and indirect RL. We employ a Gridworld task to verify the influence of different forms of PG, suggesting their differences and relationships experimentally. Finally, we classify current mainstream RL algorithms using the direct and indirect taxonomy, together with other ones, including value-based and policy-based, model-based and model-free.
An effective framework for semistructured document classification via hierarchical attention model

Recent years have witnessed the rapidly growing of the amount of semistructured documents in real-world applications. Due to the huge size of the real-world data, how to manage semistructured documents effectively is a big challenge for researchers. As a fundamental task in natural language processing field, document classification is a feasible way to handle the large-scale semistructured documents. However, existing methods fail to explicitly take advantage of the hierarchical semantics in semistructured documents. It's known that the contained semantics is beneficial for understanding the semistructured documents. Considering the hierarchical structure of a given semistructured document, we propose a semistructured document classification framework which explicitly utilizes the semantic hierarchical attention mechanism. More specifically, the hierarchical attention mechanism and graph neural network are employed to model semistructured documents, by which the multilevel semantic relationships and grammatical information are considered. Moreover, we propose an adaptive class cost learning method to treat the issue of data imbalance. Comprehensive experiments are conducted on two real-world data sets, and the results demonstrate that our framework performs better than selected baselines for semistructured document classification.
Analysis of Faces in a Decade of US Cable TV News

Cable (TV) news reaches millions of US households each day. News stakeholders such as communications researchers, journalists, and media monitoring organizations are interested in the visual content of cable news, especially who is on-screen. Manual analysis, however, is labor intensive and limits the size of prior studies. We conduct a large-scale, quantitative analysis of the faces in a decade of cable news video from the top three US cable news networks (CNN, FOX, and MSNBC), totaling 244,038 hours between January 2010 and July 2019. Our work uses technologies such as automatic face and gender recognition to measure the "screen time" of faces and to enable visual analysis and exploration at scale. Our analysis method gives insight into a broad set of socially relevant topics. For instance, male-presenting faces receive much more screen time than female-presenting faces (2.4x in 2010, 1.9x in 2019). To make our dataset and annotations accessible, we release a public interface at https://tvnews.stanford.edu that allows the general public to write queries and to perform their own analyses.
A Study of Cybersecurity Education Using a Present-Test-Practice-Assess Model

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> Based on a previous exploratory study, this research further investigated learning mode contributions from simulations and live competitive activities compared to a traditional classroom, laboratory, and testing approach to instruction in order to assess the applied behavioral contributions of each. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> Cybersecurity capabilities in organizations lag behind the threats; consequently, there is a renewed emphasis on cybersecurity education. However, education appears to lack its full potential in most settings. Few empirical studies have systematically tested the efficacy of various training methods and modes, and those that have been conducted have yielded inconsistent findings. Recent literature on the use of gamified simulations have suggested that they may improve cybersecurity behaviors. Similarly, live activities, such as hackathons and capture the flag events, have been surmised to augment learning and capabilities. This study sought to systematically investigate this question. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research Questions:</i> 1) Do cybersecurity simulations improve applied learning performance; 2) do live competitive activities improve applied learning performance; and 3) do any or each of these modes amplify applied learning performance when used in combination? <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</i> This study randomly assigned computer science students to one of four sections using different modalities. It used pretest scores on an applied exam as the co-variate, with post-test scores as the dependent variable. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> Simulations improved learning performance over traditional classroom/lab instruction alone whereas live competitive activities did not. However, the greatest learning outcomes achieved was structured cybersecurity simulated environments combined with live competitive activities.
Building a trust model system to avoid cloud services reputation attacks

The safety of cloud services within a Trust Model System (TMS) is certainly compromised by a lack of defense against security threats as well as by inaccuracy of the trust results. Our proposed model addresses well-known security threats to the reputation trust model system, and is shown to deal with all possible potential attack threats, such as Sybil, on–off, and collusion attacks, by specifying the identity of users and tracking activities undertaken by them in order to easily track unauthorized consumers or attackers and to provide proof of any kind of data leakage. The TMS can also oversee the authorization of whoever uploads feedback into the system. It can also identify invalid feedback and discard it from the system. The algorithms of the TMS first establish a variety of trust criteria in which trustworthiness is calculated. Then, feedback from the cloud service provider nodes is accepted only according to the rules of the TMS. A consumer’s trust value is finally computed using a flexible system capable of guaranteeing a good balance of consumer trust and owners' feedback. Furthermore, a majority of the existing TMS models do not take full account of interaction importance, thus impeding the accuracy of the trust values, a shortcoming that has been rectified in our proposed model.
Curve and surface reconstruction based on MTLS algorithm combined with k-means clustering

Curve and surface reconstruction methods play an important role in many research and engineering fields. It is an imperative procedure to carry out surface reconstruction from measurement data in reverse engineering, which is complicated with the presence of outliers. To achieve better accuracy and robustness of reconstruction, an improved moving total least squares (MTLS) algorithm based on k-means clustering called a KMTLS method is proposed in this article. Based on MTLS, KMTLS adjusts the weight of discrete points within the support domain by adopting a two-step fitting procedure. Firstly, an ordinary least squares (OLS) method is adopted to obtain the pre-fitting result and calculate the residuals as the input of k-means clustering. In k-means clustering, abnormal nodes are classified into one cluster and a weight function based on clustering information is introduced to deal with these nodes. Secondly, based on the compact weight function in MTLS and the weight obtained in the pre-fitting procedure, a weighted total least squares method is conducted to determine the final estimated value. The process of detecting outliers is automatic without setting threshold artificially. The simulation and experiment show that KMTLS has great robustness and accuracy.
A Neural Network-based Time-Series Model for Predicting Global Solar Radiations

Global solar radiation is variable in nature, hence its forecasting is very important. This paper aims to predict the global solar radiations using real experimental data collected over time by developing time series based neural networks in MATLAB. In this research work three models are developed, one for the daily prediction of global solar radiations and two for the hourly (with and without night times) prediction of the global solar radiations. The results obtained are evaluated using statistical analysis which confirmed that nonlinear autoregressive models are a good choice for predicting the global solar radiations. The regression coefficient was found high in the case of hourly prediction (including night hours) followed by hourly prediction (excluding night hours) and daily prediction (after averaging) respectively. The value of the best validation performance for hourly prediction (including night hours) was found 0.060887 with a regression coefficient of 0.96481. To further validate the applicability of the proposed algorithm, the model was compared with four time-series models developed in Waikato Environment for Knowledge Analysis. The results confirmed the high efficiency of our proposed models among others, based on the performance parameters root mean square error and mean Absolute error. The root mean square error for our proposed model came 1.28 which is the lowest as compared to other time series models. Also, the accuracy of the hourly prediction model was found higher as compared to daily prediction models. Further, the critical analysis of our method with various available methods in the literature is also discussed.
Oversampling Method Based on Gaussian Distribution and K-Means Clustering

Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years.When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority.Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications.Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise.In this paper, we propose a simple oversampling method based on Multivariate Gaussian distribution and K-means clustering, called GK-Means.The new method aims to avoid generating noise and control imbalances between and within classes.Various experiments have been carried out with six classifiers and four oversampling methods.Experimental results on different imbalanced datasets show that the proposed GK-Means outperforms other oversampling methods and improves classification performance as measured by F1-score and Accuracy.
Nazief &amp; Adriani Stemming Algorithm With Cosine Similarity Method For Integrated Telegram Chatbots With Service

Abstract Information systems have developed a number of questions that are often asked to customers service with a high level of question similarity. For help it create a FAQ related to information systems. The FAQ has a lot of information so that the user is confused and it takes time to find information. More users to ask questions to customer service. Chatbot is one way to help users and customers service in this matter. Customer service can answer questions automatically and users can ask questions as if to ask customer service live. In this study, researchers will apply Nazief &amp; Adriani algorithm used to do stemming because this algorithm is an algorithm which is effective in stemming Indonesian, and method cosine similarity to find the similarity level of questions with FAQs that are in the FAQ database. With implement algorithms and methods in chatbot with the help of the Telegram Messanger service produce a relative answer according to that the user expected. This method is an effective way to answer questions automatically. And if the question asked did not find an answer or the answer is not suitable then the question will be conveyed in e-layanan Information technology development center of Universitas Negeri Surabaya.
SACNN: Self-attentive Convolutional Neural Network Model for Natural Language Inference

Inference has been central problem for understanding and reasoning in artificial intelligence. Especially, Natural Language Inference is an interesting problem that has attracted the attention of many researchers. Natural language inference intends to predict whether a hypothesis sentence can be inferred from the premise sentence. Most prior works rely on a simplistic association between the premise and hypothesis sentence pairs, which is not sufficient for learning complex relationships between them. The strategy also fails to exploit local context information fully. Long Short Term Memory (LSTM) or gated recurrent units networks (GRU) are not effective in modeling long-term dependencies, and their schemes are far more complex as compared to Convolutional Neural Networks (CNN). To address this problem of long-term dependency, and to involve context for modeling better representation of a sentence, in this article, a general Self-Attentive Convolution Neural Network (SACNN) is presented for natural language inference and sentence pair modeling tasks. The proposed model uses CNNs to integrate mutual interactions between sentences, and each sentence with their counterparts is taken into consideration for the formulation of their representation. Moreover, the self-attention mechanism helps fully exploit the context semantics and long-term dependencies within a sentence. Experimental results proved that SACNN was able to outperform strong baselines and achieved an accuracy of 89.7% on the stanford natural language inference (SNLI) dataset.
Children's speaker verification in low and zero resource conditions

Our efforts towards developing an automatic speaker verification (ASV) system for child speakers are presented in this paper. For the majority of the languages, children's speech data for training the ASV system is either unavailable (zero-resource) or very limited (low-resource). Under low- and zero-resource conditions, developing an ASV system becomes a very challenging problem. To overcome this issue, we have studied the effectiveness of in-domain and out-of-domain data augmentation in this work. Speed and pitch modifications of children's speech are employed for synthetically creating data in the case of in-domain data augmentation. On the other hand, a limited amount of adults' speech is used when out-of-domain data augmentation is performed. Using adults' speech leads to severe acoustic mismatch due to dissimilarity in the attributes of speech data from adult and child speakers. To address this drawback, speech data from adult speakers are subjected to voice conversion (VC) to alter the acoustic attributes. A cycle-consistent generative adversarial network is used in this work for voice conversion. Voice conversion renders adults' speech perceptually similar to children's speech. The voice converted adults' data can then be used for augmentation, ensuring that the acoustic mismatch is minimal. To study the effectiveness of proposed data augmentation techniques experimentally, x-vector-based ASV system architecture is employed. At the same time, the role of i-vector is also studied in this paper. As a consequence of data augmentation, both equal error rate and minimum decision cost function are reduced significantly in low- and zero-resource conditions. At the same time, employing i-vectors for modeling speaker characteristics is noted to be superior. Finally, we have also presented a detailed study on the effect of data augmentation with child speakers' age variation.
Low-Cost Network Scheduling of 3D-CNN Processing for Embedded Action Recognition

The recent 3D convolutional neural network (3D-CNN) is a promising candidate for solving the action recognition problem by providing attractive algorithm-level performance.Due to the excessive amount of computational costs, however, it is almost impractical to apply the advanced 3D-CNN architecture to the resource-limited real-time embedded system.In this work, we present several optimization schemes that can relax the complexity of 3D-CNN processing without sacrificing recognition accuracy.More precisely, we first develop several 3D-CNN architectures for exploiting the trade-off between the network complexity and recognition performance.Evaluating the current confidential level, then, the proposed method dynamically changes the network structure to be used for the next clip-level inference.In addition, we introduce a systematic way of managing the network sequence for minimizing the computing overheads while supporting the acceptable algorithm-level performance.Compared to the previous works, as a result, the proposed approaches drastically relax the processing costs as well as the energy consumption by selecting the simplest 3D-CNN architecture at the run time, allowing the cost-effective action recognition for embedded edges.
Large-Scale Subspace Clustering via k-Factorization

Subspace clustering (SC) aims to cluster data lying in a union of low-dimensional subspaces. Usually, SC learns an affinity matrix and then performs spectral clustering. Both steps suffer from high time and space complexity, which leads to difficulty in clustering large datasets. This paper presents a method called k-Factorization Subspace Clustering (k-FSC) for large-scale subspace clustering. K-FSC directly factorizes the data into k groups via pursuing structured sparsity in the matrix factorization model. Thus, k-FSC avoids learning affinity matrix and performing eigenvalue decomposition, and has low (linear) time and space complexity on large datasets. This paper proves the effectiveness of the k-FSC model theoretically. An efficient algorithm with convergence guarantee is proposed to solve the optimization of k-FSC. In addition, k-FSC is able to handle sparse noise, outliers, and missing data, which are pervasive in real applications. This paper also provides online extension and out-of-sample extension for k-FSC to handle streaming data and cluster arbitrarily large datasets. Extensive experiments on large-scale real datasets show that k-FSC and its extensions outperform state-of-the-art methods of subspace clustering.
Object Detection for Autonomous Driving using YOLO algorithm

In this paper, we have presented the work that we have done pertaining to object detection for the purpose of aiding autonomous vehicles during navigation. The detections are carried out using the state-of-the-art YOLO model which is trained on a custom-made model on a custom-made dataset. The main objective of this paper is to highlight the building of the model on low-cost computing resources. After training, we have implemented the model on video data as well as on web camera.
Joint Coding Strategy of the Phase Domain and Intensity Domain for Absolute Phase Retrieval

Codewords used to determine fringe orders are significant for absolute phase retrieval based on the phase-coding method. However, because of inevitable interference from image blurring, image noise, and system nonlinearity, it is challenging to significantly increase the number of codewords for high-accuracy phase retrieval. To address this challenge, this article proposes a joint coding strategy to perform the coding of high-density stripes. More specifically, codewords are simultaneously embedded into the phase domain and the intensity domain of composite fringe patterns, which greatly extends the encoding range of valid codewords. Moreover, to improve the anti-interference capability of the proposed coding strategy, a regional correction technique (RCT) is presented to remove jump phase errors induced by the inevitable interference. The results of experiments indicate that the proposed method is effective and accurate for both complex objects and a standard sphere.