Reviewerly/exHarmony huggingface dataset:
Applying Machine Learning to LTE Traffic Prediction: Comparison of Bagging, Random Forest, and SVM

Today, a significant share of smartphone applications use Artificial Intelligence (AI) elements that, in turn, are based on Machine Learning (ML) principles. Particularly, ML is also applied to the Edge paradigm aiming to predict and optimize the network load conventionally caused by human-based traffic, which is growing each year rapidly. The application of both standard and deep ML techniques is expected to improve the networks' operation in the most complex heterogeneous environment. In this work, we propose a method to predict the LTE network edge traffic by utilizing various ML techniques. The analysis is based on the public cellular traffic dataset, and it presents a comparison of the quality metrics. The Support Vector Machines method allows much faster training than the Bagging and Random Forest that operate well with a mixture of numerical and categorical features.
Mixture Correntropy-Based Kernel Extreme Learning Machines

Kernel-based extreme learning machine (KELM), as a natural extension of ELM to kernel learning, has achieved outstanding performance in addressing various regression and classification problems. Compared with the basic ELM, KELM has a better generalization ability owing to no needs of the number of hidden nodes given beforehand and random projection mechanism. Since KELM is derived under the minimum mean square error (MMSE) criterion for the Gaussian assumption of noise, its performance may deteriorate under the non-Gaussian cases, seriously. To improve the robustness of KELM, this article proposes a mixture correntropy-based KELM (MC-KELM), which adopts the recently proposed maximum mixture correntropy criterion as the optimization criterion, instead of using the MMSE criterion. In addition, an online sequential version of MC-KELM (MCOS-KELM) is developed to deal with the case that the data arrive sequentially (one-by-one or chunk-by-chunk). Experimental results on regression and classification data sets are reported to validate the performance superiorities of the new methods.
WF-GAN: Fighting Back Against Website Fingerprinting Attack Using Adversarial Learning

Website Fingerprinting (WF) attack is an side-channel attack which aims at encrypted web traffic. WF attackers recognize encrypted website traffic through constructing fingerprinting for each website using the flow-based features extracted from encrypted traffic. WF defense typically aims at modifying the features of the encrypted websites. However, those countermeasures either cause high overhead or fail to counter the subsequent WF attacks. Especially, the newest WF attacks, which are based on deep neural network, is able to classify the defended traffic by directly learning from the labeled defended traffic. In this paper, we propose an novel defense through making use of the trick that machine learning models are vulnerable to adversarial exmaples. We design WF-GAN, a GAN with an additional WF classifier component, to generate adversarial examples for WF classifiers through adversarial learning. As the website set is divided into source and target website, WF-GAN are trained to map websites features from source set to adversarial examples and make adversarial examples more similar to the website features in the target set. The experimental result shows that WF-GAN achieves 90% success rate with at most 15% overhead for untargeted defense, which outperforms previous defense. In addition, adversarial examples based defense support targeted defense, which is not support by traditional defense. The result shows that WF-GAN achieves over 90% targeted defense success rate when the target websites set is twice as many as the source website set.
Can We Forecast Presidential Election Using Twitter Data? An Integrative Modelling Approach

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Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations

Eye movements are vital for human vision, and it is therefore important to understand how observers decide where to look. Meaning maps (MMs), a technique to capture the distribution of semantic information across an image, have recently been proposed to support the hypothesis that meaning rather than image features guides human gaze. MMs have the potential to be an important tool far beyond eye-movements research. Here, we examine central assumptions underlying MMs. First, we compared the performance of MMs in predicting fixations to saliency models, showing that DeepGaze II – a deep neural network trained to predict fixations based on high-level features rather than meaning – outperforms MMs. Second, we show that whereas human observers respond to changes in meaning induced by manipulating object-context relationships, MMs and DeepGaze II do not. Together, these findings challenge central assumptions underlying the use of MMs to measure the distribution of meaning in images.
Resonator Networks, 1: An Efficient Solution for Factoring High-Dimensional, Distributed Representations of Data Structures

The ability to encode and manipulate data structures with distributed neural representations could qualitatively enhance the capabilities of traditional neural networks by supporting rule-based symbolic reasoning, a central property of cognition. Here we show how this may be accomplished within the framework of Vector Symbolic Architectures (VSAs) (Plate, 1991; Gayler, 1998; Kanerva, 1996), whereby data structures are encoded by combining high-dimensional vectors with operations that together form an algebra on the space of distributed representations. In particular, we propose an efficient solution to a hard combinatorial search problem that arises when decoding elements of a VSA data structure: the factorization of products of multiple codevectors. Our proposed algorithm, called a resonator network, is a new type of recurrent neural network that interleaves VSA multiplication operations and pattern completion. We show in two examples-parsing of a tree-like data structure and parsing of a visual scene-how the factorization problem arises and how the resonator network can solve it. More broadly, resonator networks open the possibility of applying VSAs to myriad artificial intelligence problems in real-world domains. The companion article in this issue (Kent, Frady, Sommer, & Olshausen, 2020) presents a rigorous analysis and evaluation of the performance of resonator networks, showing it outperforms alternative approaches.
Developing English Conversation Chatbot Using Dialogflow

Despite the fact that the use of English language is inevitable nowadays, some people find it difficult to learn. Moreover, the advance of technology has brought impacts on the way how people learn the language such as speech recognition technology that is used to develop learning tools. However, some existing learning media have several shortcomings. Therefore, to bridge this gap, such media to learn and practice the language are needed. This paper focuses on developing English conversation chatbot using speech recognition and artificial intelligence technology with Dialogflow platform as the artificial intelligence engine. Evaluations toward the chatbot were done by the expert review to identify the achievement of indicators and the users to know the level of responses accuracy. The result indicated that almost all agents have reached all indicators and most of the responses had accuracy rate of 100%. It is expected that the availability of the chatbot helps students to improve their conversation skills.
Hyper-parameter-evolutionary latent factor analysis for high-dimensional and sparse data from recommender systems

High-dimensional and Sparse (HiDS) data generated by recommender systems (RSs) contain rich knowledge regarding users’ potential preferences. A Latent factor analysis (LFA) model enables efficient extraction of essential features from such data. However, an LFA model relies heavily on its hyper-parameters like learning rate and regularization coefficient, which must be chosen with care. However, traditional grid-search-based manual tuning is extremely time-consuming and computationally expensive. To address this issue, this study proposes a hyper-parameter-evolutionary latent factor analysis (HLFA) model. Its main idea is to build a swarm by taking the hyper-parameters of every single LFA-based model as particles, and then apply particle swarm optimization (PSO) to make its both hyper-parameters, i.e., the learning rate and regularization coefficient, self-adaptive according to a pre-defined fitness function. Experimental results on six HiDS matrices from real RSs indicate that an HLFA model outperforms several state-of-the-art LF models in terms of computational efficiency, and most importantly, without loss of prediction accuracy for missing data of an HiDS matrix.
Almost-exact state transfer by leakage-elimination-operator control in a non-Markovian environment

We investigate the feasibility of using an effective pulse control method to realize an almost exact state transmission through a uniform chain where the communication channel is immersed in a quantum environment. This pulse control is treated in a nonperturbative fashion, allowing for a more precise view. Using the non-Markovian quantum state diffusion equation, we calculate the dynamics of the transmission fidelity. The results show that the memory effects of the environment can support an effective transmission control that can be boosted under a non-Markovian environment. Our scheme provides another way to realize almost exact state transmission in a spin chain system.
Comparison of Eye-Based and Controller-Based Selection in Virtual Reality

Eye tracking, or pointing, in head-mounted displays enables new input modalities for point-select tasks. The goal of this paper is to explore the Fitts’ modeling of the eye-based selection in a virtual reality environment with controller-based input, providing the baseline for two types of eye-based interaction (dwell and physical trigger) in both three-dimensional and two-dimensional environment. In general, the controller-based interaction offered the highest throughput, best accuracy, and preferences of most participants. The eye-trigger interaction performed roughly between the other two. However, the performance difference between the three interaction modes had become smaller when it comes to three-dimensional targets. The performance of eye-movement interactions had been slightly better in terms of accuracy. Generally speaking, eye-based interaction still has a long way to go before becoming one of the mainstream interaction modalities in virtual reality due to the absence of a more stable and precise eye-tracking device with better calibration methods. Still, in some specific virtual reality environments, eye-based interaction has irreplaceable potential.
Improving Harris hawks optimization algorithm for hyperparameters estimation and feature selection in v‐support vector regression based on opposition‐based learning

Abstract Many real problems have been solved by support vector regression, especially v‐support vector regression (v‐SVR), but there are hyperparameters that usually needed to tune. In addition, v‐SVR cannot perform feature selection. Nature‐inspired algorithms have been used as a feature selection and as hyperparameters estimation procedure. In this paper, the opposition‐based learning Harris hawks optimization algorithm (HHOA‐OBL) is proposed to optimize the hyperparameters of the v‐SVR with embedding the feature selection simultaneously. The experimental results over four datasets show that the HHOA‐OBL outperforms the standard Harris hawks optimization algorithm, grid search, and cross‐validation methods, in terms of prediction, number of selected features, and running time. Besides, the experimental results of the HHOA‐OBL confirm the efficiency of the proposed algorithm in improving the prediction performance and computational time compared with other nature‐inspired algorithms, which proves the ability of HHOA‐OBL in searching for the best hyperparameters values and selecting the most informative features for prediction tasks. Thus, the experiments and comparisons support the performance of the proposed approach in making predictions in other real applications.
Fast high-precision ellipse detection method

Obtaining an optimal tradeoff between accuracy and efficiency in ellipse detection is a significant challenge. In this paper, we propose a fast, high-precision ellipse detection method that utilizes arc selection and grouping strategies to significantly reduce the computation amount. A fast corner detection algorithm is also proposed. In the proposed method, to generate ellipse candidates comprehensively, both grouped and ungrouped-salient arcs are fitted. Further, the salient ellipse candidates are selected as final detections that are subject to the selection strategy, which realizes both validation and de-redundancy (clustering) functions. A complexity analysis of the method revealed that the detection time is linearly related to the number of edge points. The results of extensive experiments conducted on three public datasets demonstrate that the proposed method is approximately 75% faster than state-of-the-art methods with comparable or higher precision, and its detection time is less than 30 ms in most cases.
Incentivizing Resource Cooperation for Blockchain Empowered Wireless Power Transfer in UAV Networks

In unmanned aerial vehicle (UAV) networks, UAVs-assisted wireless power transfer is a promising approach to charge low-power smart devices for energy replenishment. However, security and privacy concerns associated with the energy micro-transactions in untrusted wireless trading environment present serious challenges. In this paper, we exploit Directed Acyclic Graph (DAG) and consortium blockchain to propose a new distributed and secure UAVs-assisted wireless power transfer framework named aerial-ground chain, where heterogeneous consensus is developed to verify the energy micro-transactions. Specifically, the UAVs verify energy micro-transactions simultaneously but asynchronously to form an aerial tangle. The smart devices also participate in maintaining the aerial tangle. We define timeout for the energy micro-transactions, and leverage a set of Access Points (APs) to cooperatively confirm the timeout energy micro-transaction by utilizing consortium blockchain, to form a ground main chain. Furthermore, a contract theory based resource cooperation scheme is designed to motivate the UAVs to participate in wireless power transfer, and to incentivize the APs to contribute their resources in cooperatively verifying the timeout energy micro-transactions. Security analysis and numerical results illustrate that the developed aerial-ground chain and the designed contract theory based resource cooperation scheme are secure and efficient for UAVs-assisted wireless power transfer.
Alteration and structural features mapping in Kacho-Mesqal zone, Central Iran using ASTER remote sensing data for porphyry copper exploration

(2021). Alteration and structural features mapping in Kacho-Mesqal zone, Central Iran using ASTER remote sensing data for porphyry copper exploration. International Journal of Image and Data Fusion: Vol. 12, No. 2, pp. 155-175.
Investigating college EFL learners’ perceptions toward the use of Google Assistant for foreign language learning

Recent studies on the use of Intelligent Personal Assistant (IPA) for second language (L2) learnring have found that IPAs such as Amazon's Alexa is useful and motivating for L2 learners and that learners' language proficiency might influence their perceptions toward IPAs. However, most existing studies focused on the potentials of Alexa and paid little attention to Google Assistant (GA). This study was thus conducted to investigate how L2 learners at different proficiency levels perceive the potentials of GA. Twenty-nine EFL college students were recruited to try various voice commands for an hour in order to investigate learners' perceptions toward the potentials of GA for language learning. Results of the survey and interview show that these learners enjoyed interacting with GA and considered GA an inspiring tool to learn English. They also found that GA can be useful in improving their speaking and listening skills. They perceived GA's pronunciation to be quite natural and its utterances to be easily comprehensible. Results also reveal that higher level learners achieved better mutual comprehensibility with GA, whereas lower level learners encountered more challenges due to their mispronunciations. Based on these findings, future research directions and pedagogical suggestions on IPA-assisted L2 learning are provided.
Gosig

Existing Byzantine fault tolerance (BFT) protocols face significant challenges in safety, scalability, throughput, and latency. We present a new BFT protocol, Gosig, for the consortium blockchains. Gosig guarantees safety even in asynchronous networks fully controlled by adversaries, by combining secret leader selection with multi-round voting. We co-design both the consensus protocol and the underlying gossip network to optimize performance. In particular, we adopt transmission pipelining to fully utilize the network bandwidth while use aggregated signature gossip to reduce the number of messages. These optimizations help Gosig to achieve unprecedented single-chain performance. On a public cloud testbed spanning multiple data centers consisting of 280 nodes across 14 cities on five continents, Gosig achieves over 15,000 transactions per second with 15.8-second confirmation time. When the system scales to 5,000 nodes, Gosig can still achieve 3,000 transactions per second with about 23.9-second confirmation time.
Attention-Based Gated Recurrent Unit for Gesture Recognition

Gesture recognition becomes a thriving research area in modern human motion recognition systems. The intensification of demands on efficient interactive human-machine-interface systems, commercial objectives, and many other factors contributes to fuel this revival dynamics. Understanding human gestures becomes essential for prevention and health monitoring applications. In particular, analyzing hand gestures is of paramount importance in personalized healthcare-related applications to help practitioners providing more qualitative assessments of subject's pathologies, such as Parkinson's diseases. This work proposes a novel deep neural network approach to forecast future gestures from a given sequence of hand motion using a wearable capacitance sensor of an innovative gesture recognition hardware system. To do this, we use an attention-based recurrent neural network to capture the temporal features of hand motion to unveil the underlying pattern between the gesture and these sequences. While the attention layers capture patterns from the weights of the short term, the gated recurrent unit (GRU) neural network layer learns the inherent interdependency of long-term hand gesture temporal sequences. The efficiency of the proposed model is evaluated with respect to cutting-edge work in the field using several metrics. Note to Practitioners-In this article, the problem of human hand gesture recognition is analyzed using deep learning techniques. The proposed model uses input historical motion sequences collected from a wearable capacitance sensor to predict hand gestures. The model leverages the intrinsic correlation of motion sequences and extracts the salient part of the sequences by taking into consideration their temporal, complex, and nonlinear features. The approach studies the effect of different lengths of historical motion sequences in prediction outcomes. This allows for avoiding using cumbersome data collection, heavy data treatment, and high computational cost. The model performance is trained and assessed on real-world data by performing comparisons with alternative approaches, including well-known classifiers. The model yields very encouraging results and demonstrates that the proposed approach is quite competitive as it can reproduce typical activity trends for important channels. The present findings could help in the development of intelligent wearable devices for predicting hand gestures using a limited number of channels. This work could also help practitioners to provide a more qualitative appraisal of patients suffering from different pathologies such as Parkinson's diseases to personalized healthcare-related applications and to develop wearable gesture recognition devices on a large scale.
Black-box adversarial attacks by manipulating image attributes

Although there exist various adversarial attacking methods, most of them are performed by generating adversarial noises. Inspired by the fact that people usually set different camera parameters to obtain diverse visual styles when taking a picture, we propose the adversarial attributes, which generate adversarial examples by manipulating the image attributes like brightness, contrast, sharpness, chroma to simulate the imaging process. This task is accomplished under the black-box setting, where only the predicted probabilities are known. We formulate this process into an optimization problem. After efficiently solving this problem, the optimal adversarial attributes are obtained with limited queries. To guarantee the realistic effect of adversarial examples, we bound the attribute changes using Lp norm versus different p values. Besides, we also give a formal explanation for the adversarial attributes based on the linear nature of Deep Neural Networks (DNNs). Extensive experiments are conducted on two public datasets, including CIFAR-10 and ImageNet with respective to four representative DNNs like VGG16, AlexNet, Inception v3 and Resnet50. The results show that at most 97.79% of images in CIFAR-10 test dataset and 98.01% of the ImageNet images can be successfully perturbed to at least one wrong class with only ⩽300 queries per image on average.
Learning Flow-Based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision

Despite recent advances in deep learning-based face frontalization methods, photo-realistic and illumination preserving frontal face synthesis is still challenging due to large pose and illumination discrepancy during training. We propose a novel Flow-based Feature Warping Model (FFWM) which can learn to synthesize photo-realistic and illumination preserving frontal images with illumination inconsistent supervision. Specifically, an Illumination Preserving Module (IPM) is proposed to learn illumination preserving image synthesis from illumination inconsistent image pairs. IPM includes two pathways which collaborate to ensure the synthesized frontal images are illumination preserving and with fine details. Moreover, a Warp Attention Module (WAM) is introduced to reduce the pose discrepancy in the feature level, and hence to synthesize frontal images more effectively and preserve more details of profile images. The attention mechanism in WAM helps reduce the artifacts caused by the displacements between the profile and the frontal images. Quantitative and qualitative experimental results show that our FFWM can synthesize photo-realistic and illumination preserving frontal images and performs favorably against the state-of-the-art results. Our code is available at https://github.com/csyxwei/FFWM.