Reviewerly/exHarmony huggingface dataset:
Implementation of nature-inspired optimization algorithms in some data mining tasks

Data mining optimization received much attention in the last decades due to introducing new optimization techniques, which were applied successfully to solve such stochastic mining problems. This paper addresses implementation of evolutionary optimization algorithms (EOAs) for mining two famous data sets in machine learning by implementing four different optimization techniques. The selected data sets used for evaluating the proposed optimization algorithms are Iris dataset and Breast Cancer dataset. In the classification problem of this paper, the neural network (NN) is used with four optimization techniques, which are whale optimization algorithm (WOA), dragonfly algorithm (DA), multiverse optimization (MVA), and grey wolf optimization (GWO). Different control parameters were considered for accurate judgments of the suggested optimization techniques. The comparitive study proves that, the GWO, and MVO provide accurate results over both WO, and DA in terms of convergence, runtime, classification rate, and MSE.
Multi-domain sentiment analysis with mimicked and polarized word embeddings for human–robot interaction

This paper presents a state-of-the-art approach for sentiment polarity classification. Our approach relies on an ensemble of Bidirectional Long Short-Term Memory networks equipped with a neural attention mechanism. The system makes use of pre-trained word embeddings, and is capable of predicting new vectors for out-of-vocabulary words, by learning distributional representations based on word spellings. Also, during the training process the recurrent neural network is used to perform a fine-tuning of the original word embeddings, taking into account information about sentiment polarity. This step can be particularly helpful for sentiment analysis, as word embeddings are usually built based on context information, while words with opposite sentiment polarity often occur in similar contexts. The system described in this paper is an improved version of an approach that competed in a recent challenge on semantic sentiment analysis. We evaluate the performance of the system on the same multi-domain test set used by the organizers of the challenge, showing that our approach allows reaching better results with respect to the previous top-scoring system. Last but not least, we embedded the proposed sentiment polarity approach on top of a humanoid robot to lively identify the sentiment of the speaking user.
P-wave first-motion polarity determination of waveform data in western Japan using deep learning

Abstract P-wave first-motion polarity is the most useful information in determining the focal mechanisms of earthquakes, particularly for smaller earthquakes. Algorithms have been developed to automatically determine P-wave first-motion polarity, but the performance level of the conventional algorithms remains lower than that of human experts. In this study, we develop a model of the convolutional neural networks (CNNs) to determine the P-wave first-motion polarity of observed seismic waveforms under the condition that P-wave arrival times determined by human experts are known in advance. In training and testing the CNN model, we use about 130 thousand 250 Hz and about 40 thousand 100 Hz waveform data observed in the San-in and the northern Kinki regions, western Japan, where three to four times larger number of waveform data were obtained in the former region than in the latter. First, we train the CNN models using 250 Hz and 100 Hz waveform data, respectively, from both regions. The accuracies of the CNN models are 97.9% for the 250 Hz data and 95.4% for the 100 Hz data. Next, to examine the regional dependence, we divide the waveform data sets according to the observation region, and then we train new CNN models with the data from one region and test them using the data from the other region. We find that the accuracy is generally high ( $${ \gtrsim }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>≳</mml:mo></mml:math> 95%) and the regional dependence is within about 2%. This suggests that there is almost no need to retrain the CNN model by regions. We also find that the accuracy is significantly lower when the number of training data is less than 10 thousand, and that the performance of the CNN models is a few percentage points higher when using 250 Hz data compared to 100 Hz data. Distribution maps, on which polarities determined by human experts and the CNN models are plotted, suggest that the performance of the CNN models is better than that of human experts.
An Edge Computing-enhanced Internet of Things Framework for Privacy-preserving in Smart City

To supervise massive generated data by the Internet of Things (IoT) efficiently, we face two issues that should be addressed which are: (1) heterogeneity or satisfying diversity among IoT devices, and (2) privacy-preserving or preventing unintentional disclosure of sensitive data. Through observation, we found that existing solutions apply one common privacy-preserving rule for all devices while they address the heterogeneity issue separately that lead to unappealing performance. In this paper, we propose a framework for addressing the heterogeneity issue and privacy-preserving of IoT devices at the network edge using a novel proposed ontology data model. Besides, it leverages the proposed ontology to obtain a privacy-preserving method by frequently changing the privacy-preserving behaviors of IoT devices. Through simulation, we show that our solution overhead is less than 9 percent in the worst situation so that it is affordable to most IoT devices in one of its applications that is smart city.
Probabilistic Tile Visibility-Based Server-Side Rate Adaptation for Adaptive 360-Degree Video Streaming

In this article, we study the server-side rate adaptation problem for streaming tile-based adaptive 360-degree videos to multiple users who are competing for transmission resources at the network bottleneck. Specifically, we develop a convolutional neural network (CNN)-based viewpoint prediction model to capture the nonlinear relationship between the future and historical viewpoints. A Laplace distribution model is utilized to characterize the probability distribution of the prediction error. Given the predicted viewpoint, we then map the viewport in the spherical space into its corresponding planar projection in the 2-D plane, and further derive the visibility probability of each tile based on the planar projection and the prediction error probability. According to the visibility probability, tiles are classified as viewport, marginal and invisible tiles. The server-side tile rate allocation problem for multiple users is then formulated as a non-linear discrete optimization problem to minimize the overall received video distortion of all users and the quality difference between the viewport and marginal tiles of each user, subject to the transmission capacity constraints and users' specific viewport requirements. We develop a steepest descent algorithm to solve this non-linear discrete optimization problem, by initializing the feasible starting point in accordance with the optimal solution of its continuous relaxation. Extensive experimental results show that the proposed algorithm can achieve a near-optimal solution, and outperforms the existing rate adaptation schemes for tile-based adaptive 360-video streaming.
Feature Interaction in Terms of Prediction Performance

There has been considerable development in machine learning in recent years with some remarkable successes. Although there are many high-performance methods, the interpretation of learning models remains challenging. Understanding the underlying theory behind the specific prediction of various models is difficult. Various studies have attempted to explain the working principle behind learning models using techniques like feature importance, partial dependency, feature interaction, and the Shapley value. This study introduces a new feature interaction measure. While recent studies have measured feature interaction using partial dependency, this study redefines feature interaction in terms of prediction performance. The proposed measure is easy to interpret, faster than partial dependency-based measures, and useful to explain feature interaction, which affects prediction performance in both regression and classification models.
An Improved Implementation Approach for Quantum Phase Estimation on Quantum Computers

Quantum phase estimation (QPE) is one of the core algorithms for quantum computing. It has been extensively studied and applied in a variety of quantum applications such as the Shor's factoring algorithm, quantum sampling algorithms and the calculation of the eigenvalues of unitary matrices. The QPE algorithm has been combined with Kitaev's algorithm and the inverse quantum Fourier transform (IQFT) which are utilized as a fundamental component of such quantum algorithms. In this paper, we explore the computational challenges of implementing QPE algorithms on noisy intermediate-scale quantum (NISQ) machines using the IBM Q Experience (e.g., the IBMQX4, 5-qubit quantum computing hardware platform). Our experimental results indicate that the accuracy of finding the phase using these QPE algorithms is severely constrained by the NISQ computer's physical characteristics such as coherence time and error rates. To mitigate these physical limitations, we propose implementing a modified solution by reducing the number of controlled rotation gates and phase shift operations, thereby increasing the accuracy of the finding phase in near-term quantum computers.
A Dynamic Service-Oriented Software Architecture for Highly Automated Vehicles

We present an ecosystem comprised of multiple building blocks that are centered around a simple, pragmatic concept for service-oriented architectures (SOA). Due to rigid design-time integration, today's prevailing automotive electric, electronic and software architectures are often unsuitable for infield updates or system reconfigurations. As statically integrated architectures do not provide the flexibility required to keep up with shorter development and technology life cycles of connected and automated vehicles (AVs), service-oriented architectures are a promising way forward. We present a SOA concept that allows for dynamic, runtime integrated software architectures. Our implementation stack equally supports full-scale computers and resource-constrained platforms. We facilitate architecture specification through a web-based description tool, which follows a simple paradigm that has applications beyond software architectures. The evaluation provides benchmarks of our implementation on an automotive-grade embedded system and a trial login to our architecture specification tool.
Convolutional neural networks performance comparison for handwritten Bengali numerals recognition

Handwritten recognition has drawn profound attention since decades ago due to its numerous potential applications in real life. Research on unconstrained handwritten recognition in some languages has achieved attractive advancement, but it lags behind for Bengali even though it is the major language spoken by about 230 million people in the Indian subcontinent, and even the first and official language of Bangladesh. Recently, the use of convolutional neural network (CNN) has been reported with high accuracy in pattern recognition and computer vision problems. The main purpose of this study is to provide an architecture of a CNN to improve the accuracy of handwritten Bengali numerals recognition (HBNR) and compare its performance with the existing ones. We proposed a new CNN architecture, VGG-11M, which improves an existing one (VGG-11). The normalized and rescaled images of each numeral were augmented by different transformation operations to increase the training samples and to add diversity in the dataset. Then, the images were used to train the proposed VGG-11M model. The recognition accuracy of the developed system was tested on both training and test sets of three publicly available handwritten Bengali numerals database at different resolutions. Finally the performance of the model was compared with four other architectures (LeNet-5, ResNet-50, VGG-11, and VGG-16). The highest accuracy 99.80%, 99.66%, and 99.25% was obtained using the proposed architecture on the test set of ISI, CMATERDB, and NUMTADB dataset, respectively, at resolution $$32\times {32}$$ . The proposed VGG-11M outperformed the existing architectures of CNN on HBNR.
Supervised People Counting Using An Overhead Fisheye Camera

We propose two supervised methods for people counting using an overhead fisheye camera. As opposed to standard cameras, fisheye cameras offer a large field of view and, when mounted overhead, reduce occlusions. However, methods developed for standard cameras perform poorly on fisheye images since they do not account for the radial image geometry. Furthermore, no large-scale fisheye-image datasets with radially-aligned bounding box annotations are available for training. We adapt YOLOv3 trained on standard images for people counting in fisheye images. In one method, YOLOv3 is applied to 24 rotated, overlapping windows and the results are post-processed to produce a people count. In another method, YOLOv3 is applied to windows of interest extracted by background subtraction. For evaluation, we collected and annotated an indoor fisheye-image dataset that we make public. Experiments on this dataset show that our methods reduce the people counting MAE of two natural benchmarks by over 60%.
Towards Evaluation of NIDSs in Adversarial Setting

Signature-based Network Intrusion Detection Systems (NIDSs) have traditionally been used to detect malicious traffic, but they are incapable of detecting new threats. As a result, anomaly-based NIDSs, built on neural networks, are beginning to receive attention due to their ability to seek out new attacks. However, it has been shown that neural networks are vulnerable to adversarial example attacks in other domains. But, previously proposed anomaly-based NIDSs have not been evaluated in such adversarial settings. In this paper, we show how to evaluate an anomaly-based NIDS trained on network traffic in the face of adversarial inputs. We show how to craft adversarial inputs in the highly constrained network domain, and we evaluate 3 recently proposed NIDSs in an adversarial setting.
Classification of Cultural Heritage Sites Using Transfer Learning

India is a country endowed with rich cultural heritage especially renowned architectural sites of which 37 are UNESCO listed heritage sites. Cultural heritages connects generations over time and we need to preserve them. Architects, historians, travelers etc. They visit many historical sites where it often becomes difficult for them to identify and get historical details about the monument they are interested in. The task of archiving, documenting and sharing the knowledge of these cultural assets is challenging due to the scale and reliability of the information. An accurate prediction of the images to its correct label (heritage site) allows more proficient searches through specific terms, thus helping in the studying and understanding the heritage assets. Classification of data, which involve images, is complex and also time consuming. The present state of art of machine learning techniques can be harnessed to atomize the classification of images. The advent of high processing computing resources, state of art machine learning and deep learning algorithms provide tangible solutions. In this paper, we propose a crowdsourcing platform to collect Indian Digital Heritage Space (IHDS) monuments data, perform image classification and query based retrieval of Image labels. Further we designed a transfer learning based image classifier, which retrained using IHDS dataset on pre-trained MobileNet V2 architecture over ImageNet Dataset. The proposed transfer learning algorithm shows inference accuracy of 98.75%. We demonstrate our crowd source framework using a web application, which is part of Indian Digital Heritage Space (IHDS) project funded by DST, Government of India, New Delhi.
MVP Matching: A Maximum-Value Perfect Matching for Mining Hard Samples, With Application to Person Re-Identification

How to correctly stress hard samples in metric learning is critical for visual recognition tasks, especially in challenging person re-ID applications. Pedestrians across cameras with significant appearance variations are easily confused, which could bias the learned metric and slow down the convergence rate. In this paper, we propose a novel weighted complete bipartite graph based maximum-value perfect (MVP) matching for mining the hard samples from a batch of samples. It can emphasize the hard positive and negative sample pairs respectively, and thus relieve adverse optimization and sample imbalance problems. We then develop a new batch-wise MVP matching based loss objective and combine it in an end-to-end deep metric learning manner. It leads to significant improvements in both convergence rate and recognition performance. Extensive empirical results on five person re-ID benchmark datasets, i.e., Market-1501, CUHK03-Detected, CUHK03-Labeled, Duke-MTMC, and MSMT17, demonstrate the superiority of the proposed method. It can accelerate the convergence rate significantly while achieving state-of-the-art performance. The source code of our method is available at https: //github.com/IAAI-CVResearchGroup/MVP-metric.
A Winograd-Based Integrated Photonics Accelerator for Convolutional Neural Networks

Neural Networks (NNs) have become the mainstream technology in the artificial intelligence (AI) renaissance over the past decade. Among different types of neural networks, convolutional neural networks (CNNs) have been widely adopted as they have achieved leading results in many fields such as computer vision and speech recognition. This success in part is due to the widespread availability of capable underlying hardware platforms. In parallel, hardware specialization can expose us to novel architectural solutions, which can outperform general purpose computers for the tasks at hand. Although different applications demand for different performance measures, they all share speed and energy efficiency as high priorities. Meanwhile, photonics processing has seen a resurgence due to its inherited high speed and low power nature. Here, we investigate the potential of using photonics in CNNs by proposing a CNN accelerator design based on Winograd filtering algorithm. Our evaluation results show that while a photonic accelerator can compete with current state-of-the-art electronic platforms in terms of both speed and power, it has the potential to improve the energy efficiency by up to three orders of magnitude.
Modifiable Public Blockchains Using Truncated Hashing and Sidechains

The immutability of the blockchain technology facilitates it to establish a general consensus in a trustless environment, enabling a wide range of new applications, including distributed general-purpose data management and digital data sharing marketplace. This immutability, however, presents disadvantages for the blockchain technology, when it is used in other areas where the modification of data in blockchain is demanded. In this study, we propose a method for building modifiable blockchains in decentralized public network. To be specific, in computing the hash value of the block, the proposed method uses truncated hash values (these are called ‘target values’ in this paper) of the transactions that are modifiable upon future requests, instead of transactions themselves. By doing so, the proposed method provides an opportunity to modify those transactions by making truncated hash values of modified versions equal to their original target values. The proposed method uses several cryptographic techniques to prevent the modification of the transaction from being performed for malicious purposes, and a multichain structure to improve the efficiency in transaction modification. By accommodating the modification feature to the blockchain, proposed architecture complies to key demands of the data protection regulations such as ‘right to rectification’, ‘right to withdraw consent’, and ‘right to be forgotten’, et cetera. In addition, detailed threat analysis demonstrates that the proposed truncated hash-based modification is sufficiently secure to open up a wide range of new blockchain based services through added modifiability feature.
Improving aspect-based sentiment analysis via aligning aspect embedding

Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task, which aims to predict sentiment polarities of given aspects or target terms in text. ABSA contains two subtasks: Aspect-Category Sentiment Analysis (ACSA) and Aspect-Term Sentiment Analysis (ATSA). Aspect embeddings have been extensively used for representing aspect-categories on ACSA task. Based on our observations, existing aspect embeddings cannot properly represent the relation between aspect-categories and aspect-terms. To address this limitation, this paper presents a learning method which trains aspect embeddings according to the relation between aspect-categories and aspect-terms. According to the cosine measure metric we proposed in this paper, the limitation is successfully alleviated in the aspect embeddings which are trained by our method. The trained aspect embeddings can be used as initialization in existing models to solve ACSA task. We conduct experiments on SemEval datasets for ACSA task, and the results indicate that our pre-trained aspect embeddings are capable of improving the performance of sentiment analysis.
Spatial and Temporal Consistency-Aware Dynamic Adaptive Streaming for 360-Degree Videos

The 360-degree video allows users to enjoy the whole scene by interactively switching viewports. However, the huge data volume of the 360-degree video limits its remote applications via network. To provide high quality of experience (QoE) for remote web users, this paper presents a tile-based adaptive streaming method for 360-degree videos. First, we propose a simple yet effective rate adaptation algorithm to determine the requested bitrate for downloading the current video segment by considering the balance between the buffer length and video quality. Then, we propose to use a Gaussian model to predict the field of view at the beginning of each requested video segment. To deal with the circumstance that the view angle is switched during the display of a video segment, we propose to download all the tiles in the 360-degree video with different priorities based on a Zipf model. Finally, in order to allocate bitrates for all the tiles, a two-stage optimization algorithm is proposed to preserve the quality of tiles in FoV and guarantee the spatial and temporal smoothness. Experimental results demonstrate the effectiveness and advantage of the proposed method compared with the state-of-the-art methods. That is, our method preserves both the quality and the smoothness of tiles in FoV, thus providing the best QoE for users.