Reviewerly/exHarmony huggingface dataset:
Measuring distance between quantum states on a quantum computer

We propose protocols for determining the distances in Hilbert space between pure and mixed quantum states prepared on a quantum computer. In the case of pure quantum states, the protocol is based on measuring the square of modulus of scalar product between certain states. Determination of the distance between mixed quantum states is reduced to measuring the squares of modules of scalar products between all pure states included in the mixed states. In addition, we develop a protocol that allows one to determine the speed of evolution of the spin system simulated by a quantum computer. We apply these protocols to measure distances and speeds of evolution of different quantum systems implemented on the ibmq-santiago quantum computer.
Real Time Sign Language Recognition Framework For Two Way Communication

The need to express oneself clearly and freely is a fundamental desire and must be available to all regardless of any handicap they face. Often, people that endure hearing and speech impairments use sign language as their only way of communication. This leaves a major communication barrier between people who speak sign language and those that communicate otherwise. Not a lot of people who communicate via vocal languages are inclined to learn sign language. The contribution of this paper is to develop a model which converts signs from the American Sign Language (ASL) into English characters using a CNN which segments the signs to their English language alphabets. The model was able to detect 26 alphabets and 3 additional characters with upto 99.78% accuracy. This paper also proposes inclusion of a speech to text module in order to facilitate seamless two way communication.
Content-Aware Listwise Collaborative Filtering

Recently, listwise collaborative filtering (CF) algorithms are attracting increasing interest due to their efficiency and prediction quality. Different from rating-oriented (pointwise) CF, they recommend a preference ranking of items to each user without estimating the absolute value of the ratings. In practice, there are extensive side information about users and items in many corpora that can improve recommendation performance and specially help CF methods to address the cold-start problem. However, a model has not been proposed that incorporates side information to listwise CF. Therefore, in this work, a Bayesian graphical model, called Content-Aware Listwise Collaborative Filtering (CALCF), is developed which incorporates text information, represented as a bag of words, to listwise CF using topic models. We propose a Gibbs sampler with closed-form samples using data augmentation techniques to infer the latent variables. CALCF has been validated by comparison with previous listwise and pointwise algorithms on implicit (click, view, purchase) and explicit (numeric ratings) feedback data in both cold-start and warm-start scenarios. The results demonstrate that in most cases CALCF achieves better Normalized Discounted Cumulative Gain (NDCG) and Recall at top M recommendation compared with the previous models.
Estimating high-spatial resolution surface daily longwave radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product

In this paper, time extension methods, originally designed for clear-sky land surface conditions, are used to estimate high-spatial resolution surface daily longwave (LW) radiation from the instantaneous Global LAnd Surface Satellite (GLASS) longwave radiation product. The performance of four time methods were first tested by using ground based flux measurements that were collected from 141 global sites. Combined with the accuracy of daily LW radiation estimated from the instantaneous GLASS LW radiation, the linear sine interpolation method performs better than the other methods and was employed to estimate the daily LW radiation as follows: The bias/Root Mean Square Error (RMSE) of the linear sine interpolation method were −6.30/15.10 W/m2 for the daily longwave upward radiation (LWUP), −1.65/27.63 W/m2 for the daily longwave downward radiation (LWDN), and 4.69/26.42 W/m2 for the daily net longwave radiation (LWNR). We found that the lengths of the diurnal cycle of LW radiation are longer than the durations between sunrise and sunset and we proposed increasing the day length by 1.5 h. The accuracies of daily LW radiation were improved after adjusting the day length. The bias/RMSE were −4.15/13.74 W/m2 for the daily LWUP, −1.3/27.52 W/m2 for the daily LWDN, and 2.85/25.91 W/m2 for the daily LWNR. We are producing long-term surface daily LW radiation values from the GLASS LW radiation product.
Automated Computer Aided Diagnosis Using Altered Multi-Phase Level Sets in Application to Categorize the Breast Cancer Biopsy Images

As macroscopic Breast Cancer (BC) imaging diagnostics do not provide satisfied data, microscopic image analysis is used to get optimum cell-nuclei segmentation for image categorization. The paper presents a Novel Hybrid Segmentation Method which uses the energy detail coefficients attained from Non-Subsampled Contourlet Transform for cell-nuclei segmentation of BC histopathological images. NHSM solves the local inhomogeneities problem while preserving all necessary details. The system evaluation is done using training dataset of 98 BC histopathological images and testing dataset of 26 images. Total of 25 segmented features was extracted for classification using Decision Trees, k-Nearest Neighbor, multilayer perceptron, Multiclass-Support Vector Machine (M-SVM). The proposed NHSM segmentation results were undergone through both subjective and objective performance evaluation. Result evaluation proved that NHSM along with Multi Class-Support Vector Machine classifier provides highly precise and accurate classified result for BC tissue images compared to state-of-the-art segmentation methods. Also, the edge detection model initialization is highly robust and insensitive.
Learning Graph Representations With Maximal Cliques

Non-Euclidean property of graph structures has faced interesting challenges when deep learning methods are applied. Graph convolutional networks (GCNs) can be regarded as one of the successful approaches to classification tasks on graph data, although the structure of this approach limits its performance. In this work, a novel representation learning approach is introduced based on spectral convolutions on graph-structured data in a semisupervised learning setting. Our proposed method, COnvOlving cLiques (COOL), is constructed as a neighborhood aggregation approach for learning node representations using established GCN architectures. This approach relies on aggregating local information by finding maximal cliques. Unlike the existing graph neural networks which follow a traditional neighborhood averaging scheme, COOL allows for aggregation of densely connected neighboring nodes of potentially differing locality. This leads to substantial improvements on multiple transductive node classification tasks.
Co-Teaching: an Ark to Unsupervised Stereo Matching

Stereo matching is a key component of autonomous driving perception. Recent unsupervised stereo matching approaches have received adequate attention due to their advantage of not requiring disparity ground truth. These approaches, however, perform poorly near occlusions. To overcome this drawback, in this paper, we propose CoT-Stereo, a novel unsupervised stereo matching approach. Specifically, we adopt a co-teaching framework where two networks interactively teach each other about the occlusions in an unsupervised fashion, which greatly improves the robustness of unsupervised stereo matching. Extensive experiments on the KITTI Stereo benchmarks demonstrate the superior performance of CoT-Stereo over all other state-of-the-art unsupervised stereo matching approaches in terms of both accuracy and speed. Our project webpage is https://sites.google.com/view/cot-stereo.
A Machine Learning Approach to Classify Network Traffic

There is a significant increase of cloud and networking-enabled applications, leading to an exponential growth of network traffic. Monitoring all these applications and their generated traffic is a challenging and complex task, especially in regard to anonymous networks used to access the Dark Web or darknet. Manual investigation of the network traffic to determine patterns of malicious activity is a very difficult task and the results might not be satisfactory. The usage of machine learning is the most viable approach for the classification of the traffic as normal or malign activity. This paper analyzes CIC-Darknet 2020 dataset to classify the benign and darknet traffic. Before applying any classifiers to our dataset, we have balanced it using Synthetic Minority Oversampling Technique (SMOTE). We have applied PCA to reduce dimensionality, furthermore, ensemble techniques, logistic classifiers, tree-classifiers, and Naive Bayes have been compared and evaluated thoroughly with various evaluation metrics Accuracy, Precision, Recall, F1-Score, and Mathew’s Correlation Coefficient (MCC).
Recent Developments in Sentiment Analysis on Social Networks: Techniques, Datasets, and Open Issues

In recent years, sentiment analysis has been highly used on social media datasets to get conclusive information, opinions of users about different topics, such as politics, events, and products, and predicting users' mood or emotions. Marketing companies are very interested in finding out people's opinions and consider that in designing marketing strategies. However, filtering and monitoring the sentiments remain a formidable task due to the proliferation of diverse websites. Researchers have proposed numerous methods for automating the process of filtering and summarizing users' sentiment and opinion on social media. In this chapter, we present a systematic review of sentiment analysis techniques, including machine learning-based, deep learning-based, lexicon-based, graph-based, and hybrid approaches. We further provide a concise list of openly available datasets that have been used for research studies. Finally, we cover future directions and the respective challenges that should be looked at for further enhancing the sentiment analysis for microblog datasets of social networks.
Fast black-box quantum state preparation based on linear combination of unitaries

Black-box quantum state preparation is a fundamental primitive in quantum algorithms. Starting from Grover, a series of techniques have been devised to reduce the complexity. In this work, we propose to perform black-box state preparation using the technique of linear combination of unitaries (LCU). We provide two algorithms based on a different structure of LCU. Our algorithms improve upon the existed best results by reducing the required additional qubits and Toffoli gates to 2log(n) and n, respectively, in the bit precision n. We demonstrate the algorithms using the IBM Quantum Experience cloud services. The further reduced complexity of the present algorithms brings the black-box quantum state preparation closer to reality.
Improving Image Quality In Low-Field MRI With Deep Learning

Low-field magnetic resonance (MR) images suffer from inherent low Signal-to-noise ratio (SNR) compared to images acquired using high-field MRI scanners. Denoising these images could help and improve further processing, such as image segmentation. In this paper a Convolutional Neural Network AutoEncoder was designed with a dedicated loss function for noise reduction. A transfer learning approach was employed in which high-field high-SNR MR images, served as targets for learning from their noise-added counterparts. Evaluation of network performances was measured on both noisy high-field and low-field MR images that had not been included in the learning step. The proposed method outperformed major denoising methods applied to MR images. SNR improvements were quantified on low-field MR images.
Quasi-Identifier Recognition Algorithm for Privacy Preservation of Cloud Data Based on Risk Reidentification

Cloud computing plays an essential role as a source for outsourcing data to perform mining operations or other data processing, especially for data owners who do not have sufficient resources or experience to execute data mining techniques. However, the privacy of outsourced data is a serious concern. Most data owners are using anonymization-based techniques to prevent identity and attribute disclosures to avoid privacy leakage before outsourced data for mining over the cloud. In addition, data collection and dissemination in a resource-limited network such as sensor cloud require efficient methods to reduce privacy leakage. The main issue that caused identity disclosure is quasi-identifier (QID) linking. But most researchers of anonymization methods ignore the identification of proper QIDs. This reduces the validity of the used anonymization methods and may thus lead to a failure of the anonymity process. This paper introduces a new quasi-identifier recognition algorithm that reduces identity disclosure which resulted from QID linking. The proposed algorithm is comprised of two main stages: (1) attribute classification (or QID recognition) and (2) QID dimension identification. The algorithm works based on the reidentification of risk rate for all attributes and the dimension of QIDs where it determines the proper QIDs and their suitable dimensions. The proposed algorithm was tested on a real dataset. The results demonstrated that the proposed algorithm significantly reduces privacy leakage and maintains the data utility compared to recent related algorithms.
Overview on the Blockchain-Based Supply Chain Systematics and Their Scalability Tools

Modern IT technologies shaped the shift in economic models with many advantages on cost, optimization, and time to market. This economic shift has increased the need for transparency and traceability in supply chain platforms to achieve trust among partners. Distributed ledger technology (DLT) is proposed to enable supply chains systems with trust requirements. In this paper, we investigate the existing DLT-based supply chain projects to show their technical part and limitations and extract the tools and techniques used to avoid the DLT scalability issue. We then set the requirements for a typical DLT-based supply chain in this context. The analyses are based on the scalability metrics such as computing, data storage, and transaction fees that fit the typical supply chain system. This paper highlights the effects of Blockchain techniques on scalability and their incorporation in supply chains systems. It also presents other existing solutions that can be applied to the supply chain. The investigation shows the necessity of having such tools in supply chains and developing them to achieve an efficient and scalable system. The paper calls for further scalability enhancements throughout introducing new tools and/or reutilize the current ones. Doi: 10.28991/esj-2021-SP1-04 Full Text: PDF
Slim: Explicit Slot-Intent Mapping with Bert for Joint Multi-Intent Detection and Slot Filling

Utterance-level intent detection and token-level slot filling are two key tasks for spoken language understanding (SLU) in task-oriented systems. Most existing approaches assume that only a single intent exists in an utterance. However, there are often multiple intents within an utterance in real-life scenarios. In this paper, we propose a multi-intent SLU framework, called SLIM, to jointly learn multi-intent detection and slot filling based on BERT. To fully exploit the existing annotation data and capture the interactions between slots and intents, SLIM introduces an explicit slot-intent classifier to learn the many-to-one mapping between slots and intents. Empirical results on three public multi-intent datasets demonstrate (1) the superior performance of SLIM compared to the current state-of-the-art for SLU with multiple intents and (2) the benefits obtained from the slot-intent classifier.
Blockchain-Based Secure Communication for Space Information Networks

Space information networks (SINs) can be viewed as an expansion of a conventional network with a strong demand for high-speed, reliable, and real-time wireless communication, especially in extreme circumstances. However, there are a number of challenges associated with deploying SINs. With the emergence and wide application of blockchain technology, it is expected to relieve the trust issues of communication systems in SINs. In this article, we first propose a decentralized blockchain architecture for SINs, taking advantage of the increasing computation and communication capabilities of satellites. Then, by utilizing blockchain, we present two communication protocols, Fulgor and Rayo, designed for different application scenarios in SINs: Fulgor is a secure communication protocol between users and the ground station based on our proposed efficient authentication and key agreement protocol. Rayo is a user-to-user priva-cy-preserving communication protocol to protect the user's privacy and avoid DoS attacks. Finally, experiments are conducted and performed on a widely known blockchain platform, Hyperledger Fabric, and the findings demonstrate our proposal's feasibility.
Anomaly detection using Autoencoders and Deep Convolution Generative Adversarial Networks

The current trend in transport (road, railway, unmanned aerial vehicle) introduces autonomous transport means. As the autonomy of vehicles increases, situations arise for which the models have not been trained, which increases the safety risk. Therefore, we propose systems that would detect such "anomaly" situations. We designed two anomaly detectors - an Adversarial Autoencoder (AAE) and a Deep Convolutional Generative Adversarial Networks (DCGAN). These models are build up on models from resources Autoencoders (2020) and Deep (2020). Networks are trained using picture datasets MNIST, Fashion-MNIST and CIFAR10. With DCGAN network, cumulative and reverse cumulative distribution functions are used to find an optimal decision threshold. In the case of autoencoder networks – an optimal number of latent variables is found using reconstruction error ratio function. Then, in both cases (AAE and DCGAN network), cumulative and reverse cumulative distribution functions are used to find an optimal decision threshold. Finally, influence of image picture complexity on the anomaly detection is discussed. We got best results with anomaly detectors trained on the less complex datasets comparing to test datasets. For both detectors trained on the simplest database MNIST, under a given anomaly expectation probability equal to 0.5, we reached the anomaly detection error 0.08% (AAE) and 1.89% (DCGAN).
Isolation kernel: the X factor in efficient and effective large scale online kernel learning

Large scale online kernel learning aims to build an efficient and scalable kernel-based predictive model incrementally from a sequence of potentially infinite data points. Current state-of-the-art large scale online kernel learning focuses on improving efficiency. Two key approaches to gain efficiency through approximation are (1) limiting the number of support vectors, and (2) using an approximate feature map. They often employ a kernel with a feature map with intractable dimensionality. While these approaches can deal with large scale datasets efficiently, this outcome is achieved by compromising predictive accuracy because of the approximation. We offer an alternative approach that puts the kernel used at the heart of the approach. It focuses on creating a sparse and finite-dimensional feature map of a kernel called Isolation Kernel. Using this new approach, to achieve the above aim of large scale online kernel learning becomes extremely simple—simply use Isolation Kernel instead of a kernel having a feature map with intractable dimensionality. We show that, using Isolation Kernel, large scale online kernel learning can be achieved efficiently without sacrificing accuracy.
Multiscale IOU: A Metric for Evaluation of Salient Object Detection with Fine Structures

General-purpose object-detection algorithms often dismiss the fine structure of detected objects. This can be traced back to how their proposed regions are evaluated. Our goal is to renegotiate the trade-off between the generality of these algorithms and their coarse detections. In this work, we present a new metric that is a marriage of a popular evaluation metric, namely Intersection over Union (IoU), and a geometrical concept, called fractal dimension. We propose Multiscale IoU(MIoU) which allows comparison between the detected and ground-truth regions at multiple resolution levels. Through several reproducible examples, we show that MIoU is indeed sensitive to the fine boundary structures which are completely overlooked by IoU and fl-score. We further examine the overall reliability of MIoU by comparing its distribution with that of IoU on synthetic and real-world datasets of objects. We intend this work to re-initiate exploration of new evaluation methods for object-detection algorithms.
Research on Measurement Modeling of Spherical Joint Rotation Angle Based on RBF–ELM Network

The acquisition of rotation angle and pose information of precision spherical joint is of great importance for error motion prediction analysis and motion control. In the early stage, an analytical measurement model based on the equivalent magnetic charge method has been purposed to realize the rotation direction identification and rotation angle measurement. However, several shortcomings were observed such as complicated calculations and time-consuming, and the solution accuracy of the theoretical model was decreasing with the expansion of measurement range. To improve this situation, new modeling methods based on artificial neural network have been researched. This paper employs radial basis function (RBF), extreme learning machine (ELM), and RBF–ELM hybrid neural networks to construct measurement models, then analyzes and compares their effectiveness to obtain the optimal algorithm. Analysis results show that the RBF–ELM hybrid neural network has a better calculation accuracy than the others. Finally, the experimental data are used to train and test the network model, and the error between output angle of the model and the actual measured rotation angle is calculated. The comparison results show that the measurement model based on the RBF–ELM hybrid neural network has a higher calculation accuracy and generalization capability. Within the range of ±20°, the maximum error of rotation angle around the X and Y axes are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9'~48''$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6'~55''$ </tex-math></inline-formula> , respectively, and the root mean squared error is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1' 59''$ </tex-math></inline-formula> .