Reviewerly/exHarmony huggingface dataset:
Feed Forward Neural Network Sparsificationwith Dynamic Pruning

A recent hot research topic in deep learning concerns the reduction of the model size of a neural network by pruning, in order to minimize its training and inference cost and thus, being capable of running on devices with memory constraints. In this paper, we employ a pruning technique to sparsify a Multi-Layer Perceptron (MLP) during training, in which the number of topology connections, being pruned and restored, is not stable, but it adopts either one of the following rules: Linear Decreasing Variation (LDV) rule or Oscillating Variation (OSV) rule or Exponential Decay (EXD) rule. We conducted experiments on three MLP Network topologies, implemented with Keras, using the Fashion-MNIST dataset and results showed that the EXD method is a clear winner since, in that case our proposed sparse network has a faster convergence than the dense version of the same one, while it achieves approximately the same high accuracy (around 90%). Furthermore, it is shown that the memory footprint of the aforementioned sparse techniques is at least 95% less instead of the dense version of the network, due to the weights removed. Finally, we present an improved version of the SET implementation in Keras, using Callbacks API, making the SET implementation more efficient.
Content-based Fashion Recommender System Using Unsupervised Learning

Data mining today is much slower than before because of the advancement of computing and information systems. Relevant recommendation based on customers' preferences and needs in e-commerce gets more complicated. In the recent pandemic, people are reluctant to go out and has engaged more on internet to get their daily food and services. This phenomenon exacerbated the existing recommendation system, as the data has grown up drastically. In this study, the author recommends a relevant image quality based on the quality queries of the clothes and footwear dataset by observing their highest similarity score. Fashion MNIST images used were existing dataset for clothes and footwear. The testing on image reconstruction using training and validation approaches has shown an accurate result by showing only 0.01 loss in the dataset. Using 11 classes of the image queries, the system image has been identically reconstructed according to the queries supplied. With this result, businesses will have an implementation alternative to a faster and more efficient data mining method. Hence, this alternative will boost the speed of many recommendation systems in the e-commerce platforms and will create a better customer experience.
ClusterRR: a record and replay framework for virtual machine cluster

The Record and Replay (RnR) technology provides the ability to reproduce past execution of systems deterministically. It has many prominent applications, including fault tolerance, security analysis, and failure diagnosis. In system virtualization, previous RnR researches mainly focus on individual VM, including coherent replaying of multi-core systems, reducing performance penalty and storage overhead. However, with the emerging of distributed systems deployed in virtual machine clusters (VMC), the existing RnR technology of individual VM can not meet the requirements of analyzers and developers. The critical challenge for VMC RnR is to maintain the consistency of global state. In this paper, we propose ClusterRR, a RnR framework for VMC. To solve the inconsistency problem, we propose coordination protocols to schedule the record and replay process of VMs. Meanwhile, we employ a Hybrid RnR approach to reduce the performance penalty and storage costs caused by recording network events. Moreover, we implement ClusterRR on QEMU/KVM platform and utilize a network packets retransmission framework to guarantee the reproducibility of VMC replay. Last, we conduct a series of experiments to measure its efficiency and overhead. The results show that ClusterRR would efficiently replay the execution of the whole VMC at instruction-level granularity.
GaborNet: investigating the importance of color space, scale and orientation for image classification

Content-Based Image Retrieval (CBIR) is the cornerstone of today’s image retrieval systems. The most distinctive retrieval approach used, involves the submission of an image-based query whereby the system is used in the extraction of visual characteristics like the shape, color, and texture from the images. Examination of the characteristics is done for ensuring the searching and retrieval of proportional images from the image database. Majority of the datasets utilized for retrieval lean towards to comprise colored images. The colored images are regarded as in RGB (Red, Green, Blue) form. Most colored images use the RGB image for classifying the images. The research presents the transformation of RGB to other color spaces, extraction of features using different color spaces techniques, Gabor filter and use Convolutional Neural Networks for retrieval to find the most efficient combination. The model is also known as Gabor Convolution Network. Even though the notion of the Gabor filter being induced in CNN has been suggested earlier, this work introduces an entirely different and very simple Gabor-based CNN which produces high recognition efficiency. In this paper, Gabor Convolutional Networks (GCNs or GaborNet), with different color spaces are used to examine which combination is efficient to retrieve natural images. An extensive experiment using Cifar 10 dataset was made and comparison of simple CNN, ResNet 50 and GCN model was also made. The models were evaluated through a several statistical analysis based on accuracy, precision, recall, F-Score, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results shows GaborNet model effectively retrieve images with 99.68% of AUC and 99.09% of Recall. The results also shows different images are effectively retrieved using different color space. Therefore research concluded it is very significance to transform images to different color space and use GaborNet for effective retrieval.
A theoretical analysis based on causal inference and single-instance learning

Although using single-instance learning methods to solve multi-instance problems has achieved excellent performance in many tasks, the reasons for this success still lack a rigorous theoretical explanation. In particular, the potential relation between the number of causal factors (also called causal instances) in a bag and the model performance is not transparent. The goal of our study is to use the causal relationship between instances and bags to enhance the interpretability of multi-instance learning. First, we provide a lower bound on the number of instances required to determine causal factors in a real multi-instance learning task. Then, we provide a lower bound on the single-instance learning loss function when testing instances and training instances follow the same distribution and extend this conclusion to the situation where the distribution changes. Thus, theoretically, we demonstrate that the number of causal factors in the bag is an important parameter that affects the performance of the model when using single-instance learning methods to solve multi-instance learning problems. Finally, combining with a specific classification task, we experimentally validate our theoretical analysis.
Multifactorial Evolutionary Algorithm for Simultaneous Solution of TSP and TRP

We study two problems called the Traveling Repairman Problem (TRP) and Traveling Salesman Problem (TSP). The TRP wants to minimize the total time for all customers that have to wait before being served, while the TSP aims to minimize the total time to visit all customers. In this sense, the TRP takes a customer-oriented view, whereas the TSP is server-oriented. In the literature, there exist numerous algorithms that are developed for two problems. However, these algorithms are designed to solve each problem independently. Recently, Multifactorial Evolutionary Algorithm (MFEA) has been a variant of Evolutionary Algorithm (EA) aiming to solve multiple optimization tasks simultaneously. The MFEA framework has yet to be fully exploited, but the realm has recently attracted much interest from the research community. This paper proposed a new approach using the MFEA framework to solve these two problems simultaneously. The MFEA has two tasks simultaneously: the first is solving the TRP problem, and the second is solving the TSP. Experiment results show the efficiency of the proposed MFEA: 1. for small instances, the algorithm reaches the optimal solutions of both problems; 2. for large instances, our solutions are better than those of the previous MFEA algorithms.
Efficient Publicly Verifiable Proofs of Data Replication and Retrievability Applicable for Cloud Storage

Using Proofs of Retrievability (PORs), a file owner is able to check that a cloud server correctly stores her files.Using Proofs of Retrievability and Reliability (PORRs), she can even verify at the same time that the cloud server correctly stores both her original files and their replicas.In 2020, a new PORR combined with Verifiable Delay Functions (VDFs) was presented by Gritti.VDFs are special functions whose evaluation is slow while verification is fast.Therefore, those functions help guarantee that the original files and their replicas are stored at rest.Moreover, an important feature of the 2020 PORR solution is that anyone can verify the cloud provider's behaviour, not only the file owner.This paper extends Gritti's version.In particular, a realistic cloud framework is defined in order to implement and evaluate accurately.Results show that this PORR solution is well suitable for services provided for cloud storage.
Identifying language varieties

This study is concerned with the coherence of a newly-emerged standard variety of Italian, i.e., the so-called neo-standard Italian, as compared with the traditional standard variety. Our analysis aims to test whether, and to what extent, linguistic features which are conventionally associated with these two standard varieties co-occur in actual speech data. A principal component analysis on a corpus of spoken Italian reveals coherent patterns of aggregation of linguistic variables across the two language varieties. The results of our analysis are relevant to the more general problem of identifying language varieties in speakers’ usage. On a theoretical level, we suggest that the identification of aggregates of linguistic variables is better captured by a continuum with focal points rather than by a pure continuum of values or by a set of discrete categories. On an empirical level, the analysis indicates that the traditional standard is a more coherent variety which is strongly associated with precise situational contexts (e.g., in our data, oral examinations at the university). In contrast, the new standard appears to be a more dispersed range of linguistic elements which is called on in different verbal interactions.
Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News

There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TF-IDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news.
Joint metric learning of local and global features for vehicle re-identification

Abstract Vehicle re-identification (ReID) means to identify the target vehicle in large-scale surveillance videos captured by multiple cameras, where robust and distinctive visual features of vehicles are critical to the performance. Recently, the researchers have approached the problem with attention based models. However, most of these models use strongly-supervised methods, which rely on expensive extra labels, e.g., keypoints(vehicle wheels , logo and lamps) and attributes(e.g., color and type). Therefore, we propose a joint metric learning approach to solve the problem. We present an end-to-end Partition and Fusion Multi-branch Network (PFMN), a novel approach to effectively learn discriminative features without any annotations or additional attributes. For hard samples, which means different vehicles with similar appearance or the same vehicle with different appearances, a novel variant of hard sampling triplet loss is proposed. Based on extensive experiments, we have proved the effectiveness of our proposed method. On the challenging public data sets VeRi-776 and VehicleID, our model outperforms most state-of-the-art algorithms on mAP and rank-1. Especially on mINP, which measures the cost of model retrieval hard samples, we can achieve a significant improvement.
Arbitrary coherent distributions in a programmable quantum walk

The coherent superposition of position states in a quantum walk (QW) can be precisely engineered towards the desired distributions to meet the need of quantum information applications. The coherent distribution can make full use of quantum parallel in computation and simulation. Particularly, the uniform superposition provides the robust nonlocality, which has wide applications such as the generation of genuine multibit random numbers without postprocessing. We experimentally demonstrate that the rich dynamics featured with arbitrary coherent distributions can be obtained by introducing different sets of the time- and position-dependent operations. Such a QW is realized by a resource-constant and flexible optical circuit, in which the variable operation is executed based on a Sagnac interferometer in an intrinsically stable and precisely controlled way. Our results contribute to the practical realization of quantum-walk-based quantum computation, quantum simulations, and quantum information protocols.
Evaluating the quantum Ziv–Zakai bound for phase estimation in noisy environments

In the highly non-Gaussian regime, the quantum Ziv-Zakai bound (QZZB) provides a lower bound on the available precision, demonstrating the better performance compared with the quantum Cramér-Rao bound. However, evaluating the impact of a noisy environment on the QZZB without applying certain approximations proposed by Tsang [Phys. Rev. Lett.108, 230401 (2012)10.1103/PhysRevLett.108.230401] remains a difficult challenge. In this paper, we not only derive the asymptotically tight QZZB for phase estimation with the photon loss and the phase diffusion by invoking the variational method and the technique of integration within an ordered product of operators, but also show its estimation performance for several different Gaussian resources, such as a coherent state (CS), a single-mode squeezed vacuum state (SMSVS) and a two-mode squeezed vacuum state (TMSVS). In this asymptotically tight situation, our results indicate that compared with the SMSVS and the TMSVS, the QZZB for the CS always shows the better estimation performance under the photon-loss environment. More interestingly, for the phase-diffusion environment, the estimation performance of the QZZB for the TMSVS can be better than that for the CS throughout a wide range of phase-diffusion strength. Our findings will provide an useful guidance for investigating the noisy quantum parameter estimation.
Semi-Supervised Formality Style Transfer with Consistency Training

Formality style transfer (FST) is a task that involves paraphrasing an informal sentence into a formal one without altering its meaning.To address the data-scarcity problem of existing parallel datasets, previous studies tend to adopt a cycle-reconstruction scheme to utilize additional unlabeled data, where the FST model mainly benefits from target-side unlabeled sentences.In this work, we propose a simple yet effective semi-supervised framework to better utilize source-side unlabeled sentences based on consistency training.Specifically, our approach augments pseudo-parallel data obtained from a source-side informal sentence by enforcing the model to generate similar outputs for its perturbed version.Moreover, we empirically examined the effects of various data perturbation methods and propose effective data filtering strategies to improve our framework.Experimental results on the GYAFC benchmark demonstrate that our approach can achieve state-of-the-art results, even with less than 40% of the parallel data 1 .
Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting

Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in producing rational and factual responses.However, to establish long-term relationships with users, the KDG model needs the capability to generate responses in a desired style or sentiment.Thus, we study a new problem: Stylized Knowledge-Grounded Dialogue Generation (SKDG).It presents two challenges: (1) How to train a SKDG model where no <context, knowledge, stylized response> triples are available.(2) How to cohere with context and preserve the knowledge when generating a stylized response.In this paper, we propose a novel disentangled template rewriting (DTR) method which generates responses via combing disentangled style templates (from monolingual stylized corpus) and content templates (from KDG corpus).The entire framework is end-to-end differentiable and learned without supervision.Extensive experiments on two benchmarks indicate that DTR achieves a significant improvement on all evaluation metrics compared with previous state-of-the-art stylized dialogue generation methods.Besides, DTR achieves comparable performance with the state-of-the-art KDG methods in standard KDG evaluation setting.
Geometric picture for SLOCC classification of pure permutation symmetric three-qubit states

The quantum steering ellipsoid inscribed inside the Bloch sphere offers an elegant geometric visualization of two-qubit states shared between Alice and Bob. The set of Bloch vectors of Bob’s qubit, steered by Alice via all possible local measurements on her qubit, constitutes the steering ellipsoid. The steering ellipsoids are shown to be effective in capturing quantum correlation properties, such as monogamy, exhibited by entangled multiqubit systems. We focus here on the canonical ellipsoids of two-qubit states realized by incorporating optimal local filtering operations by Alice and Bob on their respective qubits. Based on these canonical forms, we show that the reduced two-qubit states drawn from pure entangled three-qubit permutation symmetric states, which are inequivalent under stochastic local operations and classical communication (SLOCC), carry distinct geometric signatures. We provide detailed analysis of the SLOCC canonical forms and the associated steering ellipsoids of the reduced two-qubit states extracted from entangled three-qubit pure symmetric states: We arrive at (i) a prolate spheroid centered at the origin of the Bloch sphere—with longest semiaxis along the z-direction (symmetry axis of the spheroid) equal to 1—in the case of pure symmetric three-qubit states constructed by permutation of 3 distinct spinors and (ii) an oblate spheroid centered at (0, 0, 1/2) inside the Bloch sphere, with fixed semiaxes lengths $$(1/\sqrt{2},\, 1/\sqrt{2},\, 1/2)$$ , when the three-qubit pure state is constructed via symmetrization of 2 distinct spinors. We also explore volume monogamy relations formulated in terms of the volumes of the steering ellipsoids of the SLOCC inequivalent pure entangled three-qubit symmetric states.
Revealing the Galaxy–Halo Connection through Machine Learning

Understanding the connections between galaxy stellar mass, star formation rate, and dark matter halo mass represents a key goal of the theory of galaxy formation. Cosmological simulations that include hydrodynamics, physical treatments of star formation, feedback from supernovae, and the radiative transfer of ionizing photons can capture the processes relevant for establishing these connections. The complexity of these physics can prove difficult to disentangle and obfuscate how mass-dependent trends in the galaxy population originate. Here, we train a machine learning method called Explainable Boosting Machines (EBMs) to infer how the stellar mass and star formation rate of nearly 6 million galaxies simulated by the Cosmic Reionization on Computers (CROC) project depend on the physical properties of halo mass, the peak circular velocity of the galaxy during its formation history $v_\mathrm{peak}$, cosmic environment, and redshift. The resulting EBM models reveal the relative importance of these properties in setting galaxy stellar mass and star formation rate, with $v_\mathrm{peak}$ providing the most dominant contribution. Environmental properties provide substantial improvements for modeling the stellar mass and star formation rate in only $\lesssim10\%$ of the simulated galaxies. We also provide alternative formulations of EBM models that enable low-resolution simulations, which cannot track the interior structure of dark matter halos, to predict the stellar mass and star formation rate of galaxies computed by high-resolution simulations with detailed baryonic physics.
A Comprehensive Review for Video Anomaly Detection on Videos

Video Surveillance Systems (VSS) are widely utilized in public and private areas to increase public safety, such as shopping malls, markets, banks, hospitals, educational institutions, streets, and smart cities. The accuracy and fast identification of video anomalies is usually the major goal of security applications. However, because of varying environmental factors, the complexities of human activity, the ambiguous nature of the anomaly, and the absence of appropriate datasets, detecting video anomalies is challenging. This paper surveys the last three years, a comprehensive study of detecting video anomalies, and the recently used dataset. Moreover, a comparison study on different approaches has been performed, which are used for anomalies detection. We have noticed that deep learning has outperformed other methods in this field.
Entanglement improvement via a single-side squeezing-based quantum scissors

The entanglement improvement is theoretically investigated when applying a single-side quantum scissors (SSQS) with a local squeezing operation and two-asymmetrical beam splitters (BSs) to one mode of an input two-mode squeezed vacuum state (TMSV). It is found that the gain factor can be significantly enhanced with the increasing of local squeezing parameter at the expense of the success probability. The entanglement can also be further improved adjusting the local-squeezing or the transmissivity of BSs in a small initial squeezing region. In addition, our scheme is robust against the photon loss in TMSV. The improved effect becomes more obvious due to the presence of local squeezing. However, the case is not true for a more realistic SSQS. In both cases, the asymmetric BSs play a positive role for the entanglement improvement. These results suggest that the squeezing-based SSQS at single-photon level is beneficial to effectively improve the entanglement, which may have potential applications in quantum communication.