Reviewerly/exHarmony huggingface dataset:
Memory Characterization Of Big Data Applications

This paper explores Memory Characterization of Big Data Applications for numerous big data applications. The team use Docker to run them. To get pertinent information, the team used our samples extensively with the cache simulator Dinero IV. In a cache memory, our main goal is to cut down on the amount of miss-rate while also speeding up fetching. So, using these tools and the rigorous standards that Cloud Suite supplied as big data apps, the team produced a large amount of data. Then, in order to plot the findings and observations, the team examined the data and looked for a relationship among the different components, such as cache size, block size, and associativity. Finding a pattern in the miss rates across various combinations of sizes and associativity in the benchmarks is our major goal. By doing so, the team will be able to identify a general pattern in how the demand miss rates change under the limits mentioned above. Demand miss rates fall when the team raise the associativity, cache size and line size, where d size is the cache size and I size is the line size.
Image Segmentation and Classification for Fashion Apparel

This paper aims at building an AI solution that can recognize clothes and recommend outfits based on the user's preferences (color, style, type, etc). The idea is to utilize machine learning models for image segmentation and classifying a picture of clothing according to a set of predetermined features (Example: tops, trouser, skirt, etc). It then uses a recommendation system to provide a few combinations of outfits according to the parameters (Example: color, occasion, sleeve length, etc) set by the user. We plan on building a ML model for automatic categorization of fashion apparels. Deep Fashion2 dataset is being used totrain U-net model for classification of input image segments into top, bottom, dress etc on the segmented parts of the image.
“Ending the first cycle of teaching Georgian as a foreign language - with the first stage students”

The first cycle of teaching and meeting with the students is of great importance. Learning materials should be provided purposefully and completely. The methods of providing the material should be carefully selected, planned and organized, which is essential to achieve the goal. Practical experience gives us right to offer the third methodical frame of the first cycle with its strategical and sequential plan, which our Language Center “Semita” utilizes with the great success. We consider it preferable to provide new material in a spiral manner. We choose active communication method for the lessons. The last part of the first cycle covers the following: question words: Who? What? Where?; Personal Pronouns: We, You, They; Possessive Pronouns: My, your, his, her, our, their; Adverbs of place: here, there; Plural form of the verb “to be”: are; Vocabulary: a boy, a girl, a school, A bag, friends (plural form); Phrases: may I..?; I would like..; Here you are...; Yes, of course; Hold on…
LCNNet: Light-weight convolutional neural networks for understanding the highly congested scenes

With the development of convolutional neural networks, many improved algorithms have been successively proposed to promote the accuracy of dense crowd counting. However, these algorithms are deployed with expensive computing resources, which is unbearable for small devices such as embedded systems with limited computing resources. To realize the real-time counting on the small devices, it is of great significance how to trade off the computation cost and processing accuracy of the dense crowd-counting algorithm. Thus, we propose a lightweight dense crowd-counting algorithm (LCNNet) to improve this issue. Specifically, the proposed LCNNet consists of two subnetworks, a feature extraction subnetwork, and a regression subnetwork, with a bottleneck depth-separable convolution with a residuals module as the basic module. The LCNNet effectively improves computational efficiency and reduces the computational cost, which can be performed on small devices. Extensive evaluations on four benchmark datasets well demonstrate the effectiveness of the proposed LCNNet for dense crowd-counting models. Meanwhile, the proposed LCNNet can maintain a comparable level of computational accuracy and computational cost on Vehicle counting datasets.
Transferable graph neural networks with deep alignment attention

When analyzing graph-structured data, graph neural networks (GNNs) use well-designed propagation mechanism to extract node features. However, these methods only incorporate data from nodes' fixed neighborhoods or whole graph, not accounting for the influence of important nodes outside of the neighborhoods. We offer transferable graph neural networks with deep alignment attention (TGDAA), which is a novel neural network framework that designs aligned attention to assign different weights to related important nodes. In such a scenario, we concurrently remove a number of graph neural network obstacles and make TGDAA accessible for transfer learning issues. Extensive experiments in four aspects: traditional metrics, attention-related methods, varying label rates and ablation study show that the suggested TGDAA performs better than ten state-of-the-art techniques on ten benchmark network datasets.
DO INSTITUTIONAL QUALITY AND POLITICAL RISK MATTER FOR FINANCIAL DEVELOPMENT? EVIDENCE FROM THE SELECTED EMERGING ECONOMIES

Institutional quality and political risk have emerged as critical factors for financial development in emerging countries. If the financial markets in emerging economies are to progress further, they require strong institutional support. Following the argument, this study examines the role of institutional quality and political risk on financial development in selected eight emerging countries. The study covers the yearly data from 2000 to 2019 and applies the dynamic panel data estimation technique to reach the overall objective. The results conclude that institutional quality and political stability are significant factors for financial development in the panel countries. Furthermore, our results find that institutional quality has emerged as a stronger determinant than political risk that promotes financial development. Policymakers need to devise and implement policies that promote voice and accountability and help to control corruption. The improved situation of rule of law and stable political environment increase investor confidence at home and abroad, thereby increasing the financial development in emerging countries. Since the financial institutions of emerging countries rapidly integrate with a global financial system, there are opportunities for inheriting good governance practices from technologically advanced countries. This would create a win–win situation.
Classical verification of quantum measurement for the computational basis and the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>X</mml:mi><mml:mi>Y</mml:mi></mml:mrow></mml:math> -plane basis

Recent advances in quantum technologies raise the urgent need of verifying the correct functionality of a quantum device. Certifying the correctness of a quantum device in a fully classical manner is an important research branch. In this paper, we present a measurement protocol that allows a classical verifier to interact with an efficient quantum prover to verify the computational basis or the $XY$-plane basis measurement on a quantum state. With the help of two adaptive hardcore bit properties of a noisy trapdoor claw-free family, the security of measurement protocol is proved, which is under quantum hardness of the learning with error. The security characterizes the distance between the output distribution of measurement protocol and the distribution obtained by measuring certain quantum states in the designated basis, with or without the presence of honest behavior of the prover. Moreover, exploiting the measurement protocol, we present two device-noise-independent verification protocols of graph states and a classical verification protocol of delegated quantum computing whose soundness is 0.5757.
Computer Vision Using Pose Estimation

Pose estimation involves estimating the position and orientation of objects in a 3D space, and it has applications in areas such as robotics, augmented reality, and human-computer interaction. There are several methods for pose estimation, including model-based, feature-based, direct, hybrid, and deep learning-based methods. Each method has its own strengths and weaknesses, and the choice of method depends on the specific requirements of the application, object being estimated, and available data. Advancements in computer vision and machine learning have made it possible to achieve high accuracy and robustness in pose estimation, allowing for the development of a wide range of innovative applications. Pose estimation will continue to be an important area of research and development, and we can expect to see further improvements in the accuracy and robustness of pose estimation methods in the future.
Dynamic identity‐based broadcast proxy re‐encryption for data sharing in autonomous vehicles

Summary Autonomous vehicles (AVs), as the essential part of intelligent autonomous transportation system, improves vehicle communication efficiency and supports diverse kinds of social services. However, with the development of AVs, more data has been generated by the sensors about the road, traffic, street light, and weather conditions. This also inevitably bringing new security problems, such as secure data sharing. Although the identity‐based broadcast proxy re‐encryption (IBBPRE) can be used to realize one‐to‐many data sharing, most existing schemes require access to the data owner for the group shared key update phases since the data owner's private key is mandatory for the group shared key generation. Therefore, frequent interaction with the data owner increases the operational burden and risks leaking the data owner's private key. Thus, we design a dynamic identity‐based broadcast proxy re‐encryption (DIB‐BPRE) to solve the problem of re‐encryption update. In the proposed scheme, a vehicle cloud server directly revokes or adds a group of data receivers, designated by the data owner, from the re‐encryption key. Finally, the theoretical and experimental analyses demonstrate that the DIB‐BPRE scheme is efficient, and it is suitable for data sharing in AVs.
Taxonomy and Recent Advance of Game Theoretical Approaches in Adversarial Machine Learning: A Survey

Carefully perturbing adversarial inputs degrades the performance of traditional machine learning (ML) models. Adversarial machine learning (AML) that takes adversaries into account during training and learning emerges as a valid technique to defend against attacks. Due to the complexity and uncertainty of adversaries’ attack strategies, researchers utilize game theory to study the interactions between an adversary and an ML system designer. By configuring different game rules and analyzing game outcomes in an adversarial game, it is possible to effectively predict attack strategies and to produce optimal defense strategies for the system designer. However, the literature still lacks a holistic review of adversarial games in AML. In this paper, we extend the scope of previous surveys and provide a thorough overview of existing game theoretical approaches in AML for adaptively defending against adversarial attacks. For evaluating these approaches, we propose a set of metrics to discuss their merits and drawbacks. Finally, based on our literature review and analysis, we raise several open problems and suggest interesting research directions worthy of special investigation.
Insurance Industry and Block Chain Technology: An Analysis of Opportunities and Challenges

Blockchain is incorporated into our distributed platform design as a system service to facilitate the execution of transactions in insurance procedures. The insurance sector is highly reliant on several procedures between parties engaged in a transaction for opening, retaining, and closing various types of policies. Our view of data management and security has undergone a paradigm shift as a result of the emergence of blockchain technology. Blockchain technology is essential to the insurance industry for optimization of claims processing and policy automation, resulting in lower costs and greater efficiency. Additionally, the ability of blockchain to increase transparency and reduce fraudulent activity promotes credibility and trust between the insured and insurer. This paper explains how the insurance sector might profit from blockchain technology investments. We review the fundamentals of blockchain technology, major platforms currently in use, and offer a simple explanation of the insurance sub-processes that blockchain might enhance. This paper also discuss about the challenges that must be overcome for blockchain solutions to be completely adopted in the insurance industry.
Sentiment Analysis and Corpus: Cognitive Perspective and Overhead-Accuracy Tradeoff

Human logical thinking is in the form of natural language. With the development of computer science technique, it becomes easier and more convenient for natural language processing. Therefore, a variety number of natural language processing applications have emerged. Sentiment analysis is one of these novel applications, and has been applied in many areas. In Amazon.com, there are a large number of user comments and product discussions, which can help a person to decide whether to buy a product or not without asking the opinions from friends and family members. Therefore, sentiment analysis on user comments and product discussions, such as Amazon review becomes increasingly useful and important. In this paper, the effect of corpus on sentiment analysis of Amazon review dataset with the aid of support vector machine are studied. We generate eight different size datasets from Amazon review dataset filtered by different word frequency in Corpus of Contemporary American, and conduct some experiments on these eight datasets. According to the experimental result, we make some conclusion and give some suggestions to facilitate researchers to make a trade-off between accuracy and experimental cost.
Collaborative Optimization of English Online Teaching Informatization Based on Intelligent Multimedia Image Technology

With the rapid development of computer technology and the deepening of educational concepts, the traditional English teaching methods have also changed. As a new teaching method, online teaching has received extensive attention. Online teaching has introduced intelligent multimedia image technology in English teaching, which has transformed the essence of English teaching by organically integrating traditional teaching and multimedia teaching. With the rapid growth of Internet and intelligent multimedia image technology, the number of digital images is growing exponentially. How to extract the required image from the large amount of multimedia image data has been an important topic at present. In this case, intelligent multimedia image technology was appeared, which extracted the characteristics of an image from the content of the image itself, and then retrieved it through the characteristics. This paper proposed a retrieval and classification method based on intelligent multimedia image technology. Among them, Support Vector Machine (SVM) was used to classify multimedia resources, which had a good effect. The results of this experiment showed that before the experiment, the listening score of the multimedia group was 65.5, and the speaking score was 59.7. After the experiment, the listening score of the multimedia group was 79.8, and the speaking score was 80.6. It can be found that the performance of the multimedia group has been greatly improved after the experiment, which shows that online English teaching based on intelligent multimedia image technology is more conducive to the improvement of students’ academic performance.
Foreign Dissertations on Digital Law: Statistical and literature review.

Modern domestic dissertations in law are often criticized for insufficient reliance on previous research. Society’s demand for the study of digital aspects of law, the frequent use of the comparative legal method, and, to a certain extent, the current limited access to foreign sources, necessitate a review of foreign dissertations on digital law. We chose to use qualitative and quantitative methods to select the dissertations examined in this study. Firstly, we selected dissertations defended at universities from the QS World University Ranking. Then, we refined our search by using accessible national and university research repositories. Based on comparative legal and statistical methods, we consolidated the dissertations into a single list and divided them according to different criteria. While doing this, we also defined the main trends in preparing Ph.D. dissertations on digital law. The main results of our study include a subject-thematic index of Ph.D. dissertations in the field of digital law, which is designed to facilitate searches for relevant sources, help researchers determine topics for study, and outline the main aspects of the study of digital issues from the standpoint of various branches of law.
Outlying Aspect Mining via Sum-Product Networks

Outlying Aspect Mining (OAM) is the task of identifying a subset of features that distinguish an outlier from normal data, which is important for downstream (human) decision-making. Existing methods are based on beam search in the space of feature subsets. They need to compute outlier scores for all examined subsets, and thus rely on simple outlier scoring algorithms. In this paper, we propose SOAM, a novel OAM algorithm based on Sum-Product Networks (SPNs), a class of probabilistic circuits that can accurately model high-dimensional distributions. Our approach needs to fit an SPN only once, and leverages the tractability of marginal inference in SPNs to compute outlier scores in feature subsets. This way, computing outlier scores in subsets is fast, while being based on a flexible and accurate density estimator. We empirically show that SOAM clearly outperform the state-of-the-art method in search-based OAM, and even outperforms recent deep learning-based methods in the majority of the investigated cases. (Available at github.com/stefanluedtke/Sum-Product-Network-OAM).
Semantic‐aware visual consistency network for fused image harmonisation

Abstract With a focus on integrated sensing, communication, and computation (ISCC) systems, multiple sensor devices collect information of different objects and upload it to data processing servers for fusion. Appearance gaps in composite images caused by distinct capture conditions can degrade the visual quality and affect the accuracy of other image processing and analysis results. The authors propose a fused‐image harmonisation method that aims to eliminate appearance gaps among different objects. First, the authors modify a lightweight image harmonisation backbone and combined it with a pretrained segmentation model, in which the extracted semantic features were fed to both the encoder and decoder. Then the authors implement a semantic‐related background‐to‐foreground style transfer by leveraging spatial separation adaptive instance normalisation (SAIN). To better preserve the input semantic information, the authors design a simple and effective semantic‐aware adaptive denormalisation (SADE) module. Experimental results demonstrate that the authors’ proposed method achieves competitive performance on the iHarmony4 dataset and benefits from the harmonisation of fused images with incompatible appearance gaps.
Label Distribution Learning with Discriminative Instance Mapping

Label distribution learning (LDL) is an effective tool to tackle label ambiguity since it allows one instance to be associated with multiple labels in different degrees. Therefore, the more complex but informative label space makes it challenging to directly model the relationship between original features and label distributions. In this paper, an algorithm called Label Distribution Learning with Discriminative Instance Mapping (LDLDIM) is proposed to select a discriminative instance pool (DIP) to map the original features into a more discriminative space. First, we design a criterion that incorporates label information to quantify the discriminative power of each instance. Second, we select several instances with the highest discriminative ability to construct the DIP, and map the instances to the discriminative space through the DIP. By exploiting label information, this criterion enables the selected DIP to ensure that instances that are close (far away) in label space remain close (far away) in the discriminative space. Finally, multiple regressions for prediction are trained on the label distributions and the new features that are obtained by distance mapping with DIP. Experiments and comparisons on 16 datasets illustrate that our algorithm outperforms 6 state-of-the-art LDL methods in most cases.
The Causal Strength Bank: A New Benchmark for Causal Strength Classification

Causal relation extraction is essential in the causality discovery of natural language processing. The development of causal relation extraction from the model-driven is staggering, so we resort to the data-driven method. More causal information is necessary because most current datasets only label the locations of causal entities or events, which may restrict the learning capacity of models. In this paper, we introduce a novel benchmark causal strength classification and corresponding dataset, Causal Strength Bank (CSB), consisting of a Chinese dataset (C-CSB) and an English dataset (E-CSB) which merge causal strength, causal polarity, and causal entity. To ensure credibility, we select four canonical English datasets and clean Wikipedia passages for the Chinese corpus. The corpus is then annotated and cross-checked by professional annotators in two stages, ensuring the accuracy of CSB. We evaluate various baseline methods on CSB and show that causal strength information benefits causal relation extraction, demonstrating the value of the proposed dataset. Our dataset is available at https://github.com/yuanxs21/CSB-dataset .
Diagonal Barzilai-Borwein Rules in Stochastic Gradient-Like Methods

Minimization problems involving a finite sum as objective function often arise in machine learning applications. The number of components of the finite-sum term is typically very large, by making unfeasible the computation of its gradient. For this reason stochastic gradient methods are commonly considered. The performance of these approaches strongly relies on the selection of both the learning rate and the mini-batch size employed to compute the stochastic direction. In this paper we combine a recent idea to select the learning rate as a diagonal matrix based on stochastic Barzilai-Borwein rules together with an adaptive subsampling technique to fix the mini-batch size. Convergence results of the resulting stochastic gradient algorithm are shown for both convex and non-convex objective functions. Several numerical experiments on binary classification problems are carried out to compare the proposed method with other state-of-the-art schemes.