Reviewerly/exHarmony huggingface dataset:
Multi-Attribute Featured Layout Generation forGraphic Design using Capsule Networks

Graphic design is a very individualized and variable undertaking for which it is challenging to find a single answer. Layout modeling is an essential initial stage in graphic design. The generation of visual layouts has improved recently, especially with the development of generative adversarial networks (GANs). The issue of defining the positions and dimensions of design components, however, often includes restrictions with regard to element properties, such as size, aspect ratio, and reading-order. This concept of "inverse graphics," which is the opposite of how computers (often) generate pictures, is one that Caps Net seeks to adopt. The network here begins with a picture and attempts to identify the items it includes as well as its instantiation properties, including posture (size, orientation, and relative position). When creating a typographic piece that combines text and an image, designers first place the design elements over the proper area before adjusting the size and location of the original component arrangement to get the best visual result. Position, dimension, and scale factors are used to alter different design components for this. For cutting-edge engineering applications like virtual reality and augmented reality (VR/AR), image reconstructions, contemporary sports analytics, target tracking, pose-based video games, driving assistance, and posture estimation is a crucial job. In this study, we use a multi-GPU infrastructure to build Capsule Networks to tackle the pose estimation problem for rigid object computer graphics. The necessity for reciprocal interactions among the intended components distinguishes layout creation from pixel-level picture synthesis. Experiments in both the quantitative and qualitative realms show how visually comparable the produced layouts are to actual design layouts. For a deeper knowledge of the aesthetic quality, we can create actual designs based on expected layouts. Finally, we provide a real-world example of layout suggestion.
Energy-Aware Workflow Scheduling in a Fog-Cloud Computing Environment Using Non-Dominated Sorting Genetic Algorithm

Cloud Computing is no longer just a fad and announcement. Since the beginning of this decade and up to the present, cloud computing has changed and evolved. Workflows are employed in many different scientific disciplines to coordinate data demanding applications, but their virtualized apps, platforms, compute, and storage must be promptly provisioned, scaled, and released instantly. The vast majority of researchers agree that using scientific workflows as a paradigm to define, arrange, and communicate complex scientific analysis is helpful. Workflow scheduling aims to optimally assign and execute sequence of tasks on various virtual machine instances shared and controlled by the workflow scheduler. Numerous scheduling algorithms have been proposed to tackle difficult problems more quickly than metaheuristic ones due to the NP-complete nature of this problem and its dependence on theproblem size. Energy consumption has emerged as a major issue in the cloud computing environment, in addition to the standard quality of service(QoS) restrictions like time and cost to handle this issue. The goal of this paper is to develop the Dynamic Provisioning Based on Demand (DPBD) algorithm for workflow scheduling. While sequential task completion and task priority are design restrictions, the dual objectives of the cloud service are to minimize makespan and energy usage. Using the non-dominated sorting genetic algorithm (NSGA-II), the multi-objective optimization problem's Pareto optimal solutions are found. Our approach is validated by simulating a complex workflow application.
μ: A bolt-on approach for faster and cheaper serverless computing

Serverless computing shifts the responsibility of managing and configuring the cloud infrastructure from the user to the provider. However, in the FaaS model, users are still faced with non-trivial resource allocation and management choices, in particular with respect to the way that intra- and inter-function parallelism is managed. As a consequence, deploying a cloud function with an incorrect configuration for either of these two dimensions of parallelism can lead to an unnecessary increase in execution time, cost, or both.
Understanding the Neglected Cost of Serverless Cluster Management

Serverless computing enables the cloud platform to optimize resource management under the hood to improve performance and resource-efficiency. However, today's serverless cluster managers are designed by simply retrofitting legacy workload orchestration systems, despite the unique characteristics of serverless workloads. We study Knative-on-K8s as a representative state-of-the-art cluster manager for serverless and show that it can cause second-scale delays and contribute to over 65% of end-to-end latency for function invocations experiencing cold starts. These overheads occur when the cluster experiences high sandbox churn, which is common in production serverless deployments. We analyze the root cause of current cluster manager overheads for serverless workloads and propose a set of design principles to improve end-to-end latency and peak throughput by rethinking the cluster manager system architecture.
Limited Access: The Truth Behind Far Memory

Memory capacity in data centers is becoming a scarce resource. To address this issue, emerging runtimes enable applications to supplement their local memory with additional tiers of compressed, non-volatile, or far memory, often accessed via OS-supported paging. In these systems, minimizing page faults is crucial for good performance. Yet, there is little common understanding of which parts of application code are responsible for triggering page faults. In this paper, we analyze page-fault behavior across a suite of 26 applications and find that the vast majority of page faults are triggered by a very small number of lines of application code. In the light of this and related observations, we discuss the feasibility of several ways to reduce page faults.
Evicting for the greater good: The case for Reactive Checkpointing in serverless computing

Evictable cloud resources give providers extra flexibility in managing their infrastructure and help reduce resource idleness. Due to its user-transparent and ephemeral nature, serverless computing appears to be a logical fit to make use of this type of resources. However, in its current form, this flexibility comes at the cost of semantics, namely by imposing execution time limits and only guaranteeing at least once semantics. As such, developers must either implement idempotent code or resort to proactive checkpointing or logging approaches, which pessimistically introduce runtime overheads in every invocation. In this paper, we make the case for an alternative approach to handling function interruptions, based on a reactive checkpoint-based mechanism. Following this approach, we present R-Check, a system designed to efficiently migrate function instances running on top of evictable cloud resources when interruptions are imminent. Our evaluation shows that significant resource savings can be achieved using such resources, and that function migration is possible with minimal runtime overhead.
Enabling In-Vitro Serverless Systems Research

Serverless is an increasingly popular cloud computing paradigm that has stimulated new systems research opportunities. However, developing and evaluating serverless systems in a research setting (i.e., "in-vitro", without access to a large-scale production cluster and real workloads) is challenging yet vital for innovation. Recently, several serverless providers have released production traces consisting of large sets of functions with their invocation inter-arrival time, execution time, and memory footprint distributions. However, executing the workload synthesized from these traces requires a massive cluster, making experiments expensive and time-consuming.
Deep Learning in Chinese Text Information Extraction Model for Coastal Biodiversity

In the coastal areas of China, scientists have collected nearly 500 species of coastal plants and seaweeds. The collected information includes species description, morphological characteristics, habitat distribution and resource value of plants in China. By effectively extracting Chinese text information, this article establishes a Chinese text information extraction model based on DL. This article is based on short-term and short-term memory artificial neural networks for short text classification. In addition, this article also integrates the L-MFCNN models of MFCNN for short text classification. Comparing the two methods with traditional text recognition algorithms, information extraction based on syntax analysis and deep learning, the results show that, compared with the comparison method, the recognition accuracy of Chinese text information of this neural network model can reach 96.69%. Through model training and parameter adjustment, Chinese text information of coastal biodiversity can be quickly extracted, and species categories or names can be identified.
GHZ Distillation using Quantum LDPC Codes

Entanglement distribution is a critical task in quantum networks. Since the distributed entanglement can suffer from noise in the channel, it is necessary to develop methods that distill higher quality entanglement from the shared noisy entangled states. In this work, we propose a protocol to distill multi-qubit Greenberger-Horne-Zeilinger (GHZ) states among the nodes of a network using quantum error correcting codes. The method builds upon a Bell state distillation protocol by Wilde et at. (2007) that we recently generalized to 3-qubit GHZ states. The key technical result that enables our protocol shows how measuring a Pauli operator, or in general a set of code stabilizers, on one subsystem of a multipartite GHZ state affects the other subsystems. The design and analysis of the protocol is driven by the stabilizer formalism for measurements, and we provide discussions to elucidate the steps of the protocol. A similar approach can be applied to distill other multipartite entangled states as long as they are stabilizer states.
The Application of Improved Decision Tree Algorithm in College Employment Management

The role of employment guidance in employment management is very important, but there is a problem of inaccurate outcome evaluation. The didactic teaching model cannot solve the problem of employment guidance in employment management, and the evaluation is unreasonable. Therefore, this paper proposes an improved decision tree method to optimize employment guidance analysis. First, the multiple intelligence theory is used to evaluate students, and the indicators are divided according to the requirements of employment guidance to reduce employment guidance in the interfering factor. Then, the multiple intelligence theory optimizes the employment guidance for students, forms the employment guidance program, and conducts the employment guidance results Comprehensive analysis. MATLAB simulation shows that under the condition of certain evaluation criteria, the employment rate and internship rate of improved decision tree method for students' career guidance are improved All are superior to the didactic teaching mode.
A Unified Framework for Multi-Language Sentiment Analysis

The unified framework for multi-language sentiment analysis is a vital aspect of understanding customer opinions, emotions, and feedback. This paper presents a unified framework to increase the performance of the multi-language sentimental analysis. Two popular machine translation services, Google Translate, and Yandex Translate are employed to unify the sentiment analysis for the considered languages including English, Turkish, Arabic, and French. Our findings highlight the importance of machine translation services in facilitating and enhancing the performance of sentiment analysis algorithms for different languages. Our framework was evaluated on several datasets and showed promising results, with improvements in accuracy ranging from 1% to 22% depending on the language. Our approach outperforms language-specific models and demonstrates the effectiveness of the proposed translation-based multi-language framework. In addition, we found that the performance of sentiment analysis varies among the different languages, with Google Translate exhibiting better performance in sentiment analysis of Turkish and Arabic translations while Yandex Translate shows better results in sentiment analysis of English and French translations.
An Approach for Integrating Interactive Detection of Code Smells on Agile Software Development

Code smells pose significant challenges in software development. Integrating Interactive Detection (ID) techniques, which enable real-time identification of code smells, into the Scrum framework has the potential to enhance software quality. However, there is a lack of studies and practical guidelines on integrating ID into Scrum, creating a knowledge gap in the software engineering community. This paper proposes an approach for integrating ID into Scrum and discusses the results of a focus group performed with eight professionals to validate it. The results indicate positive perceptions and improved code quality, product delivery, and maintenance. This research highlights the value of integrating ID into Scrum to enhance code quality.
Testing the First Hungarian CV-QKD System on a Real Optical Line

Secure communication is becoming increasingly important as the computing power of quantum computers grows. For some of today’s most widely used classical encryption methods, there is a quantum algorithm that can be used to crack them efficiently. It is therefore very important to be prepared for attackers who have such powerful quantum computers. One possible solution is the One-Time Pad symmetric-key encryption scheme, which requires both communicating parties to have the same key to work effectively. This is achieved in a secure and scalable way by quantum key distribution (QKD). The underlying quantum-mechanical phenomenona guarantee that the parties can detect an eavesdropper and therefore generate a key, that is only known to them. In this paper we present the first Hungarian continuous variable quantum key distribution (CVQKD) system and its first test results, carried out on an optical line of a telecommunication operator. This is the first step towards providing QKD as a service in the future.
Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. EVA systems and their enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.
Image Security Using Triple DES and DES Algorithms

Data is always the source of all information and must be more secured at all cost. Cryptography helps in preventing data from outer malicious attacks while it is transferred through the network. In this we use both Triple data encryption standard (TDES) and Data Encryption Standard (DES). It uses a security key of 16 bit and 8 bit respectively to protect the safety of the image being transferred. The main applications of these algorithms rely on medical and military basis as information or images shared must maintain the privacy. The images shared are first encrypted and then decrypted using the key that the receiver must know in prior.
Machine Learning for Accurate Software Development Cost Estimation in Economically and Technically Limited Environments

Cost estimation for software development is crucial for project planning and management. Several regression models have been developed to predict software development costs, using historical datasets of previous projects. Accurate cost estimation in software development is heavily influenced by the relevance and quality of the cost estimation dataset and its suitability to the software development environment. The currently available cost estimation datasets are limited to North American and European environments, leaving a gap in the representation of other economically and technically constrained software industries. In this article, the authors evaluate the performance of regression models using the SEERA dataset, which highly represents these constrained environments. This study provides insights into selecting regression models for cost estimation in software development. It highlights the importance of using appropriate models based on the specific software development model and dataset used in the estimation process. In the performance evaluations of eight regression models, including elastic net, lasso regression, linear regression, neural network, RANSACRegressor, random forest, ride regression, and SVM, for cost estimation in different software models, along with correlation coefficients and accuracy indicators, were reported. The results showed that SVM and random forest indicated superior performance. However, the elastic net, lasso regression, linear regression, neural network, and RANSACRegressor models also demonstrated exemplary performance in cost estimation.
A fuzzy planar subgraph formation model for partitioning very large-scale integration networks

This study focuses on forming planar subgraphs in fuzzy graphs where the planarity value is not equal to 1. The paper commences by introducing the concepts of vertex-deletion (VD) and edge-deletion (ED) operations applied to fuzzy graphs. These operations aim to derive the fuzzy planar subgraphs and extensively examine the consequential significant findings. Additionally, maximal and maximum planar subgraphs in vertex-deletion (VD) and edge-deletion (ED) are presented alongside illustrative examples. A novel approach called the Planar Partition subgraph (PP subgraph) is introduced for identifying the fuzzy planar subgraphs from a fuzzy graph with an algorithm rooted in PP subgraphs for enhanced efficiency. Furthermore, the concept of thickness value in fuzzy graphs is constructed using PP-subgraphs is introduced. The discussion involves the thickness value of a fuzzy planar graph and its connection to the planarity value, supported by illustrative examples. Finally, an application made on PP subgraphs where the network needs to be partitioned into a finite number of subgraphs is demonstrated in a very large-scale integration network.
Nighttime Traffic Object Detection via Adaptively Integrating Event and Frame Domains

Intelligent perception is crucial in Intelligent Transportation Systems (ITS), with vision cameras as critical components. However, traditional RGB cameras exhibit a significant decline in performance when capturing nighttime traffic scenes, limiting their effectiveness in supporting ITS. In contrast, event cameras possess a high dynamic range (140 dB vs. 60 dB for traditional cameras), enabling them to overcome frame degradation in low-light conditions. Recently, multimodal learning paradigms have made substantial progress in various vision tasks, such as image-text retrieval. Motivated by this progress, we propose an adaptive selection and fusion detection method that leverages both event and RGB frame domains to optimize nighttime traffic object detection jointly. To address the challenge of unbalanced multimodal data fusion, we design a learnable adaptive selection and fusion module. This module performs feature ranking and fusion in the channel dimension, allowing efficient multimodal fusion. Additionally, we construct a novel multi-level feature pyramid network based on multimodal attention fusion. This network extracts potential features to enhance robustness in detecting nighttime traffic objects. Furthermore, we curate a dataset for nighttime traffic scenarios comprising RGB frames and corresponding event streams. Through experiments, we demonstrate that our proposed method outperforms current state-of-the-art techniques in event-based, frame-based, and event and frame fusion methods. This highlights the effectiveness of integrating the event and frame domains in enhancing nighttime traffic object detection.
A Brave New World Requires Courage: New Directions for Literacy Research and Teaching

I offer a meditation on current challenges faced by literacy educators and researchers and uses those challenges to suggest new directions for the field. Citing the precipitous decline in interest in the humanities and the field of literacy education, I consider the significance of tools such as ChatGPT for the teaching of writing. I explore the significance of out-of-school literacies and the linguistic diversity of today’s students in terms of their implications for literacy instruction. I also remind us of the chilling political climate in which we find ourselves, especially with regard to LGBTQ+ identities. Given these contemporary challenges, I suggest that we in the field of literacy education rethink the nature of writing instruction, restructure our research paradigm to be more inclusive and democratic, and continue to be forceful political advocates for pedagogies, practices, and policies that will ensure a just and equitable literacy education for all.
Utilizing a feature-aware external memory network for helpfulness prediction in e-commerce reviews

With the explosive growth of reviews on e-commerce websites, consumers usually have trouble finding helpful information in massive reviews. Therefore, predicting review helpfulness has drawn the attention of researchers in the research community. Previous works have focused on identifying the helpfulness of reviews based on content, but we found that the helpfulness of a review is determined by the content of reviews and the matching degree between items and reviews. To meet this challenge, this paper proposes a deep matching model based on a feature-aware external memory network (DMMN) to predict review helpfulness. Specifically, the DMMN contains a memory matrix that can store typical features of items via memory reading/writing operations. We then derive item features by properly reading the memory matrix. Meanwhile, a self-attention mechanism is adopted to obtain review features. Finally, the helpfulness score of the review is acquired by the matching degree between item features and review features. Experiments conducted on Amazon datasets, compared with the baseline models, the DMMN obtains an about 1–2% performance improvement on the eight datasets. The experimental results demonstrate that our efforts to predict helpful reviews are feasible and effective.