Reviewerly/exHarmony huggingface dataset:
Pemodelan Deteksi Black Campaign Sumber Media Berita Online Menggunakan Long Short Term Memory

Developed a black campaign detection model to obtain an accuracy level of classifying online news using the LSTM (Long Short Term Memory) algorithm.Aggregation of neural networks in sheet layers to iteratively learn from the data collected by emulating the workings of the like human brain does the job so computers can be trained in abstraction with the problem is not well defined. The LSTM (Long Short Term Memory) text-processing learning method is used for text classification through the stages of data collection, data preprocessing, word representation, classification, and evaluation. LSTM adds capability and erases information from the cell state. Gates consist of a sigmoid part of layers and multiplication operation. Hyperparameter value is determined from Vocab_size, Embedding_dim Activation Function, Number of epoc, and Learning rate. Evaluation of the showing of the data training performance on data testing shows that the value of LSTM is in identifying online news MAPE 8%. The RMSE evaluation shows that the parameter Number of epochs has a high value of 0.053728.
Perceptions of ESL Undergraduates Regarding the Impact of Smartphones on Learning the English Language

This study explores the significance of mobile phone usage in English language learning among students at the National University of Modern Languages. By administering an open-ended questionnaire to a sample of 100 Pakistani students enrolled in Advanced English for the Master of English program, the research investigates how smartphones are used, the attitudes and behaviors of students, and the challenges encountered in utilizing mobile technology within the learning environment. The findings demonstrate that smartphones play a crucial role in enhancing students’ English language learning, as most participants express a positive view regarding their use in language instruction. This research presents a novel teaching approach for ESL students, underscoring the importance of a robust pedagogical foundation and appropriate training. Additionally, the study suggests the need for further exploration through experimental studies to examine the efficacy of smartphones in English language learning. The outcomes of this study contribute to a comprehensive understanding of the effective integration of mobile technology in ESL education, providing valuable insights for educators and researchers in the field.
RRDD: An ATT&amp;CK-based ICS Network Security Risk Assessment Method

The current network security situation of industrial control systems is becoming increasingly severe, and the ICS ATT&CK framework provides a unified knowledge base for attack tactics and techniques for industrial control system network security. This paper presents a RRDD model for network security risk assessment of ICS, which consists of four parts: Risk identification (R), Risk calculation (R), Defense strategy (D), and Defense measures (D). Risk identification is based on the ICS ATT&CK to analyze the network security risk assessment indicators of the ICS, to clarify the vulnerability of the industrial control system, the importance of assets, the threats faced and the existing mitigation measures. Risk calculation is based on the analysis results of risk assessment indicators, establishing a comparative judgment matrix to analyze the relative importance of each indicator, calculating the weight of each indicator, and calculating and grading the overall risk of ICS. Furthermore, the model adopts corresponding safety control strategies and measures based on the risk calculation results, and conducts closed-loop risk assessment and elimination. It can effectively evaluate and disposal the network security risks in ICS.
Prediction of High Skilled Talent Demand Network Model Based on Improved Ant Colony Classification Algorithm

The network model for predicting the demand for highly skilled talents is a complex problem that can be solved using an improved ant colony classification algorithm. In order to solve the contradiction between the demand for highly skilled talents and the actual development of enterprises, this article applied an improved ant colony classification algorithm to the prediction of the demand network model for highly skilled talents, thus aiming to provide more talents for enterprises. This article applied experimental design and data comparison methods to analyze the demand system for highly skilled talents based on backpropagation neural network algorithm. The experimental data showed that the root mean square error predicted by the backpropagation model was 12.6. Compared to the grey prediction model, its effect was better.
Āpta: Fault-tolerant object-granular CXL disaggregated memory for accelerating FaaS

As cloud workloads increasingly adopt the fault-tolerant Function-as-a-Service (FaaS) model, demand for improved performance has increased. Alas, the performance of FaaS applications is heavily bottlenecked by the remote object store in which FaaS objects are maintained. We identify that the upcoming CXL-based cache-coherent disaggregated memory is a promising technology for maintaining FaaS objects. Our analysis indicates that CXL's low-latency, high-bandwidth access characteristics coupled with compute-side caching of objects, provides significant performance potential over an in-memory RDMA-based object store. We observe however that CXL lacks the requisite level of fault-tolerance necessary to operate at an inter-server scale within the datacenter. Furthermore, its cache-line granular accesses impose inefficiencies for object-granular data store accesses. We propose Āpta, a CXL-based object-granular memory interface for maintaining FaaS objects. Āpta's key innovation is a novel fault-tolerant coherence protocol for keeping the cached objects consistent without compromising availability in the face of compute server failures. Our evaluation of Āpta using 6 full FaaS application workflows (totaling 26 functions) indicates that it outperforms a state-of-the-art fault-tolerant object caching protocol on an RDMA-based system by 21-90% and an uncached CXL-based system by 15-42%.
A Sentiment Analysis of Data on Outings from Mothers with Infants and Toddlers during the COVID-19 Pandemic

Global travel demand has been severely suppressed worldwide since the outbreak of the COVID-19 pandemic. In this study, we conducted a sentiment analysis of data on outings provided by mothers with infants and toddlers before and after the COVID-19 pandemic. These data were expressed in Japanese and consisted of log data from a smartphone application called mamari in which users communicate to solve and share questions and concerns about pregnancy, childbirth, and childcare. A sentiment classifier was trained using an annotated list of positive and negative sentiment words and a pretrained word embedding. Then, we calculated the mean sentiment analysis scores in the question sentences and examined the relationship between the sentiment scores of the questions and the daily confirmed cases of COVID-19 in Japan. The results show that the sentiment analysis values for the question data decreased with increasing numbers of confirmed cases after the COVID-19 pandemic began.
SHADE: Speaker-History-Aware Dialog Generation Through Contrastive and Prompt Learning

Traditional persona-based dialog generation models typically rely on textual descriptions of persona information, but this approach is limited by this and its inability to capture all aspects of a persona’s speaking style. To solve this problem, we propose a novel dialog generation model called Speaker-History-Aware Dialogue GEneration (SHADE) through Contrastive and Prompt Learning, which utilizes contrastive learning to model speaking style from historical conversation, resulting in more personalized and distinctive response. Based on the idea of, Prompt learning, on the one hand, we embed small-scale trainable parameters according to Continuous Prompts to stimulate the knowledge of the pre-trained language model, so as to improve the feature extraction ability of the model. On the other hand, we add speaker tag to the decoder input according to Discrete Prompts to improve the consistency of speaking style. We evaluate our model on two popular text-only datasets, DailyDialog and PersonaChat. The results show that SHADE outperforms other multiturn hierarchical dialog generation models in terms of response consistency, logic, and diversity. SHADE is optimal for metrics PPL, NIST, ROUGEL, METEOR, BLUE2, etc. Our proposed SHADE offers a promising direction for persona-based dialog generation, addressing the limitation of existing approaches and paving the way for more personalized and engaging conversational agents in various applications.
Design of digital watermarking algorithm based on compression sensing

With the rapid development of multimedia technology and the Internet, the security of multimedia data is facing more and more threats. Digital watermarking technology can effectively solve this problem, and compressed sensing theory is widely used in the field of digital watermarking. As the theoretical basis of the new generation of information processing, this theory can effectively solve the problem of multimedia data being attacked. Based on compressed sensing theory, combined with discrete wavelet transform and SVD decomposition, this research realized the embedding and extraction of watermark in the observation domain after image thinning, and tested different forms of watermark attacks such as salt pepper noise, Gaussian noise and filtering on the watermarked image. The experimental data shows that the PSNR value and NC of the algorithm are in line with expectations, and it has good robustness, transparency, and extraction resistance.
Firms' Investment Decisions in the Shadow of Populist ideology: Unraveling the Impact of Electoral Institutions

Existing research on corporate investment strategies has extensively examined how companies manage the challenges posed by political pressures and the role of institutional frameworks. However, less attention has received the role of electoral institutions in shaping business decision-making. We investigate how the rise of governments with populist ideology influence corporate investment, unraveling the intricate connection between electoral institutions, political pressures, and firms' investment behavior. Based on a data set covering 286,000 companies from 31 countries, spanning the period 1996 to 2020, our study reveals that the detrimental influence of populist ideology on corporate investment is less harmful for firms under proportional formula compared to countries that have majoritarian institutions. We broaden our investigation to emerging economies, contributing to a deeper understanding of how firms manage this type of political pressure in different electoral institutional contexts. To address potential endogeneity concerns arising from uncertainty and populist governments, we utilize an instrumental variable approach. By adopting this method, we aim to address any biases and provide more robust findings regarding the relationship between electoral institutions, populist ideology, and corporate investment decisions. We advocate exploring more about the implications of electoral institutions and populist ideologies on firms' investment choices.
On the Quantum Security of HAWK

In this paper, we prove the quantum security of the signature scheme HAWK, proposed by Ducas, Postlethwaite, Pulles and van Woerden (ASIACRYPT 2022). More precisely, we reduce its strong unforgeability in the quantum random oracle model (QROM) to the hardness of the one-more SVP problem, which is the computational problem on which also the classical security analysis of HAWK relies. Our security proof deals with the quantum aspects in a rather black-box way, making it accessible also to non-quantum-experts.
Portrait stylized rendering for 3D light-field display based on radiation field and example guide

With the development of three-dimensional (3D) light-field display technology, 3D scenes with correct location information and depth information can be perceived without wearing any external device. Only 2D stylized portrait images can be generated with traditional portrait stylization methods and it is difficult to produce high-quality stylized portrait content for 3D light-field displays. 3D light-field displays require the generation of content with accurate depth and spatial information, which is not achievable with 2D images alone. New and innovative portrait stylization techniques methods should be presented to meet the requirements of 3D light-field displays. A portrait stylization method for 3D light-field displays is proposed, which maintain the consistency of dense views in light-field display when the 3D stylized portrait is generated. Example-based portrait stylization method is used to migrate the designated style image to the portrait image, which can prevent the loss of contour information in 3D light-field portraits. To minimize the diversity in color information and further constrain the contour details of portraits, the Laplacian loss function is introduced in the pre-trained deep learning model. The three-dimensional representation of the stylized portrait scene is reconstructed, and the stylized 3D light field image of the portrait is generated the mask guide based light-field coding method. Experimental results demonstrate the effectiveness of the proposed method, which can use the real portrait photos to generate high quality 3D light-field portrait content.
Robustness of random-control quantum-state tomography

In a recently demonstrated quantum-state tomography scheme [P. Yang, M. Yu, R. Betzholz, C. Arenz, and J. Cai, Phys. Rev. Lett. 124, 010405 (2020)], a random-control field is locally applied to a multipartite system to reconstruct the full quantum state of the system through single-observable measurements. Here, we analyze the robustness of such a tomography scheme against measurement errors. We characterize the sensitivity to measurement errors using the condition number of a linear system that fully describes the tomography process. Using results from random matrix theory we derive the scaling law of the logarithm of this condition number with respect to the system size when Haar-random evolutions are considered. While this expression is independent of how Haar randomness is created, we also perform numerical simulations to investigate the temporal behavior of the robustness for two specific quantum systems that are driven by a single random-control field. Interestingly, we find that before the mean value of the logarithm of the condition number as a function of the driving time asymptotically approaches the value predicted for a Haar-random evolution it reaches a plateau whose length increases with the system size.
The Age of fighting machines: the use of cyber deception for Adversarial Artificial Intelligence in Cyber Defence

Cyber deception has emerged as a valuable technique in the field of cybersecurity, closely linked with adversarial Artificial Intelligence. In an era of pervasive automation, it is getting prominence as a research topic aimed at understanding how novel machine learning algorithms can be deceived using adversarial attacks that exploit vulnerabilities of their models. To this end, the paper describes the state-of-the-art of cyber deception for adversarial AI purposes, focusing on its benefits, challenges, and advanced techniques. In addition, this exploratory research attempts to extend its applicability to the fact that an appropriate and timely discovery of adversarial plans and associated actions may enhance own cyber resilience by introducing analytical findings of the adversary's intent into decision-making for cyber situational awareness. The study of adversarial thinking is as old as history and is one of the most relevant subjects rapidly incorporated into the operational planning process – a methodology to understand the operational environment. Adversarial knowledge is used for adapting own cyber defences in response to the cyber threat landscape.
Collective Human Opinions in Semantic Textual Similarity

Despite the subjective nature of semantic textual similarity (STS) and pervasive disagreements in STS annotation, existing benchmarks have used averaged human ratings as the gold standard. Averaging masks the true distribution of human opinions on examples of low agreement, and prevents models from capturing the semantic vagueness that the individual ratings represent. In this work, we introduce USTS, the first Uncertainty-aware STS dataset with ~15,000 Chinese sentence pairs and 150,000 labels, to study collective human opinions in STS. Analysis reveals that neither a scalar nor a single Gaussian fits a set of observed judgements adequately. We further show that current STS models cannot capture the variance caused by human disagreement on individual instances, but rather reflect the predictive confidence over the aggregate dataset.
Base Fee Manipulation In Ethereum's EIP-1559 Transaction Fee Mechanism

In 2021 Ethereum adjusted the transaction pricing mechanism by implementing EIP-1559, which introduces the base fee - a network fee that is burned and dynamically adjusts to the network demand. The authors of the Ethereum Improvement Proposal (EIP) noted that a miner with more than 50% of the mining power could be incentivized to deviate from the honest mining strategy. Instead, such a miner could propose a series of empty blocks to artificially lower demand and increase her future rewards. In this paper, we generalize this attack and show that under rational player behavior, deviating from the honest strategy can be profitable for a miner with less than 50% of the mining power. We show that even when miners do not collaborate, it is at times rational for smaller miners to join the attack. Finally, we propose a mitigation to address the identified vulnerability.
Findings of the SIGMORPHON 2023 Shared Task on Interlinear Glossing

This paper presents the findings of the SIG-MORPHON 2023 Shared Task on Interlinear Glossing.This first iteration of the shared task explores glossing of a set of six typologically diverse languages: Arapaho, Gitksan, Lezgi, Natügu, Tsez and Uspanteko.The shared task encompasses two tracks: a resource-scarce closed track and an open track, where participants are allowed to utilize external data resources.Five teams participated in the shared task.The winning team Tü-CL achieved a 23.99%-point improvement over a baseline RoBERTa system in the closed track and a 17.42%-point improvement in the open track.
Design of image recognition system based on deep learning hybrid model

The development of image processing technology has provided strong support for image recognition technology. At present, image recognition technology has gradually broken through the concept limit and has been applied to many fields. Image recognition technology is an innovation and upgrading of image processing technology, which is mainly used to collect and transmit various information through computer operation. At present, image processing technology mostly adopts the method based on depth learning model, but the traditional depth learning model has many disadvantages in image processing. In order to solve the problems of a single depth learning model, an image recognition system design based on depth learning hybrid model is propose.
Deep learning based multi-branch structure detection network

Convolutional neural networks perform extremely well in current image classification tasks based on deep learning. In our research experiment, we found a flaw and made a simple but extremely effective improvement. In this article, we used the traditional ResNet and VGG16 as the benchmark networks. By parallelizing multiple networks into a multi-branch structure model, we designed a multi-branch structure classification header, arranged in parallel order into a whole model, and sent the entire image into the detection header of the multi-branch structure. Through different convolution kernels, we obtained the category scores of multiple categories, and output the highest score. Adding a multi-branch architecture as an external module to the target detection network has been experimentally found to improve performance. Our experiments on cifar-10, cifar-100, and ImageNet 2012 datasets have shown that our method can achieve extremely high classification network prediction accuracy under extreme conditions. Under conventional conditions, the accuracy rate also reaches 97%. Applying related modules to target detection networks also has relatively improved performance.
Boost Your Survey Response Rate With Incentives and Impact

Nonprofit Communications ReportVolume 21, Issue 9 p. 3-3 Seeking Outside Input Boost Your Survey Response Rate With Incentives and Impact First published: 10 August 2023 https://doi.org/10.1002/npcr.32238Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume21, Issue9September 2023Pages 3-3 RelatedInformation
DMAGNet: Dual‐path multi‐scale attention guided network for medical image segmentation

Abstract In recent years, convolutional neural networks (CNN)‐based automatic segmentation of medical images has become one of the hot topics in clinical disease diagnosis. It is still a challenging task to improve the segmentation accuracy of the network model with the large variation of pathological regions in different patients and the fuzzy boundary of pathological regions. A Dual‐path Multi‐scale Attention Guided network (DMAGNet) for medical image segmentation is proposed in this paper. First, the Dual‐path Multi‐scale Attention Fusion Module (DMAF) is proposed as a novel skip connection strategy, which is applied to encode semantic dependencies between high‐level and low‐level channels. Second, the Multi‐scale Normalized Channel Attention Module (MNCA) based on the atrous convolution, normalization channel attention mechanism, and the Depthwise Separable Convolutions (DSConv) is developed to strengthen dependencies between channels. Finally, the encoder–decoder backbone employs the DSConv, as well as the pretrained Resnet34 block is combined in the encoder part to further improve the backbone network performance. Comprehensive experiments on brain, lung, and liver segmentation tasks show that the proposed DMAGNet outperforms the original U‐Net method and other advanced methods.
BRNet: A Bilateral Residual Network for Real-Time Semantic Segmentation

The lightweight semantic segmentation network makes use of various excellent residual designs, and these residual modules not only improve the efficiency of semantic segmentation but also enhance the accuracy of semantic segmentation. Inspired by the structure of residuals, we propose a lightweight network, based on the modified BiSeNet V2 model, termed the Bilateral Residual Network (BRNet). Additionally, the Three Branches Residual Stem (TBR-Stem) module is also designed. Specifically, the two-branch structure of the original Stem Block is modified to a three-branch structure, and the asymmetric convolution operation is adopted, while depth-wise separable convolution and dilated convolution are introduced. As a result, TBR-Stem significantly reduces the computation, expands the receptive field, and enhances the feature extraction capability. Besides, the Channel Attention Module (CAM) is added to the Semantic Branch to reduce channel noise. The method achieves 73.3% MIoU on the CityScapes validation set and 143 FPS inference speed on a single NVIDIA GeForce GTX 1080Ti, finally, demonstrating that the method improves segmentation accuracy.