Reviewerly/exHarmony huggingface dataset:
Training $$\beta $$-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder

The reconstruction loss and the Kullback-Leibler divergence (KLD) loss in a variational autoencoder (VAE) often play antagonistic roles, and tuning the weight of the KLD loss in $$\beta $$ -VAE to achieve a balance between the two losses is a tricky and dataset-specific task. As a result, current practices in VAE training often result in a trade-off between the reconstruction fidelity and the continuity/disentanglement of the latent space, if the weight $$\beta $$ is not carefully tuned. In this paper, we present intuitions and a careful analysis of the antagonistic mechanism of the two losses, and propose, based on the insights, a simple yet effective two-stage method for training a VAE. Specifically, the method aggregates a learned Gaussian posterior $$z \sim q_{\theta } (z|x)$$ with a decoder decoupled from the KLD loss, which is trained to learn a new conditional distribution $$p_{\phi } (x|z)$$ of the input data x. Experimentally, we show that the aggregated VAE maximally satisfies the Gaussian assumption about the latent space, while still achieves a reconstruction error comparable to when the latent space is only loosely regularized by $$\mathcal {N}(\textbf{0},I)$$ . The proposed approach does not require hyperparameter (i.e., the KLD weight $$\beta $$ ) tuning given a specific dataset as required in common VAE training practices. We evaluate the method using a medical dataset intended for 3D skull reconstruction and shape completion, and the results indicate promising generative capabilities of the VAE trained using the proposed method. Besides, through guided manipulation of the latent variables, we establish a connection between existing autoencoder (AE)-based approaches and generative approaches, such as VAE, for the shape completion problem. Codes and pre-trained weights are available at https://github.com/Jianningli/skullVAE .
On Formal Models of Interactions Between Detectors and Trackers in Crowd Analysis Tasks

In crowd analysis tasks (crowds of humans, cattle, birds, drones, etc.) the low-level vision tools are usually the same, i.e. detection and tracking of either individuals or groups. The required results, however, are more complicated (e.g. patterns of group splitting/merging, changes in group sizes and membership, group formation and disappearance, etc.). To complete such tasks, raw results of detection/tracking are converted into data associations representing crowd structure/evolution. Normally, those associations are deterministic and based on target labeling. However, performances of detectors/trackers are non-perfect, i.e. their outcomes are effectively non-deterministic. We discuss matrix-based mathematical models of interactions between detectors and trackers to represent such data associations non-deterministically. In particular, a methodology for reconstructing weak or missing associations by alternative sequences of matrix operations is proposed. This can provide more reliable label correspondences between selected moments/points of monitored scenes. Apart from mathematical details, the paper presents examples illustrating feasibility of the proposed approach.
A triple-spark guiding strategy to enhance the loser-out tournament-based fireworks algorithm

&lt;abstract&gt;&lt;p&gt;Loser-out tournament-based fireworks algorithm (LoTFWA) is a new baseline of fireworks algorithm (FWA). However, its ability to search deeply in local areas and communicate among fireworks is not satisfying enough. Therefore, this paper proposes a new triple-spark guiding strategy for LoTFWA to deal with the mentioned problems. Among the three sparks generated for guiding, one is exactly the same as the original one in LoTFWA, the second one uses the centroid of good sparks to enhance the local exploitation, and the last one is based on Differential evolution (DE) mutation and used to enhance cooperation and exploration. Experimental results show that with low computational cost, the proposed guiding strategy attains significantly better results than LoTFWA and is competitive with state-of-the-art FWA variants. Furthermore, comprehensive experiments show that the proposed strategy has the potential to combine with other FWA variants to achieve better results.&lt;/p&gt;&lt;/abstract&gt;
OMT: A Demand-Adaptive, Hardware-Targeted Bonsai Merkle Tree Framework for Embedded Heterogeneous Memory Platform

Novel flash-based, crash-tolerant, non-volatile memory (NVM) such as Intel's Optane DC memory brings about new and exciting use-case scenarios for both traditional and embedded computing systems involving Field-Programmable Gate Arrays (FPGA). However, NVMs cannot be proper replacement for existing DDR memory modules due to low write endurance and are more well-suited for a hybrid NVM + Volatile memory system. They are also well-known to be vulnerable to different memory-based adversaries that demand the use of a robust authentication method such as Bonsai Merkle Tree. However, typical update process of a BMT (eager update) requires updating the entire update chain frequently, affecting run-time performance even for the data that is not persistence-critical. The latest intermittent BMT update techniques can help provide better real-time throughput, but they lack crash-consistency.
Correlating global trends in COVID-19 cases with online symptom checker self-assessments

Background Online symptom checkers are digital health solutions that provide a differential diagnosis based on a user’s symptoms. During the coronavirus disease 2019 (COVID-19) pandemic, symptom checkers have become increasingly important due to physical distance constraints and reduced access to in-person medical consultations. Furthermore, various symptom checkers specialised in the assessment of COVID-19 infection have been produced. Objectives Assess the correlation between COVID-19 risk assessments from an online symptom checker and current trends in COVID-19 infections. Analyse whether those correlations are reflective of various country-wise quality of life measures. Lastly, determine whether the trends found in symptom checker assessments predict or lag relative to those of the COVID-19 infections. Materials and methods In this study, we compile the outcomes of COVID-19 risk assessments provided by the symptom checker Symptoma ( www.symptoma.com ) in 18 countries with suitably large user bases. We analyse this dataset’s spatial and temporal features compared to the number of newly confirmed COVID-19 cases published by the respective countries. Results We find an average correlation of 0.342 between the number of Symptoma users assessed to have a high risk of a COVID-19 infection and the official COVID-19 infection numbers. Further, we show a significant relationship between that correlation and the self-reported health of a country. Lastly, we find that the symptom checker is, on average, ahead (median +3 days) of the official infection numbers for most countries. Conclusion We show that online symptom checkers can capture the national-level trends in coronavirus infections. As such, they provide a valuable and unique information source in policymaking against pandemics, unrestricted by conventional resources.
Curbing modern cyberthreats

The current hybrid way of working has become a new playground for cyber criminals. Attacks of all kinds have increased as malicious actors have sought to exploit weaknesses in infrastructures that were hurriedly constructed to support remote working. The good news is that technology is rising to the challenge.
Learning Heuristics for the Maximum Clique Enumeration Problem Using Low Dimensional Representations

Approximate solutions to various NP-hard combinatorial optimization problems have been found by learned heuristics using complex learning models. In particular, vertex (node) classification in graphs has been a helpful method towards finding the decision boundary to distinguish vertices in an optimal set from the rest. By following this approach, we use a learning framework for a pruning process of the input graph towards reducing the runtime of the maximum clique enumeration problem. We extensively study the role of using different vertex representations on the performance of this heuristic method, using graph embedding algorithms, such as Node2vec and DeepWalk, and representations using higher-order graph features comprising local subgraph counts. Our results show that Node2Vec and DeepWalk are promising embedding methods in representing nodes towards classification purposes. We observe that using local graph features in the classification process produce more accurate results when combined with a feature elimination process. Finally, we provide tests on random graphs to show the robustness and scalability of our method.
SAViP: Semantic-Aware Vulnerability Prediction for Binary Programs with Neural Networks

Vulnerability prediction, in which static analysis is leveraged to predict the vulnerabilities of binary programs, has become a popular research topic. Traditional vulnerability prediction methods depend on vulnerability patterns, which must be predefined by security experts in a time-consuming manner. The development of Artificial Intelligence (AI) has yielded new options for vulnerability prediction. Neural networks allow vulnerability patterns to be learned automatically. However, current works extract only one or two types of features and use traditional models such as word2vec, which results in the loss of much instruction-level information. In this paper, we propose a model named SAViP to predict vulnerabilities in binary programs. To fully extract binary information, we integrate three kinds of features: semantic, statistical, and structural features. For semantic features, we apply the Masked Language Model (MLM) pre-training task of the RoBERTa model to the assembly code to build our language model. Using this model, we innovatively combine the beginning token and the operation-code token to create the instruction embedding. For the statistical features, we design a 56-dimensional feature vector that contains 43 kinds of instructions. For the structural features, we improve the ability of the structure2vec network to obtain the characteristic of the network by emphasizing node self-attention. Through these optimizations, we significantly increase the accuracy of vulnerability prediction over existing methods. Our experiments show that SAViP achieves a recall of 77.85% and Top 100∼600 accuracies all above 95%. The results are 10% and 13% higher than those of the state-of-the-art V-Fuzz, respectively.
Research into the construction of audio database based on intelligent recognition algorithm

Language resource database is a language protection project gradually carried out in various regions in China in recent years, which aims to permanently retain the language characteristics of various regions through information technology. Through professional database architecture, the language characteristics of various regions in China are recorded and archived, so as to provide reference data resources for future generations&apos; learning and research, help Chinese people fully understand the language resources and national conditions in various regions of China, scientifically plan the development of language resources, drive the promotion of Putonghua and realize the scientific development of language resources. In order to further extract the language resources needed to build the knowledge map, the author analyzes the structured data in the Internet through Python crawler technology. After that, the crawler software automatically grabs the topic information of network language resources and extracts the corresponding language resources through regular expressions.
Implementation of parallelization of data mining algorithms based on big data and cloud computing

Although traditional data mining techniques have done a lot of research to improve the accuracy of the original algorithms, most of these improved algorithms are very complex and difficult to adapt to the increase in PCs. Therefore, how to balance the accuracy and computing speed, and how to properly improve the traditional data mining algorithm in the cloud computing environment, has become an important research topic. This paper studies the parallelization of data mining algorithms for big data and cloud computing, and understands the theoretical knowledge of data mining algorithms on the basis of literature, and then designs the parallel implementation of data mining algorithms for big data and cloud computing. The algorithm was tested, and the test results showed that compared with K-means++, the algorithm in this paper reduced the misclassification rate by 1.3% and the average error by 0.06, because this paper merged the cluster centers that were too close. This makes the clustering more accurate.
Deep learning based on word vector for improving bug triage performance

Bug triage is the process of quickly assigning bug reports to the most suitable developer so that the defect can be fixed quickly. Usually, the manual triage method not only consumes a lot of time for developers, but also has low ac-curacy. Therefore, how to automatically and efficiently allocate defects has become a hot research problem in the field of software maintenance. At present, the automatic bug triage method has become the mainstream technology, but there are also problems such as feature extraction and insufficient text representation ability. In view of the above problems, the task of automatic bug triage is regarded as a text classification problem. The method for automatic bug triage of multi-scale convolutional neural networks based on word vectors MCNN- BT can more effectively capture the important text features of bug reports, thereby providing more accurate defect repairer recommendation services. Empirical research is carried out in five software projects, Platform, Pde, Jdt, Cdt, Birt. The experimental results show that the prediction accuracy of MCNN-BT method is higher than that of the Naive Bayes method and the Latent Dirichlet Allocation(LDA) method.
Unraveling the establishment of residual risk in cybersecurity

This paper addresses the background, concepts, related variables, and current problems related to the calculation of Residual Risk (RR) in cybersecurity management systems. The objective of this paper is to clarify the concept of residual risk and identify the main problems that prevent the establishment of an adequate residual risk calculation at the organizational level and to provide possible solutions to this problem that will serve as a starting point for both practitioners and researchers in measuring the effectiveness of the countermeasures applied.
Eir-Ripp: Enriching Item Representation for Recommendation with Knowledge Graph

To improve the performance of recommendation models and enhance the interpretability of results, knowledge graph is often used to add rich semantic information to the recommended items. Existing methods either use knowledge graph as an auxiliary information to mine users’ interests, or use knowledge graph to establish relationships between items via their hidden information. However, these methods usually ignore the interaction between users and items. As a result, the hidden relationship between users and items are not well explored in the item representation. To address this issue, we propose an enhancement model to learn item representation based on RippleNet (Eir-Ripp). By mining the users’ historical behavior and user characteristics, users’ preference and the correlation between users and items are extracted to complement the semantic information of items. We conduct extensive experiments to evaluate our proposal on three public data sets. Experimental results show that our model outperforms the baseline methods in terms of an up to 8.8% improvement in the recommendation.
Neural text generation in regulatory medical writing

Background: A steep increase in new drug applications has increased the overhead of writing technical documents such as medication guides. Natural language processing can contribute to reducing this burden. Objective: To generate medication guides from texts that relate to prescription drug labeling information. Materials and Methods: We collected official drug label information from the DailyMed website. We focused on drug labels containing medication guide sections to train and test our model. To construct our training dataset, we aligned “source” text from the document with similar “target” text from the medication guide using three families of alignment techniques: global, manual, and heuristic alignment. The resulting source-target pairs were provided as input to a Pointer Generator Network, an abstractive text summarization model. Results: Global alignment produced the lowest ROUGE scores and relatively poor qualitative results, as running the model frequently resulted in mode collapse. Manual alignment also resulted in mode collapse, albeit higher ROUGE scores than global alignment. Within the family of heuristic alignment approaches, we compared different methods and found BM25-based alignments to produce significantly better summaries (at least 6.8 ROUGE points above the other techniques). This alignment surpassed both the global and manual alignments in terms of ROUGE and qualitative scoring. Conclusion: The results of this study indicate that a heuristic approach to generating inputs for an abstractive summarization model increased ROUGE scores, compared to a global or manual approach when automatically generating biomedical text. Such methods hold the potential to significantly reduce the manual labor burden in medical writing and related disciplines.
Sustainability Think Tank Is the First of Five for Water 2050 Initiative

Journal AWWAVolume 115, Issue 1 p. 7-9 Water 2050 Sustainability Think Tank Is the First of Five for Water 2050 Initiative Greg Kail, Corresponding Author Greg Kail [email protected] Search for more papers by this author Greg Kail, Corresponding Author Greg Kail [email protected] Search for more papers by this author First published: 23 January 2023 https://doi.org/10.1002/awwa.2031Read 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 No abstract is available for this article. Volume115, Issue1January/February 2023Pages 7-9 RelatedInformation
Machine Learning Based Finding of Similar Sentences from French Clinical Notes

Finding similar sentences or paragraphs is a key issue when dealing with text redundancy. This is particularly the case in the clinical domain where redundancy in clinical notes makes their secondary use limited. Due to lack of resources, this task is a key challenge for French clinical documents. In this paper, we introduce a semantic similarity computing approach between French clinical sentences based on supervised machine learning algorithms. The proposed approach is implemented in a system called CONCORDIA, for COmputing semaNtic sentenCes for fRench Clinical Documents sImilArity. After briefly reviewing various semantic textual similarity measures reported in the literature, we describe the approach, which relies on Random Forest (RF), Multilayer Perceptron (MLP) and Linear Regression (LR) algorithms to build different supervised models. These models are thereafter used to determine the degrees of semantic similarity between clinical sentences. CONCORDIA is evaluated using traditional evaluation metrics, EDRM (Accuracy in relative distance to the average solution) and Spearman correlation, on standard benchmarks provided in the context of the DEFT 2020 challenge. According to the official results of this challenge, our MLP based model ranked first out of the 15 submitted systems with an EDRM of 0.8217 and a Spearman correlation coefficient of 0.7691. The post-challenge development of CONCORDIA and the experiments performed after the DEFT 2020 edition showed a significant improvement of the performance of the different implemented models. In particular, the new MLP based model achieves a Spearman correlation coefficient of 0.80. On the other hand, the LR one, which combines the output of the MLP model with word embedding similarity scores, obtains the higher Spearman correlation coefficient with a score of 0.8030. Therefore, the experiments show the effectiveness and the relevance of the proposed approach for finding similar sentences on French clinical notes.
Creating a Seeding System

Back in Chapter 2, we learned that randomization takes a couple forms in game development. The first and most common is randomization where we don’t explicitly control the seed used. That’s what we used to randomize the behavior of the nodes in that chapter.
Nonlinear remote monitoring system of manipulator based on network communication technology

Abstract In order to study the nonlinear remote monitoring system of the manipulator based on the network communication technology, a research method of the nonlinear remote monitoring system of the manipulator based on the network communication technology is proposed. In this article, in the Visual C++ environment, the transmission control protocol/internet protocol technology is used to build a remote monitoring system for the robotic arm, and it mainly realizes the function of the robotic arm running according to the trajectory in the remote. When storing video, you need to specify the ID of the video source, i.e. , the synchronization source in the real-time transport protocol data packet header identifies the robot, which can monitor more clearly. The remote monitoring technology is widely used in the modern automation industry, which not only further extends the human space activity ability, but also removes the human from the dangerous and complex working environment. Therefore, the remote monitoring technology has broad application prospects.
Forensic Mobile Analysis on Social Media Using National Institute Standard of Technology Method

Instagram and WhatsApp have become popular social media applications, and the number of active users grows significantly each year.The increased use of Instagram and WhatsApp has increased the number of digital crimes, which are frequently committed by utilizing information obtained and available through the social media accounts of potential victims.Special forensic tools are required for digital crime policing using smartphones.As a result, it is necessary to investigate the functionality of existing forensic tools for processing digital crime cases involving Android phones, particularly for the social media platforms Instagram and WhatsApp.The goal of this study was to evaluate and compare two forensic technologies for obtaining digital evidence from Instagram and WhatsApp using experimental methods.Magnet Axiom discovered 92.31% of all digital evidence, whereas MOBILedit Forensic discovered 79.49% of digital evidence.Using the process of comparing the two study outcomes with forensic technology, Magnet Axiom outperforms MOBILedit Forensic in detecting digital evidence of Instagram and WhatsApp since MOBILedit Forensic cannot restore video data for more than 20 minutes.