Version Updates Research Articles

D-Bot: Database Diagnosis System using Large Language Models

Database administrators (DBAs) play an important role in managing database systems. However, it is hard and tedious for DBAs to manage vast database instances and give timely response (waiting for hours is intolerable in many online cases). In addition, existing empirical methods only support limited diagnosis scenarios, which are also labor-intensive to update the diagnosis rules for database version updates. Recently large language models (LLMs) have shown great potential in various fields. Thus, we propose D-Bot , an LLM-based database diagnosis system that can automatically acquire knowledge from diagnosis documents, and generate reasonable and well-founded diagnosis report (i.e., identifying the root causes and solutions) within acceptable time (e.g., under 10 minutes compared to hours by a DBA). The techniques in D-Bot include ( i ) offline knowledge extraction from documents, ( ii ) automatic prompt generation (e.g., knowledge matching, tool retrieval), ( iii ) root cause analysis using tree search algorithm, and ( iv ) collaborative mechanism for complex anomalies with multiple root causes. We verify D-Bot on real benchmarks (including 539 anomalies of six typical applications), and the results show D-Bot can effectively identify root causes of unseen anomalies and significantly outperforms traditional methods and vanilla models like GPT-4.

Android malware detection framework based on sensitive opcodes and deep reinforcement learning

Malware attack is a growing problem on the Android mobile platform due to its popularity and openness. Although numerous malware detection approaches have been proposed, it still remains challenging for malware detection due to a large amount of constantly mutating apps. The opcode, as the most fundamental part of Android app, possesses good resistance against obfuscation and Android version updates. Due to the large number of opcodes, most opcode-based methods employ statistical-based feature selection, which disrupts the correlation and semantic information among opcodes. In this paper, we propose an Android malware detection framework based on sensitive opcodes and deep reinforcement learning. Firstly, we extract sensitive opcode fragments based on sensitive elements and then encode the features using n-gram. Next, we use deep reinforcement learning to select the optimal subset of features. During the process of handling opcodes, we focus on preserving semantic information and the correlation among opcodes. Finally, our experimental results show an accuracy of 0.9670 by using the 25 opcode features we obtained.

Few-shot encrypted traffic classification via multi-task representation enhanced meta-learning

Encrypted traffic classification requires identifying the services and programs running behind the content-invisible traffic data for improving quality of service and providing security assurance. Mainstream solutions achieve reliable performance by training on large-scale datasets. However, with the continuous emergence and development of encryption services, collecting and labeling sufficient amounts of encrypted traffic becomes impractical. Therefore, it is critical to utilize the few labeled data for accurate encrypted traffic classification. In this paper, we propose a Multi-task Representation Enhanced Meta-learning model (MetaMRE) for few-shot encrypted traffic classification. Specifically, we design a flow discrepancy enhancement module that combines supervised learning and clustering-based unsupervised learning to boost the discrepancy of encrypted traffic representations from a few labeled data. Moreover, MetaMRE introduces a multi-task collaborative meta-learning module that makes full use of non-target task data to learn the optimal initialization parameters suitable for the encrypted traffic classification, and then only a small amount of labeled encrypted traffic is required to adapt to the target classification task. Extensive evaluations on various real-world datasets show that the MetaMRE outperforms existing state-of-the-art methods and copes well with version updates and cross-domain problems in encrypted traffic classification.

Analysis of Population Competition and User Competition between Tiktok and WeChat

Competition exists in many fields of nature and human society. In the current diverse new media competition in economic and life, precise and detailed user research is conducive to improving the competitiveness of enterprises. Whether Tiktok or wechat, their rise is inseparable from embracing the market demand. Competition for the number of users of the two is conducive to understanding the user needs, clarifying the development positioning, and defining the orientation of future product design. Therefore, it is necessary to study in depth the competition between Tiktok and wechat. Based on the empirical facts and the consideration of the appropriate simplified model, this paper first proposes four basic assumptions. Based on the two group competition models in the presence of competition, we transform the —— based on the special movement rules of market economy, introduce the concept of effective competition, and make it conform to the user competition between Tiktok and wechat. Then, according to the ratio relationship between user growth rate and competitive factor, the linkage law between Tiktok and the number of wechat users is discussed. Since both belong to the social application software of video communication, the core of which is manipulation and feedback, the user "psychological factor" is further introduced into the model to increase durability, playability and other indicators. Integrate the update of the application platform, discuss the situation and get the balance point. At the same time, we mined the user data of the platform, and discussed the user number competition between Tiktok and wechat based on the update situation of the platform and the user experience classification. It is found that the change curve of the number of users of Tiktok and wechat follows the s-shaped curve. In order to improve the user dependence, it is necessary to increase the maximum sample size of ——, that is, to improve the experience of user manipulation and feedback, rather than playing a zero-sum game between the two. The persistence and play of the APP are positively correlated with the change in the number of users, which is positively correlated with the increase and decrease of the user experience brought by the number of version updates, rather than simply depending on the number of updates. Only from the perspective of users, to improve the use experience of applications, and to clarify their own marketing strategy and strategic positioning, can they remain invincible in the competition of the number of users. The model established here is simple and feasible. They can be extended to competitive analysis of multiple groups and even a broad field of competitive analysis of various economic entities. According to the different conditions, the equilibrium level and the coexistence situation can be combined. After analytical verification, the model established in this paper has reasonable and practical significance.

Dissecting the impact of molecular T-cell HLA mismatches in kidney transplant failure: A retrospective cohort study.

Kidney transplantation is the optimal treatment in end-stage kidney disease, but de-novo donor specific antibody development continues to negatively impact patients undergoing kidney transplantation. One of the recent advances in solid organ transplantation has been the definition of molecular mismatching between donors and recipients' Human Leukocyte Antigens (HLA). While not fully integrated in standard clinical care, cumulative molecular mismatch at the level of eplets (EMM) as well as the PIRCHE-II score have shown promise in predicting transplant outcomes. In this manuscript, we sought to study whether certain T-cell molecular mismatches (TcEMM) were highly predictive of death-censored graft failure (DCGF). We studied a retrospective cohort of kidney donor:recipient pairs from the Scientific Registry of Transplant Recipients (2000-2015). Allele level HLA-A, B, C, DRB1 and DQB1 types were imputed from serologic types using the NMDP algorithm. TcEMMs were then estimated using the PIRCHE-II algorithm. Multivariable Accelerated Failure Time (AFT) models assessed the association between each TcEMM and DCGF. To discriminate between TcEMMs most predictive of DCGF, we fit multivariable Lasso penalized regression models. We identified co-expressed TcEMMs using weighted correlation network analysis (WGCNA). Finally, we conducted sensitivity analyses to address PIRCHE and IMGT/HLA version updates. A total of 118,309 donor:recipient pairs meeting the eligibility criteria were studied. When applying the PIRCHE-II algorithm, we identified 1,935 distinct TcEMMs at the population level. A total of 218 of the observed TcEMM were independently associated with DCGF by AFT models. The Lasso penalized regression model with post selection inference identified a smaller subset of 86 TcEMMs (56 and 30 TcEMM derived from HLA Class I and II, respectively) to be highly predictive of DCGF. Of the observed TcEMM, 38.14% appeared as profiles of highly co-expressed TcEMMs. In addition, sensitivity analyses identified that the selected TcEMM were congruent across IMGT/HLA versions. In this study, we identified subsets of TcEMMs highly predictive of DCGF and profiles of co-expressed mismatches. Experimental verification of these TcEMMs determining immune responses and how they may interact with EMM as predictors of transplant outcomes would justify their consideration in organ allocation schemes and for modifying immunosuppression regimens.

Data-driven method development and evaluation for indie mobile game publishing

With the emergence of mobile distribution channels, the traditional game value chain has produced new changes, leading to the emergence of the mobile value chain. Independent (Indie) game developers can upload their games directly through third-party app stores and publish them themselves. However, many indie game developers have issues with game publishing, especially updating the new version, promoting the market, and forecasting revenue for their games. This paper aims to provide a method to guide indie mobile game developers with mobile publishing. This new method mainly focuses on addressing the main challenges from the indie game developer’s side. The method includes a new concept of mobile game publishing logic and an online analysis tool along with the guidelines. It shows how to collect and analyze data and guide new version updates, marketing promotion, and revenue forecasts. In practice, the method was provided to six indie game companies and guided their mobile game publishing, and related data were collected and analyzed for evaluation. Based on the survey and interview results, the usefulness, usability, and confidence in the method were positive, and the method improved the indie game developers’ mobile game publishing and benefited their game business.

Chaos to Clarity: Pragmatic Approaches to Overcome Challenges for Successful Implementation of Additional Risk Minimisation Measures in the European Union and the UK by a Marketing Authorisation Holder.

Most national competent authorities issue guidance on the dissemination and implementation of additional risk minimisation measures (aRMMs), like the European Medicines Agency. However, national competent authorities guidance includes additional regulatory requirements, the reasons for which are unclear. The purpose of this study was to identify significant barriers to risk management implementation and methodological challenges encountered by local safety managers in the European Union and the UK owing to differences in country-specific regulations and regional national competent authorities guidance. The European Medicines Agency and national competent authorities guidance for each of the aRMM programme's formative components were compared. A survey was conducted to ascertain the challenges encountered by local safety managers throughout the implementation phases and the responses analysed. Twenty-seven national guidance documents were compared with the European Medicines Agency's guidance, and it was observed that national competent authorities provide additional aRMM dissemination and implementation parameters for the format and layout of aRMM, translation requirements, guidance on aRMM content, submission requirements, monitoring parameters and aRMM update requirements, among others. The survey was completed by 11 out of 21 local safety managers (52.38%). Eight local safety managers rated submission of an implementation plan as the most challenging aspect. Multiple national competent authorities requests, frequent updates, and delayed approval impacted planning and implementation, resulting in modifications and delays. Moreover, the study discovered that managing frequent version updates of digital aRMMs was challenging, and most national competent authorities neglected the post-implementation phase activities. Further, most local safety managers followed both the local and global processes to maintain documentation of aRMM implementation. It was evident that the European Medicines Agency and national competent authorities empower marketing authorisation holders to implement and disseminate aRMM materials tailored to their local healthcare settings. However, this poses a challenge for marketing authorisation holders because of a lack of clarity in guidance in executing an aRMM programme and an added burden of complying with both European Medicines Agency and national competent authority requirements.

When to Be Agile: Ratings and Version Updates in Mobile Apps

Lean and agile models of product development organize the flexible capacity to rapidly update individual products in response to customer feedback. Although agile operations have been adopted across numerous industries, neither the benefits nor the factors explaining when firms choose to become agile are validated and understood. We study these questions using data on the development of mobile apps, which occurs through the dynamic release of new versions into the mobile app marketplace, and the apps’ customer ratings. We develop a structural model estimating the dependence of product versioning on (a) market feedback in the form of customer ratings against (b) project and work-based considerations, such as development timelines, scale economies, and operational constraints. In contrast to when they actually benefit from operational agility, firms become agile when launching riskier products (in terms of uncertainty in initial customer reception) and less agile when they are able to exploit scale economies from coordinating development over a portfolio of apps. Agile operations increase firm payoffs by margins of 20% to 80%, and interestingly, partial agility is often sufficient to capture the bulk of these returns. Finally, turning to a question of marketplace design, we study how the mobile app marketplace should design the display of ratings to incentivize quality (increasing app categories’ average user satisfaction rates by as much as 22%). This paper was accepted by Jayashankar Swaminathan, operations management.

What is all this new MeSH about?

The Medical Subject Headings (MeSH) thesaurus is a controlled vocabulary widely used in biomedical knowledge systems, particularly for semantic indexing of scientific literature. As the MeSH hierarchy evolves through annual version updates, some new descriptors are introduced that were not previously available. This paper explores the conceptual provenance of these new descriptors. In particular, we investigate whether such new descriptors have been previously covered by older descriptors and what is their current relation to them. To this end, we propose a framework to categorize new descriptors based on their current relation to older descriptors. Based on the proposed classification scheme, we quantify, analyze, and present the different types of new descriptors introduced in MeSH during the last fifteen years. The results show that only about 25% of new MeSH descriptors correspond to new emerging concepts, whereas the rest were previously covered by one or more existing descriptors, either implicitly or explicitly. Most of them were covered by a single existing descriptor and they usually end up as descendants of it in the current hierarchy, gradually leading toward a more fine-grained MeSH vocabulary. These insights about the dynamics of the thesaurus are useful for the retrospective study of scientific articles annotated with MeSH, but could also be used to inform the policy of updating the thesaurus in the future.

NGScloud2: optimized bioinformatic analysis using Amazon Web Services.

BackgroundNGScloud was a bioinformatic system developed to perform de novo RNAseq analysis of non-model species by exploiting the cloud computing capabilities of Amazon Web Services. The rapid changes undergone in the way this cloud computing service operates, along with the continuous release of novel bioinformatic applications to analyze next generation sequencing data, have made the software obsolete. NGScloud2 is an enhanced and expanded version of NGScloud that permits the access to ad hoc cloud computing infrastructure, scaled according to the complexity of each experiment.MethodsNGScloud2 presents major technical improvements, such as the possibility of running spot instances and the most updated AWS instances types, that can lead to significant cost savings. As compared to its initial implementation, this improved version updates and includes common applications for de novo RNAseq analysis, and incorporates tools to operate workflows of bioinformatic analysis of reference-based RNAseq, RADseq and functional annotation. NGScloud2 optimizes the access to Amazon’s large computing infrastructures to easily run popular bioinformatic software applications, otherwise inaccessible to non-specialized users lacking suitable hardware infrastructures.ResultsThe correct performance of the pipelines for de novo RNAseq, reference-based RNAseq, RADseq and functional annotation was tested with real experimental data, providing workflow performance estimates and tips to make optimal use of NGScloud2. Further, we provide a qualitative comparison of NGScloud2 vs. the Galaxy framework. NGScloud2 code, instructions for software installation and use are available at https://github.com/GGFHF/NGScloud2. NGScloud2 includes a companion package, NGShelper that contains Python utilities to post-process the output of the pipelines for downstream analysis at https://github.com/GGFHF/NGShelper.

Implications for Emotion: Using Anatomically Based Facial Coding to Compare Emoji Faces Across Platforms.

Emoji faces, which are ubiquitous in our everyday communication, are thought to resemble human faces and aid emotional communication. Yet, few studies examine whether emojis are perceived as a particular emotion and whether that perception changes based on rendering differences across electronic platforms. The current paper draws upon emotion theory to evaluate whether emoji faces depict anatomical differences that are proposed to differentiate human depictions of emotion (hereafter, “facial expressions”). We modified the existing Facial Action Coding System (FACS) (Ekman and Rosenberg, 1997) to apply to emoji faces. An equivalent “emoji FACS” rubric allowed us to evaluate two important questions: First, Anatomically, does the same emoji face “look” the same across platforms and versions? Second, Do emoji faces perceived as a particular emotion category resemble the proposed human facial expression for that emotion? To answer these questions, we compared the anatomically based codes for 31 emoji faces across three platforms and two version updates. We then compared those codes to the proposed human facial expression prototype for the emotion perceived within the emoji face. Overall, emoji faces across platforms and versions were not anatomically equivalent. Moreover, the majority of emoji faces did not conform to human facial expressions for an emotion, although the basic anatomical codes were shared among human and emoji faces. Some emotion categories were better predicted by the assortment of anatomical codes than others, with some individual differences among platforms. We discuss theories of emotion that help explain how emoji faces are perceived as an emotion, even when anatomical differences are not always consistent or specific to an emotion.

GUI-Based DL-Network Designer for KISTI’s Supercomputer Users

With the increase in research on AI (Artificial Intelligence), the importance of DL (Deep Learning) in various fields, such as materials, biotechnology, genomes, and new drugs, is increasing significantly, thereby increasing the number of deep-learning framework users. However, to design a deep neural network, a considerable understanding of the framework is required. To solve this problem, a GUI (Graphical User Interface)-based DNN (Deep Neural Network) design tool is being actively researched and developed. The GUI-based DNN design tool can design DNNs quickly and easily. However, the existing GUI-based DNN design tool has certain limitations such as poor usability, framework dependency, and difficulty encountered in changing GUI components. In this study, a deep learning algorithm that solves the problem of poor usability was developed using a template to increase the accessibility for users. Moreover, the proposed tool was developed to save and share only the necessary parts for quick operation. To solve the framework dependency, we applied ONNX (Open Neural Network Exchange), which is an exchange standard for neural networks, and configured it such that DNNs designed with the existing deep-learning framework can be imported. Finally, to address the difficulty encountered in changing GUI components, we defined and developed the JSON format to quickly respond to version updates. The developed DL neural network designer was validated by running it with KISTI’s supercomputer-based AI Studio.

Smartphone and Tablet Application (App) Life Cycle Characterization via Apple App Store Rank

With the rapid growth of the smartphone and tablet market, mobile application (App) industry that provides a variety of functional devices is also growing at a striking speed. Product life cycle (PLC) theory, which has a long history, has been applied to a great number of industries and products and is widely used in the management domain. In this study, we apply classical PLC theory to mobile Apps on Apple smartphone and tablet devices (Apple App Store). Instead of trying to utilize often-unavailable sales or download volume data, we use open-access App daily download rankings as an indicator to characterize the normalized dynamic market popularity of an App. We also use this ranking information to generate an App life cycle model. By using this model, we compare paid and free Apps from 20 different categories. Our results show that Apps across various categories have different kinds of life cycles and exhibit various unique and unpredictable characteristics. Furthermore, as large-scale heterogeneous data (e.g., user App ratings, App hardware/software requirements, or App version updates) become available and are attached to each target App, an important contribution of this paper is that we perform in-depth studies to explore how such data correlate and affect the App life cycle. Using different regression techniques (i.e., logistic, ordinary least squares, and partial least squares), we built different models to investigate these relationships. The results indicate that some explicit and latent independent variables are more important than others for the characterization of App life cycle. In addition, we find that life cycle analysis for different App categories requires different tailored regression models, confirming that inner-category App life cycles are more predictable and comparable than App life cycles across different categories.

Overview of Intelligent Online Banking System Based on HERCULES Architecture

The online banking transaction system is the application system with the most complex business, the most demanded, and frequent version updates in the software engineering application system. The existing online banking business sub-module is intelligent and faces major challenges in security. Traditional online banking systems cannot meet this capability. This article combines machine learning and online banking business module design to implement a business agent online banking system based on a new architecture. The article first proposes new features and new challenges of the online banking system, discusses the technical problems solved by the intelligent online banking system, and analyzes HERCULES Architecture business intelligent machine learning algorithm model, smart deposits and white-collar loans and other core processes, and then designed and implemented an intelligent online banking system business model, focusing on the issues of the intelligent online banking system, soft load balancing implementation and transaction security, through business implementation The effectiveness of the proposed business agent is verified. The actual use results show that the intelligent online banking of the HERCULES architecture has greatly improved the intelligence and security of the traditional online banking system. Finally, we summarize and analyze the value and innovation of the intelligent online banking system, and look forward to the shortcomings of the system.

When to Be Agile: Ratings and Version Updates in Mobile Apps

Lean and agile models of product development organize the flexible capacity to rapidly update individual products in response to customer feedback. While agile operations have been adopted across numerous industries, neither the benefits nor the factors explaining when firms choose to become agile are validated and understood. We study these questions using data on the development of mobile apps, which occurs through the dynamic release of new versions into the mobile app marketplace, and the apps' customer ratings. We develop a structural model estimating the dependence of product versioning on (A) market feedback in the form of customer ratings, against (B) project and work-based considerations, such as development timelines, scale economies, and operational constraints. In contrast to when they actually benefit from operational agility, firms become agile when launching riskier products (in terms of uncertainty in initial customer reception) and less agile when able to exploit scale economies from coordinating development over a portfolio of apps. Agile operations increase firm payoffs by margins of 20-80%, and interestingly partial agility is often sufficient to capture the bulk of these returns. Finally turning to a question of marketplace design, we study how the mobile app marketplace should design the display of ratings to incentivize quality (increasing app categories' average user satisfaction rates by as much as 22%).

An outdoor sound propagation model in concert with geographic information system software

As industrial technology advances, man-made noise has increasingly contributed to natural environment soundscapes. To predict how this anthropogenic noise can affect these natural environments, engineers build acoustic models over given terrain; however many current models are not compatible with common Geographic Information System (GIS) software, could become outdated due to software version updates, or are written as proprietary packages unavailable to park management. The goal of this study was to create a true open source outdoor sound propagation model compatible with (but not dependent on) outside GIS software. The model was developed to include uneven terrain, atmospheric absorption, screening, wind effects, and ground effects using ISO 9613-2, an international standard for attenuation of sound during propagation outdoors. Given sound source inputs and locations over an input Digital Elevation Map, GIS compatible file types of spatially explicit sound pressure level predictions can be produced by this model. This tool allows for ecologists and park managers to get a better understanding of how anthropogenic noise is affecting soundscapes in natural environments. [Work supported by United Technologies Corporation Professorship.]

A new test suite reduction method for wearable embedded software

The verification of wearable embedded software trustability plays a prominent role in the research of wearable embedded technology. Software testing is an alternative way by which to improve the trustability of embedded software systems. The test suites developed are often reused because of the frequent version updates of wearable embedded software; as a result, many test cases in the test suite become redundant. In this paper, a new Boolean subtraction operation is proposed, one that does not alter the coverage of test requirements by the test suites. Redundant requirements and cases are reduced by a column–row transformation operation through the newly defined operation, and the interrelation among the testing requirements and test cases is taken into consideration. The results of experiments on some wearable embedded software test suite reduction indicate that the proposed method results in improved properties compared with H, GE, GRE, and can generate a minimal test suite with which all test requirements can be sufficiently tested.

A Systematic Literature Review of Agile Maturity Model Research

Background/Aim/Purpose: A commonly implemented software process improvement framework is the capability maturity model integrated (CMMI). Existing literature indicates higher levels of CMMI maturity could result in a loss of agility due to its organizational focus. To maintain agility, research has focussed attention on agile maturity models. The objective of this paper is to find the common research themes and conclusions in agile maturity model research. Methodology: This research adopts a systematic approach to agile maturity model research, using Google Scholar, Science Direct, and IEEE Xplore as sources. In total 531 articles were initially found matching the search criteria, which was filtered to 39 articles by applying specific exclusion criteria. Contribution:: The article highlights the trends in agile maturity model research, specifically bringing to light the lack of research providing validation of such models. Findings: Two major themes emerge, being the coexistence of agile and CMMI and the development of agile principle based maturity models. The research trend indicates an increase in agile maturity model articles, particularly in the latter half of the last decade, with concentrations of research coinciding with version updates of CMMI. While there is general consensus around higher CMMI maturity levels being incompatible with true agility, there is evidence of the two coexisting when agile is introduced into already highly matured environments. Future Research: Future research direction for this topic should include how to attain higher levels of CMMI maturity using only agile methods, how governance is addressed in agile environments, and whether existing agile maturity models relate to improved project success.

Image-based kernel fingerprinting

The correct identification of operating system kernel versions is the first critical step in deep memory analysis–it enables the precise parsing of the kernel data structures and the correct interpretation of the observed system state. Identifying the exact kernel version is particularly challenging for open source operating systems where kernel upgrades are released frequently, and custom versions can be created on demand. State of the practice approaches, such as Volatility's, rely on small and fragile signatures; state of the art research work relies on intricate understanding of architecture-specific implementation details, which limits them to Intel x86 environments, and requires continuous updates to identify the distinguishing characteristics of new kernels.In contrast, our work builds robust signatures based solely on the content of the kernel images on disk, and is able to efficiently distinguish among incremental kernel version updates. The approach is entirely content-driven and requires no low-level analysis of the operation of the kernel. It utilizes an approximate matching tool–sdhash–to extract kernel fingerprints, and can be applied across different architectures without the need to parse and interpret the RAM snapshot. In addition, our evaluation data which contains hundreds of kernels, provides insights into the typical levels of content similarity across related kernels.

Modular reweighting software for statistical mechanical analysis of biased equilibrium data

Here a simple, useful, modular approach and software suite designed for statistical reweighting and analysis of equilibrium ensembles is presented. Statistical reweighting is useful and sometimes necessary for analysis of equilibrium enhanced sampling methods, such as umbrella sampling or replica exchange, and also in experimental cases where biasing factors are explicitly known. Essentially, statistical reweighting allows extrapolation of data from one or more equilibrium ensembles to another. Here, the fundamental separable steps of statistical reweighting are broken up into modules – allowing for application to the general case and avoiding the black-box nature of some “all-inclusive” reweighting programs. Additionally, the programs included are, by-design, written with little dependencies. The compilers required are either pre-installed on most systems, or freely available for download with minimal trouble. Examples of the use of this suite applied to umbrella sampling and replica exchange molecular dynamics simulations will be shown along with advice on how to apply it in the general case. New version program summaryProgram title: Modular reweighting version 2Catalogue identifier: AEJH_v2_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJH_v2_0.htmlProgram obtainable from: CPC Program Library, Queenʼs University, Belfast, N. IrelandLicensing provisions: GNU General Public License, version 3No. of lines in distributed program, including test data, etc.: 179 118No. of bytes in distributed program, including test data, etc.: 8 518 178Distribution format: tar.gzProgramming language: C++, Python 2.6+, Perl 5+Computer: AnyOperating system: AnyRAM: 50–500 MBSupplementary material: An updated version of the original manuscript (Comput. Phys. Commun. 182 (2011) 2227) is availableClassification: 4.13Catalogue identifier of previous version: AEJH_v1_0Journal reference of previous version: Comput. Phys. Commun. 182 (2011) 2227Does the new version supersede the previous version?: YesNature of problem: While equilibrium reweighting is ubiquitous, there are no public programs available to perform the reweighting in the general case. Further, specific programs often suffer from many library dependencies and numerical instability.Solution method: This package is written in a modular format that allows for easy applicability of reweighting in the general case. Modules are small, numerically stable, and require minimal libraries.Reasons for new version: Some minor bugs, some upgrades needed, error analysis added.Summary of revisions:1.analyzeweight.py/analyzeweight.py2 has been replaced by “multihist.py”. This new program performs all the functions of its predecessor while being versatile enough to handle other types of histograms and probability analysis.2.“bootstrap.py” was added. This script performs basic bootstrap resampling allowing for error analysis of data.3.“avg_dev_distribution.py” was added. This program computes the averages and standard deviations of multiple distributions, making error analysis (e.g. from bootstrap resampling) easier to visualize.4.WRE.cpp was slightly modified purely for cosmetic reasons.5.The manual was updated for clarity and to reflect version updates. Examples were removed from the manual in favor of online tutorials (packaged examples remain).6.Examples were updated to reflect the new format. An additional example is included to demonstrate error analysis.Running time: Preprocessing scripts 1–5 minutes, WHAM engine <1 minute, postprocess script ∼1–5 minutes.