Characteristics of real datsets and parameter settings.

1. Animal social network analysis (SNA) often uses proximity data obtained from automated tracking of individuals. Identifying associations based on proximity requires deciding on quantitative criteria such as the maximum distance or the longest time interval between visits of different individuals to still consider them associated. These quantitative criteria are not easily chosen based on a priori biological arguments alone.
2. Here we propose a procedure for optimising proximity-based association criteria in SNA, whereby different spatial and temporal criteria are screened to determine which combination detects more network structure. If we assume that biologically-relevant associations among individuals are non-random, and that proximity data are mostly influenced by those associations, then it is logical to select criteria that minimise random associations and show the underlying network structure more clearly.
3. We first used simulations to evaluate which of four simple descriptors of network structure remain unbiased (i.e., do not change directionally) when reducing the number of observations, since unbiased descriptors are necessary for comparing the structure of networks using different association criteria. Then, using two of those descriptors (coefficient of variation of the strength of associations, and network entropy), and empirical proximity data from automated tracking of common waxbills (Estrilda astrild) in a mesocosm environment, we found that the structure-based optimisation procedure selected the most biologically-relevant combination of spatial and temporal proximity criteria, in the sense that those criteria were also the best at distinguishing between previously known social sub-groups of individuals.
4. These results indicate that, provided that the assumptions for structure-based optimisation are met, this procedure can find the most biologically-relevant association criteria. Thus, under the condition that proximity data are shaped by non-random social associations, and if using adequate descriptors of network structure, structure-based optimisation may be a useful tool for SNA, particularly when a priori biological arguments are insufficient to inform the choice of proximity-based association criteria.

treatment

Evaluation DatasetThis is the dataset for ASE 2024 paper "How Does Code Optimization Impact Third-party Library Detection for Android Applications?". If you are interested in our work, you are welcome to cite our paper.## Folder Structure - apks_5_compilation_configuration: 200 Apps with 5 compilation configurations: (1) D8 alone, (2) Obfuscation, (3) Shrinking, (4) Optimization+ Shrinking, and (5) Obfuscation + Optimization + Shrinking (R8’s default strategy).- apk_13_different_optimization_strategies: Randomly selected 50 Apps with different 13 optimization strategies, individually optimized by R8. Also enabling Shrinking during compilation.- dexs: Contains the library dex files used as candidate library. These dex files are produced by the D8 compiler.- groundtruth: Groundtruth for library detection.- params_tuning_apks: APKs selected for the param tuning experiment. - mapping_files_for_dataset1: Includes seed files, mapping files, and usage files generated during APK production. These files are useful for debugging.- mapping_files_for_dataset2: Includes seed files, mapping files, and usage files generated during APK production. These files are useful for debugging.## Citation@inproceedings{10.1145/3691620.3695554,author = {Xie, Zifan and Wen, Ming and Li, Tinghan and Zhu, Yiding and Hou, Qinsheng and Jin, Hai},title = {How Does Code Optimization Impact Third-party Library Detection for Android Applications?},year = {2024},isbn = {9798400712487},publisher = {Association for Computing Machinery},address = {New York, NY, USA},url = {https://doi.org/10.1145/3691620.3695554},doi = {10.1145/3691620.3695554},booktitle = {Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering},pages = {1919–1931},numpages = {13},keywords = {code optimization, third-party library, android},location = {Sacramento, CA, USA},series = {ASE '24}}

Dataset

This dataset was created by Dhitology

Contents

Management Software for Association Market Outlook

The global management software for association market size was valued at approximately USD 3.2 billion in 2023 and is projected to reach USD 6.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.1% during the forecast period. This impressive growth can be attributed to the increasing digital transformation initiatives undertaken by associations worldwide, aiming to streamline their operations, enhance member engagement, and optimize resource management.

One of the primary growth factors driving the management software for association market is the rising adoption of cloud-based solutions. Cloud technology offers significant advantages such as scalability, flexibility, cost-efficiency, and remote accessibility. Associations are increasingly leveraging cloud-based management software to facilitate seamless operations and real-time collaboration among members, regardless of geographical barriers. Additionally, the advent of advanced analytics and artificial intelligence (AI) capabilities within these software solutions is enabling associations to gain deeper insights into member behavior and preferences, further enhancing their strategic decision-making processes.

Another key driver propelling the market growth is the growing demand for integrated management systems that can handle multiple functions such as membership management, event planning, communication, and content dissemination. Associations are recognizing the need for a holistic platform that can streamline various administrative tasks, thereby reducing manual efforts and minimizing errors. This demand for comprehensive solutions is encouraging software developers to innovate and offer feature-rich platforms that cater to the diverse needs of associations, ensuring they remain competitive and efficient in their operations.

Moreover, the increasing emphasis on member engagement and retention is playing a significant role in the market's expansion. Associations are focusing on enhancing the member experience by offering personalized services, interactive platforms, and timely communication. Management software solutions equipped with CRM (Customer Relationship Management) capabilities are enabling associations to better understand their members' needs, preferences, and engagement patterns. This, in turn, is fostering stronger member relationships and improving retention rates, contributing to the overall growth of the market.

In the realm of association management, Award Management Software has emerged as a crucial tool for organizations looking to streamline their award processes. This software facilitates the efficient handling of nominations, evaluations, and award distributions, ensuring a seamless experience for both administrators and participants. By automating these processes, associations can save time and resources, allowing them to focus on enhancing member engagement and delivering value. The integration of Award Management Software with existing management systems further enhances its utility, providing a comprehensive solution that addresses the diverse needs of associations. As associations continue to embrace digital transformation, the adoption of such specialized software is expected to grow, driving innovation and efficiency in award management.

From a regional perspective, North America holds a significant share of the management software for association market, owing to the high adoption rate of advanced technologies and the presence of numerous professional associations and non-profit organizations. Europe is also witnessing substantial growth, driven by the increasing digitalization efforts and government initiatives aimed at supporting the non-profit sector. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, fueled by the rising number of associations and the growing awareness about the benefits of adopting management software solutions.

Deployment Mode Analysis

Deployment mode plays a crucial role in the management software for association market, with two primary segments: On-Premises and Cloud. The on-premises deployment mode involves installing the software on the associationÂ’s local servers and managing it within their own IT infrastructure. Organizations that prioritize data security and have stringent compliance requirements often prefer on-premises solutions. This mode provides greater control over data and custo

The Association Management Software (AMS) market is experiencing robust growth, driven by increasing demand for efficient member management, streamlined communication, and enhanced event planning capabilities among associations of all sizes. The market's expansion is fueled by several key factors: the rising adoption of cloud-based solutions offering scalability and accessibility, the growing need for data-driven decision-making within associations, and the increasing complexity of managing diverse member needs and engagement strategies. This trend is further amplified by the integration of features like online payment processing, fundraising tools, and sophisticated reporting functionalities, creating a comprehensive platform for association operations. We estimate the current market size to be approximately $1.5 billion, with a Compound Annual Growth Rate (CAGR) of 12% projected through 2033. This growth signifies a significant opportunity for vendors to innovate and cater to the evolving needs of associations seeking to optimize their operational efficiency and member engagement. The competitive landscape is highly fragmented, with a mix of established players and emerging niche providers. Major players like iMIS, MemberSuite, and Fonteva for Associations hold significant market share, leveraging their established brand recognition and comprehensive feature sets. However, smaller, specialized vendors are gaining traction by catering to specific association types or offering highly focused solutions. Geographic expansion, particularly in regions with a growing number of associations and increasing digital adoption, represents another key growth driver. We anticipate strong growth in regions such as North America and Europe, driven by high levels of technological adoption and a large number of established associations. The increasing focus on data security and compliance regulations is creating further opportunities for vendors that offer robust security features and meet industry standards.

Problem Statement

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A real estate company managing multiple smart buildings faced increasing energy costs and challenges in achieving their sustainability goals. Inefficient energy usage, despite advanced infrastructure, led to higher utility bills and a significant carbon footprint. The company required a solution to optimize energy consumption while maintaining occupant comfort and aligning with environmental commitments.

Challenge

Optimizing energy consumption in smart buildings presented the following challenges:

Managing data from numerous IoT devices, including HVAC systems, lighting, and appliances, across multiple buildings.

Identifying and addressing inefficiencies in energy usage patterns without compromising building performance.

Implementing a scalable and adaptive solution to accommodate varying occupancy levels and seasonal changes.

Solution Provided

An AI-based energy management system was developed, leveraging IoT integration and advanced analytics to monitor, analyze, and optimize energy usage. The solution was designed to:

Analyze real-time data from IoT sensors and devices to identify inefficiencies.

Provide actionable insights to adjust energy settings dynamically based on occupancy, weather, and time of day.

Automate energy-saving actions, such as adjusting HVAC and lighting systems during off-peak hours.

Development Steps

Data Collection

Aggregated data from IoT devices, including smart meters, HVAC sensors, lighting controls, and occupancy detectors, across all buildings.

Preprocessing

Cleaned and standardized data to ensure accurate analysis and eliminate inconsistencies from different IoT devices.

Model Training

Built machine learning models to predict energy consumption trends and identify optimization opportunities.Integrated reinforcement learning algorithms to dynamically adjust energy settings based on real-time data.

Validation

Tested the system on historical and real-time building data to ensure accuracy in energy usage predictions and optimization recommendations.

Deployment

Deployed the energy management system across all smart buildings, integrating it with existing building management systems (BMS) for seamless operation.

Monitoring & Improvement

Implemented a feedback loop to monitor system performance, refine models, and continuously improve optimization strategies.

Results

Reduced Energy Consumption

The AI-powered system reduced overall energy consumption by 22%, significantly lowering the company’s carbon footprint.

Lowered Utility Costs

Optimized energy usage resulted in substantial cost savings across all buildings.

Achieved Sustainability Goals

The energy management system enabled the company to meet its sustainability targets, enhancing its reputation as an environmentally conscious organization.

Improved Operational Efficiency

Automated energy adjustments minimized manual intervention, streamlining building management processes.

Scalable Solution

The system’s scalability allowed the company to extend energy optimization across new buildings seamlessly.

Description of experimental data.

The HOA and Condo Association Management Software market, valued at $1205 million in 2025, is experiencing robust growth, projected at a 7.1% CAGR from 2025 to 2033. This expansion is driven by several key factors. The increasing number of homeowners and condo associations, particularly in rapidly urbanizing regions across North America, Europe, and Asia-Pacific, fuels the demand for efficient management solutions. Furthermore, the rising adoption of cloud-based technologies offers improved accessibility, scalability, and cost-effectiveness compared to traditional on-premise systems. The trend towards automation in tasks like payment processing, communication, and maintenance scheduling further enhances operational efficiency and reduces administrative burdens for association managers. While the market faces restraints such as the initial investment costs for software implementation and the need for staff training, the long-term benefits of streamlined operations and improved resident satisfaction outweigh these challenges. The market segmentation reveals a strong preference for cloud-based solutions across all application areas (Homeowners Associations, Condo Associations, and Properties), indicating a clear industry shift towards digital transformation. The competitive landscape is populated by a diverse range of established players and emerging startups, offering a variety of features and pricing models to cater to different association needs and sizes. Future growth will likely be influenced by advancements in AI-powered features, integrated communication platforms, and robust data analytics capabilities within the software. This will further enhance decision-making, improve resident engagement, and optimize resource allocation for associations of all sizes. The North American market currently holds a significant share, driven by high HOA and condo ownership and early adoption of technological solutions. However, significant growth opportunities exist in other regions, especially in rapidly developing economies of Asia-Pacific and parts of South America, where the number of residential communities is increasing at a rapid pace. The success of individual vendors will hinge on their ability to provide user-friendly interfaces, robust security features, and scalable solutions that adapt to the evolving needs of a diverse range of association sizes and complexities. Strategic partnerships and mergers and acquisitions will likely shape the market landscape in the coming years, fostering innovation and expanding the reach of leading providers. The increasing demand for integrated solutions that encompass financial management, communication, and maintenance tracking will further drive market growth.

Dataset

This dataset was created by furu-nag

Contents

Across all model building strategies and training populations we identify 27 distinct associations that are both S-PrediXan significant and with significant evidence of colocalization. This spans 11 unique protein models and 8 phenotypes. (XLSX)

IT Cost Optimization Service Market Outlook

The IT Cost Optimization Service market size is projected to grow from USD 15 billion in 2023 to USD 30 billion by 2032, demonstrating a compound annual growth rate (CAGR) of 8.0% over the forecast period. This growth is primarily driven by the increasing need for businesses to manage and reduce their IT expenses while maximizing value and efficiency, particularly in the face of economic uncertainties and rapidly evolving technological landscapes.

One significant growth factor for the IT Cost Optimization Service market is the increasing adoption of digital transformation strategies across various industries. Businesses are increasingly recognizing the importance of IT infrastructure in maintaining competitive advantage, and as they undergo digital transformation, the need to optimize IT costs becomes crucial. This is particularly pertinent in industries such as BFSI, healthcare, and retail, where IT spending forms a substantial part of overall operational costs. Consequently, organizations are investing more in consulting, implementation, and managed services to streamline their IT operations and reduce unnecessary expenditures.

Another key driver of growth in this market is the widespread adoption of cloud computing. As businesses migrate their operations to cloud platforms, they seek ways to optimize their cloud expenses. This includes managing subscription models, optimizing cloud storage and computing resources, and ensuring efficient utilization of cloud services. The need to balance scalability with cost-efficiency further spurs the demand for specialized IT cost optimization services that can help businesses navigate the complexities of cloud cost management.

The rise of artificial intelligence (AI) and machine learning (ML) technologies also bolsters the IT Cost Optimization Service market. These advanced technologies enable more sophisticated and automated analysis of IT operations, helping businesses identify inefficiencies and opportunities for cost savings. AI and ML can predict future IT needs, optimize resource allocation, and provide actionable insights, leading to more effective cost management strategies. This capability is particularly valuable for large enterprises with complex IT environments, driving significant demand for AI-driven cost optimization solutions.

Regionally, North America is expected to dominate the IT Cost Optimization Service market due to the high concentration of technology-driven companies and robust adoption of advanced IT solutions. The presence of major market players, coupled with a strong focus on innovation and efficiency, drives the demand for cost optimization services. However, the Asia Pacific region is anticipated to exhibit the highest growth rate, attributed to the rapid digitalization of emerging economies and increasing IT investments in countries like China and India. Europe and Latin America also present substantial growth opportunities, driven by the expanding IT infrastructure and the need to optimize costs amidst economic fluctuations.

Service Type Analysis

The IT Cost Optimization Service market can be segmented by service type into consulting, implementation, and managed services. Consulting services play a crucial role in helping organizations assess their current IT expenditure, identify cost-saving opportunities, and develop strategies to optimize their IT investments. Consulting firms leverage their expertise to provide tailored recommendations that align with the business goals and IT infrastructure of the client. This segment is expected to witness significant growth as businesses increasingly seek expert guidance to navigate the complexities of IT cost management.

Implementation services involve the actual deployment of cost optimization strategies and solutions. This includes integrating new technologies, reconfiguring existing systems, and automating processes to achieve cost savings. The implementation phase is critical as it translates strategic plans into actionable outcomes. Demand for implementation services is expected to remain strong, driven by the need for hands-on support in executing cost optimization initiatives. These services ensure that the recommended strategies are effectively put into practice, delivering tangible benefits to the organization.

Managed services represent a comprehensive approach to IT cost optimization, where service providers take over the management of the organization’s IT infrastructure and operations. This includes continuous monitoring, maintenance, and optimization

This dataset contains the data collected during the SNSF BRIDGE GREENsPACK project (Grant no. 187223) in association with the recent publication entitled “Design optimization of a phase-change capacitive sensor for irreversible temperature threshold monitoring and its eco-friendly and wireless implementation”. This work aims to study the capacitive response of a resonating capacitive device coated with phase changing material (jojoba oil) as it melts when crossing its melting temperature. Several configuration were simulated with different electrode spacing, oil volume and encapsulation thickness and the induced changes in capacitance were tested experimentaly. An eco-friendly implementation of the optimized spiral resonating devices was tested wirelessly over a custom made near field antenna and the frequency of resonance was measured as the oil melted over the structure, irreversibly changing its resonance frequency. The data that was collected in the frame of this work is present in this repository. More information about the content of the dataset is present in the included README file.

Cloud-Based MRO Inventory Optimization Software Market size stood at USD 2,519.04 Million in 2024 and is projected to reach USD 4,156.81 Million by 2032. The Market is projected to grow at a CAGR of 6.45% from 2026 to 2032.Maintenance, repair, and operations (MRO) inventory optimization software that is cloud-based is a specialized digital solution that uses cloud computing to improve MRO inventory management, forecasting, and replenishment in a variety of industries. Cloud-based MRO optimization platforms, in contrast to traditional inventory management systems, offer scalable tools, data-driven insights, and real-time access to optimize the availability and use of tools, consumables, equipment, and spare parts required to maintain industrial processes. These technologies minimize surplus inventory, guarantee ideal stock levels, and avoid downtime from stockouts by utilizing machine learning algorithms, artificial intelligence, and advanced analytics.

Asset optimisation solutions market was valued USD 4.2 billion in 2022 and is expected to rise to USD 10.9 billion by 2030 at a CAGR of 12.5%.

About this dataset

This is a dataset that contains nutritional values of household commodities. The dataset was used in the "stigler"-problem where ones tries to figure out how to minimise the costs of food while still getting enough nutrients. This dataset was created by calmcode.io and contains more than 230 household Items along with their Price, Nutritional properties and other features such as: - Unit size - Vitamins Content - Protein Content - and more.

How to use this dataset

• Optimize the Nutritial content of a Household basket while minimizing the price, do so using the "CVXpy" package.
• More datasets

Acknowledgements

If you use this dataset in your research, please credit the authors

This survey, developed by Austin Public Health in collaboration with the City of Austin, is designed to evaluate how ArcGIS tools and licenses are currently being used across our organization. The purpose of this survey is to gather detailed feedback from users to better understand their workflows, tool preferences, and specific GIS needs.By identifying how GIS resources are utilized, we aim to optimize license allocations, streamline processes, and ensure that every team member has access to the right tools for their role. This effort will help reduce costs, enhance operational efficiency, and foster more effective GIS integration across departments.The input is invaluable in shaping the future of GIS resource management within Austin Public Health and the City of Austin.Made with ESRI ArcGIS Survey123 Technology

Test statistics for ANOVA and permuted F test comparing the predictive performance of different training populations for a particular model building strategy. ANOVA is run using the training population and the aptamer model ID as factors and Spearman Correlation as response. For our permuted F test the aptamer model ID is treated as a blocking factor for permutation. (XLSX)