Miracle-dz/UniFAR-FTUs huggingface dataset:
Cold-formed steel bolted connections without washers on oversized holes shear and bearing failures in sheets

In cold-formed steel (CFS) construction, the bolted connections without washers on oversized holes may expedite the building process and lower the cost, at the same time provides satisfied strength. The current design specifications do not stipulate provisions for such connections, and washers are required to be installed on oversized holes. In order to investigate the behavior and determine the strength of CFS bolted connections without washers on oversized holes, a test program was developed and conducted at University of North Texas. This research was focused on the shear failure and the bearing failure of the connected sheets. No washer was used for the test specimens. The studied parameters included the steel sheet thickness: from 118 mil to 33 mil; the connection type: single shear and double shear; the number of bolts: one and two; the bolt type: ASTM A307, A325; the bolt diameter: 1/4 in. and 1/2 in. ; and the ductility in the sheet steel: low and high. Based on the test results, new design method for bearing strength was proposed. The paper presents the test program, test specimens, and the proposed design for CFS bolted connections without washers on oversized holes.
Empirical Evaluation of Data Visualizations by Non-Expert Users

— With the increased release of Open Government Data (OGD), several problems hinder the breakthrough of the Open Data agenda into the mainstream. One of these problems is the slow acceptance of OGD by non-expert end-users. They do not have the technical skills and prefer a human-readable format compared to the experts who demand machine-readable data. Recently, some OGD portals added interactive visualizations to ease the use of OGD by non-expert users. However, the question of human-usability or what makes it easier for non-experts to interact with OGD visualizations, remains open. With the aim to answer this question, we report results on the evaluation of OGD visualizations from the field experiment conducted with non-expert users. We discuss results and insights to inform designers and OGD providers.
AI, Organizing, and Management

As organizations increasingly use artificial intelligence (AI) technologies to improve their operations [1] and as we see the emergence of novel forms of human-AI hybrids [2], we need new management theories, frameworks, and methodologies that can help us get at the relationship between AI, organizing, and management [3]. AI-based agents often rely on complex internal processing and their behavior is less predictable than that of the types of IT artifacts we are used to dealing with [4]. Consequently, any theorizing about the role of AI for organizing and management needs to attend to the material properties of AI-based systems [5]. We contend that information systems, computer science, and related fields have a two-pronged role in this context as both explanatory and predictive sciences and prescriptive sciences [6]. First, in the spirit of explanatory and predictive science, they can contribute to our understanding of how AI systems revamp organizational structures and require novel management strategies. Second, in the spirit of prescriptive science, both academic and industry researchers can design interventions that help organizations develop strategies intended to facilitate the management of AI or that are based on AI. Such strategies are likely to be based on prescriptive knowledge that spells out how to implement AI solutions in a way that considers organizational factors. The two prongs of explanation/prediction and prescription are reflected in the papers that were submitted to this track, which cover a range of methodological perspectives, ranging from explanation to design. Four papers will be presented, each addressing different aspects of AI, organizing, and management. The first paper, “Innovating with Artificial Intelligence: Capturing the Generative Functional Capabilities of Deep Generative Learning” [7], identifies a set of functional capabilities of Deep Generative Learning (DGL) to generate outputs, and the authors discuss DGL’s potential for innovation. The second paper, “Computer, Whom Should I Hire? —Acceptance Criteria for the Usage of Artificial Intelligence in the Recruitment Process” [8], identifies factors that influence AI acceptance in the context of the recruitment process. The third paper, “Opening the Black Box of Music Royalties with the Help of Hybrid Intelligence” [9], proposes design requirements for hybrid intelligence systems in the music industry. The authors use a design science research approach to present a prototypical instantiation. Finally, the fourth paper, “Can AI Become a State Servant? A Case Study of an Intelligent Chatbot Implementation in a Scandinavian Public Service” [10], describes and analyzes a chatbot in the customer center of a public service entity. The authors highlight how the implementation affected job characteristics such as skill variety, autonomy, and feedback. These articles provide a taste of the varied implications of AI for organizing and management— addressing algorithmic capabilities, techniques of designing AI systems for particular organizational contexts, and, more broadly, the ways that AI-based systems change the nature of work.
Studies on a Hybrid Full-Bridge/Half-Bridge Bidirectional CLTC Multi-Resonant DC-DC Converter with a Digital Synchronous Rectification Strategy

This study presents a new bidirectional multi-resonant DC-DC converter, which is named CLTC. The converter adds an auxiliary transformer and an extra resonant capacitor based on a LLC resonant DC-DC converter, achieving zero-voltage switching (ZVS) for the input inverting switches and zero-current switching (ZCS) for the output rectifiers in all load range. The converter also has a wide gain range in two directions. When the load is light, a half-bridge configuration is adopted instead of a full-bridge configuration to solve the problem of voltage regulation. By this method, the voltage gain becomes monotonous and controllable. Besides, the digital synchronous rectification strategy is proposed in forward mode without adding any auxiliary circuit. The conduction time of synchronous rectifiers equals the estimation value of body diodes’ conduction time with the lightest load. Power loss analysis is also conducted in different situations. Finally, the theoretical analysis is validated by a 5 kW prototype.
CREATING MEANINGFUL BODILY EXPRESSION IN VIRTUAL WORLDS

This paper describes an investigation into possible participatory design methods for creating an avatar’s performance to support meaningful interpersonal interaction in virtual worlds. The questions proposed for this study include how to observe and analyse people’s bodily expression and how to use that material in creation of meaningful animations of bodily expression that might be incorporated in avatar behaviour. Three possible approaches were identified and evaluated in practice: observation in public places, role-play, and analysing movie clips. Observations in public places and role-play appear to be limited and unproductive in this study. Analysing movie clips could be considered as a useful approach to engage people in creating meaningful bodily expression for avatars. Besides, a preliminary scheme for understanding people’s experiences and viewpoints on bodily expressions of interpersonal interaction was concluded as follows: the phenomenon of empathy, the balance of status, the effects of environment, and the difference reactions in gender.
Cloud-based Virtual Reality Technology for Representing Design Guideline of Urban Residential Environment

This PhD research targets on how cloud-based virtual reality technology representing design guideline of urban residential environment. We attempted to employ cloud-based virtual reality technology to support stakeholders’ comprehension of urban design guidelines, compared with conventional planning documents and drawings. The research starts from using 3d model to present and checkout the design guideline. In this research we attempt to achieve new design method of the residential environment of low-rent Housing, we attempted to employ a practical project to present the concept. Firstly we investigate the low-rent housing residential area in Tianjin, China. With the interview of the local, the characteristics of the residential towards to the environment are analyzed. We think the outdoor open space of the low-rent housing residential environment could be diversified to partly meet the interior functions, by which way the cost of the implementation of low-rent housing is reduced to a certain extent. The detailed design work with some data is given necessarily. Then the 3D modeling helps to express the design guideline to the first party (non-professionals), proving the effectiveness. Then we endeavor to represent urban design guidelines to enhance public understanding. In particular, we concentrated on employing a tool for stakeholders to improve their understanding of urban design guidelines determined by related laws and regulations. We suggest that Cloud-based virtual reality technology (Google Earth) not only can display a detailed district plan of the urban design guidelines or related documents but can also offer a possible method for stakeholders to experience the planning site from anywhere via the internet. Stakeholders can refer to other planning projects on Google Earth at the same time. Thus, urban design guidelines can be more intuitive and dramatic to both professional planners and stakeholders. After that, we use the example of Shibuya bridge concept design to summarize the advantages how the cloud-based virtual reality technology do favors to represent urban design guideline. We used SAAS(Software as a Service) to facilitate the procedure of the pedestrian bridge design in Shibuya, Japan, also in communication on design evaluation, thus the support of public comprehension and participation through cloud computing is discussed.
Effects of rainfall, forage biomass, and population density, on survival and growth of juvenile kangaroos

A central goal of ecology is to understand how environmental variation affects populations. Long-term studies of marked individuals can quantify the effects of environmental variation on key life-history traits. We monitored the survival and growth of 336 individually marked juvenile eastern grey kangaroos (Macropus giganteus), a large herbivore living in a seasonal but unpredictable environment. During our 12-year study, the population experienced substantial variation in rainfall, forage biomass, and density. We used structural equation modeling to determine how variation in temperature and rainfall affected juvenile survival and growth through its effect on forage biomass and population density. Independently of population density, forage biomass had strong positive effects on survival from 10 to 21 months. At low population density, forage biomass also had a positive effect on skeletal growth to 26 months. Increasing maternal body condition improved rearing success for daughters but not for sons. High population density reduced skeletal growth to 26 months for both sexes. Rainfall had an increasingly positive effect on forage biomass at high temperatures, indicating a seasonal effect on food availability. Our study reveals interacting effects of environmental variation on juvenile survival and growth for a large mammal with a conservative reproductive strategy that experiences substantial stochasticity in food availability.
Microwave conical horn antenna with dual circular polarization—Close‐form design equations

In this article, we have proposed the closed form expressions for the optimized dimensions of different parts of the metallic stepped septum in a circular waveguide. Using these relations, a dual circularly polarized (CP) horn can be realized with good AR bandwidth, impedance bandwidth, and interport isolation. The relations are generalized for application at all frequency bands. Using these relations, horns have been designed and simulated at different frequency bands. Prototypes have been fabricated at some frequency bands and are experimentally validated. This design yields impedance bandwidth (S11 ≤ −10 dB) of about 18%, AR bandwidth more than 15%, and interport isolation about 24 dB at the band center. Experimental data for the Ka‐band prototype are provided in detail. At the design frequency, the measured axial rations are <1 dB revealing cross‐polar discrimination over 25 dB.
Decoupling processor and memory hierarchy simulators for efficient design space exploration

Virtual prototyping is typically used for HW/SW co-design and architectural exploration. Due to the increasing design complexity and tight time-to-market, virtual prototyping platforms should achieve fast simulation speed while keeping model accuracy reasonable. Yet, the simulation speed decreases as the target design complexity increases. This paper presents a simulation approach based on decoupling processor emulation and memory hierarchy simulation for the purpose of efficiency. Our approach reduces the overhead of simulating complex memory hierarchies without jeopardizing the accuracy of the performance evaluation results. Experiments show a significant improvement in simulation speed with a good level of accuracy compared to sequential simulation.
When F2FS meets address remapping

While gaining popularity in mobile devices, F2FS, a flash-friendly variation of log-structured file system, reveals three drawbacks: segment cleaning overhead, metadata update overhead, and file fragmentation, which becomes conspicuous under random update workloads. This paper suggests for the first time to leverage the address-remap technique in flash storage to remedy such pitfalls in F2FS. Our approach can, while preserving the benefit of log-structured writes, achieve the eventual effect of in-place update, completely preventing three drawbacks of F2FS. It can thus significantly outperform ext4 as well as vanilla F2FS under random update workloads. Armed with another write mode, F2FS will become competitive for a wider range of applications.
A principled approach for selecting block I/O traces

We present IOTAP, a tool that analyzes and profiles block I/O traces. IOTAP computes the (dis)similarities among a set of workloads and sets a guideline for selecting a subset of traces for benchmarking. By doing so, we avoid experimentally running all workloads or, even worse, arbitrarily selecting a subset that skews the results. We demonstrate the usefulness of IOTAP by comparing its results with experiments on real SSDs, achieving a high correlation of 0.92 for an NVMe SSD.
Lifetime-leveling LSM-tree compaction for ZNS SSD

The Log-Structured Merge (LSM) tree is considered well-suited to zoned namespace (ZNS) storage devices since the write requests to LSM-tree is sequential. However, zones can be partially invalidated and be fragmented during LSM-tree compaction. The partially-invalid zones cannot be utilized and thus space amplification becomes significant. To reclaim the invalid space, host-managed garbage collection (GC) is required, which increases the write amplification of ZNS storage and degrades I/O performance. We introduce a lifetime-leveling compaction (LL-compaction) tailored for ZNS SSD, which can alleviate space amplification without GC by making the sorted string tables in a zone have similar lifetimes. In our experiments using LevelDB, the LL-compaction achieved 1.7x better performance by removing GCs.
Infusing pub-sub storage with transactions

The need to support new features in existing storage systems is an ongoing concern for storage developers. So is the desire to develop next generation storage systems that can adopt newly developed feature improvements with relative ease. Extending storage systems is challenging because of the inherent complexity of their codebases and the need to ensure that the storage state does not become corrupt or inconsistent when enabling new features. In this work, we examine a new storage architecture, FDMI, that uses the well-established publish-subscribe model for extending the feature set of a host storage system using plugins. A central mechanism in FDMI is transactional coupling. With transactional coupling, the subscribed plugin can either create new transactions that execute asynchronously following the successful completion of the precipitating event or can participate in the pending transaction and control whether the precipitating event itself will or will not be committed. We further create a classification of transactional mechanisms as well as possible desired plugin functionality and explore the matrix of these two classifications to create a new model for faster, safer distributed storage development.
Radar Perception for Autonomous Unmanned Aerial Vehicles: a Survey

The advent of consumer and industrial Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, has opened business opportunities in many fields, including logistics, smart agriculture, inspection, surveillance, and construction. In addition, the autonomous operations of UAVs reduce risks by minimizing the time spent by human workers in harsh environments and lowering costs by automating tasks. For reliability and safety, the drones must sense and avoid potential obstacles and must be capable of safely navigating in unknown environments. UAVs’ perception requires reliability in various settings, such as high dust levels, humidity, intense sun glare, dark, and fog that can severely obstruct many conventional sensing methods. Radar systems have unique strengths; they can reliably estimate how far an object is and measure its relative speed via the Doppler effect. In addition, because radars exploit radio waves to sense, they perform well in rain, fog, snow, or smoky environments. This stands in contrast to optical technologies, such as cameras or LIght Detection And Ranging (Lidars), which are more susceptible to the same challenges as the human eye. This survey paper aims to address the signal processing challenges for the exploitation of radar systems in unmanned aerial vehicles for advanced perception, considering recent integration trends and technology capabilities. The focus is on signal processing techniques for low-cost and power-efficient radar sensors, which operate onboard the UAVs in real-time to ensure their needs in terms of perception, situational awareness, and navigation. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, safe, and autonomous way for UAVs to perceive and interact with the world.
Hello bytes, bye blocks: PCIe storage meets compute express link for memory expansion (CXL-SSD)

Compute express link (CXL) is the first open multi-protocol method to support cache coherent interconnect for different processors, accelerators, and memory device types. Even though CXL manages data coherency mainly between CPU memory spaces and memory on attached devices, we argue that it can also be useful to reform existing block storage as cost-efficient, large-scale working memory. Specifically, this paper examines three different sub-protocols of CXL from a memory expander viewpoint. It then suggests which device type can be the best option for PCIe storage to bridge its block semantics to memory-compatible, byte semantics. We then discuss how to integrate a storage-integrated memory expander into an existing system and speculate how much effect it does have on the system performance. Lastly, we visit various CXL network topologies and explore a new opportunity to efficiently manage the storage-integrated, CXL-based memory expansion.
Lifting and Dropping VMs to Dynamically Transition Between Time- and Space-sharing for Large-Scale HPC Systems

As HPC environments increasingly integrate with edge based systems, system architectures will need to handle a broader class of workloads and scheduling requirements. One result of this shift will be the need to simultaneously support bulk-synchronous parallel (BSP) and on-demand service based applications on the same infrastructure. This in turn will require that future resource management approaches utilize both space-shared as well as time-shared resource scheduling strategies. In this work we introduce the concept of "VM-lifting'' (and its inverse "VM-Dropping'') which allows dynamically switching an HPC workload between space-shared and time-shared scheduling regimes. Our work targets co-kernel based HPC system software environments, in which multiple specialized OS kernels execute natively on dedicated physical resource partitions inside a single compute node. With VM-lifting, a native co-kernel can be migrated at runtime to and from locally hosted Virtual Machine Environments due to changing scheduling requirements of the node. This allows an HPC node to be dynamically (re-)configured as either a time-shared Infrastructure-as-a-Service (IaaS) resource or a dedicated space shared resource based on the current workload demands. We have implemented this approach in the context of the Hobbes Exascale System Software stack and have demonstrated that a node can be reconfigured with minimal impact on the running applications.
MFGAN: A Lightweight Fast Multi-task Multi-scale Feature-fusion Model based on GAN

Cell segmentation and counting is a time-consuming task and an important experimental step in traditional biomedical research. Many current counting methods require exact cell locations. However, there are few such cell datasets with detailed object coordinates. Most existing cell datasets only have the total number of cells and a global segmentation labelling. To make more effective use of existing datasets, we divided the cell counting task into cell number prediction and cell segmentation respectively. This paper proposed a lightweight fast multi-task multi-scale feature fusion model based on generative adversarial networks (MFGAN). To coordinate the learning of these two tasks, we proposed a Combined Hybrid Loss function (CH Loss) and used conditional GAN to train our network. We proposed a Lightweight Fast Multitask Generator (LFMG) which reduced the number of parameters by 20% compared with U-Net but got better performance on cell segmentation. We used multi-scale feature fusion technology to improve the quality of reconstructed segmentation images. In addition, we also proposed a Structure Fusion Discrimination (SFD) to refine the accuracy of the details of the features. Our method achieved non-Point-based counting that no longer needs to annotate the exact position of each cell in the image during the training and successfully achieved excellent results on cell counting and cell segmentation.
Reproducibility Companion Paper: Human Object Interaction Detection via Multi-level Conditioned Network

To support the replication of ? Human Object Interaction Detection via Multi-level Conditioned Network", which was presented at ICMR'20, this companion paper provides the details of the artifacts. Human Object Interaction Detection (HOID) aims to recognize fine-grained object-specific human actions, which demands the capabilities of both visual perception and reasoning. In this paper, we explain the file structure of the source code and publish the details of our experiments settings. We also provide a program for component analysis to assist other researchers with experiments on alternative models that are not included in our experiments. Moreover, we provide a demo program for facilitating the use of our model.