Miracle-dz/UniFAR-FTUs huggingface dataset:
GraphBolt: Dependency-Driven Synchronous Processing of Streaming Graphs

Efficient streaming graph processing systems leverage incremental processing by updating computed results to reflect the change in graph structure for the latest graph snapshot. Although certain monotonic path-based algorithms produce correct results by refining intermediate values via numerical comparisons, directly reusing values that were computed before mutation does not work correctly for algorithms that require BSP semantics. Since structural mutations in streaming graphs render the intermediate results unusable, exploiting incremental computation while simultaneously providing synchronous processing guarantees is challenging. In this paper we develop GraphBolt which incrementally processes streaming graphs while guaranteeing BSP semantics. GraphBolt incorporates dependency-driven incremental processing where it first tracks dependencies to capture how intermediate values get computed, and then uses this information to incrementally propagate the impact of change across intermediate values. To support wide variety of graph-based analytics, GraphBolt provides a generalized incremental programming model that enables development of incremental versions of complex aggregations. Our evaluation shows that GraphBolt's incremental processing eliminates redundant computations and efficiently processes streaming graphs with varying mutation rates, starting from just a single edge mutation all the way up to 1 million edge mutations at a time. Furthermore, being specialized for graph computations, GraphBolt extracts high performance compared to Differential Dataflow.
A Lightweight Framework for Fine-Grained Lifecycle Control of Android Applications

The lifecycle of Android apps is dynamically managed by the system in an ad hoc manner, which leads to apps' abusing lifecycle entry points to automatically start up and gaming the priority-based memory management mechanism to evade being killed. Such apps exhibit diehard behaviors that keep them long-running in the background, resulting in excessive battery consumption and device performance degradation. Existing battery-saving features are far from being effective in restricting diehard behaviors, due to the lack of systematic, fine-grained control of app lifecycle. In this paper, we propose the Application Lifecycle Graph (ALG), a holistic modeling of system-wide app lifecycle. We present a lightweight runtime framework that builds ALG and utilizes it to realize fine-grained lifecycle control of apps. The framework exposes APIs that provide ALG information and lifecycle control capabilities to developers and device vendors, empowering them to leverage the framework to implement rich functionalities. Evaluation results show that the proposed framework is competent and incurs low performance overhead. It introduces 4.5MB additional memory usage on average, and approximately 5% and 0.2% CPU usage during system booting and at idle state.
On contention resolution protocols and associated probabilistic phenomena

Consider an on-line scheduling problem in which a set of abstract processes are competing for the use of a number of resources. Further assume that it is either prohibitively expensive or impossible for any two of the processes to directly communicate with one another. If several processes simultaneously attempt to allocate a particular resource (as may be expected to occur, since the processes cannot easily coordinate their allocations), then none succeed. In such a framework, it is a challenge to design efficient contention resolution protocols. Two recently-proposed approaches to the problem of PRAM emulation give rise to scheduling problems of the above kind. In one approach, the resources (in this case, the shared memory cells) are duplicated and distributed randomly. We analyze a simple and efficient deterministic algorithm for accessing some subset of the duplicated resources. In the other approach, we analyze how quickly we can access the given (nonduplicated) resource using a simple randomized strategy. We obtain precise bounds on the performance of both strategies. We anticipate that our results will find other applications.
SkyBridge: Fast and Secure Inter-Process Communication for Microkernels

Microkernels have been extensively studied over decades. However, IPC (Inter-Process Communication) is still a major factor of runtime overhead, where fine-grained isolation usually leads to excessive IPCs. The main overhead of IPC comes from the involvement of the kernel, which includes the direct cost of mode switches and address space changes, as well as indirect cost due to the pollution of processor structures. In this paper, we present SkyBridge, a new communication facility designed and optimized for synchronous IPC in microkernels. SkyBridge requires no involvement of kernels during communication and allows a process to directly switch to the virtual address space of the target process and invoke the target function. SkyBridge retains the traditional virtual address space isolation and thus can be easily integrated into existing microkernels. The key idea of SkyBridge is to leverage a commodity hardware feature for virtualization (i.e., VMFUNC) to achieve efficient IPC. To leverage the hardware feature, SkyBridge inserts a tiny virtualization layer (Rootkernel) beneath the original microkernel (Subkernel). The Rootkernel is carefully designed to eliminate most virtualization overheads. SkyBridge also integrates a series of techniques to guarantee the security properties of IPC. We have implemented SkyBridge on three popular open-source microkernels (seL4, Fiasco.OC, and Google Zircon). The evaluation results show that SkyBridge improves the speed of IPC by 1.49x to 19.6x for microbenchmarks. For real-world applications (e.g., SQLite3 database), SkyBridge improves the throughput by 81.9%, 1.44x and 9.59x for the three microkernels on average.
Deep residual refining based pseudo-multi-frame network for effective single image super-resolution

Single image super-resolution (SISR) has gained great attraction and progress in recent years. Since the SISR is an ill-posed inverse problem, most researchers are concentrated on making efforts to learn effective and reasonable mapping functions from low-resolution observation to its potential high-resolution (HR) counterpart. In this study, the authors have proposed a deep residual refining based pseudo-multi-frame network for efficient SISR. A channel-wise attention mechanism is employed for residual refinement. It can ease residual learning process through explicitly modelling non-linear dependencies between channels by using global information embedding. Multiple potential HRs from different deconvolutional layers are further artificially learned, and then adaptively fused into final desired HR image. The authors call this strategy as pseudo-multi-frame SR. It could make full use of available redundant information possessed in hierarchical layers. They have evaluated the proposed network on several popular benchmark datasets. The experimental results have shown that the two highlights proposed can consistently boost final performance. The proposed network can outperform most of the state-of-the-art methods with acceptable less parameters.
Analysing volunteer engagement in humanitarian crowdmapping

Organisers of large crowdsourcing initiatives need to consider how to produce outcomes, but also how to build volunteer capacity. Central concerns include the impact of the first-time contributor experience, and the interplay of different modes of participation in larger organisations that host multiple strands of activity. How can volunteer capacity be built proactively, so that trained volunteers are available when needed? How important are opportunities for social encounter, either online or in person? We present four empirical studies of the Humanitarian OpenStreetMap Team (HOT), a novel setting where thousands of volunteers produce maps to support humanitarian aid. Its diversity of settings and activities provides an opportunity to observe the effects of different coordination practices within a single organisation. Participation is online and open to all, however volunteers need to learn specialist tools and workflows. To support newcomers, HOT organises offline events to learn the practice under expert guidance. Our research is motivated by a dual aim: first, to produce empirical evaluations of novel practices, informed by existing community concerns. Second, to revisit existing theories in social and behavioural science through the lens of this novel setting. We use statistical methods to observe the activity and retention of HOT volunteers. The full HOT contribution history is our primary source of empirical evidence, covering multiple years of activity. We can demonstrate that coordination practices have a marked impact on contributor retention. Complex task designs can be a deterrent, while social contribution settings and peer feedback are associated with a significant increase in newcomer retention. We further find that event-centric campaigns can be significant recruiting and reactivation events, however that this is not guaranteed. Our analytical methods provide a means of interpreting key differences in outcomes. We relate our findings to comparable settings, and close with a discussion of the theoretical and practical implications.
Efficient deep learning methods for human pose estimation

Human pose estimation is a very active research area in the field of computer vision. The goal is to infer the body pose of highly articulated people in images and videos. It has applications in gaming, human computer interaction, analysis, image retrieval, robotics and autonomous driving. In the past few years, impressive success has been achieved on the task of human pose estimation in uncontrolled environments. The success can be mainly attributed to deep learning. However, the trend is to push for maximum performance by using computationally expensive deep learning methods. Despite good performance, the methods require high end graphical processing units for efficient performance. The recent increase in deployment of service robots and autonomous vehicles has raised the demand for human pose estimation methods that run efficiently and robustly on low end graphics processing units that are mostly available on robots and autonomous vehicles. In this thesis, we propose efficient and robust deep learning methods for human pose estimation. We start with 3D human pose estimation from depth data. We build on a regression forest method to handle partial occlusion from objects without any increase in method’s complexity. Our proposed method can be used in indoor scenarios to estimate body poses of people partially occluded by objects. We then move to 2D single person pose estimation from RGB images. We propose an efficient and robust deep learning method for 2D single person pose estimation. The proposed method achieves comparable performance to the recent state-of-the-art 2D single person pose estimation methods. Our proposed method runs efficiently on low-end graphics processing units. We then approach the task of multi-person pose estimation. We propose a bottom up multiperson method with a novel pairwise relative offsets based association. Our approach simultaneously predicts body parts and relative offsets maps respectively. The relative offsets are used to associate body joints with a greedy procedure. Compared to stateof-the-art bottom up approaches the proposed method does not require any expensive post-processing. Finally, we propose a correspondence matching method with an efficient correlation layer. The correlation layer efficiently computes similarity heat-maps for all the query key points over the target image. The method is applied to the tasks of semantic key points matching, dense matching and instance key points matching. In the context of multi-person pose tracking, we apply instance key points matching for associating body joints over time.
Modelling the Perinatal Network System

The topic is that hospital capacity for patient beds runs short. We wish to predict when this will occur. An inter-disciplinary approach to this problem is taken incorporating a Management Science / Operational Research perspective. The subject is the Perinatal Network System, which is described, analysed and modelled. An illustrative Case Study is taken of an English local neonatal unit, where new-born babies are cared for. The focus is High dependency cots. Recommendations produced are subject to human factors and implementation difficulties. In this work, Systems Thinking facilitates an understanding of relationships; Enterprise Architecture helps embed the context and address complexity; while Clinical Medicine underpins decision-making for individual patients. Research outputs include the Conceptual Research Framework, a Quality Metric, a Cot Predictor Tool and a Markovian model Design, which can be adapted in the future. Furthermore there is the milieu or connective ‘glue’, to provide unity. The methodology or Enterprise Modelling helps address the issue by facilitating understanding of both overview and detail.
Detecting Volumetric Attacks on loT Devices via SDN-Based Monitoring of MUD Activity

Smart environments equipped with IoT devices are increasingly under threat from an escalating number of sophisticated cyber-attacks. Current security approaches are inaccurate, expensive, or unscalable, as they require static signatures of known attacks, specialized hardware, or full packet inspection. The IETF Manufacturer Usage Description (MUD) framework aims to reduce the attack surface on an IoT device by formally defining its expected network behavior. In this paper, we use SDN to monitor compliance with the MUD behavioral profile, and develop machine learning methods to detect volumetric attacks such as DoS, reflective TCP/UDP/ICMP flooding, and ARP spoofing to IoT devices. Our first contribution develops a machine for detecting anomalous patterns of MUD-compliant network activity via coarse-grained (device-level) and fine-grained (flow-level) SDN telemetry for each IoT device, thereby giving visibility into flows that contribute to a volumetric attack. For our second contribution we measure network behavior of IoT devices by collecting benign and volumetric attacks traffic traces in our lab, label our dataset, and make it available to the public. Our last contribution prototypes a full working system (built with an OpenFlow switch, Faucet SDN controller, and a MUD policy engine), demonstrates its application in detecting volumetric attacks on several consumer IoT devices with high accuracy, and provides insights into cost and performance of our system. Our data and solution modules are released as open source to the community.
Defect Engineering of 2D Materials for Electrochemical Energy Storage

With the increasing demands for current clean energy technologies, researchers are paying more and more attention to the full utilization of energy storage devices. However, the development of energy storage technologies is still limited by different technical challenges that need to be well addressed. Owing to the high specific surface area, ultrahigh carrier mobility and excellent mechanical flexibility, 2D materials have shown prominent superiorities for a wide range of energy storage applications. For 2D materials, a series of intrinsic deficiencies such as comparatively low conductivity, sparse electroactive sites, and poor reversibility have been found to restrict their further applications in real devices. In particular, 2D materials could be further defectively engineered to achieve a meaningful breakthrough in further improving the power performance and cycle life that satisfies the industrial demands. For a comprehensive clarify of the defect effects, this review summarizes the controllable strategies to generate defects in 2D materials, along with various characterizations and applications to identify the mechanisms of defects on the improvement of electrochemical performance. Finally, future directions of defects engineering design and application prospects of defective 2D materials are provided.
Being the Change: Music Teachers’ Self-Reported Changes in Mindset and Practice

Music educators sometimes enter the workforce un(der)prepared to design and implement inclusive instruction. The purpose of this descriptive interview study was to explore practicing teachers’ self-reported changes in mindset and practice as they worked to become more inclusive. Participants (N = 20) were music educators with between 1 and 17 years of teaching experience. All participants taught some combination of preK–12 students in general music, choir, band, orchestra, jazz, and other music classes in 10 states. We present qualitative analysis from two sets of interviews concerning self-reported changes in mindsets and teaching practices. We conclude with implications of this study for practicing teachers who wish to enact more equitable and just classroom practices.
Ecologies of innovation as a mediator between nexus of generative influence and entrepreneurial networking among small and medium enterprises in Uganda

Abstract This paper examines the mediating role of ecologies of innovation in the relationship between nexus of generative influence and entrepreneurial networking among small and medium enterprises (SMEs) in Uganda. A cross sectional survey design using quantitative approach was employed in this study. Data were collected with the help of self-administrated questionnaire from 228 SMEs. Systematic random sampling technique was used. Multiple regression data were analyzed with the help of SPSS software. The results indicated that ecologies of innovation fully mediates the relationship between nexus of generative and entrepreneurial networking. The data was cross sectional in nature, thus limiting monitoring changes in resources accessed from entrepreneurial networks by entrepreneurs over time. The implications are that, policy makers and managers of SMEs should pay more attention to the role of nexus of generative influence in creating ecologies of innovation, conducive environment for employees to interact with mutual influence to advance creativity and innovation that enhance increased access to resources from entrepreneurial networks. The study of nexus of generative influence, ecologies of innovation and entrepreneurial networking using complexity theory among SMEs in Uganda is a contribution to literature.
HISTORY-BASED SINGLE BELIEF STATE GENERATION FOR PARTIALLY OBSERVABLE REAL-TIME STRATEGY GAMES

Researches of AI planning in Real-Time Strategy (RTS) games have been widely applied to human behavior modeling and war simulation. Due to the fog-of-war, planning in RTS games need to be implemented under partially observable environment, which poses a big challenge for researchers. This paper focuses on extending Hierarchical Task Network (HTN) Planning in partially observable environment, and proposes a partially observable adversarial hierarchical task network planning with repairing algorithm named PO-AHTNR. By adding sensing action into HTN domain knowledge, a reconnaissance strategy and a history-based single belief state generation method are presented to obtain the best action. In order to verify the proposed algorithm, an empirical study based on μRTS game is carried out, and the performance of modified algorithm is compared to that of AHTNR and other state-of-the-art search algorithms developed for RTS games.
Hierarchical Look-Ahead Conservation Voltage Reduction Framework Considering Distributed Energy Resources and Demand Reduction

This paper proposes a hierarchical look-ahead framework to conduct conservation voltage reduction (CVR) when distributed energy resources such as solar photovoltaic (PV) systems and energy storage systems (ESSs), and demand response programs are integrated into distribution systems. With the increasing deployment of PV systems in distribution systems, their frequently varying power output due to cloud movements could have a detrimental impact on the consumer’s voltage quality, consequently leading to degraded CVR performance. A two-level CVR framework for the coordination of an on-load tap changer (OLTC), capacitor banks (CBs), and the smart inverters of PV systems/ESSs is presented, in which these elements operate to reduce the voltage profile along the distribution feeder at different temporal scales. At the global level, the operations of the OLTC and the CBs are scheduled every hour to achieve the best CVR performance in an optimization problem using mixed-integer linear programming. When voltage violations occur rapidly, the smart inverters of PV systems and ESSs help to maintain a lower voltage profile every second based on the proposed piecewise droop control functions at the local level. A simulation study is carried out in an IEEE 33-bus distribution system with an OLTC, CBs, PV systems, and ESSs, and our results demonstrate the advantages of the proposed approach in terms of voltage level and energy savings. Furthermore, the impact of demand reduction on the proposed approach is quantified, and we verify that a higher demand reduction yields more energy savings in the proposed framework.
Gender Equality and Women's Empowerment in Academia: The Case of Armenia

The aim of the study is to analyze gender equality and women’s empowerment in Armenian academia over the past decade. For these purposes a Subjective Women’s Empowerment index has been developed as a result of data analysis from two small-sized surveys. The theoretical framework primarily draws upon the Human Capital and Subjective Well-being theories. Analysis of the statistical data has provided thought-provoking insights on the relationship of subjective and objective measures, which in some cases strongly differ. Thus, the findings on these conformities and discrepancies laid the foundation for further qualitative research analyzing the causal roots.
Theory of parametrically amplified electron-phonon superconductivity

Ultrafast optical manipulation of ordered phases in strongly correlated materials is a topic of significant theoretical, experimental, and technological interest. Inspired by a recent experiment on light-induced superconductivity in fullerenes [M. Mitrano et al., Nature (London) 530, 461 (2016)], we develop a comprehensive theory of light-induced superconductivity in driven electron-phonon systems with lattice nonlinearities. In analogy with the operation of parametric amplifiers, we show how the interplay between the external drive and lattice nonlinearities lead to significantly enhanced effective electron-phonon couplings. We provide a detailed and unbiased study of the nonequilibrium dynamics of the driven system using the real-time Green's function technique. To this end, we develop a Floquet generalization of the Migdal-Eliashberg theory and derive a numerically tractable set of quantum Floquet-Boltzmann kinetic equations for the coupled electron-phonon system. We study the role of parametric phonon generation and electronic heating in destroying the transient superconducting state. Finally, we predict the transient formation of electronic Floquet bands in time- and angle-resolved photoemission spectroscopy experiments as a consequence of the proposed mechanism.