Miracle-dz/UniFAR-FTUs huggingface dataset:
D2P: from recursive formulations to distributed-memory codes

Recursive formulations of programs are straightforward to reason about and write, often have good locality properties, and readily expose parallelism. We observe that it is easier to automatically generate distributed-memory codes for recursive formulations with certain properties: i) inclusive---a recursive method's parameters summarize the data access done within the method body. ii) Intersection---data-set intersection tests among method invocations can be computed efficiently. In this paper we present D2P, a system that automatically generates distributed-memory codes for recursive divide-conquer algorithms with these properties. D2P produces MPI-based implementations starting from shared-memory specifications of the recursive algorithms. We evaluate D2P with recursive Dynamic Programming (DP) algorithms, since these algorithms have the desired properties and are well known. We show that the generated implementations are scalable and efficient: D2P-generated implementations execute faster than implementations generated by recent distributed DP frameworks, and are competitive with (and often faster than) hand-written implementations.
Spread-n-share: improving application performance and cluster throughput with resource-aware job placement

Traditional batch job schedulers adopt the Compact-n-Exclusive (CE) strategy, packing processes of a parallel job into as few compute nodes as possible. While CE minimizes inter-node network communication, it often brings self-contention among tasks of a resource-intensive application. Recent studies have used virtual containers to balance CPU utilization and memory capacity across physical nodes, but the imbalance in cache and memory bandwidth usage is still under-investigated. In this work, we propose Spread-n-Share (SNS): a new batch scheduling strategy that automatically scales resource-bound applications out onto more nodes to alleviate their performance bottleneck, and co-locate jobs in a resource compatible manner. We implement Uberun, a prototype scheduler to validate SNS, considering shared-cache capacity and memory bandwidth as two types of performance-critical shared resources. Experimental results using 12 diverse cluster workloads show that SNS improves the overall system throughput by 19.8% on average over CE, while achieving an average individual job speedup of 1.8%.
Mesenchymal Stem Cells Ameliorate Renal Inflammation in Adriamycin-induced Nephropathy

Mesenchymal stem cells (MSCs) ameliorate the renal injury in Adriamycin (ADR)-induced nephropathy, but the mechanisms underlying their efficacy remain incompletely understood. In this study, we demonstrated that MSCs increased the survival, recovered body weight loss, and decreased proteinuria and serum creatinine levels in ADR-treated mice. MSCs also prevented podocyte damage and renal fibrosis by decreasing the expression of fibronectin, collagen 1α1, and α-smooth muscle actin. From a mechanistic perspective, MSCs inhibited renal inflammation by lowering the expression of CCL4, CCL7, CCL19, IFN-α/β, TGF-β, TNF-α, and chitinase 3-like 1. In summary, our data demonstrate that MSCs improve renal functions by inhibiting renal inflammation in ADR-induced nephropathy.
On the Flexible and Performance-Enhanced Radio Resource Control for 5G NR Networks

The RRC machinery of 5G NR introduced a new RRC state, RRC Inactive, which enables to suspend and quickly resume a radio connection, thus reducing the delay and network signalling needed for connection establishment, which in turn reduces the UE battery consumption. This paper describes the design of the RRC Inactive state and analyses how it can be efficiently used in conjunction with DRX, under different traffic profiles.The aim of this study is to investigate the trade-off between latency, network signalling, and UE battery consumption to enable the envisioned wide range of services over 5G networks, comprising massive MTC, in the most flexible and efficient way. The simulation carried out in a dense urban macro scenario reveals that, adopting an optimal configuration of RRC Inactive state results in more than 200% latency reduction compared to RRC Idle and around 40% UE power saving compared to RRC Connected, under infrequent traffic. The results also confirm that with more frequent traffic it is more beneficial to maintain the UEs in RRC Connected state avoiding any state switching cost.
Integrated textile sensor patch for real-time and multiplex sweat analysis

We report a sweat analysis patch based on silk-derived carbon textile for simultaneous detection of six health-related biomarkers. Wearable sweat analysis devices for monitoring of multiple health-related biomarkers with high sensitivity are highly desired for noninvasive and real-time monitoring of human health. Here, we report a flexible sweat analysis patch based on a silk fabric–derived carbon textile for simultaneous detection of six health-related biomarkers. The intrinsically N-doped graphitic structure and the hierarchical woven, porous structure provided the carbon textile good electrical conductivity, rich active sites, and good water wettability for efficient electron transmission and abundant access to reactants, enabling it to serve as an excellent working electrode in electrochemical sensors. On the basis of the above, we fabricated a multiplex sweat analysis patch that is capable of simultaneous detection of glucose, lactate, ascorbic acid, uric acid, Na+, and K+. The integration of selective detectors with signal collection and transmission components in this device has enabled us to realize real-time analysis of sweat.
SVS-JOIN: Efficient Spatial Visual Similarity Join for Geo-Multimedia

In the big data era, massive amount of multimedia data with geo-tags has been generated and collected by smart devices equipped with mobile communications module and position sensor module. This trend has put forward higher request on large-scale geo-multimedia retrieval. Spatial similarity join is one of the significant problems in the area of spatial database. Previous works focused on spatial textual document search problem, rather than geo-multimedia retrieval. In this paper, we investigate a novel geo-multimedia retrieval paradigm named spatial visual similarity join (SVS-JOIN for short), which aims to search similar geo-image pairs in both aspects of geo-location and visual content. Firstly, the definition of SVS-JOIN is proposed and then we present the geographical similarity and visual similarity measurement. Inspired by the approach for textual similarity join, we develop an algorithm named SVS-JOIN<inline-formula> <tex-math notation="LaTeX">$_{B}$ </tex-math></inline-formula> by combining the PPJOIN algorithm and visual similarity. Besides, an extension of it named SVS-JOIN<inline-formula> <tex-math notation="LaTeX">$_{G}$ </tex-math></inline-formula> is developed, which utilizes spatial grid strategy to improve the search efficiency. To further speed up the search, a novel approach called SVS-JOIN<inline-formula> <tex-math notation="LaTeX">$_{Q}$ </tex-math></inline-formula> is carefully designed, in which a quadtree and a global inverted index are employed. Comprehensive experiments are conducted on two geo-image datasets and the results demonstrate that our solution can address the SVS-JOIN problem effectively and efficiently.
Search Media and Elections

Concern about algorithmically-curated content and its impact on democracy is reaching a fever pitch worldwide. But relative to the role of social media in electoral processes, the role of search results has received less public attention. We develop a theoretical conceptualization of search results as a form of media-search media-and analyze search media in the context of political partisanship in the six months leading up to the 2018 U.S. midterm elections. Our empirical analyses use a total of over 4 million URLs, scraped daily from Google search queries for all candidates running for federal office in the United States in 2018. In our first set of analyses we characterize the nature of search media from the data collected in terms of the types of URLs present and the stability of search results over time. In our second, we annotate URLs' top-level domains with existing measures of political partisanship, examining trends by incumbency, election outcome, and other election characteristics. Among other findings, we note that partisanship trends in search media are largely similar for content about candidates from the two major political parties, whereas there are substantial differences in search media for incumbent versus challenger candidates. This work suggests that longitudinal, systematic audits of search media can reflect real-world political trends. We conclude with implications for web search designers and consumers of political content online.
A novel SMP-based survivability evaluation metric and approach in wireless sensor network

In Industrial Internet of Things (IIoT) device and network, wireless sensor network (WSN) is an important component. Routing protocol is the critical component of WSN. As the WSN may be attacked by all kinds of intruders, the survivability of WSN is important to IIoT security. To precisely evaluate the systematic survivability ability under external attack and internal security mechanism, a novel survivability entropy-based quantitative evaluation metric is proposed to calculate the systematic survivability ability of WSN routing protocol. Numerical analysis and simulation experiments are combined to precisely calculate the survivability entropy metric. To validate the evaluation approach, NS2 (Network simulator) is used to simulate the DoS attack and security mechanism in WSN. Experimental results show that the novel survivability evaluation metric and method can precisely evaluate the systematic survivability ability of WSN.
POSTER: Runtime Adaptations for Energy-Efficient VSLAM

As Visual Simultaneous Localisation and Mapping (VSLAM) formulations are increasingly deployed on low-power mobile platforms, it is essential to manage their power consumption in the context of minimal impact on their accuracy or robustness. This is particularly challenging when the runtime changes in motion or scene complexity are unknown in advanced. Further, different VSLAM formulations have different interpretations of the scene, posing a portability challenge for any management techniques. To this effect, we propose a new light-weight runtime adaptation model that relies purely on the change in sensor motion to characterise the tracking difficulty. The model is then used to adapt two general and portable parameters, namely Dynamic Voltage and Frequency Scaling (DVFS) and redundant frame-skipping. The model is evaluated in the context of both direct and indirect keyframe-based VSLAMs (DSO and ORB-SLAM) using scenes from the ICL-NUIM and EuRoC MAV datasets. Our results show a best-case power reduction of 75% with a marginal impact on the accuracy and robustness of the VSLAM formulations over multiple runs, compared to original base real-time versions of the algorithms. Analysis of the scenes showing an impact on robustness indicates that this is caused by critical points in the trajectory, motivating the search an improved characterisation metrics.
CARDS: A Mixed-Reality System for Collaborative Learning at School

Traditional computer systems based on the WIMP paradigm (Window, Icon, Menu, Pointer) have shown potential benefits at school (e.g. for web browsing). On the other hand, they are not well suited as soon as hands-on and collaborative activities are targeted. We present CARDS, a Mixed-Reality system that combines together physical and digital objects in a seamless workspace to foster active and collaborative learning. We describe the design process based on a participatory approach with researchers, teachers, and pupils. We then present and discuss the results of a user study that tends to show that CARDS has a good educational potential for the targeted activities.
Poster: Evaluating Code Coverage for System Call Fuzzers

The OS kernel, which has entire system privileges, is an attractive target of attackers. To reduce this threat, we need to find security bugs in the kernel prior to the attackers, and system call fuzzing is a widely used technique for this purpose. However, many system call fuzzers have not been evaluated for coverage performance which is an important indicator in fuzzing. In this poster, we propose a methodology to evaluate the code coverage performance of system call fuzzers with a strategy that combines virtualization and Intel Processor Trace (PT). First, we extract all the functions in the kernel that can be executed by system calls. Then we perform fuzzing with the target system call fuzzer on the guest OS, and record coverage information by leveraging the Intel PT. Finally, we evaluate system call fuzzers by comparing the list of functions related to system calls with the executed functions logged by Intel PT while fuzzing.
SSLDetecter: Detecting SSL Security Vulnerabilities of Android Applications Based on a Novel Automatic Traversal Method

Android usually employs the Secure Socket Layer (SSL) protocol to protect the user’s privacy in network transmission. However, developers may misuse SSL-related APIs, which would lead attackers to steal user’s privacy through man-in-the-middle attacks. Existing methods based on static decompiling technology to detect SSL security vulnerabilities of Android applications cannot cope with the increasingly common packed applications. Meanwhile, dynamic analysis approaches have the disadvantages of excessive resource consumption and time-consuming. In this paper, we propose a dynamic method to solve this issue based on our novel automatic traversal model. At first, we propose several new traversal strategies to optimize the widget tree according to the user interface (UI) types and the interface state similarity. Furthermore, we develop a more granular traversal model by refining the traversal level from the Activity component to the Widget and implement a heuristic depth-first traversal algorithm in combination with our customized traversal strategy. In addition, the man-in-the-middle agent plug-in is extended to implement real-time attack test and return the attack results. Based on the above ideas, we have implemented SSLDetecter, an efficient automated detection system of Android application SSL security vulnerability. We apply it on multiple devices in parallel to detect 2456 popular applications in several mainstream application markets and find that 424 applications are suffering from SSL security vulnerabilities. Compared with the existing system SMV-HUNTER, the time efficiency of our system increases by 38% and the average detection rate increases by 6.39 percentage points, with many types of SSL vulnerabilities detected.
Direction-Aided Indoor Positioning Leveraging Ultra-Wideband Radio

Many emerging applications based on robotics or augmented reality (e.g. for museums, BIM.) require accurate indoor positioning, that obviously can not be achieved by global positioning systems or traditional radio systems using received signal strength indicators. Ultra-wideband radio is a promising technology to enhance this accuracy, but its robustness still suffers in noisy environment and most of localization algorithms keep a prohibitive complexity to be embedded on target nodes. The Newton-Gauss algorithm represents a good trade-off between positioning performance and processing needs at the node level. As some recent wireless body area networks embed inertial measurement unit, the target direction can be used to further enhance the accuracy while keeping an acceptable complexity. In fact, the direction allows to narrow the research area of the mobile position. Thanks to this additional information, our direction-aided Newton-Gauss algorithm allows a gain of more than 14% in terms of accuracy over classical Newton-Gauss algorithm.
Gnu-RL: A Precocial Reinforcement Learning Solution for Building HVAC Control Using a Differentiable MPC Policy

Reinforcement learning (RL) was first demonstrated to be a feasible approach to controlling heating, ventilation, and air conditioning (HVAC) systems more than a decade ago. However, there has been limited progress towards a practical and scalable RL solution for HVAC control. While one can train an RL agent in simulation, it is not cost-effective to create a model for each thermal zone or building. Likewise, existing RL agents generally take a long time to learn and are opaque to expert interrogation, making them unattractive for real-world deployment. To tackle these challenges, we propose Gnu-RL: a novel approach that enables practical deployment of RL for HVAC control and requires no prior information other than historical data from existing HVAC controllers. To achieve this, Gnu-RL adopts a recently-developed Differentiable Model Predictive Control (MPC) policy, which encodes domain knowledge on planning and system dynamics, making it both sample-efficient and interpretable. Prior to any interaction with the environment, a Gnu-RL agent is pre-trained on historical data using imitation learning, which enables it to match the behavior of the existing controller. Once it is put in charge of controlling the environment, the agent continues to improve its policy end-to-end, using a policy gradient algorithm. We evaluate Gnu-RL on both an EnergyPlus model and a real-world testbed. In both experiments, our agents were directly deployed in the environment after offline pre-training on expert demonstration. In the simulation experiment, our approach saved 6.6% energy compared to the best published RL result for the same environment, while maintaining a higher level of occupant comfort. Next, Gnu-RL was deployed to control the HVAC of a real-world conference room for a three-week period. Our results show that Gnu-RL saved 16.7% of cooling demand compared to the existing controller and tracked temperature set-point better.
Non-Orthogonal Multiple Access in Cooperative UAV Networks: A Stochastic Geometry Model

In this paper, a unified framework for 3-hop unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) network is proposed. Aim at characterizing the performance of proposed framework, by using stochastic geometry, the analytical expressions for the outage probability of uplink/downlink transmissions are derived in closed- form for randomly deployed NOMA users. To obtain more insights of the network performance, the asymptotic analyses for the outage probability in the high signal- to-noise ratio (SNR) regime are carried out. These results reveal that for the uplink transmission, there exists an error floor due to the interference from the far user, while the performance of the far user can outperform the near one for the downlink transmission, which can be explained by the fact that the far user has a higher receiving power.
Using ground relays for low-latency wide-area routing in megaconstellations

Large low earth orbit satellite constellations have the potential to provide global high-capacity low-latency Internet access, but early deployments lack dedicated inter-satellite links (ISLs). We investigate the use of ground-based relays as a substitute for ISLs to provide low-latency wide area networking. We examine how to route such huge networks, potentially using user-terminals as relays, and devise an enhanced routing algorithm to cope. We show that even without ISLs, such networks can still beat optical fiber networks for latency.
Different is Good: Detecting the Use of Uninitialized Variables through Differential Replay

The use of uninitialized variables is a common issue. It could cause kernel information leak, which defeats the widely deployed security defense, i.e., kernel address space layout randomization (KASLR). Though a recent system called Bochspwn Reloaded reported multiple memory leaks in Windows kernels, how to effectively detect this issue is still largely behind. In this paper, we propose a new technique, i.e., differential replay, that could effectively detect the use of uninitialized variables. Specifically, it records and replays a program's execution in multiple instances. One instance is with the vanilla memory, the other one changes (or poisons) values of variables allocated from the stack and the heap. Then it compares program states to find references to uninitialized variables. The idea is that if a variable is properly initialized, it will overwrite the poisoned value and program states in two running instances should be the same. After detecting the differences, our system leverages the symbolic taint analysis to further identify the location where the variable was allocated. This helps us to identify the root cause and facilitate the development of real exploits. We have implemented a prototype called TimePlayer. After applying it to both Windows 7 and Windows 10 kernels (x86/x64), it successfully identified 34 new issues and another 85 ones that had been patched (some of them were publicly unknown.) Among 34 new issues, 17 of them have been confirmed as zero-day vulnerabilities by Microsoft.
Design and Analysis of Multi-Relay Cooperative Quadrature Spatial Modulation System

A multi-relay cooperative quadrature spatial modulation (QSM) system is proposed in this paper. The distinguishable feature of the multi-relay cooperative QSM lies in the enlarged aggregate throughput of the system since extra information bits at the source can be transmitted through the selection of the relays. During the communication process, only one relay is activated which adopts the amplify-and-forward (AF) relaying protocol. Furthermore, a closed-form expression of the average pairwise error probability (APEP) of the proposed multi-relay cooperative QSM system is given and employed to derive the tight upper bound of the average bit error probability (ABEP) over Rayleigh fading channels. Monte-Carlo simulation shows that the proposed multi-relay cooperative QSM system achieves better ABEP performance compared with the traditional multi-relay cooperative spatial modulation (SM) system.
Speeding up Reinforcement Learning by Combining Attention and Agency Features

When playing video-games we immediately detect which entity we control and we center the attention towards it to focus the learning and reduce its dimensionality. Reinforcement Learning (RL) has been able to deal with big state spaces, including states derived from pixel images in Atari games, but the learning is slow, depends on the brute force mapping from the global state to the action values (Q-function), thus its performance is severely affected by the dimensionality of the state and cannot be transferred to other games or other parts of the same game. We propose different transformations of the input state that combine attention and agency detection mechanisms which both have been addressed separately in RL but not together to our knowledge. We propose and benchmark different architectures including both global and local agency centered versions of the state and also including summaries of the surroundings. Results suggest that even a redundant global-local state network can learn faster than the global alone. Summarized versions of the state look promising to achieve input-size independence learning.