Miracle-dz/UniFAR-FTUs huggingface dataset:
IndexIt: Enhancing Data Locating Services for Parallel File Systems

While the ability to access a small fraction of data records from a large volume of scientific datasets is vital to accelerate scientific discovery, existing parallel file systems face serious challenges in managing scientific big data since data services have traditionally been decoupled from file systems. In this paper, we present IndexIt, an in-situ index and query middleware that aims to enhance record locating services for parallel file systems. When applications are writing data to parallel file systems, IndexIt allows users to index data while they are still in memory and bypass the performance bottleneck between memory and disks. By applying lightweight Bitmap-Range index and organizing the index as key-value pairs, IndexIt accelerates index building efficiently. Moreover, we propose a two-level query processing framework to process query requests. We build a prototype and evaluate its performance with real scientific datasets. Compared with existing data management tools, the index building time can be reduced dramatically. The proposed two-level query processing achieves up to 3 orders of magnitude speed-up than scanning the entire dataset and shows up to 1.35x speed-up than an existing tool.
Variation-Resilient Design Techniques for Energy-Constrained Systems

Process, voltage, and temperature (PVT) variations substantially increase the variability of digital CMOS logics and reduce the operation robustness, especially for energy-constrained systems with aggressive voltage scaling. This paper reviews several variation-resilient design techniques for addressing PVT variations to improve the energy efficiency of digital CMOS VLSI circuits. The scope includes static and adaptive design techniques for design-time and run-time optimization, respectively. In addition, an emerging adaptive design strategy combining the fully integrated voltage regulator for system-level optimization is also introduced.
Joint Downlink-Uplink Throughput optimization in Wireless Powered Communication Networks

Wireless power transfer has attracted a great deal of attentions recently, due to its ability to extend the lifetime of IoT devices. In this paper, a joint downlink (DL) and uplink (UL) optimization for sum throughput maximization in a two user wireless powered communication network (WPCN) is considered. During DL, a hybrid access point (H-AP) sequentially broadcasts energy and transmits information to each user, and during the UL, each user transmits information to the H-AP, and harvests energy from other users’ UL information transmissions. Each user exploits the total harvested energy during both DL and UL to support its circuit power consumption for the DL information receiving and decoding and WET in the UL as well as information transmit power in the UL. Under this setting, we maximize the weighted sum throughput over both DL and UL by optimizing transmit power and time allocations at H-AP and users, subject to the H-AP’s and each user’s transmit power constraint, time allocation constraint and energy harvesting constraints at each user. However, this problem is found to be a non-convex optimization problem. We transform this problem into a convex optimization problem, and obtain the optimal solutions using an efficient algorithm.
Direct Instantaneous Torque Control of Three Phase 6/4 Switched Reluctance Generator Operating at Low Speeds

In this paper, the direct instantaneous torque control strategy (DITC) is applied for the switched reluctance machine (SRM) operating as a generator at low speeds. The firing angles remain fixed in the decreasing part of the inductances. The instant torque is controlled with DITC for low operating speeds, the magnitude of the speed is maintained at low levels in order to keep the back-EMF lower than the supply voltage for an adequate torque control. The voltage generated in the capacitor is controlled in a fixed value using the Cuk converter.
Distributed Downloading Strategy for Multi-Source Data Fusion in Edge-Enabled Vehicular Network : (Invited Paper)

Multi-source data fusion to support intelligent transportation system (ITS) is a promising service offered by mobile edge computing (MEC). With the fusion results delivered in near real-time, drivers or autonomous vehicles can peak around the corner, extend sensing range, reinforce and validate local observations to make safer and smarter driving decisions. However, downloading too much data increases the service delay thus undermines the fusion computing service performance. In this paper, we analyze the optimal downloading strategies of vehicles. By establishing the optimization indicator to monitor and evaluate fusion computing service, we use a hierarchical game, which is equivalent to a mathematical programming with equilibrium constraints (MPEC), to formulate the intersection between the MEC and vehicles. Through analysis, we transform the MPEC problem into a solvable single-layer optimization problem. We also provide an unpractical centralized approach, which has immense signaling overhead and exponentially-growing complexity, as a performance upper bound. Numerical results validate the theoretical analysis and demonstrate that the proposed downloading strategy has near-optimal performance in terms of system utility and service delay.
Multi-factor based Dynamic Offloading with Coalitional Game in Mobile Cloud Computing

Distributed mobile cloud computing is a new paradigm to enhance the edge cloud computing capacity and reduce the energy of application processing. In this paper, we establish a distributed cloudlet system which consists of several cloudlets and a computation-intensive application. The application can be divided into multiple related tasks. Each task selects mobile device or the appropriate offloading cloudlet to optimize utility. Besides energy consumption, the utility includes user mobility predictive probability, channel availability and cloudlet availability. Considering these factors can overcome the problem resulting from the user mobility and reduce the probability of connection failure while ensuring processing efficiency. We formulated this dynamic offloading problem as a transferable coalition game because each task is a sensible player to maximize overall utility. Simulation results demonstrate that the dynamic offloading decision scheme we proposed can get a better offloading decision with low complexity.
Genetic Algorithm-Based Test Parameter Optimization for ADAS System Testing

In this paper, we outline the use of a genetic algorithm for test parameter optimization in the context of autonomous and automated driving. Our approach iteratively optimizes test parameters to aim at obtaining critical scenarios that form the basis for virtual verification and validation of Advanced Driver Assistant Systems (ADAS). We consider a test scenario to be critical if the underlying parameter set causes a malfunction of the system equipped with the ADAS function (i.e., near crash or crash of the vehicle). For evaluating the effectiveness of our approach, we set up an automated simulation framework, where we simulated the Euro NCAP car-to-car rear scenario. To assess the criticality of each test scenario we rely on time-to-collision (TTC), which is a well-known and often used time-based safety indicator for recognizing rear-end conflicts. Our genetic algorithm approach showed a higher chance to generate a critical scenario, compared to a random selection of test parameters.
Collaborative Optimization of Distributed Scheduling Based on Blockchain Consensus Mechanism Considering Battery-Swap Stations of Electric Vehicles

The proliferation of electric vehicles and active distribution network has brought many uncertainties to the power system. If the power system involves battery-swap stations of electric vehicles, it is difficult to ensure the data security during the distributed scheduling. To solve the problem, this paper sets up a collaborative optimization model for distributed scheduling based on blockchain consensus mechanism, considering the battery-swap stations. The power system was divided into three levels: the transmission network level, the distribution network level and the battery-swap station level. Next, the objective functions were constructed to minimize the generation cost and daily load variance on each level, and the optimal scheduling plan for the power system was solved through multi-level collaborative optimization. The blockchain consensus mechanism was adopted to verify the accuracy of the transaction data, and the production data of all entities were encoded by hash function before storage, such that the data are tamper resistant and traceable. The example analysis shows that our model can effectively reduce the generation cost, lower the daily load variance, and enhance system stability. The research findings shed new light on maintaining the optimization efficiency and data confidentiality of modern power network.
Fingerprint Similarity Indoor Localization: An AP Selection Based Approach

With the flexible collection of received signal strengths (RSSs) by wide and ubiquitous indoor Wi-Fi infrastructures, the indoor fingerprint-based localization systems based on RSSs are gaining popularity. However, the device heterogeneity significantly degrades the performance of the fingerprint-based localization. Since different devices have different reception sensitivity, the RSSs collected by different devices at the same location for the same access point (AP) may be significantly different. To handle the device heterogeneity problem, we propose an AP selection based calibration-free method, which includes offline phase and online phase. In particular, an AP selection method is designed in the offline phase and selects the APs whose RSSs are of great difference for constructing fingerprint database. Moreover, a signal strength difference ranking method is designed in the online phase, which alleviates the impact of heterogeneous devices. Especially, experiments results validate that the proposed method could improve the region localization accuracy up to 23%, compared with the existing calibration-free methods.
Challenges and Solutions for Industry-Grade Secure Connectivity

This contribution discusses challenges and solutions for secure communication that are relevant for the success of the Industry 4.0 paradigm. It shows that IT security has to be solved with respect to the technical challenges of hardware or software components to be used in the Industry 4.0 context. Aditionally, the paper highlights the organizational challenge to reduce security risks to a minimum during the complete industrial life cycle.
Predictive Service Placement in Mobile Edge Computing

Mobile edge computing is emerging to support delay-sensitive 5G applications at the edge of mobile networks. Unlike traditional centralized clouds, MEC nodes are attached to base stations or access points, thus densely deployed. Therefore, when a user moves erratically among multiple MEC nodes, a new problem arises: whether the services should be dynamically migrated to maintain the service performance (i.e., user-perceived latency). However, frequent service migration can significantly increase operational cost, incurring a conflict between improving performance and reducing cost. To address these mis-aligned objectives, this paper studies the performance optimization of mobile edge service placement under the constraint of long-term cost budget. Aiming at a paradigm shifting from reactive to predictive by leveraging the power of prediction for performance enhancement, we study the predictive service placement with limited prediction of the near-future information. By using two timescale Lyapunov optimization method, we propose a T -slot predictive service placement algorithm (TA) to incorporate the prediction of user mobility based on a frame-based design. We characterize the performance bound of TA in terms of cost-delay tradeoff theoretically. Performance evaluations using realistic data trace show that TA can achieve a superior performance gain over the existing schemes without prediction.
Design of Locally-Clocked Extended Burst-Mode AFSMs without Timing Analysis

The asynchronous paradigm is an alternative that can solve the synchronous design problems caused by the global clock signal that are worse in the Deep-Sub-Micron CMOS Technology. Asynchronous controllers based on Asynchronous Finite State Machines (AFSM) are widely used in control unit design of asynchronous systems. Therefore, this paper proposes a novel architecture for AFSMs in the local clock style. AFSMs are described in the extended burst-mode (XBM) specification. The existence of a local clock reduces requirements of asynchronous logic, but the timing demands may require the insertion of delay elements that leads to degradation of performance and reliability. The novel proposed local clock architecture is robust to setup and hold times violations, but also it interacts with environment without the assumption of fundamental mode, so they are free of timing analysis and they don’t need to introduce any kind of delays.
A Four-Component People Identification and Counting System Using Deep Neural Network

Estimation of people flow is important for marketing research and customer management. This research focuses on building a counting system through analyzing surveillance video, which classifies people into employees and customers and counts people by groups in a certain time interval. In this project, a four-module system is constructed, including sensor module which is built with Gaussian mixture model to activate system when people enter the area, identification module which deploys Mask RCNN to recognize people by face, tracking module tracking the recognized people and counting module. This structure not only makes full use of the advantages of Mask RCNN, but also makes it possible to be applied in real-time system on an affordable device. The tracking module can handle short-term occlusion but does not perform well in long-term or crowded situation, which will be improved in the future study.
Data Temperature-Aware Bloom Filters for Flash-Based Storage

Hot data identification techniques for flash memory have attracted a plethora of research interests because of its crucial impact on performance and life span of SSD. Our proposed technique, Data Temperature-Aware Bloom Filters (DTABF), is suitable for the requirements of both the Buffer Management and the Flash Translation Layer in SSD. DTABF divides the access period of LPAs into n cycles. Thus, DTABF can record the access frequency of LPAs with a round-robin fashion in each cycle by combining one Counting Bloom filter and n Bloom filters. The access pattern of each LPA, indicated by the data access frequency, recency, and access frequency changes over time, is presented with n different data temperatures in n cycles. Specifically, we employ a bijective function to characterize the data temperature of each LPA. Based on the data temperature identified by DTABF, the data stored in LPAs, which presenting the similar access pattern, can be gathered in the same flash block in SSD. Meanwhile, those data occasionally becoming cold in the buffer can be effectively identified to avoid being meaninglessly replaced from the buffer. Analytical and experimental results show that DTABF can help to improve hit rate, write performance and alleviate write amplification, while achieving lower memory cost and computational complexity.
Feature-Based Deformable Registration Using Minimal Spanning Tree for Prostate MR Segmentation

Automatic and accurate segmentation of the prostate is still a challenging task due to intensity inhomogeneity and complicated deformation of MR images. To tackle these problems with multi-atlas segmentation, in this paper, we propose a new metric for image registration and new descriptor for label fusion. First, to reduce the amount of edges in entropic graph, a modified <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-mutual information (<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-MI) based on fast minimal spanning tree (MST) is implemented for deformable registration. Second, localized <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-MI allowing for the spatial information is proposed with the stochastic gradient optimization, and the feature space is encoded by a sparse auto-encoder. Finally, a multi-scale descriptor utilizing local self-similarity is integrated into the patch-based label fusion to obtain final segmentation. Experiments were performed on two subsets of totally 46 T2-weighted prostate MR images from 46 patients. Compared to <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-MI based on <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-nearest neighbor graph, the registration time of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-MI based on fast MST can be reduced by almost half. The median Dice overlap of registration using localized <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-MI on one subset is shown to improve significantly from 0.725 to 0.764 (<inline-formula> <tex-math notation="LaTeX">$p=1.14\times 10^{-5}$ </tex-math></inline-formula>), compared to using <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-MI without the spatial information. The median Dice overlap of prostate segmentation using the proposed method on 20 testing images of another subset is 0.871, and the median Hausdorff distance is 8.013 mm, which demonstrate a comparable accuracy to state-of-the-art methods.
Whole-Genome Sequencing to Determine Origin of Diarrhea in Suckling Piglets in Southern China

Severe diarrhea in suckling piglets occurred in southern China in 2017 and a new porcine coronavirus, named as porcine enteric alphacoronavirus (PEAV), was identified. Here we reported a detailed process to sequence, assemble and analyze the whole genome of PEAV. GDS04 has 87% amino acid identity comparing to the S proteins of the HKU2 strains without recombination, and their receptor-binding domains in S protein differ at 20 amino acids, suggesting that GDS04 could be a new mutant virus from bat-like CoVs and responsible for the most recent diarrhea endemic in pig herds in southern China.
Fault Detection in Timed FSM with Timeouts by SAT-Solving

Faults in safety critical real-time systems are not only logical, but they can correspond to violations of timing constraints. They must be detected to avoid system failures with adverse consequences. Developing efficient fault detection techniques for varieties of system models is still challenging. In this paper, we deal with fault detection for timed finite state machines with timeouts (TFSMs-T). TFSM-T is an extension of FSM to model timing constraints in safety-critical real-time systems. We lift a fault detection approach developed for FSM to generate tests detecting both logical faults and violations of time constraints in TFSMs-T. The approach is based on constraint solving and uses mutation machines to represent domains of faulty implementations (mutants) of a specification TFSMs-T. It also avoids enumerating the implementations one by one. We develop a prototype tool and we conduct experiments to evaluate the scalability of the proposed methods.
A Flexible Shared-Memory Parallel Mesh Adaptation Framework

Numerical simulation is an important tool used in various fields of computational science and engineering. The models to be solved by simulation are predominantly based on equations which require the discretization of a spatial domain. The accuracy of the simulation results is heavily influenced by the properties of the underlying spatial discretization, the mesh. Thus, adapting a mesh to meet certain criteria is an integral step to achieve a desired accuracy and high computational performance. With the trend of more and more cores on a compute node, it is essential to efficiently exploit this available on-node parallelism of modern multi-core systems. Our work introduces a flexible shared-memory parallelized mesh adaptation framework. We show the integrability of available serial mesh adaptation algorithms and applicability to multi-region meshes. The first step of the framework is the partitioning of the initial mesh, where all the resulting partitions are subsequently assigned to independent sets using graph coloring algorithms. These sets are then processed in parallel using two different adaptation algorithms: A template-based algorithm and a Delaunay-based algorithm provided by the TetGen software. We perform benchmarks using a cube geometry with different mesh resolutions as well as a model of a microelectronic transistor device structure to demonstrate the scalability of our approach and investigate the resulting element quality. The obtained speedups for this inherently memory-bound problem and for constant problem sizes range between 4.6 and 8.6 using 16 threads.
Statistical Priority Control for Ad Hoc Networks with Different Communication Ranges

In the IEEE 802.11e MAC protocol, the Enhanced Distributed Channel Access (EDCA) was issued to support the quality of service (QoS) for the different categories of traffic. However, the performance will deteriorate rapidly when the network is overloaded, and the QoS required by real-time services cannot be guaranteed. In this paper, we first derive a Markov chain model to analyze the performance of the EDCA at different transmission distances. Then, we propose a dynamic access threshold-based MAC protocol that supports working at different transmission distances, called DAT-EDCA, with the goal of improving access performance when the network is overloaded. The ns3 simulation results show that the DAT-EDCA can significantly improve throughput and reduce access delay when the network is overloaded, while QoS is also guaranteed. The protocol can be seamlessly integrable with the EDCA and works without APs which has guiding significance for the design of the Ad Hoc network MAC protocol.
Parallel Latent Dirichlet Allocation Using Vector Processors

Latent Dirichlet Allocation (LDA) is a widely used machine learning technique for topic modeling. The ever-growing size of training data and topic models has been attracting great attention to parallel LDA implementation. In this paper, we present VLDA, which accelerates LDA training by exploiting the data-level and the thread-level parallelism using vector processors. The priority-aware scheduling approach is proposed to address the high memory requirement and workload imbalance issues with existing works. Experimental results on various datasets demonstrate a 3.2x~12.9x speedup of VLDA over state-of-the-art parallel LDA methods based on an x86 architecture, which are PLDA and LightLDA. We also show that VLDA is capable of processing large-scale datasets with good scalability to multiple vector processors.