Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust and Flexible Discrete Hashing for Cross-Modal Similarity Search

Multimodal hashing approaches have gained great success on large-scale cross-modal similarity search applications, due to their appealing computation and storage efficiency. However, it is still a challenge work to design binary codes to represent the original features with good performance in an unsupervised manner. We argue that there are some limitations that need to be further considered for unsupervised multimodal hashing: 1) most existing methods drop the discrete constraints to simplify the optimization, which will cause large quantization error; 2) many methods are sensitive to outliers and noises since they use $\ell _{2}$ -norm in their objective functions which can amplify the errors; and 3) the weight of each modality, which greatly influences the retrieval performance, is manually or empirically determined and may not fully fit the specific training set. The above limitations may significantly degrade the retrieval accuracy of unsupervised multimodal hashing methods. To address these problems, in this paper, a novel hashing model is proposed to efficiently learn robust discrete binary codes, which is referred as Robust and Flexible Discrete Hashing (RFDH). In the proposed RFDH model, binary codes are directly learned based on discrete matrix decomposition, so that the large quantization error caused by relaxation is avoided. Moreover, the $\ell _{2,1}$ -norm is used in the objective function to improve the robustness, such that the learned model is not sensitive to data outliers and noises. In addition, the weight of each modality is adaptively adjusted according to training data. Hence the important modality will get large weights during the hash learning procedure. Owing to above merits of RFDH, it can generate more effective hash codes. Besides, we introduce two kinds of hash function learning methods to project unseen instances into hash codes. Extensive experiments on several well-known large databases demonstrate superior performance of the proposed hash model over most state-of-the-art unsupervised multimodal hashing methods.
Semi-Supervised Cross-View Projection-Based Dictionary Learning for Video-Based Person Re-Identification

Video-based person re-identification (re-id) has attracted a lot of research interest. When facing dramatic growth in new pedestrian videos, existing video-based person re-id methods usually need large quantities of labeled pedestrian videos to train a discriminative model. In practice, labeling large quantities of pedestrian videos is a costly and time-consuming task, which will limit the application of these methods in the real environment. Therefore, it is valuable and necessary to investigate how to learn a discriminative re-id model by using limited labeled training pedestrian videos. In this paper, we propose a semi-supervised cross-view projection-based dictionary learning (SCPDL) approach for video-based person re-id. Specifically, SCPDL jointly learns a pair of feature projection matrices and a pair of dictionaries by integrating the information contained in labeled and unlabeled pedestrian videos. With the learned feature projection matrices, the influence of variations within each video to the re-id can be reduced. With the learned dictionary pair, pedestrian videos from two different cameras can be converted into coding coefficients in a common representation space, such that the differences between different cameras can be bridged. In the learning process, the labeled pedestrian videos are used to ensure that the learned dictionaries have favorable discriminability; the large quantities of unlabeled pedestrian videos are used to ensure that SCPDL can better capture the variations between pedestrian videos, such that the learned dictionaries can own stronger representative capability. Experiments on two public pedestrian sequence data sets (iLIDS-VID and PRID 2011) demonstrate the effectiveness of the proposed approach.
An Analytical Study on Different Image Segmentation Techniques for Malaria Parasite Detection

Malaria is a dangerous disease affecting the mortality rate in humans to a large extend. Plasmodium vivax is the most dangerous parasite for the cause of the widespread of malaria. In the present, many developing countries have a burgeoning scale of children being affected by this parasite, distressing the economic and social toll of a nation. Manual detection of this disease is a time-consuming process. So, an automatic or semiautomatic technique to detect the Plasmodium Vivax infected cell is utmost required for timely detection and thereby treatment of the same to eradicate the disease breakout. In this paper, an analytical study is carried out on various image segmentation techniques such as thresholding, a clustering method, histogram-based method etc which may be adopted for the segmentation of Plasmodium Vivax infected cells.
Are mobile banking apps secure? what can be improved?

Mobile banking apps, as one of the most contemporary FinTechs, have been widely adopted by banking entities to provide instant financial services. However, our recent work discovered thousands of vulnerabilities in 693 banking apps, which indicates these apps are not as secure as we expected. This motivates us to conduct this study for understanding the current security status of them. First, we take 6 months to track the reporting and patching procedure of these vulnerabilities. Second, we audit 4 state-of the-art vulnerability detection tools on those patched vulnerabilities. Third, we discuss with 7 banking entities via in-person or online meetings and conduct an online survey to gain more feedback from financial app developers. Through this study, we reveal that (1) people may have inconsistent understandings of the vulnerabilities and different criteria for rating severity; (2) state-of-the-art tools are not effective in detecting vulnerabilities that the banking entities most concern; and (3) more efforts should be endeavored in different aspects to secure banking apps. We believe our study can help bridge the existing gaps, and further motivate different parties, including banking entities, researchers and policy makers, to better tackle security issues altogether.
A Multi-Port Bidirectional Power Conversion System for Reversible Solid Oxide Fuel Cell Applications

Reversible Solid Oxide Fuel Cell/Electrolyser Cell (SOFC/EC) technology is an attractive solution for high energy storage system in the utility grid. However, the wide range of voltage and low power of single SOFC/EC stack make it difficult to design the power conversion system for SOFC/EC storage system. In this paper, a new power multi-port bidirectional conversion system is proposed to connect multiple SOFC/EC stacks with the utility grid. The converter structure contains a multi-port structure with two conversion stages. The two-stage conversion structure is first analyzed to address the wide-range of SOFC/EC stack’s voltage. The high-step-down CLLC resonant converter is implemented to achieve efficient voltage transformation, and the interleaved buck converter is employed as the second stage to control the voltage of SOFC/EC stack within a wide range. The derivation of the multi-port structure is introduced, and the control strategy of proposed conversion system is also discussed in this paper. The proposed conversion system enables a flexible control for the application of multiple SOFC/EC stacks. The feature of the proposed system is verified by the experiments from a down-scale prototype.
Mechanical Refinements for the Stress Reduction of High-Speed Synchronous Reluctance Machines

This paper deals with the design of Synchronous Reluctance Machines for high-speed applications. In particular, this work is focused on the impact of different mechanical refinements made on the rotor lamination with the aim of reducing the maximum stress at high-speed operations. A synchronous reluctance machine, with rated speed equal to 50000 rpm, is firstly designed by means of an automated tool based on optimization algorithms; during the optimization stage, fast magneto-static finite element simulations coupled to an analytical model for the mechanical sizing of the rotor structure are adopted in order to quickly evaluate thousands of candidate machines. After the optimization procedure, different mechanical refinements are manually performed on the selected machine and their influence on the overall performance is evaluated by means of accurate structural and magnetic finite element analysis. The main steps of the design procedure as well as the influence of the geometrical variations on the Von Mises stress reduction within the rotor lamination are presented and analyzed.
Analysis of TPCx-IoT: The First Industry Standard Benchmark for IoT Gateway Systems

By 2020 it is estimated that 20 billion devices will be connected to the Internet. While the initial hype around this Internet of Things (IoT) stems from consumer use cases, the number of devices and data from enterprise use cases is significant in terms of market share. With companies being challenged to choose the right digital infrastructure from different providers, there is an pressing need to objectively measure the hardware, operating system, data storage, and data management systems that can ingest, persist, and process the massive amounts of data arriving from sensors (edge devices). The Transaction Processing Performance Council (TPC) recently released the first industry standard benchmark for measuring the performance of gateway systems, TPCx-IoT. In this paper, we provide a detailed description of TPCx-IoT, mention design decisions behind key elements of this benchmark, and experimentally analyze how TPCx-IoT measures the performance of IoT gateway systems.
Recent Developments using Background Oriented Schlieren with a Plenoptic Camera

Plenoptic background oriented schlieren imaging has recently been introduced as a single-camera technique used to observe three-dimensional density gradients in a flow field. With the ability to generate focused BOS images, the signature of density gradients produced at different depth locations can be distinguished from one another. Two experiments demonstrate the capabilities of this technique. The first experiment visualized the rising plumes produced from two simple flames placed at different depths in a low magnification configuration. The second experiment used a high magnification configuration with long working distance to visualize shock waves in a 6.35 millimeter diameter underexpanded jet. These experiments demonstrate plenoptic BOS as a simple and convenient three-dimensional visualization technique that can be applied in facilities with limited optical access.
Overshadow Effect of Psl on Bacterial Response to Physiochemically Distinct Surfaces Through Motility-Based Characterization

Biofilms of Pseudomonas aeruginosa are ubiquitously found on surfaces of many medical devices, which are the major cause of hospital-acquired infections. A large amount of work has been focused on bacterial attachment on surfaces. However, how bacterial cells evolve on surfaces after their attachment is the key to get better understanding and further control of biofilm formation. In this work, by employing both single-cell- and collective-motility of cells, we characterized the bacterial surface movement on physiochemically distinct surfaces. The measurement of cell surface motility showed consistent results that gold and especially platinum surfaces displayed a stronger capability in microcolony formation than polyvinyl chloride and polycarbonate surfaces. More interestingly, we found that overproduction of Psl led to a narrower variance in cell surface motility among tested surfaces, indicating an overshadow effect of Psl for bacteria by screening the influence of physicochemical properties of solid surfaces. Our results provide insights into how Pseudomonas aeruginosa cells adapt their motion to physiochemically distinct surfaces, and thus would be beneficial for developing new anti-biofouling techniques in biomedical engineering.
Accelerated Distributed MPC of Linear Discrete-Time Systems With Coupled Constraints

This paper proposes a distributed model predictive control (MPC) approach for a family of discrete-time linear systems with local (uncoupled) and global (coupled) constraints. The proposed approach is based on the dual problem of an overall MPC optimization problem involving all systems, which is then solved distributively using a modified distributed Nesterov-accelerated-gradient algorithm. To further reduce the computational requirement, this approach allows for early termination of the distributed gradient algorithm. This is made possible via a consensus algorithm that determines the satisfaction of the termination condition and by appropriate tightening of the coupled constraints. Under reasonable assumptions, the approach is able to produce a suboptimal solution as long as the network of the systems is connected while ensuring recursive feasibility and exponential stability of the closed-loop system. The performance of the proposed approach is demonstrated by a numerical example.
FlowMon-DPDK: Parsimonious Per-Flow Software Monitoring at Line Rate

Testing experimental network devices requires deep performance analysis, which is usually performed with expensive, not flexible, hardware equipment. With the advent of highspeed packet I/O frameworks, general purpose equipments have narrowed the performance gap in respect of dedicated hardware and a variety of software-based solutions have emerged for handling traffic at very high speed. While the literature abounds with software traffic generators, existing monitoring solutions do not target worst-case scenarios (i.e., 64B packets at line rate) that are particularly relevant for stress-testing high-speed network functions, or occupy too many resources. In this paper we first analyse the design space for high-speed traffic monitoring that leads us to specific choices characterizing FlowMon-DPDK, a DPDK-based software traffic monitor that we release as an open source project. In a nutshell, FlowMon-DPDK provides tunable fine-grained statistics at both packet and flow levels. Experimental results demonstrate that our traffic monitor is able to provide per-flow statistics with 5-nines precision at high-speed (14.88 Mpps) using an exiguous amount of resources. Finally, we showcase FlowMon-DPDK usage by testing two open source prototypes for stateful flow-level end-host and in-network packet processing.
Activation-Aware Slack Assignment for Time-to-Failure Extension and Power Saving

This paper proposes a mean time-to-failure (MTTF) aware design methodology for minimizing power dissipation while satisfying target chip lifetime. The key contributions of the proposed design methodology are to explicitly introduce MTTF as a design constraint and optimize the design with activation-aware slack assignment (ASA). Conventionally, the gates included in nonintrinsic critical paths are downscaled or replaced with high- $V$ th gates for power savings, where the nonintrinsic critical paths are timing paths which originally had large timing slacks before the downscaling and replacement. On the other hand, ASA gives timing slacks to nonintrinsic critical paths and reduces the number of active paths whose delays are very close to those of intrinsic critical paths whose timing slacks cannot be increased by resynthesis and sizing. The proposed optimization includes both pre-ASA circuit design and ASA implementation. The former pre-ASA design prepares several design candidates that laid out with different timing constraints and selects the most promising candidate regarding power. For this selection, every candidate is analyzed to estimate minimum supply voltage after ASA that can achieve the target MTTF. Then, the proposed methodology selects a set of flip-flops for ASA using integer linear programming, such that it reduces the sum of gatewise failure probability maximumly, and performs ASA. We evaluate MTTF of circuits with and without ASA and examine how much power saving can be obtained while satisfying the target MTTF, e.g., 10 years. Evaluation results show that the circuits with ASA achieve up to 49.6% power saving.
Standard models for Power Electronic System Simulation

Cooperative Study Group on Practical Modeling and Simulation Techniques for Power Electronics Simulations, created under the Industry Applications Society of the IEEJ (Institute of Electrical Engineers of Japan), is active to develop standard models for simulations related to power electronics systems. The purpose of developing the standard models is to use in transient simulations related to power electronics for system simulation. Under the study group, four working groups (WGs) such are Smart Grid WG, the Motor Drive WG, the Automotive WG and the Power Conversion WG have been created for different fields of simulation. This paper provides the simulation models and examples such are voltage regulation equipment, pv and battery model for Smart Grid, DC modor model and its characteristics research for Motor Drive, engine, alternator, gear and ECU model for Automotive and some electric power conversion models.
An Interleaved Buck-Cascaded Buck-Boost Inverter for PV Grid-Connection Applications

To simplify circuit configuration and increase system efficiency, a two-switch buck-boost (TSBB) inverter has been proposed by connecting a buck-cascaded buck-boost (BuCBB) converter in series with a line-frequency unfolding circuit. In accordance with the level of input voltage and sinusoidal output voltage, this inverter can work with buck or boost operation principle. Wherefore, efficiency can be increased by delivering partial energy directly to output. This inverter is suitable for developing photovoltaic grid-connection systems with wide voltage-variation of input. In order to further increase its output power, this paper proposes to develop a high-efficiency inverter by connecting two BuCBB converters in interleaving parallel. Due to the cancelation of inductor ripple-currents, size and cost of input and output filters can be significantly reduced. Since the output power is shared equally by two parallel dc-dc converters, the overall conduction losses of power switches and inductors can also be reduced to improve efficiency. Finally, an experimental prototype is implemented with proper analysis and design. The experimental results are measured to verify the feasibility and correctness of the proposed inverter.
Third Generation MEEG Source Connectivity Analysis Toolbox (BC-VARETA 1.0) and Validation Benchmark.

This paper presents a new toolbox for MEEG source activity and connectivity estimation: Brain Connectivity Variable Resolution Tomographic Analysis version 1.0 (BC-VARETA 1.0). It relies on the third generation of nonlinear methods for the analysis of resting state MEEG Time Series. Into the state of the art of MEEG analysis, the methodology underlying our tool (BC-VARETA) brings out several assets. First: Constitutes a Bayesian Identification approach of Linear Dynamical Systems in the Frequency Domain, grounded in more consistent models (third generation). Second: Achieves Super-Resolution, through the iterative solution of a Sparse Hermitian Source Graphical Model. Third: Tackles efficiently in High Dimensional and Complex set up the estimation of connectivity. Fourth: Incorporates priors at the connectivity level by penalizing the groups of variables, corresponding to the Gray Matter anatomical segmentation, and including a probability mask of the anatomically plausible connections. Along with the implementation of our method, we include in this toolbox a benchmark for the validation of MEEG source analysis methods, that would serve for the evaluation of sophisticated methodologies (third generation). It incorporates two elements. First: A realistic simulation framework, for the generation of MEEG synthetic data, given an underlying source connectivity structure. Second: Sensitive quality measures that allow for a reliable evaluation of the source activity and connectivity reconstruction performance, based on the Spatial Dispersion and Earth Movers Distance, in both source and connectivity space.
One Size Does Not Fit All: Predicting Product Returns in E-Commerce Platforms

Providing easy and hassle-free product returns have become a norm for e-commerce companies. However, this flexibility on the part of the customer causes the respective e-commerce companies to incur heavy losses because of the delivery logistics involved and the eventual lower resale value of the product returned. In this paper, we consider data from one of the leading Indian e-commerce companies and investigate the problem of product returns across different lifestyle verticals. One of the striking observations from our measurements is that most of the returns take place for apparels/garments and the major reason for the return as cited by the customers is the “size/fit” issue. Here we develop, based on past purchase/return data, a model that given a user, a brand and a size of the product can predict whether the user is going to eventually return the product. The methodological novelty of our model is that it combines concepts from network science and machine learning to make the predictions. Across three different major verticals of various sizes, we obtain overall F-score improvements between 10%–25% over a naïve baseline where the clusters are obtained using simple random walk with restarts.
Design Guideline of an AC Hairpin Winding

This work aims to investigate the design rules and the additional losses in an AC hairpin winding. Such a winding type is more and more used in traction motor of electric and hybrid-electric vehicles. The actual trend is to require higher operating frequency. In fact, the motor power and speed are continuously increasing even though keeping a high number of poles to reduce the end winding length. Hairpin winding allows a higher slot fill factor in comparison with traditional round wire windings. However, the larger cross-area section of such solid wires cause a strong non-uniform current density distribution, which can delete the higher copper area benefit. Hereafter, the main rules design of an AC hairpin winding are described, to satisfy voltage and current constraints. Furthermore, different analysis has been carried out to determine the additional losses amount considering different coil connections. Finite Element analysis has been compared with analytical models.
Stigmergy-Based QoS Optimisation for Flexible Service Composition in Mobile Communities

Mobile users can form a service-sharing community within a geographic area by using their mobile devices. Finding Quality of Service (QoS) optimal service compositions in such mobile environments is challenging because of the inherent dynamism in services deployed on mobile devices. Existing service composition proposals for mobile environments either use template-matching composition or require a-priori knowledge about the QoS objectives' weights, which limits the composition flexibility in such environments. This paper introduces a QoS optimisation mechanism for planning-based service composition in mobile environments, where mobile software agents use stigmergic coordination to iteratively explore parts of the distributed service composition space to approximate a set of QoS optimal configurations. We present a mechanism that minimises the exploration of previously identified non-optimal solutions to encourage exploration of different parts of the service space. We evaluate the performance of the proposed approach and compare the results with a baseline variant, a Dijkstra-based, a Greedy and a Random approach. The results show that the proposed approach can achieve higher utility compared to the evaluated proposals at the cost of increased overhead.