Miracle-dz/UniFAR-FTUs huggingface dataset:
Wireless-Powered Distributed Spatial Modulation With Energy Recycling and Finite-Energy Storage

The distributed spatial modulation (DSM) protocol, which allows relays to forward the source’s data while simultaneously allowing the relays to transmit their own data, has been proposed by Narayanan et al. In this paper, we introduce two new protocols for enabling the DSM, consisting of single-antenna network nodes, with simultaneous wireless information and power transfer capability: power splitting-based DSM (PS-DSM) and energy recycling-based DSM (ER-DSM). More specifically, the PS-DSM relies on power splitters at the relay nodes to harvest energy transmitted from the source. On the other hand, the ER-DSM, by exploiting the inactive cooperating relays in DSM-based protocols, recycles part of the transmitted energy in the network, without relying on power splitters or time switches at the relays to harvest energy. This leads to an increase in the average harvested energy at the relays with reduced hardware complexity. Both the PS-DSM and the ER-DSM also retain all the original features of DSM. Due to its particular operating principle and specific advantages, we select the ER-DSM as the candidate for further mathematical analysis. More specifically, by considering a multi-state battery model, we propose an analytical framework based on a Markov chain formulation for modeling the charging/discharging behavior of the batteries at the relay nodes in the ER-DSM. Furthermore, based on the derived Markov chain model, we introduce a mathematical framework for computing the error probability of the ER-DSM, by explicitly taking into account, the effect of finite-sized batteries. The frameworks are substantiated with the aid of Monte Carlo simulations for various system setups.
Non-Interactive Privacy-Preserving Truth Discovery in Crowd Sensing Applications

In crowd sensing, truth discovery (TD) refers to finding reliable information from noisy/biased data collected from different providers. To protect providers' data while enabling truth distillation, privacy-preserving truth discovery (PPTD) has received wide attention recently. However, all existing approaches require iterative interaction between server(s) and individual providers, which inevitably demand all providers to be always online. Otherwise, the protocol would fail or expose extra provider information. In this paper, we design and implement the first non-interactive PPTD system that completely removes the online requirement with strong privacy guarantees. Our framework follows the same two-server model from the best-known prior solution, and leverages Yao's Garbled Circuit (GC). Yet, we devise non-trivial speedup techniques for TD-optimized implementation. Firstly, we identify reusable computations in TD to accelerate the circuit generation. Secondly, we securely evaluate the burdensome non-linear functions in TD via customized approximation with accuracy and improved efficiency. Thirdly, we reduce the online execution time by bridging together latest advancements of component-based GC and various computations needed in TD. Unlike prior arts, our framework does not reveal any intermediate results, and further supports “late-join” providers without protocol suspension/restart. The practical performance of our proof-of-concept implementation is verified through extensive evaluations.
Gradient Projection with Approximate L0 Norm Minimization for Sparse Reconstruction in Compressed Sensing

In the reconstruction of sparse signals in compressed sensing, the reconstruction algorithm is required to reconstruct the sparsest form of signal. In order to minimize the objective function, minimal norm algorithm and greedy pursuit algorithm are most commonly used. The minimum L1 norm algorithm has very high reconstruction accuracy, but this convex optimization algorithm cannot get the sparsest signal like the minimum L0 norm algorithm. However, because the L0 norm method is a non-convex problem, it is difficult to get the global optimal solution and the amount of calculation required is huge. In this paper, a new algorithm is proposed to approximate the smooth L0 norm from the approximate L2 norm. First we set up an approximation function model of the sparse term, then the minimum value of the objective function is solved by the gradient projection, and the weight of the function model of the sparse term in the objective function is adjusted adaptively by the reconstruction error value to reconstruct the sparse signal more accurately. Compared with the pseudo inverse of L2 norm and the L1 norm algorithm, this new algorithm has a lower reconstruction error in one-dimensional sparse signal reconstruction. In simulation experiments of two-dimensional image signal reconstruction, the new algorithm has shorter image reconstruction time and higher image reconstruction accuracy compared with the usually used greedy algorithm and the minimum norm algorithm.
Major-Subordinate-Task Learning for Image Orientation Estimation

In this work, we propose a major-subordinate-task learning framework to estimate image orientation. The involved two tasks, regression to the characteristic orientation of the image (major) and classification by visual content (subordinate), are fed with shared feature and update feature extractor collaboratively. To boost the major task, we introduce a novel module, matched gradients weight multiplier, to calculate matching degree of the two tasks and adaptively adjust feedback from the subordinate task towards the shared feature extractor accordingly. As a result, such feedback is expected to be always promotive to the major task. Experiments demonstrate the effectiveness of our proposed framework over the counterpart settings.
A Novel Comparator Offset Calibration Technique for SAR ADCs

This paper proposes a novel comparator offset calibration technique for SAR ADCs. We realize the calibration in CDAC instead of the comparator circuit, so that the power consumption, area and circuit complexity barely increase, which is a big advantage compared to traditional ones. A 10-bit 100Msps SAR ADC applying our offset calibration is designed in a 55nm CMOS process. The post simulation results show that the ENOB achieves 9.5 bits and consumes a power of 4.4mW at the sampling rate of 100MHz.
Multiple Sensitive Attributes Based Privacy Preserving Data Publishing

The advances in digital information applications facilitates the collection of huge amount of data about governments, healthcare, other organizations and individuals. To make this data available for researchers, businesses and other users, it needs to be released. This in turn increases the demand of exchanging and publishing this collected data. However, data in its original form, typically contains sensitive information about individuals and/or organizations, and publishing such data will violate individual or organizational privacy. Hence, Privacy Preserving Data Publishing (PPDP) provides methods and tools for publishing useful information while preserving data privacy. Before data is published to the concerned users, it is altered to maintain its privacy without compromising data utility, using various anonymization techniques. Real-time datasets contain different types of Multiple Sensitive Attributes (MSAs) (which could be numerical or categorical). Anonymization for only Single Sensitive Attribute is not suitable for functional usage. Thus, it is important to maintain the association between these MSAs and to preserve the privacy of Mixed (numerical and categorical) MSAs efficiently while working with high dimensional data. The main focus of this paper is to analyse the different schemes proposed in literature for PPDP of MSAs.
Energy-Efficient Job-Shop Dynamic Scheduling System Based on the Cyber-Physical Energy-Monitoring System

Nowadays, a large amount of energy is consumed by buildings in the city. Among them, industrial energy consumption accounts for a large proportion due to the high power of manufacturing devices in the job-shop. To achieve higher energy-efficiency, lots of scheduling methods in the job-shop are developed. However, most of the current scheduling method is based on the prior-experimental data and the schedule of the production cycle cannot be adjusted according to energy status of manufacturing devices. Thus a dynamic scheduling system based on the cyber-physical energy monitoring system is proposed to improve the energy efficiency of the job-shop. First, a novel process of “scheduling-monitoring-updating-optimizing” is implemented in the proposed system. In the scheduling model, tool aging condition is considered along with the geometry information of the work-piece to estimate energy consumption of the manufacturing process. At last, a modified genetic algorithm with multi-layer coding is applied to generate an energy-efficient schedule. The proposed system is implemented in a production cycle which contains 36 operations of six work-pieces and ten machine tools to prove its validity.
CoINT: Proactive Coordinator for Avoiding Interruptability Holder Preemption Problem in VSMP Environment

In a Virtual Symmetric Multiprocessing (VSMP) environment, the behavior of hypervisor scheduler can significantly influence a guest's I/O responsiveness. The interrupt remapping mechanism, which can leverage multiple virtual CPUs in the VSMP guest to process I/O events, is known to be an efficient and prevalent solution to improve the I/O performance. However, in this paper we identified a novel challenge called the “Interruptability Holder Preemption” (IHP) problem in interrupt remapping mechanism. The IHP issue presents that a virtual CPU (vCPU) disabling the interruptability of guest's network device is descheduled by the hypervisor scheduler, which can easily invalidate the efficiency of the interrupt remapping mechanism. To solve this problem, we propose CoINT, a gasket coordinator residing in the hypervisor, to substantially enhance the network I/O performance by empowering the hypervisor to be proactively aware of the interruptability information of the guest's network device. ColNT completely eliminates the “Interruptability Holder Preemption” problem and largely reduces I/Ointerrupt processing delay caused by hypervisor scheduler. We implement ColNT in KVM hypervisor and evaluate its efficiency and responsiveness using both macro and microlevel benchmarks. The results show that ColNT can improve the netperf throughput up to 3x compared with native KVM, and up to 1.3x compared with traditional interrupt remapping of hypervisor-Ievel solution, in sacrifice of an negligible and reasonable overhead in the hypervisor.
Research and Implementation of a Brand Normalization Method across E-Commerce Platforms

A brand is the basic attribute of a commodity, and it is the primary metadata for commodity information analysis. However, in different E-commerce platforms, the same brand entity may be named as different labels, which brings challenges to further data analysis on spoken language analysis, personalized recommendation and other works. Brand Entity Normalization becomes a research issue to recognize labels under a certain brand. The former methods focus on dictionary based text matching. However, it is unpractical to define matching rules in an effective way. This paper proposes a brand entity normalization algorithm integrating text similarity and commodity object similarity. Firstly, the text similarity of brand labels is computed to generate candidate brand entity set. Furthermore, the commodity information is introduced and candidate brand entities are merged by Gaussian mixture clustering. The performance of the proposed method is evaluated by involving real data from popular E-commerce platforms. Experimental results show that this method achieves solid performance in brand entity normalization.
Optimized Feature-Based Image Registration for Rgb and Nir Pairs

Image registration is a viable task in the field of computer vision with many applications. Researchers propose various local modules insensitive to illumination changes across cross-spectral image pairs to handle the registration challenges under different spectrum conditions. In this paper, we develop an optimized feature-based approach to register natural cross-spectral image pairs. It works on the phase information to quickly identify and describe reliable keypoints that are insensitive to illumination. It then employs a sequence of outlier removal processes to accurately find the matching feature points and the direct linear transformation to estimate the geometric transformation to align the image pair. We benchmark the proposed method and six state-of-the-art feature-based methods on the dataset provided by Ecole Polytechnique Fédérale De Lausanne (EPFL), which includes 477 pairs of RGB-NIR images. The comprehensive analysis demonstrates that the proposed method achieves up to 13.90% accuracy improvement over the second best registration method.
Stability of Hydronic Networks With Independent Zone Controllers

Many heating, ventilation, and air conditioning systems are implemented using one heat source, the power of which is distributed to individual zones using a pipe network. Each zone can be independently controlled using its own zone controller. Due to the hydronic network interconnection, the zone controllers interact by pressure/flow changes. This brief addresses a question of when and under what conditions the overall system is a stable system. We use a simplified linearized model with linear heat exchangers (HXs) and linear hydraulic resistances. Moreover, all HXs are assumed to be the same. A closed-form formula for linearization of the interconnection is provided. Using these simplifications, we provide simple guidelines which the controller for each zone should satisfy. The main result is that from stability point of view, the interconnection does not bring any new design limitation. On the other hand, an improperly tuned controller can destabilize the interconnected system. An extensive simulation verification is provided.
Terrestrial species adapted to sea dispersal: Differences in propagule dispersal of two Caribbean mangroves

A central goal of comparative phylogeography is to understand how species‐specific traits interact with geomorphological history to govern the geographic distribution of genetic variation within species. One key biotic trait with an immense impact on the spatial patterns of intraspecific genetic differentiation is dispersal. Here, we quantify how species‐specific traits directly related to dispersal affect genetic variation in terrestrial organisms with adaptations for dispersal by sea, not land—the mangroves of the Caribbean. We investigate the phylogeography of white mangroves (Laguncularia racemosa, Combretaceae) and red mangroves (Rhizophora mangle, Rhizophoraceae) using chloroplast genomes and nuclear markers (thousands of RAD‐Seq loci) from individuals throughout the Caribbean. Both coastal tree species have viviparous propagules that can float in salt water for months, meaning they are capable of dispersing long distances. Spatially explicit tests of the role of ocean currents on patterning genetic diversity revealed that ocean currents act as a mechanism for facilitating dispersal, but other means of moving genetic material are also important. We measured pollen‐ vs. propagule‐mediated gene flow and discovered that in white mangroves, seeds were more important for promoting genetic connectivity between populations, but in red mangroves, the opposite was true: pollen contributed more. This result challenges our concept of the importance of both proximity to ocean currents for moving mangrove seeds and the extent of long‐distance pollen dispersal. This study also highlights the importance of spatially explicit quantification of both abiotic (ocean currents) and biotic (dispersal) factors contributing to gene flow to understand fully the phylogeographic histories of species.
Genes Contributing to Domestication of Rice Seed Traits and Its Global Expansion

Asian rice (Oryza sativa) and African rice (Oryza glaberrima) are separately domesticated from their wild ancestors Oryza rufipogon and Oryza barthii, which are very sensitive to daylength. In the process of domestication, some traits that are favorable for the natural survival of wild rice such as seed dormancy and shattering have become favorable ones for human consumption due to the loss-of-function mutations in the genes that are underlying these traits. As a consequence, many genes that are related to these kinds of traits have been fixed with favorable alleles in modern cultivars by artificial selection. After domestication, Oryza sativa cultivars gradually spread to temperate and cool regions from the tropics and subtropics due to the loss of their photoperiod sensitivity. In this paper, we review the characteristics of domestication-related seed traits and heading dates in rice, including the key genes controlling these traits, the differences in allelic diversity between wild rice and cultivars, the geographic distribution of alleles, and the regulatory pathways of these traits. A comprehensive comparison shows that these genes contributed to rice domestication and its global expansion. In addition, these traits have also experienced parallel evolution by artificial selection on the homologues of key genes in other cereals.
A proposed performance evaluation of NoSQL databases in the field of IoT

As one of the largest-technology penetrations, Internet of Things (IoT) has gained a considerable attention across both, research communities and industrial domains. IoT is a network of billions of connected devices that are capable of communicating over the Internet. In IoT, data is generated exponentially by different real-time applications (e.g., social network sites and sensor-based devices). The traditional relational database management technologies are inappropriate to deal with such new generation of data due to its limited processing speed, scalability issues, and limited storage capacity. The advent of Big Data requires new advanced technologies that are capable of handling (storing, retrieving and processing) large amount of unstructured data that IoT applications produce. Not Only SQL (NoSQL) databases emerged as a solution to overcome some limitations of the traditional relational database. Recently, many NoSQL databases have been announced (e.g., Cassandra, MongoDB, HBase, Apache Hadoop, Voldemort) to deal with tremendous amounts of unstructured data. These databases depend mainly on distributed algorithms for their work in the field of IoT. One of the most used distributed algorithms is Chord protocol. However, Chord algorithm is inefficient in some cases because of its way of handling messages transferred between connected devices. All the messages in Chord are passed clockwise along the Chord ring, which leads to send a huge number of redundant messages. Chord works by creating a lookup table for each node connected to the network, which generates huge number of tables that should be updated regularly with the routing information. This paper presents a proposal for implementing an enhanced version of the distributed Chord protocol using the most two popular NoSQL databases, namely: MongoDB and Cassandra. The new version of the Chord algorithm will be evaluated and compared to the current algorithm against their performance using the two databases. Moreover, the two databases will be compared regarding their performance in terms of database operations (reading, writing, updating and deleting) and the number of messages that are sent by the nodes. The home environment controlling IoT application will be simulated using SensibleThings distributed platform for simulating the implementation of the modified protocol and to compare between the performance of the two databases.
Augmented Neuromuscular Gait Controller Enables Real-time Tracking of Bipedal Running Speed

Reproducing human locomotion in simulation has a variety of applications, from informing prosthetic and rehabilitation medicine to generating stable and human-like robot or animated character movement. In prior work, however, the focus has been on producing stable, natural gaits at a single speed. Novel neuromuscular controllers blending feedforward and reflex-like control have shown promising success in realizing bio-inspired speed-modulation of walking gaits while adapting a handful of parameters. In this work, we present a modified neuromuscular gait controller in the sagittal plane to similarly realize speed modulation for running gaits. As a result, our controller interpolates fewer than 10 parameters from a stable initialization to realize a large range of running speeds on a simulated bipedal platform. We discuss the speed-evolution and kinematic significance of these selected parameters, and analyze the controller's velocity-tracking performance over the speed range between 1.3 mls and 1.7 mis, which covers much of human running speeds once scaled from platform height.
Learning Adaptive Selection Network for Real-Time Visual Tracking

Offline-trained trackers based on convolutional neural networks (CNNs) have shown great potential in achieving balanced accuracy and real-time speed. However, offline-trained trackers are prone to drift to background clutters. In this paper, we present an adaptive selection network tracker (ASNT) to address the tracking drift problem. Inspired by feature selection technique used in other vision problems, we introduce a learnable selection unit for Siamese network based trackers. The selection unit enables the tracker to select relevant feature map automatically for the target. Channel dropout is applied in the selection unit to improve generalization performance for convolutional layers. To further improve the discrimination between background clutters and the target, an adaptive method is used to initialize the tracker for each video sequence. Experiments on OTB-2013 and VOT2014 datasets demonstrate that our ASNT tracker has a comparable performance against state-of-the-art methods, yet can run at a speed of over 100 fps.
LAS: Logical-Block Affinity Scheduling in Big Data Analytics Systems

Parallel computing combined with distributed data storage and management has been widely adopted by most big data analytics systems. Scheduling computing tasks to improve data locality is crucial to the performance of such systems. While existing schedulers target near-data scheduling on top of physical data blocks, these systems face a new scheduling problem where computing tasks process table-based datasets directly and access large physical blocks indirectly through their indices stored in associated small logical blocks. This new problem invalidates the basic assumption made by many existing algorithms on near-data scheduling. In this paper, we propose a Logical-block Affinity Scheduling (LAS) algorithm to coordinate the near-data scheduling of computing tasks and the placement of logical blocks for a desired balance between data-locality and load-balancing to maximize system throughput. The proposed algorithm is implemented and evaluated using a well-known big data benchmark and a practical production system deployed in public clouds. Extensive experimental results illustrate the performance superiority of LAS over three existing scheduling algorithms.