Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Performance Evaluation of Wake-Up Radio-Based Data Forwarding for Green Wireless Networks

The advent of low-power sensor nodes coupled with intelligent software and hardware technologies has led to the era of green wireless networks. From the hardware perspective, green sensor nodes are endowed with energy scavenging capabilities to overcome energy-related limitations. They are also endowed with low-power triggering techniques, i.e., wake-up radios, to eliminate idle listening-induced communication costs. In this paper, we present a comparative performance evaluation of three different data forwarding strategies for green wireless networks, namely, CTP-WUR, GREENROUTES, and WHARP, which have been shown to outperform previous state-of-art solutions. Through GreenCastalia-based simulations we analyze and provide insights into the impact on performance of diverse forwarding design choices, ranging from traditional tree-based routing (CTP- WUR), to end-to-end energy-driven route selection (GREENROUTES), to the use of sophisticated learning models (WHARP). Results show that tree- based routing obtains lesser packet delivery radio than WHARP, thus indicating that including energy harvesting awareness in route selection results in performance advantages. However, the proactive nature of route computation of CTP-WUR results in faster packet delivery and lower energy consumption, requesting further optimization of the cross-layer forwarding of GREENROUTES and WHARP.
Stator/Rotor Slot and Winding Pole Pair Combinations of DC-Biased Current Vernier Reluctance Machines

The recent novel dc-biased sinusoidal current reluctance machines (dc-biased VRMs) adopt a doubly salient structure and concentrated windings, which is similar to that of switched reluctance machines (SRMs). The special point is that the phase current is in the form of an ac component superimposed with a dc component. Some literary works show that these machines would have better performance than the existing SRMs and variable flux reluctance machines. In this paper, the slot/pole combinations and winding configurations of the dc-biased VRMs are deeply investigated. First, relationships among slots/poles are given based on the operation principles. Then, several feasible slot combinations are obtained, and the electromagnetic performances including magnetizing inductance, virtual back electromotive force, torque quality, and losses are compared by the finite element analysis. Finally, the correctness of the proposed analysis is validated by the experiment on two 12/10 prototypes.
An Empirical Study of Metric-based Comparisons of Software Libraries

BACKGROUND: Software libraries provide a set of reusable functionality, which helps developers write code in a systematic and timely manner. However, selecting the appropriate library to use is often not a trivial task. AIMS: In this paper, we investigate the usefulness of software metrics in helping developers choose libraries. Different developers care about different aspects of a library and two developers looking for a library in a given domain may not necessarily choose the same library. Thus, instead of directly recommending a library to use, we provide developers with a metric-based comparison of libraries in the same domain to empower them with the information they need to make an informed decision. METHOD: We use software data analytics from several sources of information to create quantifiable metric-based comparisons of software libraries. For evaluation, we select 34 open-source Java libraries from 10 popular domains and extract nine metrics related to these libraries. We then conduct a survey of 61 developers to evaluate whether our proposed metric-based comparison is useful, and to understand which metrics developers care about. RESULTS: Our results show that developers find that the proposed technique provides useful information when selecting libraries. We observe that developers care the most about metrics related to the popularity, security, and performance of libraries. We also find that the usefulness of some metrics may vary according to the domain. CONCLUSIONS: Our survey results showed that our proposed technique is useful. We are currently building a public website for metric-based library comparisons, while incorporating the feedback we obtained from our survey participants.
Co-Saliency Detection via Hierarchical Consistency Measure

Co-saliency detection is a newly emerging research topic in multimedia and computer vision, the goal of which is to extract common salient objects from multiple images. Effectively seeking the global consistency among multiple images is critical to the performance. To achieve the goal, this paper designs a novel model with consideration of a hierarchical consistency measure. Different from most existing co-saliency methods that only exploit common features (such as color and texture), this paper further utilizes the shape of object as another cue to evaluate the consistency among common salient objects. More specifically, for each involved image, an intra-image saliency map is firstly generated via a single image saliency detection algorithm. Having the intra-image map constructed, the consistency metrics at object level and superpixel level are designed to measure the corresponding relationship among multiple images and obtain the inter saliency result by considering multiple visual attention features and multiple constrains. Finally, the intra-image and inter-image saliency maps are fused to produce the final map. Experiments on benchmark datasets are conducted to demonstrate the effectiveness of our method, and reveal its advances over other state-of-the-art alternatives.
Effect of Thermal and Forming Gas Annealing on the Characteristics of Si/SiON/Ti-Pt MOS Capacitor

We report the effect of thermal and formal gas annealing on the electro-physical characteristics of Si/SiON/Ti-Pt MOScap by analyzing high-frequency capacitance-voltage (C-V) and current-voltage (I-V) characteristics. Thermal and forming gas annealing (FGA) were carried out at the temperatures of 800°C for 15 min in Nitrogen (N2) ambient and at 420°C for 20 min in 95% N2 and 5% H2ambient respectively. Ellipsometery, Field Emission Scanning Electron Microscopy (FESEM), Atomic force microscopy (AFM) and Fourier transform infrared spectroscopy (FTIR) were used to study the effect of thermal annealing on the physical characteristics of deposited silicon oxynitride film. We also present a comparative study between the as-deposited and annealed film for FGA and without FGA. It was found that annealed SiON with FGA shows significant improvement in electrical parameters $\boldsymbol{k}=6.3, {\boldsymbol{D}}_{\boldsymbol{it}}0.5\times 10^{11}\mathbf{eV}^{-1}\mathbf{cm}- 2$ & $\boldsymbol{Q_{eff}}=1.2\times 10^{12}\ \mathbf{cm}^{-2}$ which is further attributed to the improvement in Si/SiON interface properties. In the negative gate bias regime for both as-deposited and annealed SiON film, the maximum current through the Si/SiON/Ti-Pt device never exceeded a value of 100nA/cm2 which indicates that the film were very good insulators. For thermally annealed samples, the leakage current reduced from 10 nA/cm2(Without FGA) to 0.1nA/cm2 (With FGA) due to reduction in the density of defects in the SiON film. These results indicate that the obtained SiON film is an appropriate gate insulator for advanced nano electronic devices.
Virtual Redundancy for Active-Standby Cloud Applications

VM redundancy is the foundation of resilient cloud applications. While active-active approaches combined with load balancing and autoscaling are usually resource efficient, the stateful nature of many cloud applications often necessitates 1+1 (or $1+\mathbf{n}$) active-standby approaches. Keeping the standbys, however, could result in inefficient utilization of cloud resources. We explore an intriguing cloud-based solution, where standby VMs from active-standby applications are selectively overbooked to reduce resources reserved for failures. The approach requires careful VM placement to avoid a situation where multiple standby VMs activate simultaneously on the same host and thus cannot get the full resource entitlement. Indeed today's clouds do not have this visibility to the applications. We rectify this situation through ShadowBox, a novel redundancy-aware VM scheduler that optimizes the placement and activation of standby VMs, while assuring applications' resource entitlements. Evaluation on a large-scale cloud shows that ShadowBox can significantly improve resource utilization (i.e., more than 2.5 times than traditional approaches) while minimizing the impact on applications' entitlements.
Supervision of Webcasting-Anchor Behavior Evaluation Based on Barrage Emotion Analysis

With the rapid development of mobile computing, webcasts have been a novel form of entertainment. However, it is difficult to monitor anchors' behavior for live streaming. The anchors maybe insults viewers or uses pornography to seduce viewers. "Barrage" as a webcast commentary carrier can enrich the interactivity. We find that the content of the barrages can truly reflect the situation of an anchor. For this, we put forward a novel barrage-based anchor behavior evaluation system employing supervisor learning technology. We evaluate the emotion of the anchor based on the content of barrages, and predict the follow-up behavior of the anchor. If the content of the studio is violent or pornographic, the platform manager can ban him in advance to avoid even greater damage. The extensive experiment results show that the precision and recall of negative emotions can be up to 71.4% and 72.6%, respectively.
Generalized Multiple-Mode OFDM With Index Modulation

Multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM), which transmits an OFDM signal with information bits embedded onto multiple distinguishable signal constellations of the same cardinality and their permutations, is a recently proposed IM technique in the frequency domain. It is capable of achieving higher spectral efficiency and better error performance than classical OFDM and existing frequency-domain IM schemes. In this paper, we propose the scheme of generalized (G-) MM-OFDM-IM, which allows a different subcarrier to utilize a signal constellation of a different size while conveying the same number of IM bits. Considering phase shift keying constellations, we present design guidelines for GMM-OFDM-IM to achieve an optimal error performance in the asymptotically high signal-to-noise ratio region. A computationally efficient and near-optimal detector based on the idea of sequential decoding is also tailored to GMM-OFDM-IM, which avoids the detection of an illegitimate constellation permutation. Monte Carlo simulations are conducted to validate the inherent properties and advantages of GMM-OFDM-IM.
Prediction of SRAM Reliability Under Mechanical Stress Induced by Harsh En§ironments

On the example of a 28nm SRAM array, this work presents a novel reliability study which takes into account the effect of externally applied mechanical stress in circuit simulations. This method is able to predict the bit failures caused by the stress via the piezoresistive effect. The stability of each single SRAM cell is simulated using static noise margin. Finally, the whole array's behavior is reproduced by including device parameter variations in Monte-Carlo simulations. The results show good agreement with corresponding experiments in which mechanical stress was introduced into the SRAM array by indentation. This validates the presented simulation method for future use in the design of electronic products, especially for harsh environment applications, where high stress is expected.
A Levy Flight Firefly Optimizer based Piecewise Gamma Corrected Unsharp Masking Framework for Satellite Image Enhancement

In this paper, an efficiently modified lévy-flight based biologically inspired firefly optimizer is employed in association with a novel optimally weighted piecewise gamma corrected unsharp masking framework for imparting overall quality improvement of remotely sensed dark satellite images. The key intelligence is to utilize a weighted summation of intensity as well as texture based enhancement along with an efficiently defined cost function. The cost function is framed such that more and more intensity span can be explored in a positive manner. Here, the unsharp masking takes care for enhancing the high frequency content of the images. In association with it, piecewise gamma correction is also imparted to enhance the intensity channel of the input image. Rigorous experimentation is executed by employing the performance evaluation and comparison with pre-existing recently proposed and highly appreciated quality enhancement approaches.
A Functional Analysis Based Strategy for Preserving Location Privacy

Location based services become increasingly popular with the dramatic grows of smartphones. Personal location information is disclosed to the service providers while users are enjoying such applications. However, this data can be used to infer a user's detailed activities, or to track and predict the user's daily movements, which raises significant privacy concerns. Achieving both preserving privacy and high quality in locationbased services is still a challenge. To address this challenge, we provide a novel strategy which guarantees high service quality by ensuring the distance between a real location and the obfuscated one is bounded. For preserving location privacy, the key idea of our strategy is to select obfuscating function randomly to process data. Therefore, the real data can not be inferred by approximate or stochastic methods. This is the first paper to use such methods for preserving privacy. And the effectiveness of our strategy is demonstrated through extensive simulations.
A High-Speed Seam Extraction Method Based on the Novel Structured-Light Sensor for Arc Welding Robot: A Review

In the modern manufacturing, the teaching-playback mode and the off-line programming mode of the welding robots still play the important roles. However, these modes cannot meet the autonomous and adaptive capabilities of the welding robots. To improve the flexibility of the welding robots, fast and accurate seam extraction is the key link to realize the intelligent welding robots. A fast and accurate seam extraction algorithm based on the novel structured light vision system is proposed in this paper. First, the digital light processing projector is used to construct the multi-function structured light vision sensor in this paper. It could generate different pattern images to adapt different welding tasks. Second, during the current research work about seam extraction, the seam extraction algorithm based on morphological image processing has been widely used and improved. However, these algorithms include much machine vision algorithms and the speed of these algorithms is too slow to meet real-time requirements. Meanwhile, these algorithms are difficult to adapt to different types of weld seam. In order to achieve fast and accurate extraction of various types of weld seams, the target tracking algorithm based on the kernelized correlation filters algorithm is applied into this paper. Experimental results show that the proposed method could well realize fast and accurate seam extraction of various types of weld seams.
The Scale Effect on Spatial Interaction Patterns: An Empirical Study Using Taxi O-D data of Beijing and Shanghai

Spatial scale is a fundamental issue for geographical phenomena because the size of the spatial unit adopted for analysis can have a significant effect on aggregated spatial data and the corresponding analytical results. There exists much research on the scale issue of spatial distribution data while a few has paid attention to the scale effect on spatial interactions. Big geo-data with micro-level individual movements provide an unprecedented opportunity to explore spatial interactions from the bottom up and to understand the scale effect from the perspective of interaction patterns. In this paper, we introduced an empirical framework to explore spatial interaction data across scales. By incorporating two data sets of taxi trajectories in Beijing and Shanghai, we aggregated the taxi O-D trips under multiple regular grids with different cell sizes and analyzed the properties of spatial interactions in three aspects: the statistical distribution, the distance decay mechanism, and the structure pattern. Our research demonstrated the feasibility of analyzing spatial interactions from a multi-scale view and offered a basic empirical framework for future geographical research that is interested in scale and spatial interactions.
Energy-aware task scheduling strategies with QoS constraint for green computing in cloud data centers

Energy optimization with Quality-of-Service (QoS) constraint has become a timely and significant challenge for the cloud datacenters. In this paper, a hardware and software collaborative optimization strategy is implemented to minimize the energy cost while satisfying the time constraint of the cloud-computing datacenters. In the hardware aspect, a DVFS-capable CPU/GPU/FPGA heterogeneous cloud infrastructure is built. This infrastructure has high flexibility, and can adjust its hardware characteristics dynamically in terms of the software run-time contexts, so that a hardware platform which matches the software can be built. Based on this hardware platform, the cloud applications can be executed more efficiently with less energy cost. In the software aspect, the deadline-aware energy-efficient task scheduling algorithms are investigated. Different from the traditional approaches which search for the optimal scheduling solution by the heuristic approaches, a new scheduling approach based on the improved Mathematical Morphology (MM) algorithm is investigated in this paper. To evaluate the performance of our work, we calculated the energy cost of the Fourier transform (FT) and Gaussian elimination (GE) applications on the homogeneous and heterogeneous cloud computing platforms by applying the GA and MM algorithms, respectively. The results proved the MM algorithms running on the DVFS-capable heterogeneous cloud infrastructure could decrease the energy cost of the FT application and GE application respectively by 24.7% and 37.8%, if compared with the GA algorithm running on the DVFS-incapable homogeneous cloud infrastructure.
Can We Learn what People are Doing from Raw DNS Queries?

Domain Name System (DNS) is one of the pillars of today's Internet. Due to its appealing properties such as low data volume, wide-ranging applications and encryption free, DNS traffic has been extensively utilized for network monitoring. Most existing studies of DNS traffic, however, focus on domain name reputation. Little attention has been paid to understanding and profiling what people are doing from DNS traffic, a fundamental problem in the areas including Internet demographics and network behavior analysis. Consequently, simple questions like “How to determine whether a DNS query for www.google. com means searching or any other behaviors? ” cannot be answered by existing studies. In this paper, we take the first step to identify user activities from raw DNS queries. We advance a multiscale hierarchical framework to tackle two practical challenges, i.e., behavior ambiguity and behavior polymorphism. Under this framework, a series of novel methods, such as pattern upward mapping and multi-scale random forest classifier, are proposed to characterize and identify user activities of interest. Evaluation using both synthetic and real-world DNS traces demonstrates the effectiveness of our method.
Study on MU-MIMO Systems in the Perspective of Energy Efficiency with Linear Processing

Multi-User MIMO arrangement necessitate a bigger number of antenna arrays at base station, to aid relatively a larger number of users, with massive improvements in spectral and energy efficiency. Energy efficiency of MU-MIMO systems depend upon data transmission rate and entire power disbursed by the system. In this paper, we have used zero-forced linear processing for downlink and uplink communication to improve the energy efficiency. An improved power consumption model is proposed by considering power consumed for linear processing, amplifier and circuit modules at the user equipment and the stations. This model offers a significant enhancement in the results.
On Leader-Following Consensus Protocol for Positive Discrete-Time Multi-Agent Systems

In this paper, we introduce a consensus protocol to reach a leader–following consensus for positive discrete–time multi–agent systems. In order to guarantee the convergence of the proposed algorithm, some general results are proved in the framework of the stability theory. In addition, numerical examples that validate the obtained results show the effectiveness of the designed control strategy.
A Short-Circuit Calculation Method for DFIG-Based Wind Farms

Short-circuit calculations for wind farms are crucial for determining equipment ratings and providing basic information for protection coordination. In this paper, a practical calculation method of short-circuit currents (SCCs) is proposed for doubly fed induction generator (DFIG)-based wind farms. The improved calculation method of the SCCs in single DFIG is developed based on electromagnetic transient analysis. Combined with the traditional method of calculating the SCCs of the power system, an equivalent model of the DFIG-based wind farm used for SCCs is studied. The equivalent model and expressions for SCCs are later validated through PSCAD/EMTDC-based simulation results, and a comparison with the national standard calculation method is analyzed. The results show that the calculation method proposed in this paper has better accuracy and practicability, which are important elements in the electrical design of DFIG-based wind farms.
Towards Privacy-Preserving Speech Data Publishing

Privacy-preserving data publishing has been a heated research topic in the last decade. Numerous ingenious attacks on users' privacy and defensive measures have been proposed for the sharing of various data, varying from relational data, social network data, spatiotemporal data, to images and videos. Speech data publishing, however, is still untouched in the literature. To fill this gap, we study the privacy risk in speech data publishing and explore the possibilities of performing data sanitization to achieve privacy protection while preserving data utility simultaneously. We formulate this optimization problem in a general fashion and present thorough quantifications of privacy and utility. We analyze the sophisticated impacts of possible sanitization methods on privacy and utility, and also design a novel method - key term perturbation for speech content sanitization. A heuristic algorithm is proposed to personalize the sanitization for speakers to restrict their privacy leak (p-leak limit) while minimizing the utility loss. The simulations of linkage attacks and sanitization on real datasets validate the necessity and feasibility of this work.