Miracle-dz/UniFAR-FTUs huggingface dataset:
MIERank: Co-ranking Individuals and Communities with Multiple Interactions in Evolving Networks

Ranking has significant applications in real life. It aims to evaluate the importance (or popularity) of two categories of objects, i.e., individuals and communities. Numerous efforts have been dedicated to these two types of rankings respectively. Instead, in this paper, we for the first time explore the co-ranking of both individuals and communities. Our insight lies in that co-ranking may enhance the mutual evaluation on both sides. To this end, we first establish an Evolving Coupled Graph that contains a series of smoothly weighted snapshots, each of which characterizes and couples the intricate interactions of both individuals and communities till a certain evolution time into a single graph. Then we propose an algorithm, called MIERank to implement the co-ranking of individuals and communities in the proposed evolving graph. The core idea of MIERank lies in a novel unbiased random walk, which, when sampling the interplay among nodes over different generation times, incorporates the preference knowledge of ranking by utilizing nodes’ future actions. MIERank returns the co-ranking of both individuals and communities by iteratively alternating between their corresponding stationary probabilities of the unbiased random walk in a mutually-reinforcing manner. We prove the efficiency of MIERank in terms of its convergence, optimality and extensiblity. Our experiments on a big scholarly dataset of 606862 papers and 1215 fields further validate the superiority of MIERank with fast convergence and an up to 26% ranking accuracy gain compared with the separate counterparts.
Adjustable Compliance and Force Feedback as Key Elements for Stable and Efficient Hopping

Achieving efficient and human-like hopping motions with consistent consecutive patterns requires proper mechanical design and control. In this regard, we introduce a new bioinspired design and control approach comprised of a hybrid actuation system and force-based compliance control. Combining an electric motor with a pneumatic artificial muscle makes up the so-called Electric-Pneumatic Actuation (EPA) system which enables presetting the leg compliance. Using the ground reaction force for online adjustment of EPA allows controlling the leg stiffness. This approach combines a simplified version of reflex-based control and physical impedance adjustment, that resembles the human neuromuscular system in a parsimonious way. By optimizing PAM pressures and control gains for different targeted hopping heights using Bayesian optimization, we show experimentally that the proposed approach generates various stable hopping motions and yields efficient performance. Moreover, our method is shown to be capable of creating more human-like hopping patterns compared to the previous studies on the same robot.
Vision-Based Robotic Solution for Wire Insertion With an Assigned Label Orientation

This paper tackles the problem of wire insertion in switchgear assembly according to the current regulations. In particular, the wire connections require that the wire label has to be oriented facing up in order to simplify and speed up testing and maintenance of the switchgear. The proposed approach exploits the a priori knowledge of the scenario with a calibrated RGB camera and a robotic arm to estimate both wire end pose and label position. The procedure combines several techniques (gradient base, trained classifier and stereo vision) to elaborate standard images in order to extract some wire features related to its shape and label. Specific frames are fixed according to estimated features and then used to correctly complete the task by using a robotic system. Experiments are reported to verify the effectiveness of the proposed approach.
Distributed Neighbor Distribution Estimation with Adaptive Compressive Sensing in VANETs

Acquiring the geographical distribution of neighbors can support more adaptive media access control (MAC) protocols and other safety applications in Vehicular ad hoc network (VANETs). However, it is very challenging for each vehicle to estimate its own neighbor distribution in a fully distributed setting. In this paper, we propose an online distributed neighbor distribution estimation scheme, called PeerProbe, in which vehicles collaborate with each other to probe their own neighborhood via simultaneous symbol-level wireless communication. An adaptive compressive sensing algorithm is developed to recover a neighbor distribution based on a small number of random probes with non-negligible noise. Moreover, the needed number of probes adapts to the sparseness of the distribution. We conduct extensive simulations and the results demonstrate that PeerProbe is lightweight and can accurately recover highly dynamic neighbor distributions in critical channel conditions.
Small-Area Perovskite Photodiodes With High Detectivity and Stability

Small-area perovskite photodiodes with an active area of <inline-formula> <tex-math notation="LaTeX">$50\,\,\mu \text{m}\,\,\times 50\,\,\mu \text{m}$ </tex-math></inline-formula> are achieved, showing both high specific detectivity and excellent stability. The device exhibits a low dark current density of <inline-formula> <tex-math notation="LaTeX">$\sim 1.6\times 10 ^{-8}$ </tex-math></inline-formula> A/cm<sup>2</sup> at a reverse bias of 0.1 V, a peak photoresponsivity of 1.74 A/W, and a peak specific detectivity of <inline-formula> <tex-math notation="LaTeX">$1.5\times 10 ^{13}$ </tex-math></inline-formula> Jones. Moreover, the device also shows superior photoresponse and thermal stability after continuous 1000 cycles of light switching and 400 h of thermal aging at 85 °C. The high performance is ascribed to the blocking of minority carrier injection from electrodes under a reverse bias and the high crystal quality and thermal stability of the CH<sub>3</sub>NH<sub>3</sub>(MA)-free perovskite photoactive layer.
One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments

Cardiovascular diseases are considered the number one cause of death across the globe which can be primarily identified by the abnormal heart rhythms of the patients. By generating electrocardiogram (ECG) signals, wearable Internet of Things (IoT) devices can consistently track the patient’s heart rhythms. Although Cloud-based approaches for ECG analysis can achieve some levels of accuracy, they still have some limitations, such as high latency. Conversely, the Fog computing infrastructure is more powerful than edge devices but less capable than Cloud computing for executing compositionally intensive data analytic software. The Fog infrastructure can consist of Fog-based gateways directly connected with the wearable devices to offer many advanced benefits, including low latency and high quality of services. To address these issues, a modular one-dimensional convolution neural network (1D-CNN) approach is proposed in this work. The inference module of the proposed approach is deployable over the Fog infrastructure for analysing the ECG signals and initiating the emergency countermeasures within a minimum delay, whereas its training module is executable on the computationally enriched Cloud data centers. The proposed approach achieves the F1-measure score ≈1 on the MIT-BIH Arrhythmia database when applying GridSearch algorithm with the cross-validation method. This approach has also been implemented on a single-board computer and Google Colab-based hybrid Fog-Cloud infrastructure and embodied to a remote patient monitoring system that shows 25% improvement in the overall response time.
Cost-Driven Data Caching in the Cloud: An Algorithmic Approach

Data caching in the cloud is an efficient way to improve the QoS of diverse data applications. However, this benefit is not freely available, given monetary cost to manage the caches in the cloud. In this paper, we study the data caching problem in the cloud that is driven by the monetary cost reduction, instead of the hit rate under limited capacity as in traditional cases. In particular, given a stream of requests $\mathcal{R}$ to a shared data item, we present a shortest-path based optimal algorithm that can minimize the total transfer and caching costs within O(mn) time for off-line case, here m represents the number of nodes in the network, while n is the length of the request stream. The cost model in this computation is semi-homo, which indicates that all pairs of nodes have the same transfer cost, but each cache server node has its own caching cost rate. Our off-line algorithm improves the previous results not only in reducing the time complexity from O(m2n) to O(mn), but also in relaxing the cost model to be semi-homogeneous, rendering the algorithm more practical in reality. Furthermore, we also study this problem in its online form, and by extending the anticipatory caching idea, we propose a 2-competitive online algorithm based on the same cost model and show its tightness by giving a lower bound of the competitive ratio as 2 − o(1) for any deterministic online algorithm. We provably achieve these results with our deep insights into the problem and careful analysis of the solution algorithms, together with a trace-based study to evaluate their performance in reality.
Bidirectional Power Flow Control and Hybrid Charging Strategies for Three-Phase PV Power and Energy Storage Systems

The objective of this article is to propose a photovoltaic (PV) power and energy storage system with bidirectional power flow control and hybrid charging strategies. In order to optimize the battery charging performance, five charging strategies, including the constant-current charging, the pulse-ripple-current charging, the sinusoidal-ripple-current charging, the bidirectional pulse-ripple-current, and the bidirectional sinusoidal-ripple-current charging, are adopted. Traditionally, in order to realize these charging strategies, the PV charger should abandon the maximum power point tracking function to maintain the power flow balance. As a result, the output power of the PV array will be decreased. Therefore, bidirectional power flow control strategies are proposed to achieve the maximum PV power utilization as well as to realize the hybrid charging methods. In addition, with the proposed strategies, the bidirectional charging/discharging capability of the battery is able to achieve the maximum PV power utilization. All the proposed strategies can be realized by the digital signal processor without adding any additional circuit, component, and communication mechanism. Theoretical analysis and control principles will also be revealed. Finally, a 5-kW prototype circuit with both simulation and experimental results demonstrate the performance and feasibility of the proposed strategies.
Strong Secrecy of Arbitrarily Varying Multiple Access Channels

This paper investigates the strong secrecy capacity of the arbitrarily varying multiple access channel (AVMAC). First, Csiszár’s almost independent coloring lemma is generalized to establish an achievable secrecy rate region of the AVMAC with an eavesdropper, which includes existing results on both the arbitrarily varying wiretap channel and the multiple access wiretap channel. We then determine the capacity for a special case named semi-noiseless wiretap channel. In addition, a multi-letter outer bound is also presented. Finally, the results of this paper are further explained via a binary example with a numerical inner bound.
Tornadoes In The Cloud: Worst-Case Attacks on Distributed Resources Systems

Geographically distributed cloud networks are used by a variety of applications and services worldwide. As the demand for these services increases, their data centers form an attractive target for malicious attackers, aiming at harming the services. In this study we address sophisticated attackers who aim at causing maximal-damage to the service.A worst-case (damage-maximizing) attack is an attack which minimizes the revenue of the system operator, due to disrupting the users from being served. A sophisticated attacker needs to decide how many attacking agents should be launched at each of the systems regions, in order to inflict maximal damage.We characterize and analyze damage-maximization strategies for a number of attacks including deterministic attack, concur-rent stochastic agents attack, approximation of a virus-spread attack and over-size binomial attack. We also address user-migration defense, allowing to dynamically migrate demands among regions, and we provide efficient algorithms for deriving worst-case attacks given a system with arbitrary placement and demands. The results form a basis for devising resource allocation strategies aiming at minimizing attack damages.
Bayesian Online Learning for Energy-Aware Resource Orchestration in Virtualized RANs

Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We perform an in-depth experimental analysis of the energy consumption of virtualized Base Stations (vBSs) and render two conclusions: (i) characterizing performance and power consumption is intricate as it depends on human behavior such as network load or user mobility; and (ii) there are many control policies and some of them have non-linear and monotonic relations with power and throughput. Driven by our experimental insights, we argue that machine learning holds the key for vBS control. We formulate two problems and two algorithms: (i) BP-vRAN, which uses Bayesian online learning to balance performance and energy consumption, and (ii) SBP-vRAN, which augments our Bayesian optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient and have provably performance, which is paramount for carrier-grade vRANs. We demonstrate the convergence and flexibility of our approach and assess its performance using an experimental prototype.
First Demonstration of 400ZR DWDM Transmission through Field Deployable Hollow-Core-Fibre Cable

We report characterisation of ISRS, Kerr-nonlinearity and the first demonstration of error-free DWDM transmission with 38×400G signals using commercial 400ZR QSFP-DD modules through a field deployable 10.25km hollow-core NANF cable with <1.2dB/km loss from 1525-1625nm.
Online Joint Optimization on Traffic Engineering and Network Update in Software-defined WANs

State-of-the-art inter-datacenter WANs rely on centralized traffic engineering (TE) to improve the network performance, where TE computation is a periodical procedure and timely performs routing configurations (i.e., enforces TE polices via add, remove and modify forwarding rules) in response to the changing network conditions. The TE computation determines the routing configurations corresponding to the current network conditions and the network update operations change the routing configurations from last TE to current TE solution. Existing works take centralized TE computation and network update as two individual optimization procedures, which inevitably leads to suboptimal solution in the long run. In this paper we initiate the study of online joint optimization on TE computation and network update with the objective of minimizing the sum of TE cost and network update cost. We formulate this problem as an optimization program and propose a set of provable online algorithms with rigorous competitive and regret analysis. Trace-driven simulations on two empirical topologies demonstrate that our algorithms can significantly decrease the total cost.
Modern Trends in the Development of Network Synchronization Systems. From Plesiochronous to Coherent Networks

For new generations of communication networks, the tasks of increasing the speed and amount of transmitted information are being set, which requires the corresponding output from the network infrastructure. First of all, this concerns systems of clock and time network synchronization. The development processes of communication network technologies have led to the need for the evolution of the frequency (clock) synchronization system to time and frequency-time synchronization systems. And in the future, coherent systems of time-frequency support are already being considered. This article deals with the development of sources and the evolution of synchronization systems for telecommunication networks. It is based on the recommendations of the International Telecommunication Union and the authors' experience in this area of expertise.
3D Beam Tracking Method Based on Reconfigurable Intelligent Surface Assistant for Blocking Channel

In this paper, a method is proposed to track for the three-dimensional beam of the blocking channel, which can be used in the wireless communications of complex environment, and in remote villages, etc. The proposed beam tracking method can finish beam tracking accurately by establishing a new communication system with the reconfigurable intelligent surface (RIS) and optimizing the wireless parameters. To finish the main task for tracking the beam, the problem is divided into three parts. Firstly, the problem of the blocking channel is solved by configuring the RIS which is a promising technology and can reflect the received signal intelligently. Secondly, a beam tracking method is proposed based on the characteristics of both the angle velocities and the RIS. The core of this method is to estimate the angular velocities of the unmanned aerial vehicle while receiving the pilot symbols and to predict the azimuth and elevation angles in the following time blocks without sending new pilots. Finally, the communication effect can be optimized by adjusting the phase shift of each uniform linear array equipped on the RIS. The simulations are implemented in MATLAB and the final results can demonstrate the applicability of the proposed method.
Research and selection of parameters determining the efficiency of the braking system of cars to improve traffic safety and environmental performance

The present study considers the possibility of developing an integrated system for monitoring the basic parameters of the braking system of cars in operation during the braking process. The main parameters determining the braking efficiency of cars are the temperature of the brake system elements and the brake fluid pressure. Experimental studies and developments have been carried out to determine the temperature of the car's brake discs during braking.
The Feasibility of Wearable and Self-Report Stress Detection Measures in a Semi-Controlled Lab Environment

Workplace-related stressors, economic strain, and lack of access to educational and basic needs have exacerbated feelings of stress in the United States. Ongoing stress can result in an increased risk of cardiovascular, musculoskeletal, and mental health disorders. Similarly, workplace stress can translate to a decrease in employee productivity and higher costs associated with employee absenteeism in an organization. Detecting stress and the events that correlate with stress during a workday is the first step to addressing its negative effects on health and wellbeing. Although there are a variety of techniques for stress detection using physiological signals, there is still limited research on the ability of behavioral measures to improve the performance of stress detection algorithms. In this study, we evaluated the feasibility of detecting stress using deep learning, a subfield of machine learning, on a small data set consisting of electrodermal activity, skin temperature, and heart rate measurements, in combination with self-reported anxiety and stress. The model was able to detect stress periods with 96% accuracy when using the combined wearable device and survey data, compared to the wearable device dataset alone (88% accuracy). Creating multi-dimensional datasets that include both wearable device data and ratings of perceived stress could help correlate stress-inducing events with feelings of stress at the individual level and help reduce intra-individual variabilities due to the subjective nature of the stress response.
Integrating an Interconnect BIST with Crosstalk Avoidance Hardware

An online interconnect BIST for detecting and avoiding interconnect faults is proposed in this paper. This BIST structure is built upon a new shielding method, the required hardware of which is reutilized in transmitting redundant test data across the interconnect for testing it. Together, BIST and the shielding parts, provide an integrated and novel interconnect testing and crosstalk fault-avoidance platform that share the same hardware. Our proposed integrated technique is modeled in SPICE to evaluate its functionality in terms of avoiding crosstalk faults, and online detection of those that cannot be avoided. We show that faults due to crosstalk are reduced by 49%, and those that still remain are 100% detected.
Implementation of Backup Resource Management Controller for Reliable Function Allocation in Kubernetes

Resource allocation and management is a key role in network function virtualization to improve the reliability of network services. Kubernetes is a system to deploy and manage the virtual network functions automatically. Existing tools in Kubernetes does not provide a resource type to define the backup Pods. It does not provide automatic resource management based on the user requests for the backup Pods, either. This paper designs and implements a custom resource and the corresponding controller in Kubernetes to manage the primary and backup resources of network functions. The custom resource is a set of Pods with different types, which includes primary, hot backup, and cold backup Pods. The controller manages the set of Pods and maintains the current state of the different types of Pods to keep the current state consistent with the desired state of each type of Pod. Demonstration validates that the controller automatically manage the primary and backups resources correctly.
Performance Evaluation on Concurrent Connecting QUIC and TCP Nodes over Wireless LAN

In IEEE 802.11g wireless LAN, the performance of TCP parallel connections has been evaluated, and the condition that the total throughput is saturated has also been investigated to some extent. On the other hand, QUIC (Quick UDP Internet Connections), which is being in the final review process of standardization, is a new transport layer protocol that re-implements congestion control and retransmission control similar to TCP in the user space on UDP instead of the kernel space. Since it is a relatively new protocol, there are many environments where its performance has not been evaluated. In this paper, we evaluate the conditions in terms of the number of concurrent connecting nodes to obtain the highest total throughput on an IEEE 802.11g network. In addition, congestion control version, CUBIC, Reno, delay and packet loss ratio are taken into account. As a result, it was confirmed that QUIC and TCP can achieve the same level of throughput, and the influence of delay and packet loss ratio on the performance in the wireless LAN was clarified, as well as the fairness among concurrent connecting nodes.