Miracle-dz/UniFAR-FTUs huggingface dataset:
Tendon-Driven Soft Robotic Gripper for Blackberry Harvesting

Global berry production and consumption have significantly increased in recent years, coinciding with increased consumer awareness of the health-promoting benefits of berries. Of those consumed, fresh-market blackberries are primarily harvested by hand to maintain postharvest quality. However, the forces applied during hand harvesting can result in major losses of marketable berries to red drupelet reversion (RDR). What is more, manual harvesting is a costly endeavor that accounts for up to 50% of the person-hours involved in berry production. Herein, we present a novel, tendon-driven soft robotic gripper with active contact force feedback control, which leverages the passive compliance of the gripper to allow the gentle harvesting of berries. The versatile gripper can generate a desired force as low as 0.5 N with a mean error of 0.046 N. Field test results indicate that the robotic gripper is capable of harvesting berries with 16% RDR while maintaining a harvesting reliability of 95.24% at a harvesting rate of approximately 4.8 seconds per berry.
Interferometry Based Integrated Sensing and Communications with Imperfect Synchronizations

Interferometry is a powerful tool for estimating the incident angle of electromagnetic (EM) waves by calculating the correlation of received signals at different antennas. Motivated by very-long-baseline interfereometry (VLBI) in radio astronomy, an interferometry based sensing scheme is proposed as integrated sensing and communications (ISAC). It reuses the communication signal from base stations (BSs), similarly to passive radars, which improves the sensing precision and spectrum efficiency. Different from the almost-perfect synchronization in VLBI realized by atomic clocks, the synchronization in BSs of cellular commu-nication networks (usually based on GPS signals) could have significant errors. Therefore, algorithms for compensating for synchronization errors in both time and frequency are proposed. Numerical simulations demonstrate that the proposed algorithms can substantially alleviate the synchronization errors.
Adaptive Gains Super-Twisting-Algorithm: Design and Discretization

—In this paper, an eigenvalue-based discretization scheme is applied to a novel adaptive super-twisting-algorithm. Following the proposed procedure the discretization chattering effect is avoided entirely. An attractive property of the adaptation law is the insensitivity of the closed-loop system to overly large gains which in existing laws potentially leads to instability. Using Lyapunov’s direct method the stability of the feedback is loop shown. Numerical examples underline the beneﬁcial properties of the proposed methodology.
Exploiting Linear Models for Model-Free Nonlinear Control: A Provably Convergent Policy Gradient Approach

Model-free learning-based control methods have seen great success recently. However, such methods typically suffer from poor sample complexity and limited convergence guarantees. This is in sharp contrast to classical model-based control, which has a rich theory but typically requires strong modeling assumptions. In this paper, we combine the two approaches. We consider a dynamical system with both linear and non-linear components and use the linear model to define a warm start for a model-free, policy gradient method. We show this hybrid approach outperforms the model-based controller while avoiding the convergence issues associated with model-free approaches via both numerical experiments and theoretical analyses, in which we derive sufficient conditions on the non-linear component such that our approach is guaranteed to converge to the (nearly) global optimal controller.
Research and Application of IT Project Scope Management Based on Agile Process

In this paper,the theories of foundations and trend in development at home and abroad of project scope management and agile process are studied.Also,project scope management,agile process are analysed.Subsequently,IT project scope management model based on agile process(PSMAP) is proposed on the basis of modified IT project scope management definition model which uses agile process and varies previous project scope definition to answer IT project scope variation.PSMAP combines not only the essential theory of agile process but also the goal,values,principle of agile requirement modeling.On the premise of them,PSMAP is designed,which firstly adjusts the definition of products scope and working range,and then delivers verified products after designing continuous integration.PSMAP is used to direct the Financial Budget Management System(FBMS),a real project.As a result,PSMAP resolves the problem of project scope validation controlling called big troubles that worries project group a lot.Therefore the practicability,rationality and correctness are proved by using PSMAP in the software development of FBMS.PSMAP which also depresses project cost and advances its productivity.
SDN-Controller Triggered Dynamic Decision Control Mechanism for Healthcare IoT

Due to the lack of an integrated communication and computation architecture for Software-Defined Healthcare IoT (SD-HI), provisioning critical services is challenging. In this paper, we propose SD-Health, an edge-based decision making and task allocation (EDT) scheme for SD-HI. The proposed SD-HI network uses Machine Learning (ML)-based approach to predict the criticality of flows and location of mobile devices. Based on the predicted values, the controller delegates the required EDT module to the respective edge node. The controller identifies the future healthcare-related decisions for an edge node and prepares the module accordingly. The ML-based trajectory prediction allows to find the future location of mobile devices in the network. Once the location of the mobile device is predicted, a set of computation tasks is dynamically allocated to the edge node. The results of performance analysis show that SD-Health has a significant improvement in latency by 43.3% and energy consumption by 30%, compared to the existing state-of-the-art, along with a fair improvement in packet delivery ratio.
Attention-aware Multi-encoder for Session-based Recommendation

In session-based recommendation, the user's next possible click can solely be predicted based on historical interaction behavior in the ongoing session. Previously representative works mainly use sequence models and graph neural networks to model user's behaviors of the session. These works have achieved promising results, but each also has certain defects. In view of the shortcomings of the previous works, we propose a multi-encoder framework, under which the advantages of each encoder are retained. Different encoders are used to mine different session features and finally generate a more powerful session representation to improve the recommendation result. Furthermore, in order to improve the performance of recommendation, we introduce the inter-session collaboration information by designing a Inter-session Collaboration Module. Extensive experiments on two real-world datasets demonstrate the superiority of our method over state-of-the-art algorithms.
Improper Gaussian Signaling Based Covert Wireless Communication in IoT Networks

Covert communication, which can hide the communication behavior, has great potential in guaranteeing the security of information and transmission terminal to the greatest extent. In this paper, we propose a covert communication strategy based on improper Gaussian signaling (IGS) to increase the covert rate in the Internet of Things (IoT) system, in which the existing signals emitted by other transmitters were used as “Spectrum Shelter” to cover the IoT's communication. Specifically, we analyze the system's achievable transmission rate when the IoT node adopts IGS. Then, the optimal detection threshold and the minimum error detection probability of the warden are analyzed. Next, by jointly optimizing the transmission power and circularity coefficient of IGS, we maximize the covert rate under the constraints of the covertness. Finally, the simulation results verify that the proposed scheme can not only guarantee the covertness, but also improve the achievable covert rate.
An Adaptive Restart Heavy-Ball Projected Primal-Dual Method for Solving Constrained Linear Quadratic Optimal Control Problems

This paper presents a fast and flexible projected primal-dual method for solving linear quadratic optimal control problems with box constraints. Using a specific preconditioning, the algorithm achieves dead-beat convergence for unconstrained problems and has fast convergence for constrained problems. Accelerated convergence is obtained by applying a heavy-ball method to accelerate the projected primal-dual algorithm. In order to avoid missing critical points due to high momentum, an adaptive restarting procedure is used to slow the algorithm down if the solution diverges. Furthermore, convergence is proven by representing the algorithm as a Lur’e-type dynamic system and applying LaSalle’s invariance principle to show the fixed point is asymptotically stable. The resulting algorithm is simple, while also achieving competitive computational times.
Joint Node-Link Algorithm for Embedding Virtual Networks with Conciliation Strategy

Network virtualization (NV) has widely envisioned as a crucial factor for the success of the future networks by enabling a flexible, cost-effective and on-demand deployments of multiple network service requests on a shared physical infrastructure. The major challenge of NV is to efficiently and effectively embed heterogeneous virtual network requests (VNRs), consisting of a set of virtual nodes connected by virtual links, onto a shared substrate network meeting various stringent resource constraints. Most of the research papers in this field have merely focused on separate virtual node mapping (VNoM) or virtual link mapping (VLiM) with scalable heuristic algorithms. The lack of a coordination between node and link mapping stages results in low acceptance ratio as well as network revenues. In this paper, we propose a new approach based on Genetic Algorithm (GA) that jointly coordinates node and link mappings where the link mapping is relied on a path ranking method. A novel heuristic conciliation mechanism is introduced to handle a possible set of infeasible link mappings during gener-ating virtual embedding solutions in GA's operations. Extensive evaluation results show that our proposed GA-based algorithm outperforms state-of-the-art virtual embedding algorithms in all performance metrics we adopted.
Finite-time accuracy of timed discrete event systems

The explicit consideration of time is nowadays crucial for the specification and the verification of Discrete Event Systems (DESs). The behaviour of a DES is characterized by the set of event sequences it generates, its language. In many applications, the DES language is compared to a given set of observed sequences to evaluate the DES accuracy. In a timed context, however, the language is infinite, making it difficult to define and evaluate an accuracy indicator. To overcome this difficulty, the accuracy of a Time Petri net model with respect to a set of sequences is formulated in this paper, using a sliding finite-time window mechanism. In addition, an algorithm to evaluate the proposed accuracy measure is provided.
Defect Detection Method of Railway Fastener Based on SPP-improved ResNet

Missing and broken states of railway fasteners are directly related to driving safety. In order to realize the accurate detection of fasteners, virtual image technology is used to simulate defect state of fastener to expand feature information of fasteners, so as to overcome the unbalanced problem of fastener samples. Secondly, a deep network image detection method for railway fasteners based on SPP-improved ResNet (SPP-ResNet) is proposed to improve detection performance. Experimental results show that the accuracy is 95.1% when the network is trained on the virtual image extended dataset with Single size input train method, which is 2.4% higher than the accuracy of the unexpanded dataset. It is proved that virtual image can improve the accuracy of fastener detection. After the SPP-ResNet is trained by using multi size input training, the detection accuracy is 96.6%, which is 2.3% higher than the accuracy of ResNet. Therefore, the proposed method can meet the needs of railway-fastener automatic detection under high-speed railway development.
Feasibility and Synthesis of Finite-Dimensional, Linear Time-Invariant Synthetic Admittances for Self-Powered Systems

Self-powered systems are vibration control technologies that fully power their operation via the harvesting, storage, and reuse of energy from exogenous plant disturbances. This paper explores the feasibility and synthesis of colocated feedback control laws for self-powered systems. These control laws are realized through the use of switch-mode power electronics to simulate shunt admittances. The feasible domain of the resulting "self-powered synthetic admittance" (SPSA) controllers is more restrictive than classical feedback passivity, due to parasitic transmission and storage losses in the system. In this paper, we derive sufficient conditions for the feasibility of finite-dimensional, linear time-invariant SPSA controllers and present a sub-optimal synthesis procedure for their design. The proposed methodology is employed to design a controller to reduce the seismic response of a base-isolated civil structure.
Stabilization of networks of switched dissipative systems through a scattering transformation technique

A scattering transformation technique is extended to the case of switched dissipative nonlinear systems with quadratic supply rates. Specifically, under a relaxed form of the liveness condition, a construction of the scattering transformation is presented for a switched nonlinear dissipative system which allows for uniform assignment of a prescribed matrix gain with a common storage function. A scattering- based stabilization method for networks of switched dissipative systems with communication delays is subsequently developed.
Reinforcement Learning Based Energy-Efficient Component Carrier Activation-Deactivation in 5G

Carrier aggregation (CA) is considered a key enabler technology for delivering higher rates to users of LTE and 5G networks. However, the increased transmission rate comes with the price of higher energy consumption which stems from users continuously monitoring the control channel of the active component carriers (CCs) whether data transmission is ongoing or not. In order to reduce energy consumption, we exploit the activation-deactivation procedure at the medium access control (MAC) layer of LTE/5G network. In this paper, we propose a reinforcement learning-based algorithm to improve energy-efficiency by dynamically activating-deactivating secondary component carriers (SCCs) with awareness of the user traffic profiles. The proposed algorithm aims to predict the arrival of data and identify SCCs to activate for each user. In addition, a traffic splitting approach and an intelligent exploration strategy are proposed to balance users' load among CCs and improve the convergence of the algorithm, respectively. Results of the proposed algorithm are compared with three baseline algorithms. The first baseline always activates all CCs for each user, the second baseline activates one carrier only (i.e., the primary carrier) and the third baseline algorithm relies on a reactive method, where the activation-deactivation decision is performed after observing the arrival of data. Results show that Q-learning outperforms the baseline algorithms by achieving the highest sum throughput (and lowest average delay) with the lowest number of activated SCCs, which is obtained by learning to dynamically activate SCCs according to the traffic pattern. Hence, Q-learning is considered the most energy-efficient compared to the baseline algorithms.
A Review of the Application of Artificial Intelligence in the Virtual Learning Environment

VLEs provide learners with an immersive learning environment and have a positive impact on learning outcomes by enhancing learners' sense of participation. Artificial intelligence technology has more and more profound influence on all fields of society, and education is an important field of artificial intelligence application. With the development of VLEs, we are eager to understand how artificial intelligence can play a role in the education process. This paper constructs an analytical framework from the aspects of artificial intelligence technology application, applied discipline field, teaching effect, research deficiency and educational potential of intelligent VLEs. The results show that the artificial intelligence technologies used in VLEs mainly include machine learning, deep learning, natural language processing, and emotion computing, intelligent VLEs are usually used in second language acquisition, archaeology, biomedicine, negotiation and other fields. The application of intelligent VLE in the education process significantly improves students' collaborative ability and meets the needs of students' personalized learning. Intelligent VLEs still have some problems such as high cost, small sample size and technical cognitive load.
SWill-TAC: Skill-oriented Dynamic Task Allocation with Willingness for Complex Job in Crowdsourcing

Allocating tasks to the best-fit candidates is a classical problem in crowdsourcing (CS). Most of the existing approaches assume that the task and candidate knowledge is known in advance and ignore the effect of enrolled candidates' willingness on the CS system's selection decision. For instance, an unwilling candidate assigned to a task may quit without completing it, thus depreciating the utility of the CS platform. In practice, a task or candidate may arrive or leave the CS system dynamically. Moreover, a complex task may be broken into smaller sub-tasks, each requiring a variety of computations and expertise. To overcome these challenges, based on a greedy algorithm, we propose a novel approach for skill-oriented dynamic task allocation with willingness factor for complex assignments (SWill-TAC). This approach iteratively attempts to delegate candidates (workers) to tasks depending on the skills required for executing the tasks and the candidates' skill set. SWill-TAC also considers the willingness of eligible candidates and keeps track of the budget constraints of tasks. Finally, the feasibility and efficiency of our approach are demonstrated using the UpWork dataset. Experimental results show that SWill-TAC outperforms Online Greedy, TM-Uniform, Random selection-based, and Minimum payment-based task allocations in terms of the completed tasks count, the utility gained, and success ratio.
Paper Title (use style: paper title)

Driven by the increasing needs for condition monitoring and maintaining stable operation using process data, a multi-sensor data fusion method based on adaptive weighting strategy using analytic hierarchy process (AHP) algorithm and cross-correlation function fusion (CCFF) algorithm is proposed in this paper. On this basis, a health index (HI) is developed based on the distance using the time-domain characteristic value of the fused vibration signals. By considering the location of sensors and data relevance, the weight coefficients could be more reasonable. The effectiveness of the proposed condition monitoring method is demonstrated by a case study from a three-plunger reciprocating pump in the industrial field, and the test results show its advantage in operation monitoring and abnormal detection Keywords—adaptive weighting strategy, multi-sensor data fusion, cross-correlation function fusion algorithm, analytic hierarchy process algorithm, condition monitoring
Shortening the Deployment Time of SFCs by Adaptively Querying Resource Providers

We consider the SFC embedding (SFCE) problem in the Slice as a Service (SlaaS) model. In this model, a slice provider leases resources from multiple cloud and network providers in order to instantiate the Service Function Chain (SFC) requested by a slice tenant. As the slice provider has no visibility on the infrastructures of the resource providers, in which resources may be purchased and released quite rapidly, it has to query them to determine what are the possible allocations and their costs. We show that when there are many resource providers and many VNFs composing the SFC, the number of queries to be made for discovering a minimum cost SFC embedding grows quickly, leading to excessively long deployment times. In order to reduce the latter quantity, we propose to query resource providers strategically, rather than collecting the information on all possible allocations at once. We provide bounds on the number of queries to be made in this approach, and propose to exploit a Shortest Path Discovery algorithm in order to reduce this number of queries and thus the SFC deployment time. Our numerical results suggest that this algorithm is fairly efficient, and that the deployment times can be significantly shortened, in particular when initial estimates of allocation costs can be provided by the slice provider.
Patient-Driven Network Selection in multi-RAT Health Systems Using Deep Reinforcement Learning

The recent pandemic along with the rapid increase in the number of patients that require continuous remote monitoring imposes several challenges to support the high quality of services (QoS) in remote health applications. Remote-health (r-health) systems typically demand intense data collection from different locations within a strict time constraint to support sustainable health services. On the contrary, the end-users with mobile devices have limited batteries that need to run for a long time, while continuously acquiring and transmitting health-related information. Thus, this paper proposes an adaptive deep reinforcement learning (DRL) framework for network selection over heteroge-neous r-health systems to enable continuous remote monitoring for patients with chronic diseases. The proposed framework allows for selecting the optimal network(s) that maximizes the accumulative reward of the patients while considering the patients' state. Moreover, it adopts an adaptive compression scheme at the patient level to further optimize the energy consumption, cost, and latency. Our results depict that the proposed framework outperforms the state-of-the-art techniques in terms of battery lifetime and reward maximization.
Video Service-Oriented Vehicular Collaboration: A Multi-Agent Proximal Policy Optimization Approach

To guarantee heterogeneous performance requirements of diverse vehicular services, it is necessary to design a full cooperative policy for both vehicle to infrastructure (V2I) links and vehicle to vehicle (V2V) links. This paper investigates how to improve the quality of experience (QoE) of the V2I users for video services while satisfying the delay requirements of both V2I and V2V links. In specific, a QoE maximization problem is formulated with consideration of vehicular collaboration where task offloading decision, channel reuse decision and power allocation of V2V users are all included. A multi-agent reinforcement learning (MARL) framework is then designed, where a new reward function is proposed to evaluate the utility of the considered network. Thereafter, a proximal policy optimization approach is proposed to enable each V2V user to learn policy individually with the shared global network reward. The effectiveness of the proposed approach is finally validated with comparison of other baseline approaches through extensive simulation experiments.