Miracle-dz/UniFAR-FTUs huggingface dataset:
Group Recommendation via Self-Attention and Collaborative Metric Learning Model

Group recommendation has attracted wide attention owing to its significance in real applications. One of the big challenges for group recommendation systems is how to integrate individual preferences of each group member and attain overall preferences for the group. Most of the traditional group recommendation solutions regard group members as equal participants and assign a same weight to each member. As a result, performance of this type of recommendation methods is not as good as expected. To improve the performance of group recommendation, a novel group recommendation model via Self-Attention and Collaborative Metric Learning (SACML) is presented in this paper. With the employment of Self-Attention mechanism, the SACML model can learn the similarity interactions between group members and services and decide a different weight for different group member. Based on these weights, group preferences for services can be generated by the aggregation of group members’ preferences and the group’s own preference. Similar metric space between group and services is obtained via collaborative metric learning with the group preferences and positive and negative services’ features. Group recommendation is finally implemented based on the obtained metric space. Simulation has been conducted on CAMRa2011 and Meetup datasets, and experimental results show that the proposed SACML model has better performance in comparison with those baseline methods.
On Grid Modeling for Stability Assessment of Droop Voltage Control

Distributed voltage control using $\boldsymbol{Q}(\boldsymbol{U})$-characteristics is a means to facilitate higher penetrations of decentralized generation. However, stability concerns interfere the wider adoption of this technology. Based on a recent introduction of a robust stability criterion for assessment of droop-controlled inverters, this paper aims to improve the modeling of the grid from a control engineering point of view. After analyzing the impact of different types of voltage deviation on nodal voltage sensitivity, a more precise grid model is presented using algorithmic differentiation. The derived model is further implemented in a closed loop scheme for a $\boldsymbol{Q}(\boldsymbol{U})$-controller. Considering in a first step of evolution solely a single input single output system the existing stability criterion could be improved in terms of accuracy.
All-Powerful Learning Algorithm for the Priority Access in Cognitive Network

In this paper, we propose the All-Powerful Learning (APL) algorithm for multiple Secondary Users (SUs) that considers the priority access and the dynamic multi-user access, where the number of SUs changes over time. To the best of our knowledge, APL is the first learning algorithm that successfully handles the dynamic users with the priority access. APL does not require any cooperation or prior information (e.g. the number of users in the network, or the number of available channels, or the total number of iterations) as do many existing algorithms. We should emphasize that the knowledge of previous parameters can make all these algorithms impractical and difficult to apply. The experimental results show the superiority of APL compared to existing algorithms.
Comparative Study Between Extended Gopinath Observer and Back EMF-MRAS Speed Observer Coupled with Gopinath Rotor Flux Observer for Sensorless Vector Control of Induction Motor Drives

The article presents a comparison a two observers used in the simultaneous estimation of the speed, position and phasor modulus of the rotor flux of an induction motor. The analysed observers are: extended Gopinath observer (EGO) and assembly consisting of the reduced order Gopinath observer and the Peng speed observer. Both observers are used in sensorless vector control systems with direct rotor flux orientation. Comparative tests are realized by numerical simulation using the Matlab-Simulink.
Graph Transformations and Derivation of Scheduling Constraints Applied to the Mapping of Real-Time Distributed Applications

Synchronous Data-Flow as a deterministic variation of Khan Process Networks is a good model for distributed applications that allows for verification of the properties of the applications both at the design level and at run-time. Real-Time extensions exist which allow to specify Real-Time clocks for some of the processes in the graph. With the addition of Real-Time clocks, the behavior of the complete system can be easily differentiated from a nominal Real-Time mode where all the required data for processing is available when a clock tick occurs, and an error mode can be triggered when the condition is not met. Our contribution in this paper is –first– to show a set of graph transformations that allow to account for the execution and communication time on a real platform while at the same time maximizing the parallelism of execution and –second– on top of these transformations to provide the execution constraints as a linear program that must be met at run-time to guarantee the real-time requirements. It is illustrated on a subset of a real-life automotive example.
A Defect Classification Network Based on Deformation Dense Connection in Wire Rod Surface Image

Surface defects significantly impair the quality of wire rod products in steel manufacturing industry. In this paper, a defect classification network with deformation dense connection is proposed to integrate low-level texture features and high-level semantic features of defect image, in order to fully utilize the low-level texture feature. The deformation dense connection is composed of Dense Block(DB) and down-sampling cross-layer connection to enhance the utilization of low-level feature while avoiding the increasing of the depth of the network. Experimental results show that the proposed method achieved 97.02% classification accuracy. The performance of the proposed method is qualified in the application of quality control of wire rod production.
Detection, Enumeration and Localization of Underwater Acoustic Sources

The problem of passive detection, enumeration and localization of underwater objects is of great interest in several applications of sonar. Source localization requires prior knowledge of the number of sources. Therefore, detection is followed by estimation of the number of sources, and finally localization. Normally, each of the aforementioned objectives is treated as a different problem, and separate processing techniques are employed to solve them. In this paper, we propose two techniques, viz. the embedded subspace detector (ESD) and the Bartlett processor detector (BPD), for joint detection and enumeration of underwater acoustic sources using a vertical linear array of sensors. The BPD is also capable of simultaneously estimating the range and depth of each source. Simulation results indicate that the proposed detectors can achieve good detection and enumeration at low signal-to-noise ratio, and that their enumeration performance compares very favorably with those of established source enumeration techniques such as Akaike information criterion and minimum description length.
Ad-hoc mobile array based audio segmentation using latent variable stochastic model

Segmentation/diarization of audio recordings using a network of ad-hoc mobile arrays and the spatial information gathered is a part of acoustic scene analysis. In this ad-hoc mobile array network, we assume fine (sample level) synchronization of the signals only at each mobile node and a gross synchronization (frame level) across different nodes is sufficient. We compute spatial features at each node in a distributed manner without the overhead of signal data aggregation between mobile devices. The spatial features are then modeled jointly using a Dirichlet mixture model, and the posterior probabilities of the mixture components are used to derive the segmentation information. Experiments on real life recordings in a reverberant room using a network of randomly placed mobile phones has shown a diarization error rate of less than 14% even with overlapped talkers.
Semi-Blind Source Separation based on Multi-Modulus Criterion: Application for Pilot Contamination Mitigation in Massive MIMO Communications Systems

This paper presents an efficient semi-blind approach for demixing instantaneous mixtures, in a massive Multiple-Input Multiple-Output (MIMO) communications system, with pilot contamination. A hybrid cost function is defined based on the Multi-Modulus (MM) criterion, for the blind part, and the Least Squares (LS) criterion for the pilot-based part. A simple but efficient semi-blind block gradient descent procedure is put forward, in which the step size, which globally minimizes the cost function along the search direction, is algebraically computed at each iteration for each user. Simulation results show that the use of pilots enhance the convergence and allow to mitigate the inherent ambiguity of fully-blind methods. Moreover, the optimal step size notably accelerates the convergence and can further reduce the impact of local extrema. Furthermore, the proposed approach is very promising for source recovering when pilot contamination occurs in massive MIMO communications systems.
Efficient Uplink Multi-Beam Initial Access Scheme for Inactive Users in mmWave Networks

The interface between the Radio Access Network and Core Network is kept for Radio Resource Control (RRC) Inactive relative to RRC Idle, and the initial access should be conducted for inactive user equipments (UEs) to reduce the delay of link connection when inactive UEs switch to active. However, due to less interaction information between inactive UEs and transmission and reception points (TRPs), it is necessary to conduct initial access by beam sweeping rather than refined beam alignment. In this paper, an effective uplink multi-beam initial access scheme for inactive UEs in mmWave networks is proposed, where uplink beam sweeping and fast beam recovery is considered. Due to the application of digital beamforming technology and considering that TRP scanning beam number is more than that of UE scanning beams, the delay of uplink based beam sweeping is significantly reduced in the proposed initial access scheme, compared with the traditional downlink based beam sweeping. Furthermore, when the serving beam pair is blocked or not available, fast beam recovery would be conducted by using backup beam pair, where the cases that the backup beam pair and serving beam pair belonging to the same TRP or different TRPs are considered. Simulation results present the effect of density of TRPs, beam sweeping cycle and UE speed on outage probability and average rate after inactive UEs switching to active, and verify initial access performance gain of the proposed initial access scheme.
Towards a Framework for Smart Resilient Logistics

In order to remain competitive, logistics companies are forced to provide smart solutions within a network that is characterized by complexity and heterogeneity. The advancements of sensing and communication technologies stimulate logistics organizations to improve their business performances by using more advanced decision support tools. This research is devoted to improve logistics decision making by exploiting the enormous datasets originating from IoT networks in combination with Big Data Analytics. The main aim is to develop a resilient planning framework that stimulates logistics planners to combine both human experiences and pattern recognition mechanisms (e.g., machine learning, data mining, etc.). In this paper, four research deliverables are proposed to pursue this vision: (1) a state-of-the-art overview of modern decision support tools to enhance logistics resilience and efficiency; (2) the development of dynamic optimization algorithms using real-time data; (3) the construction of data-driven algorithms to identify, assess and resolve the presence of logistical disturbances and; (4) the formulation of resilient planning framework that enables real-life implementations of the algorithms developed. A brief overview of the required research activities is given as well, including a visualization of the activities' coherency. This paper concludes with a description of the preliminary results and some future research directions.
A Novel Scheduling Scheme for Ultra-Dense Networks

This paper proposes a novel packet scheduling scheme, called as throughput enhanced scheduling (TES), for future ultra-dense networks. TES modifies the popular modified largest weighted delay first (M-LWDF) scheme and introduces a new term to the scheduling decision making. The aim is to have a more balanced weight distribution between delay and throughput related parameters. The performance of TES is compared with well-known schemes - proportional fairness (PF), exponential/proportional fairness (EXP/PF) and M-LWDF. For performance evaluation, four performance metrics - average spectral efficiency and delay, quality of service violation ratio and Jain's fairness index - are investigated. The simulation results show that TES scheme can outperform all the compared scheduling schemes.
Dendritic cells generated from naïve and tumor-bearing mice uniquely restores different leukocyte subpopulations in chemotherapy-treated tumor-bearing mice

Background: Dendritic cell (DC)-based vaccination has shown promising application in tumor immunotherapy. However, it is not clear whether the presence of tumor impacts the efficacy of generation and functionality of DCs. Aim: To compare the phenotype of DCs generated from naïve or tumor bearing mice and their capability to restore leukopenia-associated chemotherapy. Materials and Methods: DCs were generated from bone marrow (BM) of naïve or Ehrlich ascites carcinoma (EAC) bearing mice. EAC is an undifferentiated breast cancer cell line with the high transplantable capability and rapid proliferation. BM cells were cultured in vitro for 7 days with granulocyte macrophage colony-stimulating factor and interleukin-4 (20 ng/ml each), loaded with different concentrations of EAC cell lysate (0.5, 1, 3 and 5 mg/106) DCs followed by activation with the toll-like receptor 3 ligand poly(I:C). For DC-based vaccination, CD1 mice (n = 5/group) were inoculated with an intraperitoneal (i.p.) injection of 0.25 × 106 EAC cells to form ascites, treated on day 14 with an i.p. injection of cyclophosphamide (4 mg/mouse) and on day 15 with subcutaneous injection of 2 × 106 DCs from control or EAC bearing mice. Injected DCs were loaded with or without EAC lysate followed by i.p. injection of 50 μg/mouse poly(I:C). On day 21, mice were bled and sacrificed for peripheral blood count and spleen and BM cellularity. Results: Yield of DCs generated from naÏve or EAC bearing mice, as well as their phenotype (CD11c+ CD11b+) and activation (CD40 and CD80) with poly(I:C) were similar. Loading DCs with 1 mg EAC lysate induced better viability and activation phenotype as compared with the other concentrations. Regardless the source of DCs, DCs vaccination restored the total numbers of leukocytes in blood but not in the spleen and BM. The effect on peripheral blood leukocytes was coincided with the restoration of the relative numbers of lymphocytes, monocytes, and granulocytes. Conclusion: These data support the use of allogenic DCs from healthy donors in anticancer DC-based vaccination.
Joint Offloading and Computation Resource Allocation in D2D Assisted Hybrid Framework

In this paper, we proposed a D2D assisted hybrid framework, where the computation-intensive task can be offloaded to the cloud or neighboring users. Aiming at minimizing the total energy consumption under delay constraints, the joint optimization of the task offloading, task scheduling and computing resource allocation problem is formulated. Additionally, we model the duration of users’ intermittent connections to study the effect of user mobility on the task success rate. Then, the original problem is divided into two sub-problems to solved separately considering the coupling multiplicative variables. Further, a flexible proximal alternating direction method of multipliers (ADMM) based algorithm is proposed to solve the nonconvex sub-problem in a distributed way. Numerical results reveal the effectiveness of the algorithm on convergence and complexity reduction, and the proposed scheme achieves excellent performance when compared with other conventional schemes.
Soft Tactile Fingertip to Estimate Orientation and the Contact State of Thin Rectangular Objects

A soft tactile fingertip was developed to provide the contact feedback on characteristics of an object for performing automatic assembly of electronic parts, such as a thin circuit board. However, designing a tactile fingertip with simple design and minimal number of sensors that can estimate orientation and the contact state of an object is a challenging task. This letter presents the simple-structured soft fingertip sensitive to external forces. The fingertip can estimate the grasping force and orientation of a thin rectangular object, and the contact state between the object and the environment based on magnetic flux density (MFD) changes. Two Hall-effect sensors were fixed in parallel at the hard base, and four cylindrical magnets were embedded in the soft body of the fingertip. All parts except the magnets were 3D-printed to simplify the fabrication process. We applied a machine learning approach to define the relationship between the MFD of sensor outputs and object's characteristics. In addition, finite element simulations were performed to evaluate the designed structure. Experimental results verified that the proposed fingertip can estimate the orientation and gripping force of thin rectangular objects. Furthermore, it can successfully classify contact states of the grasped object based on the sensor outputs.
A Hybrid Algorithm Based on WiFi for Robust and Effective Indoor Positioning

Indoor positioning based on the Wireless Fidelity (WiFi) protocol and the Pedestrian Dead Reckoning (PDR) approach is widely exploited because of the existing WiFi infrastructure in buildings and the advancement of built-in smartphone sensors. In this work, a hybrid algorithm that combines WiFi fingerprinting and PDR to both exploit their advantages as well as limiting the impact of their disadvantages is proposed. Specifically, to build a probability map from noisy Received Signal Strength (RSS), a Gaussian Process (GP) regression is deployed to estimate and construct the RSS fingerprints with incomplete data. Mean and variance of generated points are used to estimate WiFi fingerprinting position by K-nearest weights from the probability of visible RSS measurements of the online phase. In addition, a particle filter is applied to fuse PDR and WiFi fingerprinting by using the information from RSS, inertial sensors and features of indoor maps. To demonstrate the potential of the proposed framework, two case studies are considered. In the first case, a comparison is made between GP regression with K-Nearest Neighbours (KNN) method to show the improvement with a sparse input data set. In the second case, the proposed framework is compared to both the fingerprinting approach as well as the PDR algorithm. The results show significant improvements from our proposed framework. The average positioning accuracy of our proposed system can be lower than 1.2 m, which was reduced by 48% and 70% compared with the WiFi fingerprinting and the PDR method, respectively.
Multiband Cooperation for 5G HetNets: A Promising Network Paradigm

Multiband cooperative networking is being recognized as a new enabling scheme to provide superior user experience and greatly scale up system capacity for 5G cellular networks. This article presents a control/user plane split (CUPS)-based multiband cooperative network architecture. To satisfy the diversified requirements of future 5G applications, the separation of the control and user planes is applied in both the core network and radio access network (RAN). In this proposed network architecture, control information is transmitted through the control plane at sub-6 GHz, and the user equipment (UE) data are forwarded through the user plane in millimeter-wave (mm-wave) bands.
Coverage Performance Analysis of Wireless Caching Networks With Non-Orthogonal Multiple Access-Based Multicasting

This paper investigates the non-orthogonal multiple access (NOMA) based multicasting in wireless caching networks for the performance enhancement of spectral efficiency. Particularly, at the base stations (BSs), upon receiving the requests from the attached user equipments (UEs), a superposed message of the most requested <inline-formula> <tex-math notation="LaTeX">$\hat {m}$ </tex-math></inline-formula> files is broadcasted by applying NOMA to simultaneously satisfy the demands of multiple UEs, with the power allocation of the files determined by the respective number of requests. Further, at the UEs, two different decoding strategies, i.e., the conventional SIC without cache-aided interference cancellation and the SIC with cache-aided interference cancellation, are considered to facilitate the reliable data reception. To analyze the performance of the proposed NOMA based multicasting scheme, a novel framework is developed based on stochastic geometry. We first derive the probability that an ordered sequence <inline-formula> <tex-math notation="LaTeX">$\widetilde {\Psi }_{\mathbf {Y}}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$\hat {m}$ </tex-math></inline-formula> files is broadcasted by the typical BS <inline-formula> <tex-math notation="LaTeX">$\mathbf {Y}$ </tex-math></inline-formula> with the technique of NOMA. Then, we characterize the coverage probability of <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula>-th file in <inline-formula> <tex-math notation="LaTeX">$\widetilde {\Psi }_{\mathbf {Y}}$ </tex-math></inline-formula>. Further, we evaluate the average coverage probability of the wireless caching network with the studied NOMA based multicasting. It is shown by simulations that the derived analytical results are validated and the technique of wireless caching can further improve the performance of NOMA.
Blink-former: Light-aided beamforming for multiple targets enhancement

We propose a multimodal framework to enhance multiple target sound sources using a conventional microphone array, a video camera, and sound power sensors, called Blinkies, that we have recently developed. Each Blinky consists of a microphone, LEDs, a microcontroller, and a battery. One of the LEDs intensity is varied according to sound power, that is, the Blinky works as a sound-to-light conversion sensor. They are easy to distribute over a large area, and thus, the sound power information therein can be harvested by capturing the LED signals with a video camera. Although these signals are a mixture of contributions from multiple sources, we demonstrate that they can be separated into individual source activities by non-negative matrix factorization. The obtained activities are further utilized to design maximum signal-to-interference-and-noise ratio beamformers enhancing the source signals. We conduct numerical simulations and real experiments to evaluate the performance of this method in diffuse noise environment. The experimental results show that the proposed scheme using Blinkies is superior to competing algorithms, especially at low signal-to-noise ratio.