Miracle-dz/UniFAR-FTUs huggingface dataset:
Light-Weight and Privacy-Preserving Authentication Protocol for Mobile Payments in the Context of IoT

The widespread use of smart devices attracts much attention on the research for a mobile payment protocol in the context of the Internet of Things (IoT). However, payment trust and user privacy still raise critical concerns to the application of mobile payments since existing authentication protocols for mobile payments either suffer from the heavy workload on a resource-limited smart device or cannot provide user anonymity in the mobile payment. To address these challenges elegantly, this paper presents a lightweight and privacy-preserving authentication protocol for mobile payment in the context of IoT. First, we put forward a unidirectional certificateless proxy re-signature scheme, which is of independent interest. Based on this signature scheme, this paper, then, gives a new mobile payment protocol that for the first time not only achieves anonymity and unforgeability but also leaves low resource consumption on smart devices. In the proposed protocol, the efficiency is notably improved by placing the most computational cost on Pay Platform (usually with abundant computational power) instead of lightweight mobile devices. Moreover, by considering that the Pay Platform and Merchant Server needs to perform computation for each transaction, the idea of batch-verification has been adopted to mitigate the overhead for millions of users at the Pay Platform and Merchant Server to address the scalability issue. Through the formal security analysis presented in this paper, the proposed protocol is proved to be secure under the extended CDH problem. In addition, the performance evaluation shows that the proposed protocol is feasible and efficient for the resource-limited smart devices in the IoT.
Optimal Power Allocations for Relay-assisted NOMA-based 5G V2X Broadcast/Multicast Communications

Recently, as the benefit of its compelling applications, vehicle-to-everything (V2X) communications have attracted more and more attentions. Non-orthogonal multiple access (NOMA) provides a new solution for V2X services to ease traffic congestion and reduce latency. Different from existing works, we propose two NOMA-based schemes for V2X broadcasting and multicasting, and investigate the power allocation problems for them. The power allocation problems are formulated to maximize the minimum achievable vehicle rate for the purpose of fairness. In addition, we show that the formulated problem is neither convex nor concave. Fortunately, it is shown that the considered problem is quasi-concave, and hence we obtain the solution to the problem by a power allocation algorithm based on a bisection procedure. Simulation results demonstrate that the proposed scheme out-performs the scheme with fixed power allocation obviously and achieves a significant performance improvement with respect to a suboptimal fractional transmit power allocation (FTPA) method and the optimized time division multiple access (TDMA) scheme.
Robust Control of Uncertain Linear Systems Based on Reinforcement Learning Principles

In this paper, a reinforcement learning (RL) approach is developed to solve the robust control for uncertain continuous-time linear systems. The objective is to find a feedback control law for the uncertain linear system using an online policy iteration algorithm. The robust control problem is solved by constructing an extended algebraic Riccati equation with properly defined weighting matrices for a general uncertain linear system. An online policy iteration algorithm is developed to solve the robust control problem based on RL principles without knowing the nominal system matrix. The convergence of the algorithm to the robust control solution for uncertain linear systems is proved. The simulation examples are given to demonstrate the effectiveness of the proposed algorithm. The results extend the design method of robust control to uncertain linear systems.
PAST-HOAS: Fast Setup of Per-Address Sink Routing Trees by Reverse Flooding

Scaling Ethernet switched networks to sizes of hundreds of thousands of hosts is a key requirement for data center networks. PAST is a simple and scalable OpenFlow-based layer-two architecture for data center Ethernet networks that outperforms Equal Cost Multipath Forwarding. PAST computes and configures one spanning tree for every MAC destination address. However, an important bottleneck of PAST is the time taken to perform the massive installation of rules at OpenFlow switches to set up the trees. PAST-HOAS aims to solve that obstacle, building trees instantly by path discovery, either triggered by the controller with a multicast probe frame from the edge destination switch or by the standard ARP Request frame; without flow installation and without tree computation. PASTHOAS is loop-free, and provides 100–400 times faster network initialization, lower forwarding delays and increased robustness and scalability.
A Teacher-Student Learning Approach for Unsupervised Domain Adaptation of Sequence-Trained ASR Models

Teacher-student (T-S) learning is a transfer learning approach, where a teacher network is used to “teach” a student network to make the same predictions as the teacher. Originally formulated for model compression, this approach has also been used for domain adaptation, and is particularly effective when parallel data is available in source and target domains. The standard approach uses a frame-level objective of minimizing the KL divergence between the frame-level posteriors of the teacher and student networks. However, for sequence-trained models for speech recognition, it is more appropriate to train the student to mimic the sequence-level posterior of the teacher network. In this work, we compare this sequence-level KL divergence objective with another semi-supervised sequence-training method, namely the lattice-free MMI, for unsupervised domain adaptation. We investigate the approaches in multiple scenarios including adapting from clean to noisy speech, bandwidth mismatch and channel mismatch.
Adaptive Dynamic Surface Control Design for Uncertain Nonlinear Strict-Feedback Systems With Unknown Control Direction and Disturbances

This paper investigates the adaptive tracking control problem for a class of uncertain single-input and single-output strict-feedback nonlinear systems with unknown control direction and disturbances. Dynamic surface control is utilized to handle the problem of “explosion of complexity” occurred in the conventional backstepping design. In order to escape analytic calculation, a first-order filter is used to generate the command signals and their derivatives. Moreover, Nussbaum function is employed to handle the problem of the unknown control coefficient. New controllers and adaptive laws are designed by combining the compensation tracking error and the prediction error that exist between the system state and the serial–parallel estimation model. It is proved that all the variables in the closed-loop system are bounded and the tracking error is driven to the origin with a small neighborhood. Finally, the simulation results are presented to verify the effectiveness of the proposed approach.
Big Production Enterprise Supply Chain Endogenous Risk Management Based on Blockchain

In view of the influence of information’s “incompleteness” and “asymmetry” to supply chain operation efficiency, we make big production enterprise as the object and apply blockchain to its supply chain endogenous risk management, to research the specific operation mechanism and application value. In the operation process of big production enterprise supply chain, because of the information’s asymmetry, the fraud problem will produce among the business subjects; blockchain is a decentralized distributed accounting and data storage technology, and with blockchain technology, we can resolve the business subjects’ fraud problem and can provide more accurate decision information basis for each business section, and realize group decision. This paper has described the system structure and intelligent contract operation mechanism under consensus authentication of blockchain applying in big production enterprise supply chain and analyzed by the case. In view of the limitation of classical blockchain technology applying in big production enterprise supply chain, we constructed the corresponding blockchain data storage mechanism and data access mechanism. Analyzed the economic value of this paper researching from the aspects of response speed, supply accuracy, cooperation integrity, business interaction economic cost, supply quality, and supply price. This paper research will provide ideas and model structure for developing supply chain area’s blockchain system and will promote the application research development of blockchain in specific area.
Bitrate Adaptation-aware Cache Partitioning for Video Streaming over Information-Centric Networks

Recent studies suggest that performance gains for content delivery over Information-centric Networks (ICNs) may be negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieval of multimedia content. The bitrate adaptation mechanism that drives video streaming appears to clash with generic ICN caching techniques in ways that affect users' Quality of Experience (QoE). Cache performance diminishes as video consumers dynamically select content encoded at different bitrates. Motivated by preliminary evidence suggesting the merits of bitrate-based cache partitioning, we introduce a scheme to dissect the cache capacity of routers along a forwarding path according to dedicated bitrates. To facilitate this partitioning, we propose a guiding principle RippleCache, which stabilizes bandwidth fluctuation while achieving high cache utilization by safeguarding high-bitrate content on the edge and pushing low-bitrate content into the network core. We further propose a cache placement scheme, RippleFinder, to realize this RippleCache principle and highlight its impact on users' QoE by cache partitioning. The performance gains are reinforced by evaluations in NS-3. Measurements show RippleFinder can significantly reduce bitrate oscillation, while ensuring high video quality, indicating overall improvement to QoE.
Deep Learning at Scale on NVIDIA V100 Accelerators

The recent explosion in the popularity of Deep Learning (DL) is due to a combination of improved algorithms, access to large datasets and increased computational power. This had led to a plethora of open-source DL frameworks, each with varying characteristics and capabilities. End users are then left with the difficult task of determining software and hardware configurations to get optimal performance from each framework. We share our experiences and develop best practices for DL training with TensorFlow, MXNet and Caffe2. The paper also looks at DL inferencing with TensorRT on NVIDIA V100 Volta GPUs. It focuses on one of the more prominent neural network architectures, Resnet50, combined with Imagenet dataset. We quantify the impact of hardware attributes on DL workloads such as the usage of PCIe vs NVLink GPUs, performance past a single worker node, effect of high speed interconnect such as InfiniBand EDR on training and the implication of utilizing a network attached storage and its advantages.
Modeling Opinion of IPTV Viewers Based on Implicit Feedback and Content Metadata

In today’s hyper-connected world, rich social data feeds can be obtained from various sources, including the data exhaust of many commonly used systems. In this paper, we analyze the social pulse, obtained from viewer activity in an IPTV network—we attempt to validate a framework for determining public opinion and public interest through implicit feedback of IPTV viewers. First, we address the hypothesis that implicit viewer feedback in the form of channel change events, paired with the content metadata, can be used to model viewers’ opinion and interest. For this, we design a controlled experiment to collect explicit feedback by rating a set of general-interest news clips. In addition to collecting demographic information, we also survey viewers’ opinion, interest, and the probability of channel change during each clip. Furthermore, we extract weighted feature vectors from the closed captions of the video; this data, combined with the reported probability of channel change, is used to build a model that classifies opinion in five categories based on the probability of channel change and content. Next, we build a simplified model that classifies opinion in five categories based on the interest, which shows a linear relationship, but further consideration of content, in this case, provides better accuracy and possibility to analyze anomalous cases. Finally, we discuss and analyze the applications of such models in large systems and the necessary modifications to scale the system and to ensure the adequate performance on massive IPTV event data streams.
Sensitivity of Connection Schemes for Detection of Axial Displacement of Transformer Winding by Frequency Response Analysis

Axial displacement of winding is one of the most severe mechanical faults of power-transformer windings because it may result in an increase of mechanical force generated when high short-circuit current flows into the winding. Frequency response analysis (FRA) has a possibility to detect them with high sensitivity. However, the sensitivities of FRA in various connection schemes have not been fully investigated yet. In this paper, transfer functions of a transformer whose winding is axially displaced are measured in several connection schemes. The sensitivities of the measured transfer functions in these connection schemes to the axial displacement are evaluated objectively by employing some numerical indices. From the comparison of the calculated numerical indices, it is found that the inductive inter-winding measurement is the most sensitive to the axial displacement. Furthermore, the measured transfer function is reproduced by a simple circuit model. Based on discussion using the circuit model, it is found that some resonances in the TF of IIW measurement are sensitive to the variation of the mutual inductance, which would be varied due to the AD.
Influence of the Titanium Case used in Implantable Medical Devices on the Wireless Power Link

Titanium cases are usually used as the main enclosure of implantable medical devices (IMDs), such as pacemaker. Different titanium alloys with different resistivities exist. Currently, the wireless powering of such IMDs is a challenging research topic. In this work, the influence that different titanium alloys have on the wireless power transfer link is studied at 13.56 MHz. It is shown in simulations and measurements, that a worst resistivity for the case of the implantable medical device exists, which minimizes the link efficiency. A simplified analytical model is also presented to understand this effect. Additionally, we show that titanium grade 2, which has a lower resistivity (better conductor) than titanium grade 5, achieves higher link efficiency than the grade 5, in our design at 13.56 MHz. Therefore, this analysis proves that the titanium alloy electrical characteristics should be carefully considered in the design of the wireless power transfer link.
New Design of High-Rate Generalized Root Protograph LDPC Codes for Nonergodic Block Interference

In this letter, we propose high-rate generalized root protograph (GRP) low-density parity-check (LDPC) codes for a nonergodic two-state binary symmetric channel with block interference with and without block fading. In these channels, the proposed GRP LDPC codes have asymptotic and finite-length performance improvement approaching to channel outage probability.
Personalized Sketch-Based Image Retrieval by Convolutional Neural Network and Deep Transfer Learning

The sketch-based image retrieval (SBIR) finds the natural images according to the features and rules defined by human beings. The retrieval results are generally similar in contour; however, their complete semantic information of the image is missing. From the user’s point of view, the same hand-drawn image may represent many different things, due to the semantic “one-to-many” category mapping relationship between the hand-drawn image and the natural image, that is the inherent ambiguity of hand-drawn image. In addition, the user’s drawing has many different characteristics, so the retrieval results generally cannot fully match with his intent. For the above-mentioned challenges, a personalized SBIR architecture is proposed, including a deep full convolutional neural network as a general model and a personalized model using transfer learning to achieve fine-grained image semantic feature. On the basis of the pre-trained general model and the images selected by the user in history, we construct the personalized model training dataset. Moreover, the user history feedback with the current hand-drawn image is combined as the input of the transfer learning model, to fine-tune the distribution of features in vector space, so that the neural network can learn the personalized semantic information. The experiments show that the general model has strong generalization ability with the mean average precision as 0.64 on the Flickr15 K dataset. The migration model can realize fine-grained image semantic vector space division, which perfectly satisfies the personalized retrieval requirements by hand-drawn sketch-based image input.
Software Prefetching for Unstructured Mesh Applications

Applications that exhibit regular memory access patterns usually benefit transparently from hardware prefetchers that bring data into the fast on-chip cache just before it is required, thereby avoiding expensive cache misses. Unfortunately, unstructured mesh applications contain irregular access patterns that are often more difficult to identify in hardware. An alternative for such workloads is software prefetching, where special non-blocking instructions load data into the cache hierarchy. However, there are currently few examples in the literature on how to incorporate such software prefetches into existing applications with positive results. This paper addresses these issues by demonstrating the utility and implementation of software prefetching in an unstructured finite volume CFD code of representative size and complexity to an industrial application and across a number of processors. We present the benefits of auto-tuning for finding the optimal prefetch distance values across different computational kernels and architectures and demonstrate the importance of choosing the right prefetch destination across the available cache levels for best performance. We discuss the impact of the data layout on the number of prefetch instructions required in kernels with indirect-access patterns and show how to integrate them on top of existing optimisations such as vectorisation. Through this we show significant full application speed-ups on a range of processors, such as the Intel Xeon Skylake CPU (15%) as well as on the in-order Intel Xeon Phi Knights Corner (1.99x) architecture and the out-of-order Knights Landing (33%) many-core processor.
Enabling Low-Latency Applications in LTE-A Based Mixed Fog/Cloud Computing Systems

In order to enable low-latency computation-intensive applications for mobile user equipments (UEs), computation offloading becomes critical necessary. We tackle the computation offloading problem in a mixed fog and cloud computing system, which is composed of an long term evolution-advanced (LTE-A) small-cell based fog node, a powerful cloud center, and a group of UEs. The optimization problem is formulated into a mixed-integer non-linear programming problem, and through a joint optimization of offloading decision making, computation resource allocation, resource block (RB) assignment, and power distribution, the maximum delay among all the UEs is minimized. Due to its mixed combinatory, we propose a low-complexity iterative suboptimal algorithm called BTFA based joint computation offloading and resource allocation algorithm (FAJORA) to solve it. In FAJORA, first, offloading decisions are obtained via binary tailored fireworks algorithm; then computation resources are allocated by bisection algorithm. Limited by the uplink LTE-A constraints, we allocate feasible RB patterns instead of RBs, and then distribute transmit power among the RBs of each pattern, where Lagrangian dual decomposition is adopted. Since one UE may be allocated with multiple feasible patterns, we propose a novel heuristic algorithm for each UE to extract the optimal pattern from its allocated patterns. Simulation results verify the convergence of the proposed iterative algorithms, and exhibit significant performance gains could be obtained compared with other algorithms.
End-To-End Named Entity And Semantic Concept Extraction From Speech

Named entity recognition (NER) is among SLU tasks that usually extract semantic information from textual documents. Until now, NER from speech is made through a pipeline process that consists in processing first an automatic speech recognition (ASR) on the audio and then processing a NER on the ASR outputs. Such approach has some disadvantages (error propagation, metric to tune ASR systems sub-optimal in regards to the final task, reduced space search at the ASR output level,.) and it is known that more integrated approaches outperform sequential ones, when they can be applied. In this paper, we explore an end-to-end approach that directly extracts named entities from speech, though a unique neural architecture. On a such way, a joint optimization is possible for both ASR and NER. Experiments are carried on French data easily accessible, composed of data distributed in several evaluation campaigns. The results are promising since this end-to-end approach provides similar results (F-measure= 0.66 on test data) than a classical pipeline approach to detect named entity categories (F-measure=0.64). Last, we also explore this approach applied to semantic concept extraction, through a slot filling task known as a spoken language understanding problem, and also observe an improvement in comparison to a pipeline approach.
Teacher-Student Training for Text-Independent Speaker Recognition

This paper investigates text-independent speaker recognition using neural embedding extractors based on the time-delay neural network. Our primary focus is to explore the teacher-student (TS) training framework for knowledge distillation in a text-independent (TI) speaker recognition task. We report the results on both proprietary and public benchmarks, obtaining competitive results with 88–93% smaller models. Particularly, in clean testing conditions, we find TS training on neural-based TI systems achieved same or better performance than the i-vector based counterparts. Neural embeddings are less prone to short segment issues, and offer better performance particularly in the high-recall setting. They can also provide some additional insights about speakers, such as gender or how difficult a given speaker can be for recognition.