Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy-Aware Caching Policy Design Under Heterogeneous Interests and Sharing Willingness

By exploiting the storage resources of end devices, local caching becomes a promising approach to reduce the latency and improve the throughput of content delivery. Considering battery constraints at end devices, this paper investigates energy-aware caching policy to minimize the power consumed in content delivery, taking into account the heterogeneity of interests and sharing willingness of different MSs. Specifically, MSs are divided into disjoint groups based on the preferences and sharing willingness, and each group customizes the caching policy accordingly. Both the non-coordinated and coordinated caching scenarios are considered. For the non-coordinated case, the problem is formulated as an optimization problem, which is proved to be concave and solved by Lagrange methods. For the coordinated caching case, a water-filling based iterative algorithm is proposed to get the optimal caching policies. Numerical results demonstrate that the designed caching policies can reduce the system energy consumption by around 10%–20% comparing to the conventional ones without considering the heterogeneity of users.
A Flow-Based Entropy Characterization of a NATed Network and Its Application on Intrusion Detection

This paper presents a flow-based entropy characterization of a small/medium-sized campus network that uses network address translation (NAT). Although most networks follow this configuration, their entropy characterization has not been previously studied. Measurements from a production network show that the entropies of flow elements (external IP address, external port, campus IP address, campus port) and tuples have particular characteristics. Findings include: i) entropies may widely vary in the course of a day. For example, in a typical weekday, the entropies of the campus and external ports may vary from below 0.2 to above 0.8 (in a normalized entropy scale 0–1). A similar observation applies to the entropy of the campus IP address; ii) building a granular entropy characterization of the individual flow elements can help detect anomalies. Data shows that certain attacks produce entropies that deviate from the expected patterns; iii) the entropy of the 3-tuple {external IP, campus IP, campus port} is high and consistent over time, resembling the entropy of a uniform distribution's variable. A deviation from this pattern is an encouraging anomaly indicator; iv) strong negative and positive correlations exist between some entropy time-series of flow elements.
PNC-Aided Robust Secure Beamforming Design for Two-Way Relay Networks with Artificial Noise

In this paper, we study a two-way relay network (TWRN), in which eavesdroppers' channel state information is imperfect, and all the eavesdroppers collude to form joint receive beamforming for enhanced receptions. To ensure the robustness of the TWRN, we design the transmit beamforming and establish two secure beamforming design formulations, which combines the physical layer network coding with artificial noise technique at the relay. One secure beamforming design formulation is to minimize the total transmit power in the TWRN, and the other one is to guarantee a maximum secrecy sum rate (SSR) in the worst-case. With the help of approximation techniques, the former optimization problem is converted into a convex problem, whose semidefinite relaxation solution is rank-one. However, due to the maximum available power limit of the system, the power optimization problem may be infeasible. To overcome this problem, the SSR optimization formulation is presented, and the optimization problem is decoupled into two subproblems that can be transformed into the convex forms. Finally, it is verified by the numerical results that the proposed schemes are effective.
Decoupled Multiple Association in Full-Duplex Ultra-Dense Networks: An Evolutionary Game Approach

User association is indispensable for the operation of wireless network and has critical impacts on system performance. For most existing work, user associations are typically coupled, which require a user equipment (UE) to associate with the same base station (BS) in uplink (UL) and downlink (DL). However, wireless networks are becoming heterogeneous and densifying, which generates intrinsic distinctions (transmission power, data traffic and backhaul capacity etc.) between UL and DL. Accordingly, coupled association may no longer be optimal. In this work, we explore decoupled user association in full-duplex ultra-dense networks (UDNs), which allows a UE to associate with different BSs in UL and DL respectively. Furthermore, to fully exploit the benefits of UDNs, multiple association, referring to associating a UE with multiple BSs, is jointly adopted in UL and DL. Considering the dynamic and complicated association process, an evolutionary game (EG) is formulated, where UEs are players, and their strategies are association selections in UL/DL. Particularly, evolutionary equilibrium is viewed as the stable solution to the formulated problem. Moreover, an EG-based algorithm with low complexity is proposed for decoupled multiple association. Numerical results validate the convergence of the proposed algorithm for strategy adoption. Besides, the impacts of information exchange delay and learning rate are investigated for providing a better association decision.
A Novel Adaptive Routing and Switching Scheme for Software-Defined Vehicular Networks

Software-Defined Vehicular Networks (SDVNs) technology has been attracting significant attention as it can make Vehicular Ad Hoc Network (VANET) more efficient and intelligent. SDVN provides a flexible architecture which can decouple the network management from data transmission. Compared to centralized SDVN, hybrid SDVN is even more flexible and has less overhead. This hybrid technology can eliminate the burden on the central controller by moving regional routing tasks from the central controller to local controllers or vehicular nodes. In the literature, different routing protocols have been reported for SDVNs. However, these existing routing protocols lack flexibility and adaptive approaches to deal with changing and dynamic traffic conditions. Thus, this paper proposes a new software-defined routing method, namely, Novel Adaptive Routing and Switching Scheme (NARSS), deployed in the controller. This adaptive method can dynamically select routing schemes for a specific traffic scenario. To achieve this, this paper firstly presents a method for collecting road network information to describe traffic condition where the method extracts the feature data used to generate the routing scheme switching model. Secondly, we train the feature data through an artificial neural network with high training speed and accuracy. Finally, we use the model as a basis for establishing the NARSS and deploy it in the controller. Simulation results show that the proposed scheme outperforms the single traditional routing protocol in terms of both packet delivery ratio and end-to-end delay.
On the Performance of Beam Allocation Based Multi-User Massive MIMO Systems

Moving to millimeter wave (mmWave) frequencies and deploying massive multiple input multiple output (MIMO) antenna arrays have shown great potential of supporting highdata-rate communications in the fifth-generation (5G) and beyond wireless networks, thanks to the availability of huge amounts of mmWave frequency bandwidth and massive numbers of narrow and high gain beams. A number of massive MIMO beamforming techniques have been proposed, among which the fixed-beam scheme has attracted considerable interests from both academia and industry due to its simplicity and requirement of a small number of radio frequency (RF) chains compared to the number of base-station (BS) antennas. Moreover, a beam allocation based pure analog fixed-beam system requires much lower complexity and less signalling overhead than the hybrid beamforming based fixed-beam system, which can therefore be easily implemented in the practical systems. In this paper, the sum data rate of beam allocation based multi-user massive MIMO systems is studied where a near-optimal low complexity beam allocation algorithm is adopted. Simulation results show that our derived average sum data rate serves as a good approximation of the simulation results.
Interference-Free OFDM Embedding of Wake-Up Signals for Low-Power Wake-Up Receivers

The use of ultra-low power wake-up receivers (WuRx) can significantly reduce idle listening energy cost. To tailor a WuRx scheme to orthogonal frequency division multiplexing (OFDM) based systems, such as LTE-MTC or IEEE 802.11, the wake-up signal (WUS) also needs to follow OFDM principles to avoid interfering with other transmissions in the same shared bandwidth. Here, we address this particular issue and propose an approach where the OFDM transmitter is modified to also transmit WUSs designed for non-coherent low-power WuRxs, on a subset of the carriers, thus embedding the WUS correctly and ensuring orthogonality. The approach is evaluated for different system parameters and its applicability across a wide range of OFDM-based systems is verified.
Vectorized Color Modulation for Covert Camera-Screen Communication

We propose a vectorized color modulation (VCM) method for a covert camera-screen communication (CSC) system under the human visual constraint. Instead of using light intensity of each image pixel block for data modulation in the existing work, we explore the color mixing characteristics of different colors while maintaining the chromaticity perception quality. Thus a color vector in the chromatic diagram can carry information in its angle, revealing a new signal dimension and mitigating the flickering issue. The best color vector pair is searched and selected for data modulation to yield the lowest bit error rate (BER). Experimental test based on representative images from an open database shows that the proposed method yields much better communication performance than the existing intensity modulation method.
Proactive Eavesdropping of Two Suspicious Communication Links via Jamming

This paper studies a new and challenging wireless surveillance problem where a legitimate monitor (e.g., the National Security Agency in the USA) attempts to eavesdrop more than one suspicious communication links simultaneously to maximally protect public security. To facilitate concurrent eavesdropping, our multi-antenna monitor employs a proactive eavesdropping via jamming approach, by purposely jamming suspicious receivers to lower the transmission rates of the target links. In particular, we are interested in characterizing the monitor's achievable eavesdropping signal-to-interference-plus-noise ratio (SINR) region of two suspicious links, by optimizing the legitimate monitor's transmit covariance matrix for jamming the two suspicious receivers. As the monitor cannot hear more than what suspicious links transmit, the achievable eavesdropping SINR region is essentially the intersection of the achievable region for the two suspicious links and that for the two eavesdropping links, and the former region can be purposely altered by the monitor's jamming transmit covariance matrix subject to its power budget. As both suspicious links' SINRs are affected by monitor's jamming covariance matrix, we first analyze the achievable region bounds for both suspicious links and then characterize the achievable eavesdropping SINR region. Finally, numerical results are provided to corroborate our analysis.
Using minimal recursion semantics in Japanese question answering

Question answering is a research field with the aim of providing answers to a user’s question, phrased in natural language. In this thesis I explore some techniques used in question answering, working towards the twin goals of using deep linguistic knowledge robustly as well as using language-independent methods wherever possible. While the ultimate aim is cross-language question answering, in this research experiments are conducted over Japanese data, concentrating on factoid questions. The two main focus areas, identified as the two tasks most likely to benefit from linguistic knowledge, are question classification and answer extraction. In question classification, I investigate the issues involved in the two common methods used for this task—pattern matching and machine learning. I find that even with a small amount of training data (2000 questions), machine learning achieves better classification accuracy than pattern matching with much less effort. The other issue I explore in question classification is the classification accuracy possible with named entity taxonomies of different sizes and shapes. Results demonstrate that, although the accuracy decreases as the taxonomy size increases, the ability to use soft decision making techniques as well as high accuracies achieved in certain classes make larger, hierarchical taxonomies a viable option. For answer extraction, I use Robust Minimal Recursion Semantics (RMRS) as a sentence representation to determine similarity between questions and answers, and then use this similarity score, along with other information discovered during comparison, to score and rank answer candidates. Results were slightly disappointing, but close examination showed that 40% of errors were due to answer candidate extraction, and the scoring algorithm worked very well. Interestingly, despite the lower accuracy achieved during question classification, the larger named entity taxonomies allowed much better accuracy in answer extraction than the smaller taxonomies.
Multi-Source Coded Downloads

In this paper, we propose a selective-repeat (SR) automatic repeat-request (ARQ) model for multi-source download scenarios and analyze their useful throughput that we refer to as goodput. The multi-source scenario comprises a set of transmitters that send packets to a receiver. We characterize the forward channels from the transmitters to the receiver via a general hidden Markov model (HMM) and assume that the reverse channels from the receiver to the transmitter are lossless. To find the average goodput of the network, we exploit the probability-generation function. We consider different packet transmission schemes, including uncoded random, network coded and sliding window-based network coded packets, and contrast their performance. Our calculations show that using network coding in a multi-source scenario can increase the average goodput, while sliding window-based coding may also archive the theoretical maximum goodput. We show that our multi-source approach avoids the straggler problem, therefore adding more transmitters to the network increases its throughout and the system does not get limited by the weakest transmitter. We also verify our analytic results with extensive simulations.
Large-Scale Indoor Positioning using Geomagnetic Field with Deep Neural Networks

The existing RF signal based indoor localization techniques such as BLE or Wi-Fi fingerprinting are hard to apply to large scale indoor environment such as airport and department stores since the localization error grows as the physical dimension of the indoor space increases. This can be attributed to unstable received signal strengths (RSS) of the underlying RF signal, which is enlarged with the increased physical scale and the complexity of the indoor space. In this paper, instead of RF signal we use the geomagnetic sensor signal for indoor localization, whose signal strength is more stable than RF RSS. Our approach using the geomagnetic field is as follows. Although similar geomagnetic field values exist in indoor space, an object movement would experience a unique sequence of the geomagnetic field signals as the movement continues. We can locate the position of the object by tracking the geomagnetic field signal sequence sensed with the object movement by using a deep neural network model called recurrent neural network (RNN), which is good at recognizing time varying sequence of sensor data. We use two different versions of RNN model: basic RNN and Long Short-Term Memory (LSTM). We have trained RNNs to learn the magnetic field maps of both medium scale (about 94m × 26m) and large scale (about 608m × 50m area) indoor testbeds and analyze both training and test set results by tuning several training hyperparameters. For comparison, we have also implemented both Bluetooth Low Energy (BLE) and Wi-Fi based fingerprinting localization techniques and measured their localization accuracies for the testbeds. By using Google TensorFlow 1.6 and Nvdia CUDA Toolkit v9.0 with cuDNN v7.1 library as a deep learning framework, we could achieve the average localization accuracy of 0.51 and 1.04 meters for the medium and the large-scale testbeds respectively with LSTM model, substantially improving the localization performance compared to the existing RF based fingerprinting techniques.
Semi-Supervised Deep Learning-Based Methods for Indoor Outdoor Detection

Detecting whether a mobile user is indoor or outdoor is an important issue which significantly impacts user behavior contextualization and mobile network resource management. Indoor Outdoor Detection (IOD) can be performed within mobile networks or in user terminals. Implementing IOD in mobile networks is interesting for operators as it is less costly, easier to deploy, and more energy efficient with centralized computing. This paper investigates hybrid/semi-supervised Deep Learning-based methods for detecting the environment of an active mobile phone user. They are based on both labeled and unlabeled large real radio data obtained from inside the network and from 3GPP signal measurements. We empirically evaluate the effectiveness of the semi-supervised learning methods using new real-time radio data, with partial ground truth information, gathered massively from multiple typical and diversified locations (indoor and outdoor) of mobile users. We also present an analysis of such schemes as compared to the existing supervised classification methods including SVM and Deep Learning.
Detection of Multi-Stage Attacks Based on Multi-Layer Long and Short-Term Memory Network

Multi-stage attack is a new trend of cyber attack. It is difficult for existing schemes to identify multiple stages in an attack period and associate independent stages. To address these issues, we design a long and short-term memory network (LSTM) based on multi-feature layer. First, we introduce stage features layer, the historical data is stored and calculated to identify the different stages of variable durations in multi-stage attacks. Then, the time-series features layer is used to associate the independent attack stages to analyze whether the current data falls in an attack period. Extensive experiments indicate that our proposed scheme has a lower false positive rate than existing schemes by at least 65.83%, and the false negative rate is reduced by at least 65.26%.
Recent progress in tactile sensing and sensors for robotic manipulation: can we turn tactile sensing into vision?1

This paper surveys recently published literature on tactile sensing in robotic manipulation to understand effective strategies for using tactile sensing and the issues involved in tactile sensing. It consists of a brief review of existing tactile sensors for robotic grippers and hands, review of modalities available from tactile sensing, review of the applications of tactile sensing in robotic manipulations, and discussion of the issues of tactile sensing and an approach to make tactile sensors more useful. We emphasize vision-based tactile sensing because of its potential to be a good tactile sensor for robots. GRAPHICAL ABSTRACT
Finite Blocklength Analysis of the Multiple Access Relay Channel with Batch-and-Forward Strategy

This work provides a comprehensive framework regarding the analysis of the Multiple Access Relay Channel (MARC) with Time Division Multiple Access (TDMA) scheduling, subject to finite blocklength constraints. In particular, we examine the stability conditions and evaluate the maximum throughput, by applying a cognitive cooperation protocol that assumes relaying is enabled when sources are idle. Moreover, we propose the novel Batch-And-Forward (BAF) strategy, that can significantly enhance the performance of cooperative networks in the finite blocklength regime, as well as reduce the requirement in metadata. The BAF strategy is quite versatile, thus, it can be embedded in existing cooperative protocols, without imposing additional complexity on the overall scheme.
Channel Estimation and Localization for mmWave Systems: A Sparse Bayesian Learning Approach

Millimeter wave (mmWave) systems have potential advantages for high accuracy localization due to their large bandwidth and highly directional and sparse channel. The existing schemes on mmWave system localization only explore the channel's angular sparsity and restrict the representation of angular parameters to fixed grids. In this paper, we propose a joint sparse channel model with adjustable grids of angles and delays of mmWave paths, which not only enables exploiting the joint sparsity in angular and delay domain, but also mitigates the quantization error due to fixed grids. We formulate the estimation of channel, angles and delays as a sparse Bayesian learning problem and propose to solve it using an in-exact block minorization-maximization algorithm and then determine the user location. We verify the performance of the algorithm by simulations.
Joint Resource Allocation for Adaptive Fuzzy Logic Based Coordinated Multi-Cell NOMA Systems

We investigate a downlink multi-cell non-orthogonal multiple access (NOMA) system with coordinated base stations (BSs) and propose a joint resource allocation (RA) scheme alongside adaptive user association to green the system. To the best of our knowledge, this is the first work to investigate joint allocation of subchannels and power for coordinated NOMA systems, while the previous work on RA for coordinated orthogonal multiple access (OMA) systems is not applicable. A serving channel gain based joint RA (SCG-JRA) algorithm is proposed, based on the theoretical proof that the total transmission power is mono-decreasing with respect to the SCGs of non-coordinated users. As for user association, an adaptive fuzzy logic (FL) based multi-criterion approach is proposed to achieve higher robustness against the combined effect of shadowing, fading and inter-cell interference, compared to the previous single-criterion based approaches. Numerical results show that the proposed SCG-JRA with adaptive FL based user association significantly outperforms the previous RA schemes assisted by single-criterion user association, in terms of energy efficiency (EE) and total transmission power, enabling a greener system.
Fetal Birth Weight Estimation in High-Risk Pregnancies Through Machine Learning Techniques

The low weight of fetus at birth is considered one of the most critical problems in pregnancy care, affecting the newborn's health and leading it to death in more severe cases. This condition is responsible for the high infant mortality rates worldwide. In health, artificial intelligence techniques, especially those based on machine learning (ML), can early predict problems related to the fetus' health state during entire gestation, including at birth. Hence, this paper proposes an analysis of several ML techniques capable of predicting whether the fetus will born small for its gestational age. The results show that the hybrid model, named bagged tree, achieved excellent results concerning accuracy and area under the receiver operating characteristic curve, to know, 0.849 and 0.636, respectively. The importance of the early diagnosis of problems related to fetal development relies on the possibility of an increase in the gestation days through timely intervention. Such intervention would allow an improvement in fetal weight at birth, associated with a decrease in neonatal morbidity and mortality.