Miracle-dz/UniFAR-FTUs huggingface dataset:
Fixed Region Beamforming Using Frequency Diverse Subarray for Secure mmWave Wireless Communications

Millimeter-wave (mmWave) using conventional phased array (CPA) enables highly directional and fixed angular beamforming (FAB), therefore enhancing physical layer security (PLS) in the angular domain. However, as the eavesdropper is located in the direction pointed by the mainlobe of the information-carrying beam, information leakage is inevitable and FAB cannot guarantee PLS performance. To address this threat, we propose a novel fixed region beamforming (FRB) by employing a frequency diverse subarray (FDSA) architecture to enhance the PLS performance for mmWave communications. In particular, we carefully introduce multiple frequency offset increments (FOIs) across subarrays to achieve a sophisticated beampattern synthesis that ensures a confined information transmission only within the desired angle-range region (DARR) in close vicinity of the target user. More specifically, we formulate the secrecy rate maximization problem with FRB over possible subarray FOIs, and consider two cases of interests, i.e., without/with the location information of eavesdropping, both turn out to be NP-hard. For the unknown eavesdropping location case, we propose a seeker optimization algorithm to minimize the maximum sidelobe peak of the beampattern outside the DARR. As for the known eavesdropping location case, a block coordinate descend linear approximation algorithm is proposed to minimize the sidelobe level in the eavesdropping region. Moreover, we propose an inverted subarray subset technique to further randomize the sidelobes against sensitive eavesdropping. By using the proposed FRB, the mainlobes of all subarrays are constructively superimposed in the DARR while the sidelobes are destructively overlayed outside the DARR. Therefore, FRB exhibits prominent effect on confining information transmission within the DARR. Numerical simulations demonstrate that the proposed FDSA-based FRB can provide superior PLS performance over the CPA-based FAB.
A New Unsupervised Short-Utterance based Speaker Identification Approach with Parametric t-SNE Dimensionality Reduction

State-of-the-art speaker identification (SI) systems have achieved accuracies of 100% with long-duration utterances which are impractical. Recently, short-utterance based systems have gained attention although identification rates are lower. This paper presents an approach for text-dependent speaker phoneme-based (<1sec) SI with parametric t-distributed stochastic neighbor embedding (pt-SNE) for dimensionality reduction of features to provide 3D-visualization. The approach employs Gaussian mixture model enhanced by $K-means++$ and gap statistic methods. As there is no other similar work, a fair comparison is unavailable. The 75% rate achieved is comparable to other works using (i) short-utterances (ii) pt-SNE for recognition of other data types.
The place of emotions while inspecting schools: reflections of two Ofsted inspectors

ABSTRACT This article investigates the role of emotions of inspectors while inspecting schools as reported by inspectors themselves within an education context of increased accountability that arguably privileges rationality over emotions. The study is built on an emotion management framework that regards emotions not only as unavoidably natural and intimately linked to ‘rational’ judgements that people make, but also that such judgements are social constructions used to regulate expression of emotions. In-depth interview data of one retired and another, semi-retired Office for Standards in Education, inspectors were thematically analysed to assess the extent to which their emotions formed part of school inspections. In the absence of an officially sanctioned narrative of inspectors’ emotions during inspections, two kinds of self-prescribed emotions emerged as key findings: emotions for accountability, to which inspectors gave expression, and emotions for improvement, that they suppressed. For an inspection body that needs to ensure both accountability and improvement of schools to justify its viability, these research claims open up a new area of discussion that should lead to a review of inspection body’s stance on emotions and individual inspectors’ self-reflections on the moral as well as professional obligation to pursue both emotions for accountability and improvement.
Schauder's estimate for nonlocal kinetic equations and its applications

In this paper we develop a new method based on Littlewood-Paley's decomposition and heat kernel estimates of integral form, to establish Schauder's estimate for the following degenerate nonlocal equation in $\mathbb R^{2d}$ with Holder coefficients: $$ \partial_tu=\mathscr L^{(\alpha)}_{\kappa;{\rm v}} u+b\cdot\nabla u+f,\ u_0=0, $$ where $u=u(t,x,{\rm v})$ and $\mathscr L^{(\alpha)}_{\kappa;{\rm v}}$ is a nonlocal $\alpha$-stable-like operator with $\alpha\in(1,2)$ and kernel function $\kappa$, which acts on the variable ${\rm v}$. As an application, we show the strong well-posedness to the following degenerate stochastic differential equation with Holder drift $b$: $$ {\rm d}Z_t=b(t,Z_t){\rm d}t+(0,\sigma(t,Z_t){\rm d}L^{(\alpha)}_t),\ \ Z_0=(x,{\rm v})\in\mathbb R^{2d}, $$ where $L^{(\alpha)}_t$ is a $d$-dimensional rotationally invariant and symmetric $\alpha$-stable process with $\alpha\in(1,2)$, and $b:\mathbb R_+\times\mathbb R^{2d}\to\mathbb R^{2d}$ is a $(\gamma,\beta)$-Holder continuous function in $(x,{\rm v})$ with $\gamma\in\big(\frac{2+\alpha}{2(1+\alpha)},1\big)$ and $\beta\in\big(1-\frac{\alpha}{2},1\big)$, $\sigma:\mathbb R_+\times\mathbb R^{2d}\to\mathbb R^d\otimes\mathbb R^d$ is a Lipschitz function. Moreover, we also show that for almost all $\omega$, the following random transport equation has a unique $C^1_b$-solution: $$ \partial_tu(t,x,\omega)+(b(t,x)+L^{(\alpha)}_t(\omega))\cdot\nabla_x u(t,x,\omega)=0,\ \ u(0,x)=\varphi(x), $$ where $\varphi\in C^1_b(\mathbb R^d)$ and $b:\mathbb R_+\times\mathbb R^d\to\mathbb R^d$ is a bounded continuous function in $(t,x)$ and $\gamma$-order Holder continuous in $x$ uniformly in $t$ with $\gamma\in\big(\frac{2+\alpha}{2(1+\alpha)},1\big)$.
CoSegNet: Deep Co-Segmentation of 3D Shapes with Group Consistency Loss

We introduce CoSegNet, a deep neural network architecture for co-segmentation of a set of 3D shapes represented as point clouds. CoSegNet takes as input a set of unsegmented shapes, proposes per-shape parts, and then jointly optimizes the part labelings across the set subjected to a novel group consistency loss expressed via matrix rank estimates. The proposals are refined in each iteration by an auxiliary network that acts as a weak regularizing prior, pre-trained to denoise noisy, unlabeled parts from a large collection of segmented 3D shapes, where the part compositions within the same object category can be highly inconsistent. The output is a consistent part labeling for the input set, with each shape segmented into up to K (a user-specified hyperparameter) parts. The overall pipeline is thus weakly supervised, producing consistent segmentations tailored to the test set, without consistent ground-truth segmentations. We show qualitative and quantitative results from CoSegNet and evaluate it via ablation studies and comparisons to state-of-the-art co-segmentation methods.
Coded Trace Reconstruction

Motivated by average-case trace reconstruction and coding for portable DNA-based storage systems, we initiate the study of coded trace reconstruction, the design and analysis of high-rate efficiently encodable codes that can be efficiently decoded with high probability from few reads (also called traces) corrupted by edit errors. Codes used in current portable DNA-based storage systems with nanopore sequencers are largely based on heuristics, and have no provable robustness or performance guarantees even for an error model with i.i. d. deletions and constant deletion probability. Our work is a first step towards the design of efficient codes with provable guarantees for such systems. We consider a constant rate of i.i. d. deletions, and begin by analyzing marker-based code-constructions coupled with worst-case trace reconstruction algorithms. Then, we show how a more careful design of the code allows us to exploit ideas from average-case trace reconstruction to reduce the number of traces required with the same redundancy.
Road Segmentation Based on Hybrid Convolutional Network for High-Resolution Visible Remote Sensing Image

Road segmentation plays an important role in many applications, such as intelligent transportation system and urban planning. Various road segmentation methods have been proposed for visible remote sensing images, especially the popular convolutional neural network-based methods. However, high-accuracy road segmentation from high-resolution visible remote sensing images is still a challenging problem due to complex background and multiscale roads in these images. To handle this problem, a hybrid convolutional network (HCN), fusing multiple subnetworks, is proposed in this letter. The HCN contains a fully convolutional network, a modified U-Net, and a VGG subnetwork; these subnetworks obtain a coarse-grained, a medium-grained, and a fine-grained road segmentation map. Moreover, the HCN uses a shallow convolutional subnetwork to fuse these multigrained segmentation maps for final road segmentation. Benefitting from multigrained segmentation, our HCN shows impressing results in processing both multiscale roads and complex background. Four testing indicators, including pixel accuracy, mean accuracy, mean region intersection over union (IU), and frequency weighted IU, are computed to evaluate the proposed HCN on two testing data sets. Compared with five state-of-the-art road segmentation methods, our HCN has higher segmentation accuracy than them.
Multi-View Temporal Ensemble for Classification of Non-Stationary Signals

In the classification of non-stationary time series data such as sounds, it is often tedious and expensive to get a training set that is representative of the target concept. To alleviate this problem, the proposed method treats the outputs of a number of deep learning sub-models as the views of the same target concept that can be linearly combined according to their complementarity. It is proposed that the view’s complementarity be the contribution of the view to the global view, chosen in this paper to be the Laplacian eigenmap of the combined data. Complementarity is computed by alternate optimization, a process that involves the cost function of the Laplacian eigenmap and the weights of the linear combination. By blending the views in this way, a more complete view of the underlying phenomenon can be made available to the final classifier. Better generalization is obtained, as the consensus between the views reduces the variance while the increase in the discriminatory information reduces the bias. The data experiment with artificial views of environment sounds formed by deep learning structures of different configurations shows that the proposed method can improve the classification performance.
Asymmetric-ReRAM: A Low Latency and High Reliability Crossbar Resistive Memory Architecture

Resistive Memory (ReRAM) is promising to be used as the storage-class memory due to its good scalability, high density, low standby power and non-volatility. By employing the unique crossbar structure, ReRAM can be constructed with extremely high density. However, ReRAM's crossbar structure suffers from an IR drop issue, which causes the asymmetric access latency in crossbar arrays. Moreover, without access transistors in the crossbar structure, read and write disturbances lead to serious data reliability problem. In this paper, we propose Asymmetric-ReRAM (A-ReRAM) to overcome the challenges of crossbar ReRAM. By reducing the IR drops along both wordlines and bitlines, leveraging the asymmetric access latency in crossbar arrays and exposing the most appropriate write latency to the memory controller, A-ReRAM effectively reduces the access latency. By applying the circuit-architecture level solutions, A-ReRAM addresses the read and write disturbances, and significantly improves the reliability. The experimental results show that, A-ReRAM improves system performance by 29.7% and 19.2%, and reduces the energy consumption by 21.1% and 11.3% on average, compared to the baseline and the state-of-the-art crossbar ReRAM design. Meanwhile, A-ReRAM significantly improves the reliability of ReRAM-based memory systems.
Effects of Distinct Robot Navigation Strategies on Human Behavior in a Crowded Environment

State-of-the-art social robot navigation algorithms often lack a thorough experimental validation in human environments: simulated evaluations are often conducted under unrealistically strong assumptions that prohibit deployment in real world environments; experimental demonstrations that are limited in sample size do not provide adequate evidence regarding the user experience and the robot behavior; field studies may suffer from the noise imposed by uncontrollable factors from the environment; controlled lab experiments often fail to properly enforce challenging interaction settings. This paper contributes a first step towards addressing the outlined gaps in the literature. We present an original experiment, designed to test the implicit interaction between a mobile robot and a group of navigating human participants, under challenging settings in a controlled lab environment. We conducted a large-scale, within-subjects design study with 105 participants, exposed to three different conditions, corresponding to three distinct navigation strategies, executed by a telepresence robot (two autonomous, one teleoperated). We analyzed observed human and robot trajectories, under close interaction settings and participants' impressions regarding the robot's behavior. Key findings, extracted from a comparative statistical analysis include: (1) evidence that human acceleration is lower when navigating around an autonomous robot compared to a teleoperated one; (2) the lack of evidence to support the conventional expectation that teleoperation would be humans' preferred strategy. To the best of our knowledge, our study is unique in terms of goals, settings, thoroughness of evaluation and sample size.
5G A REVIEW ON EXISTING TECHNOLOGIES

over the past few decades, the mobile wireless communication system has encountered a significant change in which mobile wireless generation is very fast in terms of speed. Even the wired telephone is getting past nowadays. Different constraints are available for describing the 5G technologies which are using for enhancement of 5G but this paper presents the combine review of 5G with its state of the art techniques used by cellular network architecture and some of the key emerging technologies that are helpful in improving the architecture and meeting the demands of those users who want to connect adequate with technology. This paper elaborates all parameters which have been discussed in different papers which are related to 5G technology. 5G technologies will offer more promising amenities like secure connection with encrypted documentation of e-payments, 5G users also use mobile to wireless devices connection.
Toward the Evaluation of Written Proficiency on a Collaborative Social Network for Learning Languages: Yask

Yask is an online social collaborative network for practicing languages in a framework that includes requests, answers, and votes. Since measuring linguistic competence using current approaches is difficult, expensive and in many cases imprecise, we present a new alternative approach based on social networks. Our method, called Proficiency Rank, extends the well-known Page Rank algorithm to measure the reputation of users in a collaborative social graph. First, we extended Page Rank so that it not only considers positive links (votes) but also negative links. Second, in addition to using explicit links, we also incorporate other 4 types of signals implicit in the social graph. These extensions allow Proficiency Rank to produce proficiency rankings for almost all users in the data set used, where only a minority contributes by answering, while the majority contributes only by voting. This overcomes the intrinsic limitation of Page Rank of only being able to rank the nodes that have incoming links. Our experimental validation showed that the reputation/importance of the users in Yask is significantly correlated with their language proficiency. In contrast, their written production was poorly correlated with the vocabulary profiles of the Common European Framework of Reference. In addition, we found that negative signals (votes) are considerably more informative than positive ones. We concluded that the use of this technology is a promising tool for measuring second language proficiency, even for relatively small groups of people.
Characterizations of Mutual Coupling Effects on Switch-Based Phased Array Antennas for 5G Millimeter-Wave Mobile Communications

The fifth generation (5G) millimeter-wave (mmWave) handset demands a cost-effective mmWave array antenna with beam steering capability to overcome the high-pass loss and to ensure seamless connectivity. Unlike sub-6-GHz handsets, emerging mmWave handsets usually employ phased array antennas with a reasonably large number of elements. Unfortunately, due to the legacy of a few antennas in sub-6-GHz handsets, the mutual coupling effect on the mmWave handset has not been thoroughly investigated. In this paper, we study the mutual coupling effect on the mmWave handset performance by comparing array antennas with different inter-element spacing and different configurations. It is found that mutual coupling tends to increase the active reflection (especially at large scanning angles), which in turn reduces the realized gain and maximum scanning angle of the phased array antenna. For a sub-6-GHz multiple-input multiple-output handset with two or four antenna ports and fully digital precoding/decoding, 10-dB isolation is usually regarded as good enough. It is shown in this paper, however, that the outage capacity of the mmWave handset can be clearly improved by reducing the mutual coupling.
Analytical Analysis of SER for Beyond 5G Post-OFDM Waveforms in Presence of High Power Amplifiers

This paper studies the performance of enhanced multicarrier waveforms in the presence of a nonlinear memoryless high power amplifier (HPA). In particular, we focus on the in-band non-linear distortions (NLD) and their effects on symbol-error-rate. First, we introduce a theoretical characterization of NLD caused by HPAs. Two HPA models are considered in this paper: 1) a 4-GHz realistic HPA, modeled using a polynomial model, and this model exhibits both amplitude-to-amplitude (AM/AM) and amplitude-to-phase distortions and 2) a Rapp model, which exhibits only the AM/AM distortion. Second, we present further discussions and comparisons on the different waveforms: the cyclically prefixed orthogonal frequency division multiplexing (OFDM), the weighted overlap and add OFDM, the universal filtered multicarrier, the filtered OFDM, and the filter bank multicarrier based on offset-QAM. Then, we provide insights into the impact of in-band non-linear distortions caused by HPAs. The simulation and theoretical results are shown to be in agreement for different waveforms and various input back-off values.
“I Don't Believe You”: Investigating the Effects of Robot Trust Violation and Repair

When a robot breaks a person's trust by making a mistake or failing, continued interaction will depend heavily on how the robot repairs the trust that was broken. Prior work in psychology has demonstrated that both the trust violation framing and the trust repair strategy influence how effectively trust can be restored. We investigate trust repair between a human and a robot in the context of a competitive game, where a robot tries to restore a human's trust after a broken promise, using either a competence or integrity trust violation framing and either an apology or denial trust repair strategy. Results from a $2\times 2$ between-subjects study ($n=82$) show that participants interacting with a robot employing the integrity trust violation framing and the denial trust repair strategy are significantly more likely to exhibit behavioral retaliation toward the robot. In the Dyadic Trust Scale survey, an interaction between trust violation framing and trust repair strategy was observed. Our results demonstrate the importance of considering both trust violation framing and trust repair strategy choice when designing robots to repair trust. We also discuss the influence of human-to-robot promises and ethical considerations when framing and repairing trust between a human and robot.
A change of perspective: switching quantum reference frames via a perspective-neutral framework

Treating reference frames fundamentally as quantum systems is inevitable in quantum gravity and also in quantum foundations once considering laboratories as physical systems. Both fields thereby face the question of how to describe physics relative to quantum reference systems and how the descriptions relative to different such choices are related. Here, we exploit a fruitful interplay of ideas from both fields to begin developing a unifying approach to transformations among quantum reference systems that ultimately aims at encompassing both quantum and gravitational physics. In particular, using a gravity inspired symmetry principle, which enforces physical observables to be relational and leads to an inherent redundancy in the description, we develop a perspective-neutral structure, which contains all frame perspectives at once and via which they are changed. We show that taking the perspective of a specific frame amounts to a fixing of the symmetry related redundancies in both the classical and quantum theory and that changing perspective corresponds to a symmetry transformation. We implement this using the language of constrained systems, which naturally encodes symmetries. Within a simple one-dimensional model, we recover some of the quantum frame transformations of \cite{Giacomini:2017zju}, embedding them in a perspective-neutral framework. Using them, we illustrate how entanglement and classicality of an observed system depend on the quantum frame perspective. Our operational language also inspires a new interpretation of Dirac and reduced quantized theories within our model as perspective-neutral and perspectival quantum theories, respectively, and reveals the explicit link between them. In this light, we suggest a new take on the relation between a `quantum general covariance' and the diffeomorphism symmetry in quantum gravity.
Why Children Prefer Extrovert or Introvert Robots: A Pilot Study Using Pairwise Robot Comparison

This work presents preliminary findings from a pilot study researching the complementary and similarity effect of personality of robots on children ($\mathbf{N}=22$). We aim to advance research on robot personality by using a pairwise robot comparison study, a novel method in HRI. Our findings show that the attributed gender and the voice of the robots were the main reasons given for a preference. Based on the preliminary data we hypothesize that boys have a preference for the robot with the same gender as themselves.