Miracle-dz/UniFAR-FTUs huggingface dataset:
ACT: An Autonomous Drone Cinematography System for Action Scenes

Drones are enabling new forms of cinematography. Aerial filming via drones in action scenes is difficult because it requires users to understand the dynamic scenarios and operate the drone and camera simultaneously. Existing systems allow the user to manually specify the shots and guide the drone to capture footage, while none of them employ aesthetic objectives to automate aerial filming in action scenes. Meanwhile, these drone cinematography systems depend on the external motion capture systems to perceive the human action, which is limted to the indoor environment. In this paper, we propose an Autonomous CinemaTography system “ACT” on the drone platform to address the above the challenges. To our knowledge, this is the first drone camera system which can autonomously capture cinematic shots of action scenes based on limb movements in both indoor and outdoor environments. Our system includes the following novelties. First, we propose an efficient method to extract 3D skeleton points via a stereo camera. Second, we design a real-time dynamical camera planning strategy that fulfills the aesthetic objectives for filming and respects the physical limits of a drone. At the system level, we integrate cameras and GPUs into the limited space of a drone and demonstrate the feasibility of running the entire cinematography system onboard in real-time. Experimental results in both simulation and real-world scenarios demonstrate that our cinematography system “ACT” can capture more expressive video footage of human action than that of a state-of-the-art drone camera system.
Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository

Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most efficient and widely used supervised learning algorithms are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor (LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this paper is to implement these elegant learning algorithms on eleven different datasets from the UCI machine learning repository to observe the variation of accuracies for each of the algorithms on all datasets. Analyzing the accuracy of the algorithms will give us a brief idea about the relationship of the machine learning algorithms and the data dimensionality. All the algorithms are developed in Matlab. Upon such accuracy observation, the comparison can be built among KNN, SVM, LMNN, and ENN regarding their performances on each dataset.
PointNetGPD: Detecting Grasp Configurations from Point Sets

In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud. Compared to recent grasp evaluation metrics that are based on handcrafted depth features and a convolutional neural network (CNN), our proposed PointNetGPD is lightweight and can directly process the 3D point cloud that locates within the gripper for grasp evaluation. Taking the raw point cloud as input, our proposed grasp evaluation network can capture the complex geometric structure of the contact area between the gripper and the object even if the point cloud is very sparse. To further improve our proposed model, we generate a large-scale grasp dataset with 350k real point cloud and grasps with the YCB object set for training. The performance of the proposed model is quantitatively measured both in simulation and on robotic hardware. Experiments on object grasping and clutter removal show that our proposed model generalizes well to novel objects and outperforms state-of-the-art methods. Code and video are available at https://lianghongzhuo.github. io/PointNetGPD.
DocFace+: ID Document to Selfie Matching

Numerous activities in our daily life require us to verify who we are by showing our ID documents containing face images, such as passports and driver licenses, to human operators. However, this process is slow, labor intensive and unreliable. As such, an automated system for matching ID document photographs to live face images (selfies<xref ref-type="fn" rid="fn1"><sup>1</sup></xref>) in real time and with high accuracy is required. In this paper, we propose DocFace+ to meet this objective. We first show that gradient-based optimization methods converge slowly (due to the underfitting of classifier weights) when many classes have very few samples, a characteristic of existing ID-selfie datasets. To overcome this shortcoming, we propose a method, called dynamic weight imprinting, to update the classifier weights, which allows faster convergence and more generalizable representations. Next, a pair of sibling networks with partially shared parameters are trained to learn a unified face representation with domain-specific parameters. Cross-validation on an ID-selfie dataset shows that while a publicly available general face matcher (InsightFace) only achieves a true accept rate (TAR) of 88.78 ± 1.30% at a false accept rate of 0.01% on the problem, DocFace+ improves the TAR to 95.95 ± 0.54%. <fn id="fn1"><label><sup>1</sup></label><p>Technically, the word “selfies” refers to self-captured photos from mobile phones. But here, we define “selfies” as any self-captured live face photos, including those from mobile phones and kiosks. </p></fn>
Nonlinear Speech Enhancement Under Speech PSD Uncertainty

Most Bayesian clean speech estimators, like the Wiener filter or Ephraim and Malah's amplitude estimators, are derived under the assumption that the true power spectral density (PSD) of speech is known. In practice, however, only estimates are available. When the PSD estimation errors are neglected, they propagate through to the final speech estimate, resulting in undesired artifacts such as musical noise and speech distortions. To increase the robustness to PSD estimation errors, recently a linear estimator has been proposed that explicitly takes into account the uncertainty of the available speech PSD estimate. In this paper, we show that in the derivation of this estimator a limiting statistical assumption is made, and that avoiding this assumption leads to a novel, potentially more powerful nonlinear estimator under PSD uncertainty. In combination with a sophisticated speech PSD estimator, the proposed approach achieves a higher predicted speech quality than the linear alternative and its conventional counterpart, the Wiener filter.
Standing still whilst ‘looking back and moving forwards’: The personal accounts of POWS members in the here and now

In 2017, the BPS Psychology of Women Section (POWS) celebrated its 30th anniversary. Within the year that succeeded it was re-named the Psychology of Women and Equalities Section (POWES). This paper reflects not only on POWS (as it was named when this research was conducted) as an organization in the past, but also on the everyday accounts of those who are involved with the Section. Using an online survey including a story completion task, we explored how participants (n= 26) came to POWS; what roles they have engaged in within and outside of the section; and how they see POWS and feminist activism in Psychology moving forward. By analysing thematically, core themes around past reflections, a call for a more radical future and some central challenges were identified. We reflect on these findings and discuss how the positive virtues attributed to POWS exist alongside the tensions between embodying either a critic or a conformist status.
Tracking Position and Orientation Through Millimeter Wave Lens MIMO in 5G Systems

Millimeter wave signals and large antenna arrays are considered enabling technologies for future 5G networks. Despite their benefits for achieving high data rate communications, their potential advantages for tracking of the location and rotation angle of the user terminals are not well investigated. A joint heuristic beam selection and user position and orientation tracking approach is proposed. First, the user location is tracked in the uplink by joint beam selection together with time-of-arrival (TOA) and angle-of-arrival (AOA) tracking at the base station (BS). Then, the user rotation angle is obtained using the location information by joint beam selection and tracking at the mobile station (MS). The beam selection, TOA and AOA tracking, at the BS and MS are performed during the data transmission phase. Numerical results demonstrate that the proposed method performs close to the estimated position and rotation angle in the training phase with reduced complexity and reduced number of required pilots for the estimation.
A new RAN slicing strategy for multi-tenancy support in a WLAN scenario

Radio Access Network (RAN) slicing is a key technology, based on Software Defined Networks (SDN) and Network Function Virtualization (NFV), which aims at providing a more efficient utilization of the available network resources and the reduction of the operational costs. In that respect, in this paper a novel virtualized WiFi network hypervisor is presented. This new hypervisor, based on a time variant radio resource sharing mechanism named Weighted Air-Time Deficit Round Robin (WADRR), is able to follow the dynamicity of the traffic variations seen by the different tenants located to the network Access Points (APs). It will be shown that the proposed WADRR hypervisor is able to dynamically assign the appropriate resources per tenant in every AP of the network, according to its specific traffic requirements. To this end, all the network APs are instructed by a controller which is responsible of guaranteeing, on average (long term perspective) and over the whole network, the accomplishment of the tenant's Service Level Agreement (SLA) target, while satisfying the short term traffic requests in the individual network APs. The correct behavior of the proposed algorithm has been validated through both simulations and in a real SDN-NFV platform build upon the 5G-EmPOWER test-bed.
Integrated Route and Charging Planning for Electric Vehicles Considering Nonlinear Charging Functions

Considering the carbon emission reduction and energy saving, the electric vehicles (EVs) have been identified as a promising alternative to classical fossil fuel-driven vehicles. Consequently, the Electric Vehicle Route Planning with Recharging (EVRC) problem extends traditional routing problems to take the limited capacity of the vehicle's battery into consideration. In order to avoid the depletion of battery, planned detours should be introduced to recharge the battery. As the previous research works mainly focused on the three assumptions: (1) EVs will be fully charged when they get a CS; (2) The charging time rises linearly as the battery charge level ascends;(3) Serve time is not considered in the whole process. However, in the actual situations, the location of the used charging stations and the amount of charge are decision variables. In addition, the charging function is not linear. In this work, we propose an integrated route and charging planning for EVs, and take partial charging, nonlinear charging functions as well as serve time into consideration. The experimental result shows that it leads to infeasible or excessively costly solutions when the serve time and the features of partial and nonlinear charging process are ignored.
The Study on the Botnet and its Prevention Policies in the Internet of Things

With the rapid development of Internet of Things (IOT), IOT is more and more important. Also, it faces serious security issues. This paper analyzes Mirai's architecture. The core components are C & C server and Loader server that take charge of command and control, IOT equipments are in charge of broadcast and attack. Paper analyzes Botnet propagation model, Mirais infection attack procedure, impact factor and then proposes the corresponding anti-virus strategy.
Function Splitting and Quadratic Approximation of the Primal-Dual Method of Multipliers for Distributed Optimization Over Graphs

We propose two algorithms based on the Primal-Dual Method of Multipliers (PDMM) to be used in distributed network optimization: Function Split PDMM (FS-PDMM) and Quadratically Approximated PDMM (QA-PDMM). Our approaches simplify the local subproblems that must be solved for each node, at each update iteration, improving computational efficiency at distributed processors. FS-PDMM allows for simplified updates of distributed problems involving regularized general convex functions, while QA-PDMM allows smooth local cost functions to be approximated quadratically. In both cases, this leads to iterative updates that require mostly simple and analytic computation rather than numerical solutions to more complex subproblems, particularly when using common regularization functions such as the <inline-formula> <tex-math notation="LaTeX">$l_1$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$l_2$ </tex-math></inline-formula> norms. We show that FS-PDMM is theoretically equivalent to performing conventional PDMM on a network twice the size as the physical network, and prove convergence for QA-PDMM for a common class of problems. Experimentally, we demonstrate convergence and reduction in computational complexity for elastic net regularized least squares and ridge regularized logistic regression.
Improved Visual Relocalization by Discovering Anchor Points

We address the visual relocalization problem of predicting the location and camera orientation or pose (6DOF) of the given input scene. We propose a method based on how humans determine their location using the visible landmarks. We define anchor points uniformly across the route map and propose a deep learning architecture which predicts the most relevant anchor point present in the scene as well as the relative offsets with respect to it. The relevant anchor point need not be the nearest anchor point to the ground truth location, as it might not be visible due to the pose. Hence we propose a multi task loss function, which discovers the relevant anchor point, without needing the ground truth for it. We validate the effectiveness of our approach by experimenting on CambridgeLandmarks (large scale outdoor scenes) as well as 7 Scenes (indoor scenes) using variousCNN feature extractors. Our method improves the median error in indoor as well as outdoor localization datasets compared to the previous best deep learning model known as PoseNet (with geometric re-projection loss) using the same feature extractor. We improve the median error in localization in the specific case of Street scene, by over 8m.
A Convergent Image Fusion Algorithm Using Scene-Adapted Gaussian-Mixture-Based Denoising

We propose a new approach to image fusion, inspired by the recent plug-and-play (PnP) framework. In PnP, a denoiser is treated as a black box and plugged into an iterative algorithm, taking the place of the proximity operator of some convex regularizer, which is formally equivalent to a denoising operation. This approach offers flexibility and excellent performance, but convergence may be hard to analyze, as most state-of-the-art denoisers lack an explicit underlying objective function. Here, we propose using a scene-adapted denoiser (i.e., targeted to the specific scene being imaged) plugged into the iterations of the alternating direction method of multipliers. This approach, which is a natural choice for image fusion problems, not only yields state-of-the-art results but it also allows proving convergence of the resulting algorithm. The proposed method is tested on two different problems: hyperspectral fusion/sharpening and fusion of blurred-noisy image pairs.
The Generic Holdout: Preventing False-Discoveries in Adaptive Data Science

Adaptive data analysis has posed a challenge to science due to its ability to generate false hypotheses on moderately large data sets. In general, with non-adaptive data analyses (where queries to the data are generated without being influenced by answers to previous queries) a data set containing $n$ samples may support exponentially many queries in $n$. This number reduces to linearly many under naive adaptive data analysis, and even sophisticated remedies such as the Reusable Holdout (Dwork et. al 2015) only allow quadratically many queries in $n$. In this work, we propose a new framework for adaptive science which exponentially improves on this number of queries under a restricted yet scientifically relevant setting, where the goal of the scientist is to find a single (or a few) true hypotheses about the universe based on the samples. Such a setting may describe the search for predictive factors of some disease based on medical data, where the analyst may wish to try a number of predictive models until a satisfactory one is found. Our solution, the Generic Holdout, involves two simple ingredients: (1) a partitioning of the data into a exploration set and a holdout set and (2) a limited exposure strategy for the holdout set. An analyst is free to use the exploration set arbitrarily, but when testing hypotheses against the holdout set, the analyst only learns the answer to the question: "Is the given hypothesis true (empirically) on the holdout set? " -- and no more information, such as "how well" the hypothesis fits the holdout set. The resulting scheme is immediate to analyze, but despite its simplicity we do not believe our method is obvious, as evidenced by the many violations in practice. Our proposal can be seen as an alternative to pre-registration, and allows researchers to get the benefits of adaptive data analysis without the problems of adaptivity.
Using Accelerometric and Gyroscopic Data to Improve Blood Pressure Prediction from Pulse Transit Time Using Recurrent Neural Network

We propose a method for estimating blood pressure (BP) non-invasively from electrocardiogram (ECG) and photoplethysmogram (PPG) signals. This method has potential to be used as a continuous form of BP estimation. Along with these signals, to our knowledge, for the first time in the BP measurement studies, we included accelerometric and gyroscopic signals from a wearable device to compensate for motion during continuous BP prediction. Our prediction model is a long-short-term-memory (LSTM) architecture of a recurrent neural network (RNN), which accommodates the multiscale temporal dependency between the sequential raw signal values and the corresponding systolic and diastolic BP values. We performed a study with 50 healthy volunteers. The mean difference ± standard deviation (SD) of the RNN-based approach were $0.02\pm 4.8$ for SBP and $1.5\pm 3.7$ for DBP in seated position & $2.6\pm 6.0$ for SBP and $2.7\pm 4.5$ for DBP while walking. These values meet current validation standard requirements for measurement accuracy. Our experiments also demonstrate that the proposed RNN-based approach outperformed the classical linear regression model for BP prediction.
The reproductive strategy in a Chloris virgata population in response to precipitation regimes

Resource availability influences plant growth and reproduction. Here, a controlled experiment was conducted in order to evaluate the adaptation response of Chloris virgata to different precipitation conditions, and to further predict the reproductive strategy in a population of C. virgata under different precipitation regimes. Three regimes (low, typical and high) of water addition were used to simulate current precipitation patterns. In total 20 individuals for each treatment were analysed to compare tiller number, spike traits, seed traits, the relationship between seed size and seed number, and so on. In addition, the effects of different precipitation regimes on offspring vigour of C. virgata were also studied. Results indicated that tiller number, spike number, seed yield and seed number were unchanged under different water addition regimes, while seed size was about 0.5 mg at typical and high precipitation levels and was higher than that in the low precipitation level. The higher seed mass per spike and spike mass both occurred at typical and high precipitation levels. Significant positive correlations between seed mass and non-seed mass in C. virgata in response to precipitation regimes were largely allometric (size dependent), as was a significant negative correlation between seed size and seed number at low precipitation. The highest germination rates and seedling weights both occurred at typical and high precipitation levels. These findings showed that different precipitation regimes affected reproductive strategy of C. virgata. Chloris virgata will not benefit from low precipitation, while typical and high precipitation will improve seed traits and offspring vigour of this species.
Scalable and Performant Graph Processing on GPUs Using Approximate Computing

Graph algorithms are being widely used in several application domains. It has been established that parallelizing graph algorithms is challenging. The parallelization issues get exacerbated when graphics processing units (GPUs) are used to execute graph algorithms. While the prior art has shown effective parallelization of several graph algorithms on GPUs, a few algorithms are still expensive. In this work, we address the scalability issues in graph parallelization. In particular, we aim to improve the execution time by tolerating a little approximation in the computation. We study the effects of four heuristic approximations on six graph algorithms with five graphs and show that if an application allows for small inaccuracy, this can be leveraged to achieve considerable performance benefits. We also study the effects of the approximations on GPU-based processing and provide interesting takeaways.
Semi-Supervised Learning with Deep Neural Networks for Relative Transfer Function Inverse Regression

Prior knowledge of the relative transfer function (RTF) is useful in many applications but remains little studied. In this paper, we propose a semi-supervised learning algorithm based on deep neural networks (DNNs) for RTF inverse regression, that is to generate the full-band RTF vector directly from the source-receiver pose (position and orientation). Two typical scenarios are discussed: training on labeled RTFs only, or on additional unlabeled RTFs. Both setups utilize the low-dimensional manifold property of RTF in stationary environments. With this property as an additional regularization term, a smooth mapping solution with respect to the manifold is obtained. Experimental simulations show that the proposed method achieves a lower mean prediction error than the free field model with few labeled RTFs, and the unlabeled RTFs are essential in improving the inverse regression performance.
Classification-Based Supervised Hashing with Complementary Networks for Image Search

For realizing an efﬁcient image search system, the binary hash code representation of images is essential to reduce memory consumption and computation time. In this paper, we present a new supervised hashing method using complementary networks. We construct the framework that performs classiﬁcation tasks for discriminative hash codes and regularizes them with the assistance of adversarial networks. Speciﬁcally, our framework has an encoder network that generates relaxed hash codes and is trained with a classiﬁer to make the codes discriminative. A discriminator network takes as inputs the instances from a desired distribution and generated hash codes to learn to distinguish true and false samples. The classiﬁer presents resulting one-hot labels to the desired instances, which complements the main classiﬁcation task. As simultaneously trained with the discriminator, the encoder network produces the hash codes with the desired properties. Experiments on widely used datasets show that the proposed framework can help to produce effective hash codes for image search, and applies to hashing with both single-and multi-label classiﬁcation tasks.