Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Face Modeling and Head Pose Measurement Using Stereovision

To drive safely, it is effective to introduce the environmental recognition system such as the walker detector and the obstacle detector. It is, however, not preferable to show the driver all of information recognized by these systems, because it is difficult for the driver, especially for the elderly driver, to process too much information. Thus, it is necessary to eliminate the unnecessary information that displayed by the driving support system. It is considered that the oversight detection based on driver's gaze estimation will become powerful criterion for unnecessary information. The gaze position can be analyzed from both driver's head pose and gaze directions. This paper focuses on the head pose measurement technique in six degree of freedom. This technique is based on ICP registration between 3D face model made in advance and obtained shape data. ICP registration can achieve high accuracy alignment, but needs huge computational effort, in general. To solve this problem, this paper propose the introducing a reduction in volume of data based on pattern information in the image, coarse-to-fine search, and SIMD computing. Finally, it was confirmed that the proposed technique could measure in high accuracy by experiment.
Accelerating QUIC’s Connection Establishment on High-Latency Access Networks

A significant amount of connection establishments on the web require a prior domain name resolution by the client. Especially on high-latency access networks, these DNS lookups cause a significant delay on the client’s connection establishment with a server. To reduce the overhead of QUIC’s connection establishment with prior DNS lookup on these networks, we propose a novel QuicSocks proxy. Basically, the client delegates the domain name resolution towards the QuicSocks proxy. Our results indicate, that colocating our proxy with real-world ISP-provided DNS resolvers provides great performance gains. For example, 10% of our 474 sample nodes distributed across ISP’s in Germany would save at least 30 ms per QUIC connection establishment. The design of our proposal aims to be readily deployable on the Internet by avoiding IP address spoofing, anticipating Network Address Translators and using the standard DNS and QUIC protocols. In summary, our proposal fosters a faster establishment of QUIC connections for clients on high-latency access networks.
From Surveillance to Digital Twin: Challenges and Recent Advances of Signal Processing for Industrial Internet of Things

The Scanning the Literature column provides concise summaries of selected papers that have recently been published in the field of networking. Each summary describes the paper’s main idea, methodology, and technical contributions. The purpose of the column is to bring the state of the art of networking research to readers of IEEE Network . Authors are also welcome to recommend their recently published work to the column, and papers with novel ideas, solid work, and significant contributions to the field are especially appreciated. Authors wishing to have their papers presented in the column should contact the Editor.
Adaptive Approximate Bayesian Computation Tolerance Selection

Approximate Bayesian Computation (ABC) methods are increasingly used for inference in situations in which the likelihood function is either computationally costly or intractable to evaluate. Extensions of the basic ABC rejection algorithm have improved the computational efficiency of the procedure and broadened its applicability. The ABC-Population Monte Carlo (ABC-PMC) approach of Beaumont et al. (2009) has become a popular choice for approximate sampling from the posterior. ABC-PMC is a sequential sampler with an iteratively decreasing value of the tolerance, which specifies how close the simulated data need to be to the real data for acceptance. We propose a method for adaptively selecting a sequence of tolerances that improves the computational efficiency of the algorithm over other common techniques. In addition we define a stopping rule as a by-product of the adaptation procedure, which assists in automating termination of sampling. The proposed automatic ABC-PMC algorithm can be easily implemented and we present several examples demonstrating its benefits in terms of computational efficiency.
Channel-Correlation-Enabled Transmit Optimization for MISO Wiretap Channels

An artificial-noise (AN)-aided beamformer specific to correlated main and wiretap channels is designed in this paper. We consider slow-fading multiple-input-single-output (MISO) wiretap channels with a passive single-antenna eavesdropper, in which independent transmitter-side and correlated receiver-side are assumed. Additionally, the source has accurate main channel information and statistical wiretap channel information. To reduce the secrecy loss due to receiver-side correlation, this paper proposes the scheme of channel-correlation-enabled transmit optimization. Particularly, the correlation is viewed as a resource to acquire more knowledge about wiretap channel. Based on this, the statistical distribution of wiretap channel is described more precisely. Then, the power of AN in the null space of main channel is placed more reasonably instead of simple uniform distribution and an elaborate beamformer for the information-bearing signal is designed. Finally, an efficient algorithm for power allocation between the information-bearing signal and the AN is developed. Simulation results show that the secrecy rate under transmit power and secrecy outage constraint is improved.
On the Optimal Refresh Power Allocation for Energy-Efficient Memories

Refresh is an important operation to prevent loss of data in dynamic random-access memory (DRAM). However, frequent refresh operations incur considerable power consumption and degrade system performance. Refresh power cost is especially significant in high-capacity memory devices and battery-powered edge/mobile applications. In this paper, we propose a principled approach to optimizing the refresh power allocation. Given a model for the bit error rate dependence on power, we formulate a convex optimization problem to minimize the word mean squared error for a refresh power constraint; hence we can guarantee the optimality of the obtained refresh power allocations. In addition, we provide an integer programming problem to optimize the discrete refresh interval assignments. For an 8-bit accessed word, numerical results show that the optimized nonuniform refresh intervals reduce the refresh power by 29% at a peak signal-to-noise ratio of 50dB compared to the uniform assignment.
A Hybrid Learning Rule for Efficient and Rapid Inference with Spiking Neural Networks

The emerging neuromorphic computing (NC) architectures have shown compelling energy efficiency to perform machine learning tasks with spiking neural networks (SNNs). However, due to the non-differentiable nature of spike generation, the standard error backpropagation algorithm is not directly applicable to SNNs. In this work, we propose a novel learning rule based on the hybrid neural network with shared weights, wherein a rate-based SNN is used during the forward propagation to determine precise spike counts and spike trains, and an equivalent ANN is used during error backpropagation to approximate the gradients for the coupled SNN. The SNNs trained with the proposed learning rule have demonstrated competitive classification accuracies on the CIFAR-10 and IMAGENET- 2012 datasets with significant savings on the inference time and total synaptic operations compared to other state-of-the-art SNN implementations. The proposed learning rule offers an intriguing solution to enable on-chip computing on the pervasive mobile and embedded devices with limited computational budgets.
Work and heat fluctuations reveal the strokes of autonomous quantum heat engines

We analyze an autonomous thermoelectric engine composed of two superconducting qubits coupled to separate heat baths and connected by a Josephson junction. Work and heat are process quantities and not observables of the engine quantum system, but their rates can be derived from the steady state expectation value of appropriate system observables and their fluctuations are given by correlation functions determined with the GKLS master equation and quantum regression theorem. The correlation functions also reveal a cyclical, dynamical transfer of energy - the strokes of the engine - hiding underneath the steady state.
Size-dependence of the photothermal response of a single metal nanosphere

We study the thermal response of a single spherical metal nanoparticle to continuous wave illumination as a function of its size. We show that the particle temperature increases non-monotonically as the particle size increases, indicating that the photo-thermal response can be optimized by tuning the particle size and illumination wavelength. We also compare the size-effect on the photo-thermal effects of gold and silver nanoparticles and find somewhat surprisingly that Ag NPs are more efficient heat generators only for sufficiently small sizes. These results have importance primarily for application such as plasmon-assisted photo-catalysis, photothermal cancer therapy, etc., and provide a first step toward the study of the size-dependence of the thermo-optic nonlinearity of metal nanospheres.
Map-to-Text: Local Map Descriptor

: Map matching, the ability to match a local map built by a mobile robot to previously built maps, is crucial in many robotic mapping, self-localization, and simultaneous localization and mapping (SLAM) applications. In this paper, we propose a solution to the “map-to-text (M2T)” problem, which involves the generation of text descriptions of local map content based on scene understanding to facilitate fast succinct text-based map matching. Unlike previous local feature approaches that trade discriminativity for viewpoint invariance, we develop a holistic view descriptor that is view-dependent and highly discriminative. Our approach is inspired by two independent observations: (1) The behavior of mobile robots given a local map can often be characterized by a unique viewpoint trajectory, and (2) a holistic view descriptor can be highly discriminative if the viewpoint is unique given the local map. Our method consists of three distinct steps: (1) First, an informative local map of the robot’s local surroundings is built. (2) Next, a unique viewpoint trajectory is planned in accordance with the given local map. (3) Finally, a synthetic view is described at the designated viewpoint. Because the success of our holistic view descriptor depends on the assumption that the viewpoint is unique given a local map, we also address the issue of viewpoint planning and present a solution that provides similar views for similar local maps. Consequently, we also propose a practical map-matching framework that combines the advantages of the fast succinct bag-of-words technique and the highly discriminative M2T holistic view descriptor. The results of experiments conducted using the publicly available radish dataset verify the e ﬃ cacy of our proposed approach.
PYE

Languages like Java and C# follow a two-step process of compilation: static compilation and just-in-time (JIT) compilation. As the time spent in JIT compilation gets added to the execution-time of the application, JIT compilers typically sacrifice the precision of program analyses for efficiency. The alternative of performing the analysis for the whole program statically ignores the analysis of libraries (available only at runtime), and thereby generates imprecise results. To address these issues, in this article, we propose a two-step (static+JIT) analysis framework called precise-yet-efficient (PYE) that helps generate precise analysis-results at runtime at a very low cost. PYE achieves the twin objectives of precision and performance during JIT compilation by using a two-pronged approach: (i) It performs expensive analyses during static compilation, while accounting for the unavailability of the runtime libraries by generating partial results, in terms of conditional values, for the input application. (ii) During JIT compilation, PYE resolves the conditions associated with these values, using the pre-computed conditional values for the libraries, to generate the final results. We have implemented the static and the runtime components of PYE in the Soot optimization framework and the OpenJDK HotSpot Server Compiler (C2), respectively. We demonstrate the usability of PYE by instantiating it to perform two context-, flow-, and field-sensitive heap-based analyses: (i) points-to analysis for null-dereference-check elimination; and (ii) escape analysis for synchronization elimination. We evaluate these instantiations against their corresponding state-of-the-art implementations in C2 over a wide range of benchmarks. The extensive evaluation results show that our strategy works quite well and fulfills both the promises it makes: enhanced precision while maintaining efficiency during JIT compilation.
AIMER: Appraisal Interpersonal Model of Emotion Regulation, Affective Virtual Students to Support Teachers Training

Elementary school classrooms are emotionally stressful environments, for both students and teachers. Successful teachers use strategies that regulate students' emotions and behaviors while also controlling their own emotions (stress, nervousness). To prepare teachers for the challenges of teaching, teacher training should include emotional and behavioral management strategies. Virtual Training Environments (VTEs) are effective at providing experiences and increasing learning in many domains. Creating VTEs for teachers can improve student learning and teacher retention. We introduce our current research aimed at integrating emotionally-intelligent virtual students within a 3D classroom training system. In our simulation, virtual students' emotional states will be determined from an appraisal process of actions taken by the teacher trainee in the virtual classroom. Virtual students will then display the appropriate non-verbal behaviors and react to the teacher accordingly. We present the first steps required to implement our proposed architecture which are based on appraisal theory of emotions and emotion regulation theory.
Attempt to Visualize Cardiac Motion on Body Surface Using Active Stereoscopic Depth Camera

For the purpose of monitoring the cardiac mechanical phenomena, we propose a non-contact cardiac motion measurement method applying the active stereo depth camera. In the proposed method, the three-dimensional shape of the subject's chest is reconstructed based on the active stereo method using infrared light using RealSense F200 which is a three-dimensional image sensor manufactured by Intel Corporation. Then, by performing the active stereo measurement by projecting the green dot matrix pattern using the RealSense color camera, micro displacement of the chest surface due to the cardiac motion is acquired. In this paper, we confirm the feasibility of non-contact cardiac motion measurement by green dot matrix pattern projection, and attempt to visualize the distribution of the cardiac motion on the chest surface.
A New Look at the Essence of the Array Photoplethysmograph

Photoplethysmography (PPG) has been widely used to investigate various cardiovascular conditions. Previous studies have demonstrated the effects of temperature of the measurement environment. However, an integrated evaluation has not been established in environments with gradual air temperature variations. This study was conducted to determine the correlation between the activity frequency (AF) of array PPG image and the changes in skin surface temperature (SST) by environment temperature control. The study participants were 13 company employees aged between 27 and 48 years (mean age: 35.6 years) who were treated with an IR heater to increase the skin temperature for approximately 15 min. Measurements of finger array PPG and finger SST were obtained before infrared heating as baseline, and the intervention of 15-min infrared heating was continued in the left hand as the experiment group. The right hand was not subjected to heating, which served as the control group. The results showed that the local fingertip SST increase affected the local AF of the array PPG image, which could be related to the biphasic skin blood flow response. The array PPG image could be used for monitoring the activity control of microcirculation or endothelial function.
Characterizing Transport Perception using Social Media: Differences in Mode and Gender

Transport planners face the growing need to understand the behavior of their users, who base their mobility decisions on several factors, including travel time, quality of service, and security. However, transportation is usually designed with an average user in mind, without considering the needs of important groups, such as women. In this context, we analyzed 300K tweets about transportation in Santiago, Chile. We classified users into modes of transportation, and then we estimated the associations between mode of transportation, gender, and the categories of a psycho-linguistic lexicon. Our results include that women express more anger and sadness than expected, and are worried about sexual harassment. Conversely, men focus more on the spatial aspects of transportation, leisure, and work. Thus, our work provides evidence on which aspects of transportation are relevant in the daily experience, enabling the measurement of the travel experience using social media.
HH-Net: Image Driven Microscope Fast Auto-Focus with Deep Neural Network

Computer aid auto-focus system is necessary for accurate microscope diagnosis, especially for the high precision microscope, which leaves little physical distance for focus adjusting manually. We proposed an image-driven microscope fast auto-focus system with a deep neural network. There are two main contributions. First, combining the high-level feature learning ability advantages of convolution neural network (CNN) and the handcraft feature selection ability of statistical learning, we proposed a High-level-Handcraft Neural Network (HH-Net) to accurately determine the distance index between microscope lens and cell smear by evaluating the image focus quality. It deployed 13 layers CNN for the high-level feature extraction from image patches. While the handcraft features which provide global information from the raw image were extracted by statistical algorithms and merged into CNN features. Finally, the combined features are utilized by the fully connected layers in the network to obtain the final distance index by classifying the biomedical image focus quality. Second, cooperated with the HH-Net, we propose an end to end image driven microscope fast auto-focus system, which can learn auto-focus policies from visual input and finish at a clear spot automatically. The accuracy of our patch level focus quality prediction is 92.4% with HH-Net, while the real-time image level focus quality predication can be 99.99% with 0.025s cost time by certainty voting strategy. Our auto-focus system can also cooperate with the X-Y Micro platform to automatically scan the whole cell smear and get the real-time best-in-focus image of a microscope with fast response, accuracy, and robustness.
Sparse-Matrix Compression Primitives with OpenCL Framework to Support Halide

Halide and OpenCL now play important roles for heterogeneous multi-core computing. OpenCL provides vendor-level support and Halide provides domain-specific support such as vision processing and AI model (TVM Halide IR). Halide also provides flexible scheduling for applications on target machines. OpenCL plays a supporting role for Halide environments. In this work, we investigate the research issues in supporting sparse computation with Halide and their corresponding OpenCL support. We present sparse matrix compression primitives on Halide for sparse matrix matrix (SpMM) multiplication with OpenCL framework. Halide is a programming language designed to process image and array from numerous algorithms and scheduling primitives to achieve state-of-art performance including SIMD and heterogeneous computation. This paper proposed the implementation of sparse matrix compression for Halide scheduling primitives including COO, CSR, and hybrid CSR. The design of experiments includes Halide primitives for sparse matrix compression and matrix computations. The experimental result of computation with compressing matrix shows the performance are improved by up to 85% compared to the baseline without compression.
Extremist Propaganda Tweet Classification with Deep Learning in Realistic Scenarios

In this work, we tackled the problem of the automatic classification of the extremist propaganda on Twitter, focusing on the Islamic State of Iraq and al-Sham (ISIS). We built and published several datasets, obtained by mixing 15,684 ISIS propaganda tweets with a variable number of neutral tweets, related to ISIS, and random ones, accounting for imbalances up to 1%. We considered three state-of-the-art, deep learning techniques, representative of the main current approaches to text classification, and two strong linear machine learning baselines. We compared their performance when varying the composition of the training and test sets, in order to explore different training strategies, and to evaluate the results when approaching realistic conditions. We demonstrated that a Recurrent-Convolutional Neural Network, based on pre-trained word embeddings, can reach an excellent F1 score of 0.9 on the most challenging test condition (1%-imbalance).
HDRNET: Single-Image-based HDR Reconstruction Using Channel Attention CNN

As opposed to the low dynamic range (LDR) image, high dynamic range (HDR) image can represent a greater dynamic range of luminosity that can be perceived by human visual system. As under-/over-exposure and color quantization will cause information loss, inferring a HDR image from a single LDR input is an ill-posed problem. To tackle this, we present an end-to-end convolutional neural network (CNN) termed HDRNET to directly reconstruct HDR image given only a single 8-bit LDR image, which does not require any human expertise. Information from different scales should be considered, so our architecture is multiscale. To enhance the representational ability of CNN, we propose a hybird loss and use channel attention mechanism to adaptively rescale channel-wise features. To train the HDRNET, we build a large dataset of LDR-HDR image pairs. Comparative experiments demonstrate the superiority of the proposed algorithm both qualitatively and quantitatively.
A Survey on Provable Data Possession in Cloud Computing Systems

— In Provable data possession scheme the customer outsources the data to the remote cloud service provider which is responsible for storing and preserving the data. Customers can rent the storage infrastructure from the cloud service providers to store their data by paying fees. Therefore the customers need to verify whether the server possesses the original data and should have strong guarantee that the service provider is storing all the data copies issued as per the agreement. In this process the issues such as data security, data dynamics, integrity protection and multi cloud storage have remained the most important task. To achieve this various PDP techniques and its extensions are discussed in this paper. This paper surveyed different types of PDP techniques and the focus is done on comparing the best method for achieving the efficient and secure PDP technique.