Reviewerly/exHarmony huggingface dataset:
A modified contrastive loss method for face recognition

Contrastive Loss is frequently used as loss function in CNN for face recognition, but it can result in the overfitting and low sampling efficiency for the positive samples. In this paper, a Modified Contrastive Loss (MCL) is proposed to overcome the shortcomings of contrastive loss. MCL and ResNet are combined with a Joint Bayesian technique to develop a ResNet-Modified Contrastive Loss-Joint Bayesian (ResNet-MCL-JB) model. First, ResNet is used as the basic network structure, and several ResNets are trained to use the MCL. Then, the ResNet with the Joint Bayesian for metric learning is integrated. The state-of-the-art performance of ResNet-MCL-JB attests to its effect. For further improvement, a Progressive Soft Filter Pruning method (PSFP) is applied in the neural network. PSFP can effectively diminish the size of the network while maintaining high accuracy. This method gradually prunes the filters on each layer by the weight of each filter. We combine the MCL and PSFP together with ResNet, and thus, we achieve considerable much improvement in both accuracy and computational cost.Keywords: Modified contrastive loss; Face recognition; Joint bayesian
Multi-task ADAS system on FPGA

Advanced Driver-Assistance Systems (ADAS) can help drivers in the driving process and increase the driving safety by automatically detecting objects, doing basic classification, implementing safeguards, etc. ADAS integrate multiple subsystems including object detection, scene segmentation, lane detection, and so on. Most algorithms are now designed for one specific task, while such separate approaches will be inefficient in ADAS which consists of many modules. In this paper, we establish a multi-task learning framework for lane detection, semantic segmentation, 2D object detection, and orientation prediction on FPGA. The performance on FPGA is optimized by software and hardware co-design. The system deployed on Xilinx zu9 board achieves 55 FPS, which meets real-time processing requirement.
Step climbing method for crawler type rescue robot using reinforcement learning with Proximal Policy Optimization

It is a huge burden on an operator when he/she tele-operates a rescue robot traveling on a rough terrain. Therefore, the purpose of this study is to reduce this burden by controlling the robot autonomously. As a first step, we propose a step climbing method for a crawler type rescue robot by using reinforcement learning with Proximal Policy Optimization (PPO). The input data are the image of a camera on the robot and a posture image of the robot. We verified the effectiveness of the proposed method using a dynamics simulator.
Bitcoin futures: trade it or ban it?

This paper examines the impact of South Korea’s ban on Bitcoin futures on intraday spot volatility, liquidity and volatility–volume relationship. The results show that while reducing the permanent component of intraday spot volatility, the imposition of a ban on Bitcoin futures trading increases the transitory component. For intraday spot liquidity, different liquidity proxies indicate heterogeneous results. Moreover, we identify a positive and unidirectional effect of intraday spot volume on volatility. This effect appears to be stronger in the post-ban period. Overall, over the past few months, South Korea’s Bitcoin futures ban generally has had a significant impact on the intraday dynamics of the Bitcoin spot market.
Some new quantum MDS codes from generalized Reed–Solomon codes

Quantum maximum distance separable (MDS) codes form a significant class of quantum codes. In this paper, by using Hermitian self-orthogonal generalized Reed–Solomon codes, we construct two new classes of q-ary quantum MDS codes, which have minimum distance greater than q2. Most of these quantum MDS codes are new in the sense that their parameters are not covered by the codes available in the literature.
A Memory-Optimized and Energy-Efficient CNN Acceleration Architecture Based on FPGA

The development of Convolutional Neural Network (CNN) contributes to breakthroughs made in the field of artificial intelligence. Compared with traditional algorithms, CNN has merits in speed and accuracy concerning detection, identification and classification. GPU is of great popularity for implementing CNN on account of its computational capacity. However, its high power consumption limits the application in the embedded field. Recently, researchers accelerate CNN utilizing Field Programmable Gate Arrays (FPGA) which is demonstrated more energy-efficient than GPU, and is suitable for the applications of embedded systems. Although FPGA has the superiority in low power consumption, powerful parallel computing and high flexibility, bandwidth and memory accessing become the bottleneck of CNN accelerator design. In this paper, a novel memory-optimized and energy-efficient CNN accelerating architecture is proposed. The paper analyzes the on-chip memory and off-chip memory resources of FPGA, and proposes a memory optimization solution using specially mixed operation of FIFO and ping-pong. To ensure accuracy, a folat-16 CNN model is used to test the framework, and evaluated on Xilinx ZCU102 platform which has both Arm-Core and FPGA on one chip. After testing the VGG-16 Net and a FCN Net with 500MB weights, the architecture is 10 times faster than CPU, and has better energy-efficiency than GPU does.
The wisdom of the crowd: A case of post- to ante-mortem face matching by police super-recognisers

This case report describes novel methodology used to identify a 43-year-old post-mortem photo of a drowned male recovered from a London river in the 1970s. Embedded in an array of foils, police super-recognisers (n=25) possessing superior simultaneous face matching ability, and police controls (n=139) provided confidence ratings as to the similarity of the post-mortem photo to an ante-mortem photo of a man who went missing at about the same time. Indicative of a match, compared to controls, super-recognisers provided higher ratings to the target than the foils. Effects were enhanced when drawing on the combined wisdom of super-recogniser crowds, but not control crowds. These findings supported additional case evidence allowing the coroner to rule that the deceased male and missing male were likely one and the same person. A description of how similar super-recogniser wisdom of the crowd procedures could be applied to other visual image identification cases when no other method is feasible is provided.
A Robust Vehicle Detection Approach based on Faster R-CNN Algorithm

Video-based Intelligent Transportation Systems (V-ITS) can play an important role in developing a wide range of applications in transportation field. These systems use the outputs of video cameras to extract desired information by the means of various Artificial Intelligence techniques. Considering impressive advantages of applying Deep Neural Networks (DNNs) in different fields of object detection and classification, these methods have attracted a huge attention among researchers in recent years. In this regard, Convolutional Neural Networks (CNNs) as an important class of DNNs have been used for visual imagery goals in a wide variety of applications such as image recognition and video analysis, and even made their way through ITS applications. One of the most important steps of V-ITS applications is the process of vehicle detection in video frames and the high accuracy rate in this step can provide applicable data for other complementary modules such as vehicle tracking and classification. In this paper, a robust method to detect vehicles in video frames based on CNNs is proposed which provides an almost real-time performance and impressive accuracy. To overcome the challenges of building a precise vehicle detection model from still images, we have transformed the main architecture of a pre-trained ResNet-50 residual network to Faster Region-based Convolutional Neural Network (Faster R-CNN). Experimental results show that the system's sensitivity factor is 0.985 and it needs an average of 74 milliseconds to detect vehicles in real condition data. Consequently, our method can provide acceptable results in vehicle detection in terms of accuracy and execution time.
Operator coherence dynamics in Grover's quantum search algorithm

Coherence is one of the most basic concepts and resources in quantum information. To clear the role coherence plays on the essential operator level in Grover's search algorithm, here we discuss the coherence dynamics of the state after each basic operator is applyied. As it is known, Grover's search algorithm repeats the application of Grover operator $G$, which can be decomposed into $G={H}^{\ensuremath{\bigotimes}n}P{H}^{\ensuremath{\bigotimes}n}O$, where $H$ is Hadamard operator, $P$ is the condition phase-shift operator, and $O$ is the oracle operator. First, we show that $O$ and $P$ are incoherent operators while ${H}^{\ensuremath{\bigotimes}n}$ is coherent. Second, we prove that the amount of the operator coherence of the first ${H}^{\ensuremath{\bigotimes}n}$ and the operator coherence produced or depleted by ${H}^{\ensuremath{\bigotimes}n}$ depends not only on the size of the database and the success probability, but also on target states. Moreover, the amount of operator coherence is larger when the superposition state of targets is entangled rather than product. Third, we show that the two ${H}^{\ensuremath{\bigotimes}n}$ have different effects on coherence that one produces coherence and the other depletes coherence, and the depletion plays a major role. Therefore, the coherence is vibrating during the search process and the overall effect is that coherence is in depletion.
National Identity Predictive Models for the Real Time Prediction of European School’s Students: Preliminary Results

An experimental study is conducted to predict the real time national identity (national or immigrants) of the students based on their responses in information and communication technology (ICT) survey held in European schools. All the experiments are conducted in SPSS IBM modeler version 18.1. The target datasets were collected by ESSIE (SMART 2010/0039) during the big survey at levels 3 of schools ISCED (International Standard Classification of Education) in the year 2011. The auto classifier node selected 5 supervised machine learning classifiers filtering out of 8 classifiers. To predict the national identity of students in academic school, the highest accuracy 96.6% is achieved by decision tree C5 with filtering of 46 features out of total 156 and to predict the national identity of students in vocational school, the uppermost accuracy 94.3% is achieved by Tree-AS with reduction of total 41 features out of total 172. Hence, to predict the national identity, self-reduction and auto classifier stabilized only 46 features for C5 Tree and 41 features for Tree-AS. The findings of paper also signify that C5 classifier outperformed the Logistic Regression (LR) and Tree-AS after feature reduction at academic schools. Further, Tree-AS also outperformed the Bayesian network (BN), linear support vector machine (LSVM) and LR after feature reduction at vocational schools.
On Choosing Mixture Components via Non-Local Priors

Summary Choosing the number of mixture components remains an elusive challenge. Model selection criteria can be either overly liberal or conservative and return poorly separated components of limited practical use. We formalize non-local priors (NLPs) for mixtures and show how they lead to well-separated components with non-negligible weight, interpretable as distinct subpopulations. We also propose an estimator for posterior model probabilities under local priors and NLPs, showing that Bayes factors are ratios of posterior-to-prior empty cluster probabilities. The estimator is widely applicable and helps to set thresholds to drop unoccupied components in overfitted mixtures. We suggest default prior parameters based on multimodality for normal–T-mixtures and minimal informativeness for categorical outcomes. We characterize theoretically the NLP-induced sparsity, derive tractable expressions and algorithms. We fully develop normal, binomial and product binomial mixtures but the theory, computation and principles hold more generally. We observed a serious lack of sensitivity of the Bayesian information criterion, insufficient parsimony of the Akaike information criterion and a local prior, and a mixed behaviour of the singular Bayesian information criterion. We also considered overfitted mixtures; their performance was competitive but depended on tuning parameters. Under our default prior elicitation NLPs offered a good compromise between sparsity and power to detect meaningfully separated components.
Completeness for Game Logic

Game logic was introduced by Rohit Parikh in the 1980s as a generalisation of propositional dynamic logic (PDL) for reasoning about outcomes that players can force in determined 2-player games. Semantically, the generalisation from programs to games is mirrored by moving from Kripke models to monotone neighbourhood models. Parikh proposed a natural PDL-style Hilbert system which was easily proved to be sound, but its completeness has thus far remained an open problem. In this paper, we introduce a cut-free sequent calculus for game logic, and two cut-free sequent calculi that manipulate annotated formulas, one for game logic and one for the monotone μ -calculus, the variant of the polymodal μ -calculus where the semantics is given by monotone neighbourhood models instead of Kripke structures. We show these systems are sound and complete, and that completeness of Parikh's axiomatization follows. Our approach builds on recent ideas and results by Afshari & Leigh (LICS 2017) in that we obtain completeness via a sequence of proof transformations between the systems. A crucial ingredient is a validity-preserving translation from game logic to the monotone μ -calculus.
Self-Attention Guided Deep Features for Action Recognition

Skeleton based human action recognition is an important task in computer vision. However, it is very challenging due to the complex spatio-temporal variations of skeleton joints. In this work, we propose an end-to-end trainable network consisting of a Deep Convolutional Model (DCM) and a Self-Attention Model (SAM) for human action recognition from skeleton data. Specifically, skeleton sequences are encoded into color images and fed into DCM to extract deep features. In the SAM, handcrafted features representing the motion degree of joints are extracted and the attention weights are learned by a simple yet effective linear mapping. The effectiveness of proposed method has been verified on NTU RGB+D, SYSU-3D and UTD-MHAD datasets and achieved state-of-the-art results.
Comparative Study of Track-to-Track Fusion Methods for Cooperative Tracking with Bearings-only Measurements

Using a network of spatially distributed sensors to track a moving object can be a challenging task. In applications with limited communication between sensor nodes and packet loss, it may be impossible to process measurements from these distributed sensor nodes in a central unit. Therefore, it is often necessary to use only the locally available measurements at the sensor nodes and afterwards merge all local tracks into one consistent result. In this paper, several different track-to-track fusion algorithms are compared to cooperatively track a moving object using only bearing measurements. It is shown that the Sample-based Fusion that uses a set of deterministic samples to reconstruct the cross-covariances is a suitable fusion algorithm for the considered setup. Furthermore, it provides the means to efficiently keep track of the cross-covariances between sensor nodes and therefore outperforms conservative methods. The proposed approach is also tested in a real-world indoor localization setup using bearings-only acoustic measurements from three microphone arrays.
Deep Spectral Kernel Learning

Recently, spectral kernels have attracted wide attention in complex dynamic environments. These advanced kernels mainly focus on breaking through the crucial limitation on locality, that is, the stationarity and the monotonicity. But actually, owing to the inefficiency of shallow models in computational elements, they are more likely unable to accurately reveal dynamic and potential variations. In this paper, we propose a novel deep spectral kernel network (DSKN) to naturally integrate non-stationary and non-monotonic spectral kernels into elegant deep architectures in an interpretable way, which can be further generalized to cover most kernels. Concretely, we firstly deal with the general form of spectral kernels by the inverse Fourier transform. Secondly, DSKN is constructed by embedding the preeminent spectral kernels into each layer to boost the efficiency in computational elements, which can effectively reveal the dynamic input-dependent characteristics and potential long-range correlations by compactly representing complex advanced concepts. Thirdly, detailed analyses of DSKN are presented. Owing to its universality, we propose a unified spectral transform technique to flexibly extend and reasonably initialize domain-related DSKN. Furthermore, the representer theorem of DSKN is given. Systematical experiments demonstrate the superiority of DSKN compared to state-of-the-art relevant algorithms on varieties of standard real-world tasks.
Fault tolerance for a scientific workflow system in a Cloud computing environment

High computation power is required to execute complex scientific workflows. Cloud computing resources are used viably to perform such complex workflows. Task clustering has demonstrated to be an efficient technique to decrease system overhead and to enhance the fine computational granularity tasks of a scientific workflow executed on distributed resources. However, earlier clustering methods ignore the effect of failures on the system, despite their significant impact on large-scale distributed resources, such as Clouds. In this paper, we present a new fault-tolerant task clustering method called FT-HCC that is designed by including the workflow execution time (makespan) and execution cost constraints, which are used to increase workflow performance. The proposed method is implemented and evaluated in a simulation-based approach, using a real-time workflow execution to analyze performance improvement. The results consolidate that the proposed strategies and techniques work efficiently in terms of fault tolerance and improve both workflow makespan and execution cost when compared to existing approaches.
A Reference Architecture for Smart and Software-Defined Buildings

The vision encompassing Smart and Software-defined Buildings (SSDB) is becoming more popular and its implementation is now more accessible due to the widespread adoption of the Internet of Things (IoT) infrastructure. Some of the most important applications sustaining this vision are energy management, environmental comfort, safety and surveillance. This paper surveys IoT and SSB technologies and their cooperation towards the realization of smart spaces. We propose a four-layer reference architecture and we organize related concepts around it. This conceptual frame is useful to identify the current literature on the topic and to connect the dots into a coherent vision of the future of residential and commercial buildings.
Informative Summarization of Numeric Data

We consider the following data summarization problem. We are given a dataset including ordinal or numeric explanatory attributes and an outcome attribute. We want to produce a summary of how the explanatory attributes affect the outcome attribute. The summary must be human-interpretable, concise, and informative in the sense that it can accurately approximate the distribution of the outcome attribute. We propose a solution that addresses the fundamental challenge of this problem--handling large numeric domains--and we experimentally show the effectiveness and efficiency of our approach on real datasets.
Distortion correction of a microscopy lens system for deformation measurements based on speckle pattern and grating

Lens distortion will cause errors in the measurement of deformation by digital image correlation when using a microscope at high magnification for image recording. To improve the measurement accuracy of 2D digital image correlation, lens distortion was detected by rigid-body translation tests for a micro speckle pattern using DIC calculation and by phase analysis of a cross-grating using a Fourier transform. Based on the first-order radial distortion model, the coefficients for the lens distortion were determined using the least-squares method. Then, the displacement fields without lens distortion were obtained, and accurate measurements were achieved even for images that were deformed by lens distortion. The results demonstrate that the average standard deviation of the displacement fields decreases by 60% for lens distortion correction by rigid-body translation tests of a micro speckle pattern using DIC calculation, and they decreased by 97% for phase analysis of a cross-grating using a Fourier transform. The experimental results verify that digital image correlation is a powerful tool for micro-scale deformation measurement using a microscope at high magnification as long as one correct for lens distortion.
Discriminative Autoencoding Framework for Simple and Efficient Anomaly Detection

In this paper, a discriminative autoencoding framework is proposed for semi-supervised anomaly detection using reconstruction errors.The framework only consists of a generator and a discriminative encoder, and the output of the latter is a vector.In the training process, the framework is trained as a generative adversarial network based on quadratic potential divergence.An extra loss added in the objective function enforces the discriminative encoder to use the mean value of the output vector for discrimination, which also empowers it with encoding ability.In the testing process, the trained framework can be used as an autoencoder to reconstruct test samples, where the trained discriminative encoder works as an encoder, and samples with reconstruction errors above a predefined threshold are determined as anomalies.The properties of quadratic potential divergence ensure a simple training process with comparable performance, meanwhile the discriminative encoder with two functions makes full use of training resources and reduces required network structures.Comparisons on benchmark datasets also show the efficiency and superiorities of the proposed methods.
Parallel Computing, Graphics Processing Unit (GPU) and New Hardware for Deep Learning in Computational Intelligence Research

Graphics processing unit (GPU) is an electronic circuit which manipulates and modifies the memory for better image output. Deep learning involves huge amounts of matrix multiplications and other operations which can be massively parallelized and thus sped up on GPUs. A single GPU might have thousands of cores while a CPU usually has no more than 12 cores. GPU's practical applicability is affected by two issues: long training time and limited GPU memory, which is greatly influenced as the neural network size grows. In order to overcome these issues, this chapter presents various technologies in distributed parallel processing which improve the training time and optimize the memory, and hardware engine architectures will be explored for data size reduction. The GPUs generally used for deep learning are limited in memory size compared to CPUs, so even the latest Tesla GPU has only 16 GB of memory. Therefore, GPU memory cannot be increased to that extent easily, so networks must be designed to fit within the available memory. This could be a factor limiting progress, overcoming which would be highly beneficiary to the computational intelligence area.