Reviewerly/exHarmony huggingface dataset:
Human Crowd Detection for City Wide Surveillance

Surveillance systems are most commonly used for monitoring/surveillance of almost all public and private places. Real time behaviour of these systems add extra capacity to the surveillance activity. In such cases, any suspicious or uneven activity is detected by analysing the real time video stream of the cameras through these system. At maximum places, the surveillance activity is more manual, i.e. real time video footage of CCTV is monitored by security personals in some control room. It is difficult for any human being to monitor CCTV continuously and tirelessly without any rest. Therefore, robustness and effectiveness of such surveillance depends on technological strength of security personals, (i.e. human being), and hence pose the need of more man power for round the clock surveillance. This paper discusses a complete autonomous solution that will not only reduce the effort of individuals but will also increase the effectiveness of the law enforcement authority in patrolling the public places of a city. This system is proposed to use the existing CCTV infrastructure of the city. Various computer vision techniques are employed to detect the suspicious activity from real time video sequences. An intricate communication system is also designed to decrease the response time of system for the police authorities. This system is capable to generate an alarm signal if there founds any suspicious fellow or unusual activity in the restricted site. It also generates the complete description related to the events happenings in form of textual warning messages.
Comparison of gaze accuracy and precision in real-world and virtual reality

Abstract Virtual reality (VR) is popular across many fields and is increasingly used in sports as a training tool. The reason, therefore, is recently improved display technologies, more powerful computation capacity, and lower costs of head-mounted displays for VR. As in the real-world (R), visual effects are the most important stimulus provided by VR. However, it has not been demonstrated whether the gaze behavior would achieve the same level in VR as in R. This information will be important for the development of applications or software in VR. Therefore, several tasks were designed to analyze the gaze accuracy and gaze precision using eye-tracking devices in R and VR. 21 participants conducted three eye-movement tasks in sequence: gaze at static targets, tracking a moving target, and gaze at targets at different distances. To analyze the data, an averaged distance with root mean square was calculated between the coordinates of each target and the recorded gaze points for each task. In gaze accuracy, the results showed no significant differences between R and VR in gaze at static targets (1 m distance, p &gt; 0.05) and small significant differences at targets placed at different distances ( p &lt; 0.05), as well as large differences in tracking the moving target ( p &lt; 0.05). The precision in VR is significantly worse compared to R in all tasks with static gaze targets ( p &lt; 0.05). On the whole, this study gives a first insight into comparing foveal vision, especially gaze accuracy and precision between R and VR, and can, therefore, serve as a reference for the development of VR applications in the future.
On a Scalable Entropic Breaching of the Overfitting Barrier for Small Data Problems in Machine Learning

Overfitting and treatment of small data are among the most challenging problems in machine learning (ML), when a relatively small data statistics size [Formula: see text] is not enough to provide a robust ML fit for a relatively large data feature dimension [Formula: see text]. Deploying a massively parallel ML analysis of generic classification problems for different [Formula: see text] and [Formula: see text], we demonstrate the existence of statistically significant linear overfitting barriers for common ML methods. The results reveal that for a robust classification of bioinformatics-motivated generic problems with the long short-term memory deep learning classifier (LSTM), one needs in the best case a statistics [Formula: see text] that is at least 13.8 times larger than the feature dimension [Formula: see text]. We show that this overfitting barrier can be breached at a 10[Formula: see text] fraction of the computational cost by means of the entropy-optimal scalable probabilistic approximations algorithm (eSPA), performing a joint solution of the entropy-optimal Bayesian network inference and feature space segmentation problems. Application of eSPA to experimental single cell RNA sequencing data exhibits a 30-fold classification performance boost when compared to standard bioinformatics tools and a 7-fold boost when compared to the deep learning LSTM classifier.
Dense moment feature index and best match algorithms for video copy-move forgery detection

This paper proposes a video copy-move forgery detection method to effectively address inter/intra-frame forgeries both at the frame and pixel level. First, a unified moment framework is proposed to extract multi-dimensional dense moment features from the video effectively. Second, a novel feature representation method takes each feature sub-map index to represent its every dimensional feature and then concatenates to a 9-digit dense moment feature index. Third, an inter-frame best match algorithm is proposed to search the 9-digit dense moment feature index of each pixel to find its best matches. All the best matches construct the best match map. Fourth, an inter-frame post-processing algorithm identifies the inter-frame forgery video in the best match map firstly and then indicates the corresponding inter-frame forgery regions. Otherwise, the intra-frame post-processing algorithm re-searches the best match of every pixel in each independent frame and then indicates the intra-frame forgery regions. If the video does not belong to the intra-frame forgeries, the video is determined as a genuine one. The experimental results show that the proposed method is effective at addressing the forensics of the genuine/forgery video and locating the inter/intra-frame copy-move forgeries both at the frame and pixel level.
Tiny convolution, decision tree, and binary neuronal networks for robust and real time pupil outline estimation

In this work, we compare the use of convolution, binary, and decision tree layers in neural networks for the estimation of pupil landmarks. These landmarks are used for the computation of the pupil ellipse and have proven to be effective in previous research. The evaluated structure of the neural networks is the same for all layers and as small as possible to ensure a real-time application. The evaluations include the accuracy of the ellipse determination based on the Jaccard Index and the pupil center. Furthermore, the CPU runtime is considered to make statements about the real-time usability. The trained models are also optimized using pruning to improve the runtime. These optimized nets are also evaluated with respect to the Jaccard index and the accuracy of the pupil center estimation. Link to the framework and models.
Resource-Shared Crypto-Coprocessor of AES Enc/Dec With SHA-3

Cryptographic co-processors are integral to the modern System-on-Chips. Flexibility in such designs serves dual purpose, i.e., it enables acceleration of different essential cryptographic primitives (Encryption/Authentication/Pseudo Random Number Generation (PRNG)) and also results in design compaction via resource sharing. In this context, a novel resource-shared crypto-coprocessor, named AE$HA-3 is presented, which combines two National Institute of Standards and Technology (NIST) standardized algorithms, i.e., Advance Encryption Standard (AES) and Secure Hash Algorithm-3 (SHA-3). Due to algorithmic dissimilarities, so far no resource-shared implementation enabling AES key scheduling/ enc/dec and SHA-3 has been presented. AE$HA-3 exploits resource-sharing for area reduction, i.e., integration of Look-Up-Tables (I-Tables) for AES enc/dec; logical optimization of Six Input Equation (SixIE) for SHA-3; a Unified XOR Section to carry out both key whitening in AES and SHA-3 transformations. Furthermore, the AES key scheduling was performed using the same resource-shared hardware. The proposed AE$HA-3 on Xilinx Virtex FPGA family results in highest hardware efficiency in terms of Throughput per Slice (TPS), along with a 49.37% area consumption reduction, when compared against the smallest stand-alone implementations presented to date.
Automatic accent identification as an analytical tool for accent robust automatic speech recognition

We present a novel study of relationships between automatic accent identification (AID) and accent-robust automatic speech recognition (ASR), using i-vector based AID and deep neural network, hidden Markov Model (DNN-HMM) based ASR. A visualization of the AID i-vector space and a novel analysis of the accent content of the WSJCAM0 corpus are presented. Accents that occur at the periphery of AID space are referred to as "extreme". We demonstrate a negative correlation, with respect to accent, between AID and ASR accuracy, where extreme accents exhibit the highest AID and lowest ASR performance. These relationships between accents inform a set of ASR experiments in which a generic training set (WSJCAM0) is supplemented with a fixed amount of accented data from the ABI (Accents of the British Isles) corpus. The best performance across all accents, a 32% relative reduction in errors compared with the baseline ASR system, is obtained when the supplementary data comprises extreme accented speech, even though this accent accounts for just 14% of the test data. We conclude that i-vector based AID analysis provides a principled approach to the selection of training material for accent robust ASR. We speculate that this may generalize to other detection technologies and other types of variability, such as Speaker Identification (SI) and speaker variability.
Instance-based weighting filter for superparent one-dependence estimators

Bayesian network classifiers remain of great interest in recent years, among which semi-naive Bayesian classifiers which utilize superparent one-dependence estimators (SPODEs) have shown superior predictive power. Linear weighting schemes are effective and efficient ones for linearly combining SPODEs, whereas it is a challenging task for averaged one-dependence estimators (AODE) to find globally optimal and fixed weights for its SPODE members. The joint probability distribution of SPODE may not always fit different test instances to the same extent, thus a flexible rather than rigid weighting scheme would be a feasible solution for the final AODE to approximate the true joint probability distribution. Based on this promise, we propose a novel instance-based weighting filter, which can flexibly assign discriminative weights to each single SPODE for different test instances. Meanwhile, the weight considers not only the mutual dependence between the superparent and class variable, but also the conditional dependence between the superparent and non-superparent attributes. Experimental comparison on 30 publicly available datasets shows that SPODE with instance-based weighting filter outperforms state-of-the-art BNCs with and without weighting methods in terms of zero–one loss, bias and variance with minimal additional computation.
Optimal path planning for automated dimensional inspection of free-form surfaces

Structural dimensional inspection is vital for the process monitoring, quality control, and fault diagnosis in the mass production of auto bodies. Comparing with the non-contact measurement, the high-precision five-axis measuring machine with the touch-trigger probe is a preferred choice for data collection. It can assist manufacturers in making accurate inspection quickly. As the increase of free-form surfaces and diverse surface orientations in product design, existing inspection approaches cannot capture some new critical features in the curvature of products in an efficient way. Therefore, we need to develop new path planning methods for automated dimensional inspection of free-form surfaces. This paper proposes an optimal path planning system for automated programming of measuring point inspection by incorporating probe rotations and effective collision detection. Specifically, the methodological contributions include: (i) a dynamic searching volume-based algorithm is developed to detect potential collisions in the local path between measurement points; (ii) a local path generation method is proposed with the integration of the probe trajectory and the stylus rotation. Then, the inspection time matrix is proposed to quantify the measuring time of diverse local paths; (iii) an optimization approach of the global inspection path for all critical points on the product is developed to minimize the total inspection time. A case study has been conducted on an auto body to verify the performance of the proposed method. Results show that the collision-free path for the free-form auto body could be generated automatically with off-line programming, and the proposed method produces about 40% fewer dummy points and needs 32% less movement time in the auto body inspection process.
Special Session: The Recent Advance in Hardware Implementation of Post-Quantum Cryptography

The recent advancement in quantum technology has initiated a new round of cryptosystem innovation, i.e., the emergence of Post-Quantum Cryptography (PQC). This new class of cryptographic schemes is intended to be mathematically resistant against any known attacks using quantum computers, but, at the same time, be fully implementable using traditional semiconductor technology. The National Institutes of Standards and Technology (NIST) has already started the PQC standardization process, and the initial pool of 69 submissions has been reduced to 26 Round 2 candidates. Echoing the pace of the PQC "revolution," this paper gives a detailed and thorough introduction to recent advances in the hardware implementation of PQC schemes, including challenges, new implementation methods, and novel hardware architectures. Specifically, we have: (i) described the challenges and rewards of implementing PQC in hardware; (ii) presented the novel methodology for the design-space exploration of PQC implementations using high-level synthesis (HLS); (iii) introduced a new underexplored PQC scheme (binary Ring-Learning-with-Errors), as well as its novel hardware implementation for possible lightweight applications. The overall content delivered by this paper could serve multiple purposes: (i) provide useful references for the potential learners and the interested public; (ii) introduce new areas and directions for potential research to the VTS community; (iii) facilitate the PQC standardization process and the exploration of related new ways of implementing cryptography in existing and emerging applications.
Segmentation-free writer identification based on convolutional neural network

Handwriting recognition is one of the desired aspects of document understanding and analysis. It deals with the writing style of the document and learns the features which differentiate the writers. In this paper, a SEGmentation-free Writer Identification (SEG-WI) model is proposed based on a convolution neural network and a weakly supervised region selection mechanism. The model, SEG-WI, takes an unsegmented text document and produces the writer-ID with the region probability map. The probability vectors at each cell location in the input document constitute a region probability map. To achieve the best performance, top 10% to 50% cell regions are selected for decision making and a voting mechanism among the selected regions is used to identify the writer. The region selection, voting mechanism for decision making, and loss calculation are the main contributions in this work, which enables the proposed system as segmentation free. The proposed model is evaluated on different datasets such as IAM handwriting database (IAM), and computer vision lab (CVL) for English, Institut of communications technology/Ecole Nationale d'Ing nieurs de Tunis (IFN/ENIT) for Arabic, Kannada, and Devanagari for Indic, and outperforms compare with the state-of-the-art results. Moreover, a comparative analysis of the proposed model with and without region selection is performed to validate the effect of the region selection mechanism and it improves the performance of the model.
Deep Structure-Revealed Network for Texture Recognition

Texture recognition is a challenging visual task since various primitives along with their arrangements can be recognized from a same texture image when perceiving with different contexts. Some recent work building on CNNs exploits orderless aggregating to provide invariance to spatial arrangements. However, these methods ignore the inherent structural property of textures, which is a critical cue for distinguishing and describing texture images in the wild. To address this problem, we propose a novel Deep Structure-Revealed Network (DSR-Net) that leverages spatial dependency among the captured primitives as structural representation for texture recognition. Specifically, a primitive capturing module (PCM) is devised to generate multiple primitives from eight directional spatial contexts, in which deep features are firstly extracted under the constrains of direction map and then encoded based on the similarities of neighborhood. Next, these primitives are associated with a dependence learning module (DLM) to generate structural representation, in which a two-way collaborative relationship strategy is introduced to perceive the spatial dependencies among multiple primitives. At last, the structure-revealed texture representations are integrated with spatial ordered information to achieve real-world texture recognition. Evaluation on the five most challenging texture recognition datasets has demonstrated the superiority of the proposed model against state-of-the-art methods. The structure-revealed performances of DSR-Net are further verified on some extensive experiments, including fine-grained classification and semantic segmentation.
Semantic Example Guided Image-to-Image Translation

Many image-to-image (I2I) translation problems are in nature of high diversity that a single input may have various counterparts. The multi-modal network that can build a many-to-many mapping between two visual domains has been proposed in prior works. However, most of them are guided by sampled noises. Some others encode the reference image into a latent vector, which would eliminate the semantic information of the reference image. In this work, we aim to provide a solution to control the output based on references semantically. Given a reference image and an input in another domain, we first perform semantic matching between the two visual content and generate an auxiliary image, which explicitly encourages the semantic characteristic to be preserved. A deep network then is used for I2I translation and the final outputs are expected to be semantically similar to both the input and the reference. However, few paired data can satisfy that dual-similarity in a supervised fashion, and so we build up a self-supervised framework in the training stage. We improve the quality and diversity of the outputs by employing non-local blocks and a multi-task architecture. We assess the proposed method through extensive qualitative and quantitative evaluations and also present comparisons with several state-of-the-art models.
WaveletStereo: Learning Wavelet Coefficients of Disparity Map in Stereo Matching

Some stereo matching algorithms based on deep learning have been proposed and achieved state-of-the-art performances since some public large-scale datasets were put online. However, the disparity in smooth regions and detailed regions is still difficult to accurately estimate simultaneously. This paper proposes a novel stereo matching method called WaveletStereo, which learns the wavelet coefficients of the disparity rather than the disparity itself. The WaveletStereo consists of several sub-modules, where the low-frequency sub-module generates the low-frequency wavelet coefficients, which aims at learning global context information and well handling the low-frequency regions such as textureless surfaces, and the others focus on the details. In addition, a densely connected atrous spatial pyramid block is introduced for better learning the multi-scale image features. Experimental results show the effectiveness of the proposed method, which achieves state-of-the-art performance on the large-scale test dataset Scene Flow.
Soft Gazetteers for Low-Resource Named Entity Recognition

Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance.Although modern neural network models do not require such handcrafted features for strong performance, recent work (Wu et al., 2018) has demonstrated their utility for named entity recognition on English data.However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages.To address this problem, we propose a method of "soft gazetteers" that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking.Our experiments on four low-resource languages show an average improvement of 4 points in F1 score. 1
Severity-Aware Semantic Segmentation With Reinforced Wasserstein Training

Semantic segmentation is a class of methods to classify each pixel in an image into semantic classes, which is critical for autonomous vehicles and surgery systems. Cross-entropy (CE) loss-based deep neural networks (DNN) achieved great success w.r.t. the accuracy-based metrics, e.g., mean Intersection-over Union. However, the CE loss has a limitation in that it ignores varying degrees of severity of pair-wise misclassified results. For instance, classifying a car into the road is much more terrible than recognizing it as a bus. To sidestep this, in this work, we propose to incorporate the severity-aware inter-class correlation into our Wasserstein training framework by configuring its ground distance matrix. In addition, our method can adaptively learn the ground metric in a high-fidelity simulator, following a reinforcement alternative optimization scheme. We evaluate our method using the CARLA simulator with the Deeplab backbone, demonstraing that our method significantly improves the survival time in the CARLA simulator. In addition, our method can be readily applied to existing DNN architectures and algorithms while yielding superior performance. We report results from experiments carried out with the CamVid and Cityscapes datasets.
GAMIN: Generative Adversarial Multiple Imputation Network for Highly Missing Data

We propose a novel imputation method for highly missing data. Though most existing imputation methods focus on moderate missing rate, imputation for high missing rate over 80% is still important but challenging. As we expect that multiple imputation is indispensable for high missing rate, we propose a generative adversarial multiple imputation network (GAMIN) based on generative adversarial network (GAN) for multiple imputation. Compared with similar imputation methods adopting GAN, our method has three novel contributions: 1)We propose a novel imputation architecture which generates candidates of imputation. 2)We present a confidence prediction method to perform reliable multiple imputation. 3)We realize them with GAMIN and train it using novel loss functions based on the confidence. We synthesized highly missing datasets using MNIST and CelebA to perform various experiments. The results show that our method outperforms baseline methods at high missing rate from 80% to 95%.