Reviewerly/exHarmony huggingface dataset:
Aircraft tracking in infrared imagery with adaptive learning and interference suppression

Airborne target tracking is a crucial part of infrared imaging guidance. In contrast to visual tracking tasks, the target in infrared imagery shows different visual patterns. Moreover, severe background clutter and frequent occlusion caused by infrared interference make it a challenging task. Recently, discriminative correlation filter (DCF)-based trackers have shown impressive performance. However, the features adopted in DCF-based trackers are either handcrafted or pre-trained from a different task, which do not closely intertwine with the domain-specific video. To settle this problem, it is proposed to make full use of online training to learn domain-specific features. By integrating the correlation filter layer into the convolutional neural networks, the feature domain and the response maps of the DCF can be optimized iteratively in the initial frame. Meanwhile, utilizing the measurement of the response maps' peak strength, further adjustments to the feature domain can be made to achieve a sharper peak and suppress the interference region during the tracking process. Evaluations are conducted to prove the validity of proposed aircraft-tracking algorithm.
Probabilistic observation model correction using non-Gaussian belief fusion

This paper presents a framework for state estimation which tolerates uncertainty in observation model parameters by (1) incorporating this uncertainty in state observation, and (2) correcting model parameters to improve future state observations. The first objective is met by an uncertainty propagation approach, while the second is achieved by gradient-descent optimization. The novel framework allows state estimates to be represented by non-Gaussian probability distribution functions. By correcting observation model parameters, estimation performance is enhanced since the accuracy of observations is increased. Monte Carlo simulation experiments validate the efficacy of the proposed approach in comparison with conventional estimation techniques, showing that as model parameters converge to ground-truth over time, state estimation correspondingly improves when compared to a static model estimate. Because observation models cannot be known with perfect accuracy and existing approaches do not address parametric uncertainties in non-Gaussian estimation, this work has both novelty and usefulness in most state estimation contexts.
A Survey on Blockchain Adaptability in IoT Environments

Blockchain is a considered to be a disruptive technology as like an Internet. The features of blockchain are giving more expectations in their applications. IoT is emerging day by day with millions of connecting devices. Even though IoT is evolving, the security of the internet of things is questionable with its architecture. IoT relay on a centralized server is vulnerable to the data breach. The trust in the centralized architecture is also questionable. We require the decentralized technology for IoT to secure their communication. The blockchain is a peer to peer, decentralized, distributed network that does not support the centralized architecture where the data are stored in the common ledger. The cryptographic hash is used in the blockchain which provides the feature of tamper-proof to the blockchain. Bitcoin is the first application that proves the security of blockchain in payment systems. Blockchain is providing transparency in the network with the shared ledger. To avoid the issues in the IoT environment the suggestion from every researcher is to go for blockchain. In this paper, we are going to survey the blockchain adaptability in various IoT environments for finding future research to improve the application of blockchain with IoT.
High-Throughput ECC with Integrated Chipkill Protection for Nonvolatile Memory Arrays

New coding schemes based on generalized concatenated codes are proposed for emerging nonvolative memory technologies. Key requirements are high code rate, low latency, high throughput and the ability to correct chipkill failures while sustaining high data reliability despite raw bit error rates up to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> . New concatenated codes based on Reed-Solomon codes have been designed for payload sizes of 512B, 1kB, and 2kB; they have high rates above 0.8 and a high data reliability with a decoder target BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-15</sup> . An FPGA-based implementation of the decoder validates the low latency and high throughput: for an operating clock frequency of 250MHz, the decoding latency is 236ns and a 9.3GB/s throughput is achieved.
Reaserch on Monolingual Font Recommendation based on Target Perception

Describing the target perceptual image is the most natural way to propose a request of font recommendation. For merchants, the target perceptual image of product is even more concerned than the actual ones. For professional designers, experience can be used to effectively convey the target perception through appropriate graphic design. And font selection is one of the important aspects. For ordinary users, it is relatively easy to judge or express the perceptual image of concrete products. However, it is difficult to judge the perceptual image of abstract fonts. To explore the solution of the problem, this paper proposes a monolingual font recommendation method based on the target perception. Specifically, the user assigns the target sense in the designated perceptual image space. The font library is divided into typical fonts and other fonts. Then the system will compare the target with the perceptual image space of the typical font library. And recommended fonts will be listed according to the perceptual similarity of target and the selected typical fonts. Furthermore, because different fonts of the same text in the same language often have similar appearance characteristics. On the basis of the typeface similarity of the typical fonts and others, further font recommendations can be made. In addition, a two-dimensional space word cloud is adopted to visualize the font similarity. In this way, users will be more intuitive when exploring and choosing recommended fonts.
Regularized Flexible Activation Function Combination for Deep Neural Networks

Activation in deep neural networks is fundamental to achieving non-linear mappings. Traditional studies mainly focus on finding fixed activations for a particular set of learning tasks or model architectures. The research on flexible activation is quite limited in both designing philosophy and application scenarios. In this study, three principles of choosing flexible activation components are proposed and a general combined form of flexible activation functions is implemented. Based on this, a novel family of flexible activation functions that can replace sigmoid or tanh in LSTM cells are implemented, as well as a new family by combining ReLU and ELUs. Also, two new regularisation terms based on assumptions as prior knowledge are introduced. It has been shown that LSTM models with proposed flexible activations P-Sig- Ramp provide significant improvements in time series forecasting, while the proposed P-E2-ReLU achieves better and more stable performance on lossy image compression tasks with convolutional auto-encoders. In addition, the proposed regularization terms improve the convergence, performance and stability of the models with flexible activation functions. The code for this paper is available at https://github.com/9NXJRDDRQK/Flexible_Activation.
Acquisition of High Bandwidth Signals by Sampling an Analog Chromatic Derivatives Filterbank

A method for acquisition of high bandwidth signals is presented as a typical example of a signal processing application of chromatic derivatives and approximations. Chromatic derivatives are numerically robust differential operators based on orthogonal polynomials. Theoretical formulations required for synthesising an analog chromatic derivative filterbank based on all-pass filters is presented. Such an analog filterbank with N +1 filters can be used for digital acquisition of high bandwidth signals by simultaneously sampling the outputs of the filterbank at a rate 2/(N + 1) of the Nyquist rate, resulting in samples of the signal and higher order chromatic derivatives up to order N, which in turn are used to reconstruct the original signal back. The value of the signal at any instant is obtained from only two nearest samples of the chromatic derivatives filterbank.
Utilizing Web Trackers for Sybil Defense

User tracking has become ubiquitous practice on the Web, allowing services to recommend behaviorally targeted content to users. In this article, we design Alibi, a system that utilizes such readily available personalized content, generated by recommendation engines in real time, as a means to tame Sybil attacks. In particular, by using ads and other tracker-generated recommendations as implicit user “certificates,” Alibi is capable of creating meta-profiles that allow for rapid and inexpensive validation of users’ uniqueness, thereby enabling an Internet-wide Sybil defense service. We demonstrate the feasibility of such a system, exploring the aggregate behavior of recommendation engines on the Web and demonstrating the richness of the meta-profile space defined by such inputs. We further explore the fundamental properties of such meta-profiles, i.e., their construction, uniqueness, persistence, and resilience to attacks. By conducting a user study, we show that the user meta-profiles are robust and show important scaling effects. We demonstrate that utilizing even a moderate number of popular Web sites empowers Alibi to tame large-scale Sybil attacks.
A deep learning model S-Darknet suitable for small target detection

Abstract In order to solve the problem of low accuracy of small target detection in target detection, a small target detection model S-Darknet is proposed. The algorithm is designed based on the Darknet-53 network. First, a new backbone network is proposed, which fully extracts the feature of small objects and adapts to multi-scale detection. Then, in order to enhance the feature information of the target after the fusion, a feature enhancement module is added before each upsampling. Finally, the proposed algorithm was verified on the VOC2007, VOC2012 data sets and the actual data sets of railway freight locks. Experimental results show that this method has high detection accuracy under the premise of ensuring real-time performance.
Online mixture-based clustering for high dimensional count data using Neerchal–Morel distribution

In this paper, we propose an online learning technique for unsupervised clustering based on a mixture of Neerchal–Morel distributions (NMD). Online learning is able to overcome the drawbacks of batch learning in such a way that the mixture’s parameters can be updated instantly for any new data instances. Then, we use a novel Minorization–Maximization framework to address the issue of high dimensional optimization and the mixture’s parameters estimation. Finally, by implementing a minimum message length model selection criterion, the weights of irrelevant mixture components are driven towards zero, which resolves the problem of knowing the number of clusters beforehand. To evaluate the performance of our proposed model, we have considered 3 challenging real-world applications that involve high-dimensional count vectors, namely, topic clustering, medical diagnosis and human action recognition. The results show that the mixture model based on the NMD performs better than other similar models.
REDUCTION TECHNIQUES FOR PROVING DECIDABILITY IN LOGICS AND THEIR MEET–COMBINATION

Abstract Satisfaction systems and reductions between them are presented as an appropriate context for analyzing the satisfiability and the validity problems. The notion of reduction is generalized in order to cope with the meet-combination of logics. Reductions between satisfaction systems induce reductions between the respective satisfiability problems and (under mild conditions) also between their validity problems. Sufficient conditions are provided for relating satisfiability problems to validity problems. Reflection results for decidability in the presence of reductions are established. The validity problem in the meet-combination is proved to be decidable whenever the validity problem for the components are decidable. Several examples are discussed, namely, involving modal and intuitionistic logics, as well as the meet-combination of $\textrm {K}$ modal logic and intuitionistic logic.
Pedestrian Detection Algorithm in SSD Infrared Image Based on Transfer Learning

Because of difficulty in feature extraction of infrared pedestrian images, the traditional methods of object detection usually make use of the labor to obtain pedestrian features, which suffer from the low-accuracy problem. With the rapid development of machine vision, deep learning has gradually become a research hotspot and a mainstream method for many pattern recognition and object detection problems. In this paper, aiming at the defects of deep convolutional neural network, such as the high cost on training time and slow convergence, a new algorithm of SSD infrared image pedestrian detection based on transfer learning is proposed, which adopts a transfer learning method and the Adam optimization algorithm to accelerate network convergence. For the experiments, we augmented the OUS thermal infrared pedestrian dataset and our solution enjoys a higher mAP of 94.8% on the test dataset. After experimental demonstration, our proposed method has the characteristics of fast convergence, high detection accuracy and short detection time.
Design and implementation of an efficient program interpreter for industrial robot

Abstract Robot program interpreter is an important tool for robot to realize real-time control and fast feedback. In order to simplify the process of robot programming and improve the interpretation efficiency of robot program, an easy-to-use and efficient industrial robot program interpreter is designed and implemented in this paper. The interpreter divides the robot program into two parts: variable definition and instruction call. The process of interpretation includes lexical analysis, syntactic analysis, semantic analysis and instruction interpretation modules. Using flex and bison tools to assist in the generation of lexical and syntactic analysis programs, this paper proposes child-sibling notation (CSN) to construct a syntax tree. In semantic analysis, the red-black tree structure of the map container is used to create a symbol table and record variable information. By the way of presetting type checking codes, errors in the program can be reported and handled. Finally, the interpreter traverses the syntax tree with depth-first algorithm, and calls the corresponding control function while interpreting the instruction sentence to execute the motion of robot. The experimental results show that the designed interpreter has high efficiency and stability in interpreting the robot program and meets the operational requirements of industrial robots.
Face matching system in multi-pose changing scene

Due to the emergence of deep learning, face recognition has made remarkable achievements. But in the traditional video surveillance scene, because the monitoring object is not controlled and the face posture changes frequently, the actual face image may be a negative face image, which greatly reduces the accuracy of face recognition. And the traditional monitoring system does not have an automatic warning function, which wastes a lot of human resources. This paper focuses on the realization of a real-time and efficient face matching system in multi-pose changing scenes. The system mainly uses the MTCNN algorithm for face detection and alignment and uses a generative confrontation network to make face positive to eliminate the influence of posture change. The feature extraction network uses lightweight MobileFaceNet as the backbone network for feature extraction, and the feature comparison is carried out by cosine similarity. Experiments show that the multi-pose face matching system can greatly improve the speed of feature extraction, and has good accuracy, real-time, and robustness.
BAFPN: An Optimization for YOLO

Object detection is essential in Computer Vision and is widely applied in all areas. This paper proposes a method called BAFPN. BAFPN is a new bidirectional Feature Pyramid Network that constructs accurate object detection networks based on YOLOv4 by implementing Adaptively Spatial Feature Fusion. Besides, Exponential Moving Average is used to improve the network performance. The developed network not only maintains high computing speed but also enhances the mAP by 4.3% when testing with the MS COCO dataset and when comparing it to the original YOLOv4. To further improve the performance, the trained model was pruned using the Batch Normalization layer's scaling factor, achieving an 18% reduction in size and improving object detection speed.
Cache Compression with Golomb-Rice Code and Quantization for Convolutional Neural Networks

Cache compression schemes reduce the cache miss rate by increasing the effective cache capacity and consequently, reduce memory access and power consumption. Therefore, cache compression is beneficial for applications with heavy memory traffic, including convolutional neural network (CNN). In this paper, a new cache compression of a floating-point number is proposed for CNNs. The exponent is compressed using the Golomb-Rice code, instead of the Huffman code, for an efficient hardware implementation. The compression syntax is carefully designed so that the size of compressed data is not very far from the entropy, which is the theoretical limit, by distinguishing two different types of data used in CNNs. On the other hand, since the mantissa of CNNs data can be hardly compressed by entropy coding, it is simply quantized for data reduction that may not degrade the CNN performance significantly thanks to the error robustness of CNNs. The quantization reduces 23 bits of a mantissa to 4 bits. The experimental results show that the miss rate of a 1 MB compressed cache with the proposed compression method applied is almost similar to that of an uncompressed 2 MB cache without any decrease of the CNN accuracy.
Human-centric Relation Segmentation: Dataset and Solution

Vision and language understanding techniques have achieved remarkable progress, but currently it is still difficult to well handle problems involving very fine-grained details. For example, when the robot is told to "bring me the book in the girl's left hand", most existing methods would fail if the girl holds one book respectively in her left and right hand. In this work, we introduce a new task named human-centric relation segmentation (HRS), as a fine-grained case of HOI-det. HRS aims to predict the relations between the human and surrounding entities and identify the relation-correlated human parts, which are represented as pixel-level masks. For the above exemplar case, our HRS task produces results in the form of relation triplets 〈girl [left hand], hold, book 〉 and exacts segmentation masks of the book, with which the robot can easily accomplish the grabbing task. Correspondingly, we collect a new Person In Context (PIC) dataset for this new task, which contains 17,122 high-resolution images and densely annotated entity segmentation and relations, including 141 object categories, 23 relation categories and 25 semantic human parts. We also propose a Simultaneous Matching and Segmentation (SMS) framework as a solution to the HRS task. It contains three parallel branches for entity segmentation, subject object matching and human parsing respectively. Specifically, the entity segmentation branch obtains entity masks by dynamically-generated conditional convolutions; the subject object matching branch detects the existence of any relations, links the corresponding subjects and objects by displacement estimation and classifies the interacted human parts; and the human parsing branch generates the pixelwise human part labels. Outputs of the three branches are fused to produce the final HRS results. Extensive experiments on PIC and V-COCO datasets show that the proposed SMS method outperforms baselines with the 36 FPS inference speed. Notably, SMS outperforms the best performing baseline m-KERN with only 17.6 percent time cost. The dataset and code will be released at http://picdataset.com/challenge/index/.
Self-supervised Dense Depth Prediction in Monocular Endoscope Video for 3D Liver Surface Reconstruction

Abstract In this paper, we propose a self-supervised monocular depth prediction method which combines traditional multi-view stereo method and fully convolutional network to predict the depth map in monocular endoscopic video and achieve 3D dense reconstruction of liver surface. We adopt the sparse data generated by COLMAP supervision signal as the training data, and integrate the attention model in the fully convolutional network to effectively extract the channel features to improve the accuracy of depth prediction. Taking into account the problem of insufficient supervision ability of sparse data, the projection transformation of two images within a certain range is carried out to make up for the missing supervision points. Experimental results show that this method has achieved good results in the depth prediction of monocular endoscopic video and has good applicability to the whole liver.
VSB2-Net: Visual-Semantic Bi-Branch Network for Zero-Shot Hashing

Zero-shot hashing aims at learning hashing model from seen classes and the obtained model is capable of generalizing to unseen classes for image retrieval. Inspired by zero-shot learning, existing zero-shot hashing methods usually transfer the supervised knowledge from seen to unseen classes, by embedding the hamming space to a shared semantic space. However, this makes instances difficult to distinguish due to limited hashing bit numbers, especially for semantically similar unseen classes. We propose a novel inductive zero-shot hashing framework, i.e., VSB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net, where both semantic space and visual feature space are embedded to the same hamming space instead. The reconstructive semantic relationships are established in the hamming space, preserving local similarity relationships and explicitly enlarging the discrepancy between semantic hamming vectors. A two-task architecture, comprising of classification module and visual feature reconstruction module, is employed to enhance the generalization and transfer abilities. Extensive evaluation results on several benchmark datasets demonstrate the superiority of our proposed method compared to several state-of-the-art baselines.
Application of Broad Learning System for Image Classification Based on Deep Features

Due to large amount of image data, the accuracy and real-time performance of classification are difficult problems in image classification. On the one hand, the broad learning system (BLS) has achieved good results in the timeliness of classification. On the other hand, deep learning feature extraction effect is good, but its structure is complex and the training time is long. Therefore, based on the above advantages of both methods, this paper proposed BLS based on deep features for image classification. Firstly, a feature extractor is constructed based on the ResNet101 to obtain the deep features of the classification image. Then, the feature nodes and enhancement nodes of BLS are constructed based on the deep features. Through the experiment, our method has two performance on benchmark datasets: high classification accuracy, good real-time.