Reviewerly/exHarmony huggingface dataset:
Learning to Structure an Image With Few Colors

Color and structure are the two pillars that construct an image. Usually, the structure is well expressed through a rich spectrum of colors, allowing objects in an image to be recognized by neural networks. However, under extreme limitations of color space, the structure tends to vanish, and thus a neural network might fail to understand the image. Interested in exploring this interplay between color and structure, we study the scientific problem of identifying and preserving the most informative image structures while constraining the color space to just a few bits, such that the resulting image can be recognized with possibly high accuracy. To this end, we propose a color quantization network, ColorCNN, which learns to structure the images from the classification loss in an end-to-end manner. Given a color space size, ColorCNN quantizes colors in the original image by generating a color index map and an RGB color palette. Then, this color-quantized image is fed to a pre-trained task network to evaluate its performance. In our experiment, with only a 1-bit color space (i.e., two colors), the proposed network achieves 82.1% top-1 accuracy on the CIFAR10 dataset, outperforming traditional color quantization methods by a large margin. For applications, when encoded with PNG, the proposed color quantization shows superiority over other image compression methods in the extremely low bit-rate regime. The code is available at https://github.com/hou-yz/color_distillation.
Agile and Software Project Management Antipatterns: Clarifying the Partnership

We address the relationship between Agile and software project management (SPM), discussing the extent to which agile practices help to reduce or avoid different SPM antipatterns. Our results may be particularly useful to organizations that are becoming agile.
Image Mosaicing Based on Improved Optimal Seam-Cutting

To solve the problems of seams or ghosting caused by large exposure differences or moving objects in an image mosaic, this paper presents an image mosaicing method based on improved optimal seam-cutting.First, the feature points are extracted by the SIFT algorithm, and the false matching points are removed by the RANSAC algorithm.Next, a new energy function is constructed to perform optimal seam-cutting according to a dynamic programming algorithm that includes a colour and brightness data guarantee item in the YUV colour space and an eight-direction structural information guarantee item based on the Robinson operator.Finally, an improved weighted average method is proposed to fuse the images.The experimental results show that the method can make the transition of the image overlap area smoother and more natural, effectively eliminate the seam and ghosting and improving the image mosaic's quality.
GIFnets: Differentiable GIF Encoding Framework

Graphics Interchange Format (GIF) is a widely used image file format. Due to the limited number of palette colors, GIF encoding often introduces color banding artifacts. Traditionally, dithering is applied to reduce color banding, but introducing dotted-pattern artifacts. To reduce artifacts and provide a better and more efficient GIF encoding, we introduce a differentiable GIF encoding pipeline, which includes three novel neural networks: PaletteNet, DitherNet, and BandingNet. Each of these three networks provides an important functionality within the GIF encoding pipeline. PaletteNet predicts a near-optimal color palette given an input image. DitherNet manipulates the input image to reduce color banding artifacts and provides an alternative to traditional dithering. Finally, BandingNet is designed to detect color banding, and provides a new perceptual loss specifically for GIF images. As far as we know, this is the first fully differentiable GIF encoding pipeline based on deep neural networks and compatible with existing GIF decoders. User study shows that our algorithm is better than Floyd-Steinberg based GIF encoding.
Video Captioning With Temporal And Region Graph Convolution Network

Video captioning aims to generate a natural language description for a given video clip that includes not only spatial information but also temporal information. To better exploit such spatial-temporal information attached to videos, we propose a novel video captioning framework with Temporal Graph Network (TGN) and Region Graph Network (RGN). TGN mainly focuses on utilizing the sequential information of frames that most of existing methods ignore. RGN is designed to explore the relationships among salient objects. Different from previous work, we introduce Graph Convolution Network (GCN) to encode frames with their sequential information and build a region graph for utilizing object information. We also particularly adopt a stack GRU decoder with a coarse-to-fine structure for caption generation. Very promising experimental results on two benchmark datasets (MSVD and MSR-VTT) show the effectiveness of our model.
Determining the importance of sentence position for automatic text summarization

The methods of Automatic Extractive Summarization (AES) uses the features of the sentences of the original text to extract the most important information that will be considered in summary. It is known that the first sentences of the text are more relevant than the rest of the text (this heuristic is called baseline), so the position of the sentence (in reverse order) is used to determine its relevance, which means that the last sentences have practically no possibility of being selected. In this paper, we present a way to soften the importance of sentences according to the position. The comprehensive tests were done on one of the best AES methods using the bag of words and n-grams models with the with DUC02 and DUC01 data sets to determine the importance of sentences.
A CNN-RBPNN Model With Feature Knowledge Embedding and its Application to Time-Varying Signal Classification

A novel technique, combining the feature extraction mechanisms of a convolutional neural network (CNN) with the classification method of a radial basis probability neural network (RBPNN), is proposed for small sample set modeling and feature knowledge embedding in multi-channel time-varying signal classification. This CNN-RBPNN consists of a signal input layer, signal feature parallel extraction and integration units, and an RBPNN classifier. Each channel signal in a feature extraction unit corresponds to a 1D CNN. The extracted features are represented as feature vectors, and these vectors constitute a comprehensive feature matrix. The RBPNN classifier was designed using signal feature embedding mechanism based on radial basis kernels and the property of combining pattern subclasses into pattern classes to form complex class boundaries. A dynamic clustering algorithm was used to divide each pattern class sample into several subclasses. Typical signal samples in each pattern subclass were designated as kernel centers, in order to achieve signal categories features embedding. This process was also used to determine the number of nodes in the RBPN layer. The RBPN layer outputs were selectively summed in the pattern layer according to kernel center category, which can generate irregular class boundaries, reducing the overlap among different pattern class boundary. The proposed CNN-RBPNN replaces the full-connection layer and classifier unit of conventional CNN with RBPNN, which can extract and represent signals distribution features and structural properties, implement structural and data constraints. This can reduce the structural risks of small sample set modeling. In this study, the properties of CNN-RBPNN are analyzed and an integrated learning algorithm is proposed. An experiment was conducted using 12-lead ECG signals in a seven-classification in the case of small sample set. Results demonstrated that, the correct recognition rate is 5.7% higher than other methods in the experiment, the performance evaluation index also showed significant improvement.
A High-performance Inference Accelerator Exploiting Patterned Sparsity in CNNs

Convolutional neural networks (CNNs) have emerged as the critical technology for deep learning with significantly growing computation and memory demands. Model compression has been widely acknowledged as an effective way to achieve acceleration on CNNs. However, most proposed architectures of FPGA are inefficient for compressed models that contain a large amount of zero operations. In this work, we propose a sparse CNNs inference accelerator on FPGA utilizing uniform sparsity introduced by pattern pruning to achieve high energy efficiency. Our architecture maintains the sparse weights in a compressed format to reduce the storage demands and displays a flexible kernel-stationary dataflow to enable the extensive data reusing. In addition, we design flexible computing arrays which can be dynamically reconfigured to balance workload with low overheads. Specially, the on-chip memory applies a novel data buffering structure with slightly rearranged sequences to address the challenge of access conflict. The experiments show that our accelerator can achieve $316 .4 {GOP/s }\sim 343 .5 {GOP/s }$ for VGG16 and ResNet-50.
Video Snapshot: Single Image Motion Expansion via Invertible Motion Embedding

Unlike images, finding the desired video content in a large pool of videos is not easy due to the time cost of loading and watching. Most video streaming and sharing services provide the video preview function for a better browsing experience. In this paper, we aim to generate a video preview from a single image. To this end, we propose two cascaded networks, the motion embedding network and the motion expansion network. The motion embedding network aims to embed the spatio-temporal information into an embedded image, called video snapshot. On the other end, the motion expansion network is proposed to invert the video back from the input video snapshot. To hold the invertibility of motion embedding and expansion during training, we design four tailor-made losses and a motion attention module to make the network focus on the temporal information. In order to enhance the viewing experience, our expansion network involves an interpolation module to produce a longer video preview with a smooth transition. Extensive experiments demonstrate that our method can successfully embed the spatio-temporal information of a video into one "live" image, which can be converted back to a video preview. Quantitative and qualitative evaluations are conducted on a large number of videos to prove the effectiveness of our proposed method. In particular, statistics of PSNR and SSIM on a large number of videos show the proposed method is general, and it can generate a high-quality video from a single image.
Learning To See Faces In The Dark

Recovering details from dark images has received increasing attention due to its potential in applications such as video surveillance. We propose the first approach to detect and enhance human faces in extreme low-light images. Our method consists of two stages: a novel Face Location Network (FLNet) to locate the face, followed by a Face Enhancement Network (FE-Net) that uses concatenated sub-modules to progressively recover the face from coarse to fine grained details. Specifically, our enhancement modules exploit the semantic priors of facial landmarks to facilitate face recovery. Extensive experiments show our method is quantitatively and qualitatively superior to the state-of-the-art in terms of enhancement quality and face recognition. We have also collected a real-world dataset to support relevant research. All code and data will be shared for reproducing our experiments.
MAC Protocol Identification Using Convolutional Neural Networks

Making network nodes aware of the spectrum parameters can help to improve the spectrum utilization and network efficiency. To achieve such goals, machine learning (ML) and deep learning (DL) have been utilized to identify spectrum parameters, such as modulation formats, power levels, medium access control (MAC) protocols, etc. This paper explores MAC protocol identification using ML and DL in additive white Gaussian noise (AWGN) and Rayleigh fading environments. We transform the received signals into spectrogram and utilize convolutional neural networks (CNN) to recognize the MAC protocols. Experimentation results demonstrate the effectiveness in MAC protocol identification using ML and DL algorithms.
AF-DBSCAN: An unsupervised Automatic Fuzzy Clustering method based on DBSCAN approach

Automatic clustering problems play an important role to ameliorate the goodness of the data set's partitioning. Actually, the requirement to detect the suitable clustering solution without need for user-given parameters still remain challenging in unsupervised learning. This paper proposes an efficient and effective clustering method, named AF-DBSCAN (Automatic Fuzzy DBSCAN) based on the fuzzy clustering method FN-DBSCAN (Fuzzy Neighborhood Density-Based Spatial Clustering of Applications with Noise). The main idea of the proposed method is to cover the limitations of FN-DBSCAN by exploiting the benefits of k-neighbors plot, in purpose to determine the input parameter values. In fact, AF-DBSCAN avoids the manual intervention of non-experimental users in estimating the input parameters, the minimal threshold of neighborhood membership degree ϵ1 and the minimal neighborhood set cardinality ϵ2, which are hard to guess, and so permits to determine them more reasonably. In such way, the whole clustering process can be fully automated. Simulation experiments, carried out on a real medical data set, highlighted the AF-DBSCAN's effectiveness even for high-dimensions data sets, and showed that the proposed method outperformed the classical method since it provides a better clustering accuracy.
Motion Prediction of Virtual Patterns, Human Hand Motions, and a simplified Hand Manipulation Task with Hierarchical Temporal Memory

In this paper we utilize Numenta's Hierarchical Temporal Memory implementation NuPIC for online visual motion pattern prediction and test its performance on virtual animations as well as real world human motion data. For evaluation we run a series of progressively more complex experiments testing specific capabilities: Prediction of fixed-time noise-free motion animations, prediction of protocol-directed tasks with real-world camera captured human motion data, and lastly prediction of repetitive tasks performed without a strict protocol. Results show that the presented setup is able to predict time sequenced images as well as highly variable human motions increasingly well over several iterations. Limits are faced for non sequential variable hand motion execution: Here, predictions are made but do not improve in quality over time. The network runs online in real time and can be transferred to different tasks without expert knowledge. These characteristics qualify the setup for human robot interaction scenarios without the need for verified prediction accuracy.
Evaluation of Iterative Pagerank Algorithm for Web Page Ranking

The success behind Google Search Engine is PageRank algorithm. Many pagerank algorithms exist in the literature for ranking web pages. However, determining the optimum pagerank algorithm is an optimization problem. In this paper, we evaluated three Iterative Page Ranking methods and compare it to Power method of original Pagerank to determine the most optimum pagerank algorithm. Extensive experiment was conducted involving the four algorithms: Jacobi, Power, Gauss Seidel and induced dimension reduction (IDR). The performance metrics used to determine the efficiency and effectiveness of the algorithms includes convergence, number of iterations and time elapses. The result of the experiments indicated Jacobi and IDR method outperforms Power in the number of iterations and convergence faster than Gauss Seidel. Gauss Seidel has the shortest time elapsed followed by Jacobi, Power and IDR.
Verifiable and Private Oblivious Polynomial Evaluation

It is a challenging problem to delegate the computation of a polynomial on encrypted data to a server in an oblivious and verifiable way. In this paper, we formally define Verifiable and Private Oblivious Polynomial Evaluation (VPOPE) scheme. We design a scheme called Verifiable $${\textsf {IND}\hbox {-}\textsf {CFA}} $$ Paillier based Private Oblivious Polynomial Evaluation (VIP-POPE). Using security properties of Private Polynomial Evaluation (PPE) schemes and Oblivious Polynomial Evaluation (OPE) schemes, we prove that our scheme is proof unforgeability, indistinguishability against chosen function attack, and client privacy-secure under the Decisional Composite Residuosity assumption in the random oracle model.
Interactive-Imitation-Based Distributed Coordination Scheme for Smart Manufacturing

Concordant operations among automatic industrial devices (i.e., agents) play a significant role in achieving efficient smart manufacturing. Existing multiagent cooperation methods focus on centralized training with decentralized execution, which is unsuitable for the edge-based distributed industrial scenarios. To harmonize distributed devices' actions and enhance the production efficiency, in this article, we propose an Interactive-Imitation-based Distributed Coordination (IIDC) algorithm. Specifically, we leverage the generative adversarial imitation learning (GAIL) model to direct one agent's actions by following external professional demonstrations. We also adopt the self-imitation learning (SIL) model to direct one agent's potential actions by following its own previous good experiences. As the imperfect demonstrations in the GAIL-based interagent imitation process may degrade the imitation accuracy, we present a confidence-based matching method to reduce the gap between the professional and imitative behaviors. Furthermore, during the SIL-based intra-agent imitation process, the rewardless explorations also lead to nonoptimal imitation policy. We then present a Stein variational policy gradient based self-imitation method to learn an expected optimal policy. We validate the IIDC algorithm's effectiveness via the sequential assembly task. Evaluation results demonstrate that the IIDC algorithm can enhance the production efficiency evidently.
A Real-Time Computer Network Trend Analysis Algorithm Based on Dynamic Data Stream in the Context of Big Data

The extraction of effective trends can provide early warning, status assessment and decision support for monitoring objects. Traditional curve trend analysis algorithms include sliding window (SW) algorithm and extrapolation online data segmentation (OSO) algorithm. Compared with conventional least squares method, the overall least-squares method has a higher accuracy of straight line fitting. In addition, there is no limit to the maximum length of the sliding window for the SW algorithm. When the threshold of the detection point is relatively large, the length of the window may be long. The OSD algorithm defines the minimum sliding window length, so that the mutation point within the minimum sliding window cannot be detected. Aiming at the shortcomings of the SW algorithm and the OSD algorithm, a new data stream trend analysis method is proposed. This method adopts the overall least squares method to fit the data stream segmentally to improve the precision of the trend analysis. In addition, it also proposes a variable sliding window. The algorithm solves the fixed window problem of the SW algorithm and the OSD algorithm to achieve a reasonable segmentation of the data stream.
Optimal GPS Integrity-Constrained Path Planning for Ground Vehicles

Path planning for a ground vehicle in an urban environment is considered. The vehicle is equipped with a GPS receiver and a road map. The vehicle desires to take the shortest path to reach a target destination, while guaranteeing that integrity monitoring-based measures are satisfied along its traversed path. A path planning algorithm is proposed that yields the optimal path to follow as well as suboptimal feasible paths. The integrity monitoring-based measure considered in this paper is the horizontal protection level (HPL), which refers to the statistical bound around the vehicle that guarantees the probability of the absolute position error exceeding a desired threshold is not larger than the integrity risk. Experimental results are presented showing that choosing the optimal path from the proposed algorithm reduces the average and maximum HPL by 2 m and 20.2 m, respectively, compared to choosing the shortest-time path, while introducing a negligible additional path length.
Textual Visual Semantic Dataset for Text Spotting

Text Spotting in the wild consists of detecting and recognizing text appearing in images (e.g. signboards, traffic signals or brands in clothing or objects). This is a challenging problem due to the complexity of the context where texts appear (uneven backgrounds, shading, occlusions, perspective distortions, etc.). Only a few approaches try to exploit the relation between text and its surrounding environment to better recognize text in the scene. In this paper, we propose a visual context dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> for Text Spotting in the wild, where the publicly available dataset COCO-text [40] has been extended with information about the scene (such as objects and places appearing in the image) to enable researchers to include semantic relations between texts and scene in their Text Spotting systems, and to offer a common framework for such approaches. For each text in an image, we extract three kinds of context information: objects in the scene, image location label and a textual image description (caption). We use state-of-the-art out-of-the-box available tools to extract this additional information. Since this information has textual form, it can be used to leverage text similarity or semantic relation methods into Text Spotting systems, either as a post-processing or in an end-to-end training strategy.
Tell The Truth From The Front: Anti-Disguise Vehicle Re-Identification

Recent efforts have been increasingly made on vehicle reidentification (re-ID), which has huge contributions to intelligent transportation and criminal investigation. However, most existing methods heavily rely on the color and texture features of vehicles to discern their identities, which turn invalid under adversarial social security occasions where vehicles' color and style are always tampered or forged by crime suspects. In this paper, we propose a local feature preservation method to learn the structure-aware features from the position distribution of individual local regions within vehicle front window area, which appears more robust and discriminative upon disguise. We further develop a two-branch deep convolutional network framework to integrate the structure-aware features with vehicle model features for vehicle Re-ID. The experimental results on datasets VehicleID and Vehicle-1M show that our end-to-end framework achieves promising performance and outperforms the state-of-the-art methods proposed so far.