Reviewerly/exHarmony huggingface dataset:
AMLN: Adversarial-Based Mutual Learning Network for Online Knowledge Distillation

Online knowledge distillation has attracted increasing interest recently, which jointly learns teacher and student models or an ensemble of student models simultaneously and collaboratively. On the other hand, existing works focus more on outcome-driven learning according to knowledge like classification probabilities whereas the distilling processes which capture rich and useful intermediate features and information are largely neglected. In this work, we propose an innovative adversarial-based mutual learning network (AMLN) that introduces process-driven learning beyond outcome-driven learning for augmented online knowledge distillation. A block-wise training module is designed which guides the information flow and mutual learning among peer networks adversarially throughout different learning stages, and this spreads until the final network layer which captures more high-level information. AMLN has been evaluated under a variety of network architectures over three widely used benchmark datasets. Extensive experiments show that AMLN achieves superior performance consistently against state-of-the-art knowledge transfer methods.
Learning a Dynamic Feature Fusion Tracker for Object Tracking

Object tracking is a key component of self-driving systems and has important meanings to alleviate traffic accidents. Therefore, it is meaningful to design a high performance and real-time tracker for improving the stability and safety of self-driving systems. In this paper, an effective and efficient feature fusion tracker, which dynamically fuses gradient and color features to model the appearance of the target object, is designed with the correlation filters framework for fast tracking. To be specific, two complementary correlation filters for gradient (e.g. HOG) and color (e.g. ColorNames) features are maintained during tracking, and the proposed feature fusion method adaptively adjusts the weights of them to deal with large appearance changes of the target object in challenging tracking scenes. The weights are decided by the consistency of the final tracking result and the predicted results obtained by two correlation filters. Moreover, a failure detection scheme is designed to alleviate the model drift issue caused by undesirable model updates to improve the tracking accuracy. If a tracking result is identified as a failed case, re-detection operations are performed to accurately localize the target object. The experimental results prove that the proposed tracker can achieve competitive tracking performance and a satisfactory tracking speed of 25.3 FPS in comparison with several state-of-the-art trackers on challenging tracking benchmarks.
Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models

Fuzzy-Rough Cognitive Networks (FRCNs) are recurrent neural networks intended for structured classification purposes in which the problem is described by an explicit set of features.The advantage of this granular neural system relies on its transparency and simplicity while being competitive to stateof-the-art classifiers.Despite of their relative empirical success in terms of prediction rates, there are limited studies on FRCNs' dynamic properties and how their building blocks contribute to algorithm's performance.In this paper, we theoretically study these issues and conclude that boundary and negative neurons always converge to a unique fixed-point attractor.Moreover, we demonstrate that negative neurons have no impact on algorithm's performance and that the ranking of positive neurons is invariant.Moved by our theoretical findings, we propose two simpler fuzzyrough classifiers that overcome the detected issues and maintain the competitive prediction rates of this classifier.Toward the end, we present a case study concerned with image classification in which a Convolutional Neural Network is coupled with one of the simpler models derived from the theoretical analysis of the FRCN model.The numerical simulations suggest that, once the features have been extracted, our granular neural system performs as well as other recurrent neural networks.
A Systematic Review of the Criminogenic Potential of Synthetic Biology and Routes to Future Crime Prevention

Synthetic biology has the potential to positively transform society in many application areas, including medicine. In common with all revolutionary new technologies, synthetic biology can also enable crime. Like cybercrime, that emerged following the advent of the internet, biocrime can have a significant effect on society, but may also impact on peoples' health. For example, the scale of harm caused by the SARS-CoV-2 pandemic illustrates the potential impact of future biocrime and highlights the need for prevention strategies. Systematic evidence quantifying the crime opportunities posed by synthetic biology has to date been very limited. Here, we systematically reviewed forms of crime that could be facilitated by synthetic biology with a view to informing their prevention. A total of 794 articles from four databases were extracted and a three-step screening phase resulted in 15 studies that met our threshold criterion for thematic synthesis. Within those studies, 13 exploits were identified. Of these, 46% were dependent on technologies characteristic of synthetic biology. Eight potential crime types emerged from the studies: bio-discrimination, cyber-biocrime, bio-malware, biohacking, at-home drug manufacturing, illegal gene editing, genetic blackmail, and neuro-hacking. 14 offender types were identified. For the most commonly identified offenders (>3 mentions) 40% were outsider threats. These observations suggest that synthetic biology presents substantial new offending opportunities. Moreover, that more effective engagement, such as ethical hacking, is needed now to prevent a crime harvest from developing in the future. A framework to address the synthetic biology crime landscape is proposed.
Small object detection in aerial view based on improved YoloV3 neural network

In application scenarios such as UAV inspection, deep learning-based object detection methods are increasingly used to improve the automation of line inspection. In the aerial view scene, the drone is usually fly at a high altitude from the ground, so the proportion of the object in the image is relatively small. When the YoloV3 network identifies small objects, the detection result would not be good because there is less information in the 8x downsampling feature map. In this paper, base on the LaSOT data set, the YoloV3 network has been modified by adjusting the values of anchors and establishing the 4x downsampling prediction layer to enhance the detection effect of small objects. Compared with the original YoloV3 network, the improved YoloV3 network has a certain improvement in convergence ability and detection accuracy compared to the original YoloV3 network.
FTDL: A Tailored FPGA-Overlay for Deep Learning with High Scalability

Fast inference is of paramount value to a wide range of deep learning applications. This work presents FTDL, a highly-scalable FPGA overlay framework for deep learning applications, to address the architecture and hardware mismatch faced by traditional efforts. The FTDL overlay is specifically optimized for the tiled structure of FPGAs, thereby achieving post-place-and-route operating frequencies exceeding 88 % of the theoretical maximum across different devices and design scales. A flexible compilation framework efficiently schedules matrix multiply and convolution operations of large neural network inference on the overlay and achieved over 80 % hardware efficiency on average. Taking advantage of both high operating frequency and hardware efficiency, FTDL achieves 402.6 and 151.2 FPS with GoogLeNet and ResNet50 on ImageNet, respectively, while operating at a power efficiency of 27.6 GOPS/W, making it up to 7.7× higher performance and 1.9× more power-efficient than the state-of-the-art.
Learning to Quantize Deep Neural Networks: A Competitive-Collaborative Approach

Reducing the model size and computation costs for dedicated AI accelerator designs, neural network quantization methods have attracted momentous attention recently. Unfortunately, merely minimizing quantization loss using constant discretization causes accuracy deterioration. In this paper, we propose an iterative accuracy-driven learning framework of competitive-collaborative quantization (CCQ) to gradually adapt the bit-precision of each individual layer. Orthogonal to prior quantization policies working with full precision for the first and last layers of the network, CCQ offers layer-wise competition for any target quantization policy with holistic layer fine-tuning to recover accuracy, where the state-of-the-art networks can be entirely quantized without any significant accuracy degradation.
Smart Micro-GaS: A Cognitive Micro Natural Gas Industrial Ecosystem Based on Mixed Blockchain and Edge Computing

With the increase in natural gas consumption, distributed natural gas supply and transaction have become new development goals of the industrial Internet of Things (IoT) for natural gas. However, there are obvious disadvantages of the existing natural gas pipeline network in aspects of infrastructure warning, multilevel data transmission, automatic transaction, and security. Emerging technologies, such as blockchain, edge computing, and AI have been introduced to address these shortcomings. This article proposes Smart Micro-GaS, i.e., the concept of a cognitive micro natural gas industrial ecosystem based on mixed blockchain and edge computing. Three aspects, multilevel, multiview, and multidimension, are put forward for its design and deployment. Then, based on the most important smart contract algorithm in blockchain, a mixed transaction model for natural gas is established. Finally, a case analysis is conducted on a smart natural gas testbed for data prediction and the proposed smart contract algorithm. The framework proposed in this article makes the natural gas data have multilevel liquidity and realizes diversified transactions.
Intuitionistic fixed point logic

We study the system IFP of intuitionistic fixed point logic, an extension of intuitionistic first-order logic by strictly positive inductive and coinductive definitions. We define a realizability interpretation of IFP and use it to extract computational content from proofs about abstract structures specified by arbitrary classically true disjunction free formulas. The interpretation is shown to be sound with respect to a domain-theoretic denotational semantics and a corresponding lazy operational semantics of a functional language for extracted programs. We also show how extracted programs can be translated into Haskell. As an application we extract a program converting the signed digit representation of real numbers to infinite Gray code from a proof of inclusion of the corresponding coinductive predicates.
Part-Aware Interactive Learning for Scene Graph Generation

Generating scene graph to describe the whereabouts and interactions of objects in an image has attracted increasing attention of researchers. Most existing methods explore object-level visual context or bodypart-object cooperation with the message passing structure, which can not meet the part-aware interaction nature of scene graph. Normally, a subject interacts with an object through crucial parts in each other. Besides, the correlation among parts within an identical object can also help predicting objects and their relationships. Hence, both of subject and object parts and their intra- and inter-object correlations should be fully considered for scene graph generation. In this paper, we propose a part-aware interactive learning method, which are divided into the intra-object and inter-object scenarios. First, we detect objects from an image and further decompose each one into a set of parts. Second, the part-aware graph attention module is proposed to refine part features via the intra-object message passing, and the refined features are incorporated for object inference. Third, the visual mutual attention module is designed to discover part-aware correlated visual cues precisely for predicate inference. It can highlight the subject-related object parts and the object-related subject parts during inter-object interactive learning. We demonstrate the superiority of our method against the state of the arts on Visual Genome. Ablation studies and visualization further validate its effectiveness.
Location-based challenges for playful neighbourhood exploration

Location-based activities can challenge citizens to explore their neighbourhood in new playful ways. This paper presents a classification of such playful activities based on the literature and experience gained (1) in co-creation sessions and (2) gameplay for neighbourhood exploration with citizens in the Hague. The location-based game designed for this purpose encouraged neighbourhood exploration and social interaction that went beyond the everyday normal. Results showed that citizens are specifically interested in activities that allow them to discover new information about, and places in, their neighbourhood when these are related to their own life. The results of this paper provide new knowledge on game design to inform designers on which type of interactions and activities foster neighbourhood exploration and social interaction.
Enhancing Self-supervised Monocular Depth Estimation via Incorporating Robust Constraints

Self-supervised depth estimation has shown great prospects in inferring 3D structures using purely unannotated images. However, its performance usually drops when trained on the images with changing brightness and moving objects. In this paper, we address this issue by enhancing the robustness of the self-supervised paradigm using a set of image-based and geometry-based constraints. Our contributions are threefold, 1) we propose a gradient-based robust photometric loss which restrains the false supervisory signals caused by brightness changes, 2) we propose to filter out the unreliable areas that violate the rigid assumption by a novel combined selective mask, which is computed on the forward pass of the network by leveraging the inter-loss consistency and the loss-gradient consistency, and 3) we constrain the motion estimation network to generate across-frame consistent motions via proposing a triplet-based cycle consistency constraint. Extensive experiments conducted on KITTI, Cityscape and Make3D datasets demonstrate the superiority of our method, that the proposed method can effectively handle complex scenes with changing brightness and object motions. Both qualitative and quantitative results show that the proposed method outperforms the state-of-the-art methods.
AvatarMeeting: An Augmented Reality Remote Interaction System With Personalized Avatars

To further enhance the immersion perception of remote interaction, avatars can be involved harnessing Head Mounted Display (HMD) based Augmented Reality (AR). In our demonstration, we present an avatar based remote interaction system AvatarMeeting, enabling users to meet with remote peers through interactive personalized avatars just like face to face. Specifically, we propose a novel framework including a consumer-grade set-up, a complete transmission scheme and a processing pipeline, which consists of prescan modeling, pose detection and action reconstruction. And an angle based reconstruction approach is introduced to empower the AR avatars to perform the same actions as each remote real person do in real time smoothly while keeping a good avatar shape.
Dynamic aperture factor analysis/target transformation (DAFA/TT) for Mg-serpentine and Mg-carbonate mapping on Mars with CRISM near-infrared data

Serpentine and carbonate are products of serpentinization and carbonation processes on Earth, Mars, and other celestial bodies. Their presence implies that localized habitable environments may have existed on ancient Mars. Factor Analysis and Target Transformation (FATT) techniques have been applied to hyperspectral data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) to identify possible serpentine and Mg-carbonate-bearing outcrops. FATT techniques are capable of suggesting the presence of individual spectral signals in complex spectral mixtures. Applications of FATT techniques to CRISM data thus far only evaluate whether an entire analyzed image (≈ 3 × 105 pixels) may contain spectral information consistent with a specific mineral of interest. The spatial distribution of spectral signal from the possible mineral is not determined, making it difficult to validate a reported detection and also to understand the geologic context of any purported detections. We developed a method called Dynamic Aperture Factor Analysis/Target Transformation (DAFA/TT) to highlight the locations in a CRISM observation (or any similar laboratory or remotely acquired data set) most likely to contain spectra of specific minerals of interest. DAFA/TT determines the locations of possible target mineral spectral signals within hyperspectral images by performing FATT in small moving windows with different geometries, and only accepting pixels with positive detections in all cluster geometries as possible detections. DAFA/TT was applied to a hyperspectral image of a serpentinite from Oman for validation testing in a simplified laboratory setting. The mineral distribution determined by DAFA/TT application to the laboratory hyperspectral image was consistent with Raman analysis of the serpentinite sample. DAFA/TT also successfully mapped the spatial distribution of Mg-serpentine and Mg-carbonate previously detected in CRISM data using band parameter mapping and extraction of ratioed spectra. We applied DAFA/TT to CRISM images in some olivine-rich regions of Mars to characterize the spatial distribution of Mg-serpentine and Mg-carbonate-bearing outcrops.
Leveraging QoE Heterogenity for Large-Scale Livecaset Scheduling

Livecast streaming has received great success in recent years. Although many prior efforts have suggested that dynamic viewer scheduling according to the quality of service (QoS) can improve user engagement, they may suffer inefficiency due to their ignorance of viewer heterogeneity in how the QoS impact quality of experience (QoE).
Fast algorithms for single and multiple pattern Cartesian tree matching

Cartesian tree matching is the problem of finding every substring of a given text which has the same Cartesian tree as that of a given pattern. In this paper we propose fast algorithms for single and multiple pattern Cartesian tree matching by introducing new representations and encodings. For single pattern Cartesian tree matching, we present the framework of a binary filtration method and an efficient verification technique. Any exact string matching algorithm can be used as a filtration for Cartesian tree matching in our framework. For multiple pattern Cartesian tree matching, we present two fingerprinting methods, i.e., the parent-distance encoding and the binary encoding. By combining an efficient fingerprinting method and a conventional multiple string matching algorithm, we can efficiently solve multiple pattern Cartesian tree matching. By experiments we show that our matching algorithms provide good performances for both single and multiple pattern Cartesian tree matching.
PyMatting: A Python Library for Alpha Matting

An important step of many image editing tasks is to extract specific objects from an image in order to place them in a scene of a movie or compose them onto another background. Alpha matting describes the problem of separating the objects in the foreground from the background of an image given only a rough sketch. We introduce the PyMatting package for Python which implements various approaches to solve the alpha matting problem. Our toolbox is also able to extract the foreground of an image given the alpha matte. The implementation aims to be computationally efficient and easy to use. The source code of PyMatting is available under an open-source license at https://github.com/pymatting/pymatting.
Spatiotemporal chaos in multiple dynamically coupled map lattices and its application in a novel image encryption algorithm

Spatiotemporal chaotic systems are widely used for image encryption due to their better chaos, more chaotic sequences and more complex dynamic behavior. This paper proposes a new spatiotemporal chaotic system named Multiple dynamically coupled map lattices (MDCML). The proposed system introduces dynamic coupling parameter, which enhances ergodicity and improves pseudo-randomness and chaos of the system. According to the analysis of Kolmogorov-Sinai entropy, bifurcation diagram and information entropy, it can be proved that the chaotic state of all lattices of the proposed system is very stable. By applying the proposed system, we design a new image encryption algorithm. The proposed algorithm introduces the random-based diffusion strategy, which can greatly reduce the correlation between adjacent pixels. Finally, a large number of experimental results show that our proposed algorithm has high security to resist common attacks and is suitable for image encryption.
Occlusion Detection for Automatic Video Editing

Videos have become the new preference comparing with images in recent years. However, during the recording of videos, the cameras are inevitably occluded by some objects or persons that pass through the cameras, which would highly increase the workload of video editors for searching out such occlusions. In this paper, for releasing the burden of video editors, a frame-level video occlusion detection method is proposed, which is a fundamental component of automatic video editing. The proposed method enhances the extraction of spatial-temporal information based on C3D yet only using around half amount of parameters, with an occlusion correction algorithm for correcting the prediction results. In addition, a novel loss function is proposed to better extract the characterization of occlusion and improve the detection performance. For performance evaluation, this paper builds a new large scale dataset, containing 1,000 video segments from seven different real-world scenarios, which could be available at: https://junhua-liao.github.io/Occlusion-Detection/. All occlusions in video segments are annotated frame by frame with bounding-boxes so that the dataset could be utilized in both frame-level occlusion detection and precise occlusion location. The experimental results illustrate that the proposed method could achieve good performance on video occlusion detection compared with the state-of-the-art approaches. To the best of our knowledge, this is the first study which focuses on occlusion detection for automatic video editing.