Reviewerly/exHarmony huggingface dataset:
uICNet: Lightweight Image Segmentation

Convolutional Neural Networks have started making headway in solving the problem of semantic segmentation. The demand for increasingly lightweight neural networks has been driven by an abundance of cheap hardware capable of running such neural networks and utilization of such networks for real world applications. MobileNet’ s utilization of the depthwise separable convolution has been proven to be an efficient approach for reducing neural network size without incurring a high penalty in accuracy. In the realm of image segmentation, ICNet was a breakthrough in the ability for semantic segmentation networks to be deployed on commonly available hardware and run at close to real time. In this paper, ICNet is improved upon by utilizing lessons learned from MobileNet and applying these lessons to create a new lighter weight network, uICNet. uICNet achieves similar accuracy to ICNet while substantially improving model size.
The Deep Flow Inspection Framework Based on Horizontal Federated Learning

The deep flow inspection (DFI) can identify abnormal traffic to avoid the data congestion caused by the sudden increase of traffic, which can maintain the stability of 6G network. However, the traditional DFI cannot protect the privacy of clients. This paper proposes a deep flow inspection method based on the horizontal federated learning to achieve the traffic identification locally, which reduces the risk of data leakage. Besides, a lightweight CNN model, Simplified-MobileNet, is proposed to realize the effective traffic identification under the limited hardware environment. Federated aggregation algorithm FedAvg is also applied to promote the communication efficiency during model training. The experimental results demonstrate that the Simplified-MobileNet decreases the training time per round by about 15%, and compared with the standalone mode, the FedAvg algorithm can achieve a higher training accuracy with a limited communication time.
Clickbait Indonesian News Classification using ColBERT with Siamese Network on Headline and Content News

Clickbait, which is a technique for creating an attractive title, is a way to generate income by increasing reader and visitor traffic. The factor of the rise of clickbait in online media is the increasingly fierce competition between media to get readers or visitors to the exclusion of site visitor satisfaction using clickbait. Based on this problem, a classifier is needed to detect news that is clickbait or not clickbait. Recent research on clickbait uses M-BERT by focusing news analysis on the news headlines. But clickbait is easier to identify by assessing the similarity of meaning between the headline and the news content. It uses a combination of siamese network architecture. This study proposes a siamese network in determining the similarity between the title and scope of the news to determine whether the news is clickbait news. The combination of siamese and ColBERT networks to produce the given embedding sentences and have text similarity values can perform better than the baseline system.
HuGraph: Acceleration of GCN Training on Heterogeneous FPGA Clusters with Quantization

Graph convolutional networks (GCNs) have suc-ceeded significantly in numerous fields, but the need for higher performance and energy efficiency training GCN on larger graphs continues unabated. At the same time, since recon-figurable accelerators have the ability to fine-grained custom computing modules and data movement, FPGAs can solve problems such as irregular memory access for GCN computing. Furthermore, to scale GCN computation, the use of heteroge-neous FPGAs is inevitable due to the constant iteration of new FPGAs. In this paper, we propose a novel framework, HuGraph, which automatically maps GCN training on heterogeneous FPGA clusters. With HuGraph, FPGAs work in synchronous data parallelism using a simple ring 1D topology that is suitable for most off-the-shelf FPGA clusters. HuGraph uses three approaches to advance performance and energy efficiency. First, HuGraph applies full-process quantization for neighbor-sampling-based data parallel training, thereby reducing computation and mem-ory consumption. Second, a novel balanced sampler is used to balance workloads among heterogeneous FPGAs so that FPGAs with fewer resources do not become bottlenecks in the cluster. Third, HuGraph schedules the execution order of GCN training to minimize time overhead. We implement a prototype on a single FPGA and evaluate cluster-level performance with a cycle-accurate simulator. Experiments show that HuGraph achieves up to 102.3 ×, 4.62×, and 11.1× speedup compared with the state-of-the-art works on CPU, GPU, and FPGA platforms, respectively, with negligible accuracy loss.
Dense Reconstruction from Visual SLAM with Probabilistic Multi-Sequence Merging

This paper presents a comprehensive visual SLAM system that extends the application of ORB-SLAM3. Using it as a template, a supplementary and optional function of 3D dense reconstruction is implemented for both RGB-D and stereo cameras. With conventional datasets, TUM, EuRoC, and KITTI as benchmarks, we confirm the validity of proposed system in both indoor and outdoor scenarios. Besides, the concept of Octree is integrated into our system to generate Octomap. A compact mapping can be achieved as such, verified by the fact that the size of each dense point cloud map is reduced to approximately one-fifth after the conversion. Furthermore, a multi-sequence merging method is included in our proposed system, formulating with a probabilistic-based optimizing algorithm and map accessing functions from the original system. Multi-sequence experiments evince that the tracking accuracy profits from the exploitation of a priori knowledge gathered through the preceding sequences.
Improved Distributed-memory Triangle Counting by Exploiting the Graph Structure

Graphs are ubiquitous in modeling complex systems and representing interactions between entities to uncover structural information of the domain. Traditionally, graph analytics workloads are challenging to efficiently scale (both strong and weak cases) on distributed memory due to the irregular memory-access driven nature (with little or no computations) of the meth-ods. The structure of graphs and their relative distribution over the processing elements poses another level of complexity, making it difficult to attain sustainable scalability across platforms. In this paper, we discuss enhancements to TriC, a distributed-memory implementation of graph triangle counting using Mes-sage Passing Interface (MPI), which was featured in the 2020 Graph Challenge competition. We have made some incremental enhancements to TriC, primarily adopting a user-defined buffering strategy to overcome the startup problem for large graphs (by fixing the memory for intermediate data), and experimenting with probabilistic data structures such as bloom filter to improve the query response time for assessing edge existence, at the expense of increasing the overall false positive rate. These adjustments have led to a modest improvements in most cases, as compared to the previous version.
Literature Review of OpenAI Five’s Mechanisms in Dota 2’s Bot Player

Multiplayer Online Battle Arena (MOBA) games, such as Dota 2, present significant problems to AI systems, such as multi-agent, massive state-action space, and sophisticated action control. Those problems will become increasingly important in the development of more powerful AI systems. OpenAI Five has demonstrated that DRL (Deep Reinforcement Learning) agents can be trained to achieve superhuman competence in matches that involve thousands of steps before reaching the end goal without the need of explicit hierarchical macro-actions. These DRL agents, in general, receive high-dimensional inputs at each step and act on deep-neural-network-based policies updated by the learning mechanism to maximize the return in an end-to-end manner. This paper investigates the approaches employed by OpenAI Five to gradually acquire knowledge during training: (1) using surgeries to solve the problem of game renewals, (2) using hyperparameters instead of ordinary parameters since they cannot be processed, (3) making decisions using policies in addition to macro strategies. Finally, how the agents in the game receive and respond to observations and actions happening in each match is included, as an addition to explanations of the dense reward function for multiple agent cooperation created using the zero-sum technique.
Integrating Jess rules in Renew Tool for Smart Car Guidance

Pervasive computing environments include a diversity of interconnected smart devices, which help humans in their everyday life. The universality of these devices and the wireless Internet environment has allowed users to exploit environmental sensor data in a variety of fields. The modeling of these systems requires tools, which hold immense features such as mobility and context awareness. This paper presents a conceptual framework based on a multi-agent architecture for context-aware decision support in dynamic smart environments. The architecture is modeled using a powerful formalism called nets within net. In addition, we augmented such a formalism with the expert system (JESS) and simulated the Renew tool. We validated the proposed approach with a case study about smart car guidance.
An Action Recgonition System Based on WiFi

Compared with the action recognition method based on wearable sensors and video, the action recognition technology based on WiFi has the advantages of extensive infrastructure, simplicity and no user interference. In this paper, we use the CSI data of WiFi signal to realize human actions recognition based on WiFi through the stages of data acquisition, data preprocessing and intelligent recognition. Preprocessing includes data Deduplication / interpolation, removing DC component of signal, removing abnormal data, data de-noising, segmentation of data flow. The typical U-Net is used as the action recognition network. In the experiment, through the recognition and verification of seven actions, the average accuracy rate reached 90.83%. In addition, the influence distances of the other actions are analyzed through experiments.
Image Enhancement for Facial Landmark Detection

Facial landmark detection (FLD) is a field of study in computer vision that specializes in detecting and tracking key points from human faces. There are many applications, such as detecting a human’s head pose (position and rotation), tracking whether drivers are paying attention or not, applying augmented reality, etc. This paper proposes to evaluate and decide what are the optimal techniques of image enhancement that can be used to improve different algorithms of FLD. After proposing different pipelines of contrast enhancement and grayscale conversion to enhance the image before applying the FLD algorithm, the results show that the performance will not increase drastically if an image enhancement pipeline is used, but for the reason that this step will not consume a lot of time in the process of detection, it is a step worth considering.
Neural Cognitive Diagnosis for Empirical Q-Matrix

The growth of educational informatization has been aided by the constant updating and switching of application modes brought about by the continual development of a new generation of information technologies such as artificial intelligence, cloud computing, data mining, and mobile Internet. Cognitive diagnosis aims to assess students’ knowledge competency and offer a foundation for targeted instruction and teacher feedback. The neural cognitive diagnosis framework for the empirical Q-matrix is proposed in the research. The neural cognitive diagnosis framework for empirical Q-matrix introduces empirical Q-matrix, which to a certain extent reduces the subjectivity of artificial Q-matrix, to construct and select the most appropriate Q-matrix; accurate and interpretable diagnostic results are obtained by combining neural network and learning complex motion interaction function.
Research on Fog Ranging Based on Binocular Vision

For the robot to travel in foggy weather, the target feature information is not obvious, which affects the robot’s ranging speed and accuracy. In order to realize the rapid and stable advancement of the robot, a binocular vision ranging detection method for the target under foggy conditions is designed and implemented. First, the dehazing algorithm MSBDN (Multi-Scale Boosted Dehazing Network) is used to dehaze the target image to restore the characteristic information of the target; then, the Zhang Zhengyou calibration method is used to obtain the internal and external parameters of the camera; then, the semi-global matching algorithm SGBM (semi-global-blockmatching) to match pixels to obtain the target disparity map; Use WLS (weighted least squares) filtering to smooth and denoise the initial disparity map to obtain the optimal disparity map. Convert disparity map to depth map. The experimental simulation shows that the method can restore the actual features of the image better in the poor image quality of foggy days, improve the distance measurement accuracy, and meet the requirements of robot travel in foggy days.
Anomaly Detection in Control Systems With Interval Dissimilarity

When performing anomaly detection on data generated by control systems, one is confronted with uniquely structured time series containing both set points as well as their corresponding process values. We devise a method which exploits the presence of these two distinct signals by converting time series to time interval series and subsequently applying a distance metric between pairs of time interval series to determine their anomaly status. Contrary to common time series distance metrics, the time interval series distance metric employed here scales with the number of intervals rather than with the number of samples in the time series. The distances produced by our approach respond appropriately to the occurrence of anomalous subsequences within the time series and are robust to minor variations between time series. The proposed method’s overall profile in terms of computational efficiency and quality of the produced distances is competitive in comparison with other methods in our experiments.
Reweighted Robust Particle Filtering Approach for Target Tracking in Automotive Radar Application

In view of the decline of filtering accuracy caused by measured outliers in target tracking application, a novel reweighted robust particle filter is proposed to acquire accurate state estimates in an automotive radar system. To infer the importance of each entry in the multidimensional contaminated measurement vector, we employ a weight vector, which follows a Gamma distribution, to model the measured noise and carry out accurate state estimates. Additionally, the particle filter method is employed to perform approximate posterior inference of state estimates in the nonlinear model. The Cramer–Rao lower bound is provided for the performance evaluation of the proposed method. Both simulation and experimental results demonstrate the superiorities of the proposed algorithm over other robust solutions.
Hierarchical Model Predictive Control for Obstacle Avoidance in Airborne Radar Based Detect-and-Avoid Systems Operating in a Varying Environment

This paper presents a model predictive control (MPC) based obstacle avoidance trajectory generation in uncrewed aircraft systems (UAS). The system aims to generate desired trajectories for the avoidance of flying aircraft with constant velocity within the collision avoidance threshold. This approach presents a two-layered MPC architecture incorporating a collision cone technique to achieve real-time avoidance of dynamic obstacles in non-cooperative sensor-based beyond-visual-line-of-sight (BVLOS) operation scenarios. The efficiency of the presented real-time solution is achieved by using a fast analytic collision cone technique for path planning and effective initial guesses for path tracking.
A Brief Overview of the Darknet

This article provides an overview of the darknet and how it is used as part of the criminal enterprise. It explores some recent high-profile cases and discusses the investigative difficulties associated with darknet technology. A brief overview of forensic artifacts found in darknet cases as well as possible investigative techniques are also discussed.
Fraudulent and Counterfeit Secure Documents and Currency

Government and non-government issuing authorities issue security documents that can be trusted and authenticated with confidence. Whether for financial gain or to enable other fraudulent activities, attempts to deceive using fraudulent or counterfeit versions of these documents are internationally prevalent. This article outlines the nature of security documents that are used for deceit and the criminal activities they may assist in facilitating. It also addresses the various strategies implemented as a means of combating fraudulent and counterfeit documents, and discusses security features utilized in the manufacture of high level secure documents and currency.
A Framework for Video-Text Retrieval with Noisy Supervision

A key challenge in extending vision-linguistic models to new video domains is curating large annotated datasets. We propose a framework that leverages videos with noisy linguistic descriptions, such as sports broadcasts, to train a model using an uncurated dataset. We introduce an unsupervised model that uses the corpus membership between a target and an auxiliary corpus to assign a relevance probability to the linguistic description of examples in the target domain. We examine these probabilities to evaluate the effect of noisy data in the video-text retrieval task. Our framework provides a domain-invariant recipe for enhancing multi-modal datasets by reducing the noise without requiring the costly manual curation effort. We show that our unsupervised model improves the performance of the video-text retrieval model using readily available hockey broadcast videos with closed-captioning. Furthermore, we propose a multi-modal cross-correlation objective function to obtain additional performance gains. We showcase our proposed framework in the context of a new multi-modal dataset of temporally labeled hockey videos with noisy textual descriptions.
Impact of Downscaling on Adversarial Images

Most works on adversarial attacks consider that small images whose size already fits the model but downscaling is a necessary first step to adapt the size of the image to the model, and it can reform the adversarial signal. This paper explores attacking large images on classifiers with different input sizes and compares theoretical results with practical ones. The possibility of forging adversarial images using different interpolation methods and different deep learning structures are investigated. The distortion of the adversarial signal and the transferability over other downscaling methods are also studied. An ensemble model gathering different resizing interpolations is also proposed to increase the transferability of the attack against a set of downscaling kernels.
Future Frame Extrapolation Using Future Cost Volume

A novel future frame extrapolation algorithm using the future cost volume is proposed in this work. First, we develop the future cost volume to estimate motion vectors from a future frame to input frames. Second, we generate two future frame candidates by back-ward warping the input frames using the future motion vectors. Finally, we develop a synthesis network, which aggregates the two candidates to reconstruct the future frame faithfully. Experimental results demonstrate that the proposed algorithm significantly outperforms state-of-the-art extrapolators on various datasets.
Contrastive Learning for Online Semi-Supervised General Continual Learning

We study Online Continual Learning with missing labels and propose SemiCon, a new contrastive loss designed for partly labeled data. We demonstrate its efficiency by devising a memory-based method trained on an unlabeled data stream, where every data added to memory is labeled using an oracle. Our approach outperforms existing semi-supervised methods when few labels are available, and obtain similar results to state-of-the-art supervised methods while using only 2.6% of labels on Split-CIFAR10 and 10% of labels on Split-CIFAR100.
SFIC: Sparsity-Driven Facial Image Compression Network

Facial image compression is crucial in many areas like social media and video surveillance. Considering the sparsity of facial features, sparse representation (SR) has been applied to compress facial images, in which each image patch is sparsely represented by a small number of dictionary atoms to save bit-rates. Along this line, we propose the first end-to-end sparsity-driven facial image compression network namely SFIC. In the proposed network, the traditional convolutional sparse coding (CSC) is turned into a learnable CSC block, which is combined with discrete wavelet transform (DWT) to form the sparsity encoding module (SEM). This is the first time that CSC has been explored in facial image compression. In the decoding side, a corresponding sparsity decoding module (SDM) is used to decode the image, and we further propose a quality enhancement module (QEM) to enhance the quality of decoded image. The experimental results verify that the proposed SFIC network achieves 74%, 55%, and 33% bit-rate savings over JPEG, JPEG-2000, and HEVC.
Self-Supervised Pretraining for Deep Hash-Based Image Retrieval

Deep hashing aims to produce discriminative binary hash codes for fast image retrieval through a deep baseline network and additional trainable hash function. In a supervised deep hashing network, the baseline network is generally initialized with classification-based pretrained models, and the overall hashing network is trained in a supervised fashion. However, since classification and retrieval are two different tasks, it is necessary to reconsider the initial model for the baseline network. In this paper, we propose to use a self-supervised pretrained model as the baseline for the first time. We investigate the impact of pretrained model types by comparing deep hashing networks that use the baseline network with 1) randomly initialized weights, 2) conventional supervised pretrained weights, and 3) proposed self-supervised pretrained weights. As a result, we confirm that the performance of deep hashing differs depending on the initial baseline setting, and the proposed self-supervised baseline model shows comparable or better performance over the supervised one. Our code is released at https://github.com/HaeyoonYang/SSPH.
U-Deepdig: Scalable Deep Decision Boundary Instance Generation

For more than a decade, deep learning algorithms have consistently achieved and improved upon the state-of-the-art performance on image classification tasks. However, there is a general lack of understanding and knowledge about the decision boundaries carved by these modern deep neural network architectures. Recently, an algorithm called DeepDIG was introduced to generate boundary instances between two classes based on the decision regions defined by any deep neural network classifier. Although it is very effective in generating boundary instances, the underlying algorithm was designed to work with two classes at a time in a non-commutative fashion which makes it ill-suited for multi-class problems with hundreds of classes. In this work, we extend the DeepDIG algorithm so that it scales linearly with the number of classes. We show that the proposed U-DeepDIG algorithm maintains the efficacy of the original DeepDIG algorithm while being scalable and more efficient when applied to larger classification problems. We demonstrate this by applying our algorithm on MNIST, Fashion-MNIST and CIFAR10 datasets. In addition to qualitative comparisons, we also perform extensive quantitative comparison by analyzing the margin between the class boundaries and the instances generated.