Reviewerly/exHarmony huggingface dataset:
Improved YOLOv3-tiny Object Detector with Dilated CNN for Drone-Captured Images

The research problems on Object detection have been attracted with major issues in the computer vision domain. Object detection based on images from unmanned aerial vehicles (UAV) - drones, has versatile applications in both defence security, agriculture and GIS. However, real-time object detection in UAV scenarios remains quite a tedious problem due to environmental obstructions such as occlusion and view-invariant conditions despite the high number of solutions proposed to solve this task. This paper proposes an improved YOLOv3-tiny object detector by introducing a multi-dilated module between the convolution unit and the receptive field, where the problem of a small number of positive training samples is solved by a larger size of the predicted feature map thereby reducing the rate of label rewriting in YOLOv3-tiny. We find that the fusion of multi-scale receptive fields is effective in detecting even every single tiny object. We introduce a path aggregation module that merges the semantic information in a deeper layer and detailed information in an earlier layer. The analysis of the proposed solution shows that on the VisDrone2019-Det test set, our proposed model is more efficient and effective, running 2.96% times faster and increasing 4.0% AP50 than YOLOv3.
Traffic sign detection research based on G-GhostNet-YOLOx model

In recent years, unmanned systems have become an increasingly popular research direction, in which the fast and accurate detection of traffic signs is an important research element in unmanned systems. In order to apply the target detection algorithm to embedded devices and mobile devices with low computing power, a G-GhostNet-YOLOx algorithm is proposed, which is based on the YOLOx-m network structure with the introduction of G-GhostNet lightweight network. The model is made as small as possible with the premise of ensuring the accuracy of the model. The experimental results show that the mAP (Mean Average Precision) of the Chinese traffic sign test set reaches 93.4%, which is high and small enough for application in embedded and mobile devices, which will be of practical significance for promoting the development of driverless technology and improving traffic safety.
An Intelligent Blockchain-Assisted Cooperative Framework for Industry 4.0 Service Management

The shift towards Industry 4.0 has seen significant steps forward with the advancements in processing, communication, and storage capabilities of Internet of Things (IoT) devices. Cyber-physical systems (CPS) have become more intelligent and withhold advanced processing, storage, and communication capabilities. Rejuvenated network and service management architectures must incorporate the capabilities of intelligent CPS. With that said, this article introduces a cooperative blockchain (BC)-assisted resource and capability sharing approach to fulfill CPS tasks. The solution uses Federated Learning (FL)-enabled Intelligent IoT (IIoT) devices to support Next-Generation Networks (NGNs). A clustering multi-stage blockchain and FL algorithm is used to create local and global models for CPS tasks. Local models are created for each cluster during the first stage. At the second stage, Federated Averaging is used by fog devices to create fog models. A global deep model is then created on the cloud using Federated Aggregation. Blockchain is used to record and validate the added models and ensure that records are not altered under cyber-attacks. Simulation results have shown that the proposed solution outperforms conventional FL and blockchain approaches in terms of accuracy and delay tolerance.
Distilling Siamese Trackers with Attention Mask

In recent years, the introduction of Siamese network has brought new vitality to the object tracking community. However, high-performance Siamese trackers cannot run at a real-time speed on mobile devices due to their complex and huge model. Knowledge distillation is a common and effective model compression method, but it is difficult to be applied to the challenging task like object tracking. We find out the fundamental cause is that the imbalance between the foreground and background in the object tracking task, which aggravates the problem of insufficient feature extraction ability of small backbone. Therefore, we propose the attention mask distillation (AMD) to help the student tracker focus on the foreground area faster and more accurately. The attention mask can be easily obtained from the feature maps and brings fine-granularity to the traditional binary mask. The experimental results on OTB 100 and VOT20 18 show that our method enables the student tracker perform as well as the teacher tracker. At the same time, it's able to run on the CPU at a hyper-real-time of 66 fps and achieves nearly 9 times model compression ratio. Such low computational and storage costs make it possible to deploy high-performance trackers on resource-constrained platforms.
Efficient Subjective Video Quality Assessment Based on Active Learning and Clustering

Subjective video quality assessment (SVQA) is essential in order to produce a sufficient benchmark dataset, which is especially important for extreme environments such as underwater videos. However, SVQA has always been plagued by its disadvantages, such as high cost and time-consuming. In this paper, we propose an effective SVQA method based on active learning and clustering (AL-SVQA). Our method initializes dual regression models for video quality prediction with a few quality-known videos. During the quality assessment process with active learning and clustering results, a batch of videos are iteratively selected by a sampling strategy and their quality is scored by a subject. The scored videos are then used to continually update the prediction models until meeting the stop criteria. When the active learning stops, the prediction models will annotate the video scores instead of humans while maintaining good performance. In this way, AL-SVQA can reduce about 40% of human the workload for subjective quality assessment. Evaluation experiments were conducted with an underwater video database.
Prediction of Bitcoin Price Based on LSTM

In response to the rise of the bitcoin market, the nonlinear variation of bitcoin price has always been the center of research in the community. Using bitcoin transaction data from 2014 to 2017, this study removes the uncontrollability and unpredictability of external factors and discusses the relationship between the predict and actual price of a single-feature LSTM model and a multi-feature LSTM model that incorporates thermodynamic chart to point out potentially highly correlated variables for the bitcoin price itself only, sets up a one-day prior algorithm, uses Python 3.7, Keras and LSTM tools to plot line plots of predicted and true prices and compare the accuracy of both. We conclude that the LSTM prediction is better with multiple features, which can greatly reduce the error and hedge the risk. Even in the chance case of more drastic fluctuations, the prediction is still better, which improves the utility and applicability of the model.
Leveraging Action Affinity and Continuity for Semi-supervised Temporal Action Segmentation

We present a semi-supervised learning approach to the temporal action segmentation task. The goal of the task is to temporally detect and segment actions in long, untrimmed procedural videos, where only a small set of videos are densely labelled, and a large collection of videos are unlabelled. To this end, we propose two novel loss functions for the unlabelled data: an action affinity loss and an action continuity loss. The action affinity loss guides the unlabelled samples learning by imposing the action priors induced from the labelled set. Action continuity loss enforces the temporal continuity of actions, which also provides frame-wise classification supervision. In addition, we propose an Adaptive Boundary Smoothing (ABS) approach to build coarser action boundaries for more robust and reliable learning. The proposed loss functions and ABS were evaluated on three benchmarks. Results show that they significantly improved action segmentation performance with a low amount (5% and 10%) of labelled data and achieved comparable results to full supervision with 50% labelled data. Furthermore, ABS succeeded in boosting performance when integrated into fully-supervised learning.
Expressive Local Feature Match for Image Search

Content-based image retrieval (CBIR) aims to search the most similar images to a given query content, from a large pool of images. Existing state of the art works would extract a compact global feature vector for each image and then evaluate their similarity. Although some CBIR works utilize local features to get better retrieval results, they either would require extra codebook training or use re-ranking for improving the retrieved results. In this work, we propose a many-to-many local feature matching for large scale CBIR tasks. Unlike existing local feature based algorithms which tend to extract large amounts of short-dimensional local features from each image, the characteristic feature representation in the proposed approach is modeled for each image aiming to employ fewer but more expressive local features. Characteristic latent features are selected using k-means clustering and then fed into a similarity measure, without using complex matching kernels or codebook references. Despite the straightforwardness of the proposed CBIR method, experimental results indicate state of art results on several benchmark datasets.
Active Learning of One-Clock Timed Automata Using Constraint Solving

Active automata learning in the framework of Angluin’s $$L^*$$ algorithm has been applied to learning many kinds of automata models. In applications to timed models such as timed automata, the main challenge is to determine guards on the clock value in transitions as well as which transitions reset the clock. In this paper, we introduce a new algorithm for active learning of deterministic one-clock timed automata and timed Mealy machines. The algorithm uses observation tables that do not commit to specific choices of reset, but instead rely on constraint solving to determine reset choices that satisfy readiness conditions. We evaluate our algorithm on randomly-generated examples as well as practical case studies, showing that it is applicable to larger models, and competitive with existing work for learning other forms of timed models.
Early Dementia Identification: On the Use of Random Handwriting Strokes

Timely diagnosis plays a crucial role for the treatment of neurodegenerative diseases. In particular, Dementia Identification in early stages is important to help patients have a better quality of life and to help clinicians to find a pathway of treatments to slow the effects. To the aim, a wide set of different handwriting tasks is here considered, and Shallow and Deep Learning methodologies are compared. Furthermore, Random Hybrid Stroke (RHS) are adopted to represent the handwriting time series. This solution outperforms the classical Deep Learning methodology and it is compared to a state-of-art shallow learning approach. Finally, a decision-level fusion for the results is adopted.
Learning Hierarchy Aware Features for Reducing Mistake Severity

Label hierarchies are often available apriori as part of biological taxonomy or language datasets WordNet. Several works exploit these to learn hierarchy aware features in order to improve the classifier to make semantically meaningful mistakes while maintaining or reducing the overall error. In this paper, we propose a novel approach for learning Hierarchy Aware Features (HAF) that leverages classifiers at each level of the hierarchy that are constrained to generate predictions consistent with the label hierarchy. The classifiers are trained by minimizing a Jensen-Shannon Divergence with target soft labels obtained from the fine-grained classifiers. Additionally, we employ a simple geometric loss that constrains the feature space geometry to capture the semantic structure of the label space. HAF is a training time approach that improves the mistakes while maintaining top-1 error, thereby, addressing the problem of cross-entropy loss that treats all mistakes as equal. We evaluate HAF on three hierarchical datasets and achieve state-of-the-art results on the iNaturalist-19 and CIFAR-100 datasets. The source code is available at https://github.com/07Agarg/HAF .
How knowledge becomes a technical object: epistemic practices &#x0D; in information technology

The article analyzes the practices of production and improving the efficiency of technical knowledge. The field of information technologies was chosen as the base of research. A distinctive feature of the modern stage of development of technology is that knowledge itself becomes a technical object. The requirements of efficiency, high performance, security are beginning to apply to knowledge itself. The authors concept of epistemic practices is used as a methodological basis for the present study. Epistemic practices are defined as methods and techniques for working with technical objects, the result of which is to increase the efficiency of technical knowledge implemented into these objects. The high level of competition and the direct dependence of the profits of IT companies on the efficiency of knowledge production makes the information technology industry a kind of testing ground for the production of epistemic practices. It has been proved that epistemic practices in the field of information technology are the forms of knowledge representation in information systems, the selection of data visualization methods, the improvement of multifactorial mathematical models and the determination of the hardware load configuration. It is concluded that the production of epistemic practices is a condition for the competitiveness of IT companies operating in the sphere of high technologies.
Towards Unbiased Multi-Label Zero-Shot Learning With Pyramid and Semantic Attention

Multi-label zero-shot learning extends conventional single-label zero-shot learning to a more realistic scenario that aims at recognizing multiple unseen labels of classes for each input sample. Existing works usually exploit attention mechanism to generate the correlation among different labels. However, most of them are usually biased on several <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">major classes</i> while neglect most of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minor classes</i> with the same importance in input samples, and may thus result in overly diffused attention maps that cannot sufficiently cover <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minor classes</i> . We argue that disregarding the connection between major and minor classes, i.e., correspond to the global and local information, respectively, is the cause of the problem. In this paper, we propose a novel framework of unbiased multi-label zero-shot learning, by considering various class-specific regions to calibrate the training process of the classifier. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pyramid Feature Attention</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PFA</i> ) is proposed to build the correlation between global and local information of samples to balance the presence of each class. Meanwhile, for the generated semantic representations of input samples, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Semantic Attention</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SA</i> ) to strengthen the element-wise correlation among these vectors, which can encourage the coordinated representation of them. Extensive experiments on the large-scale multi-label benchmarks <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MS-COCO</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NUS-WIDE</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Open-Images</i> demonstrate that the proposed method surpasses other representative methods by significant margins.
Fall Detection and Activity Recognition using Hybrid Convolution Neural Network and Extreme Gradient Boosting classifier

The branch of study that focuses on ambient assisted living systems has shown a significant amount of interest in the problem of activity and fall detection. These types of systems make use of a variety of sensing technologies to track human movements and attempt to determine the activity being carried out for the goal of health monitoring as well as other applications. In this regard, in addition to activity identification, fall detection is a very essential role. Falls are a leading cause of injuries and even fatalities, hence it is imperative that falls be detected as soon as possible. This study provides a fall detection and activity identification system that not only takes into account the many activities involved in day-to-day life but also takes into account the detection of falls while taking into consideration the intensity and the direction in which the fall occurred. The data from the Inertial Measurement Unit that is included in the SisFall database is first split into non-overlapping segments that last for three seconds each. Following the appropriate augmentation of the data, exacting the feature with the help of a Convolutional Neural Network, followed by an eXtreme Gradient Boosting (XGB) final step for categorization into the different output groups. The results of the studies demonstrate that the gradient-boosted CNN works far better than previous similar approaches, with an unweighted average recall of 88 percent being achieved.
An improved line-of-sight guidance law for path following of Unmanned Surface Vehicle based on adaptive lookahead distance

This paper proposes an improved line-of-sight (LOS) guidance law for path following of Unmanned Surface Vehicle (USV) based on adaptive lookahead distance. The adaptive lookahead distance guidance algorithm of path following is designed as a new function of the USV's cross tracking error to adjust the lookahead distance with less parameters and less initial lookahead distance, so that the USV reach the desired path quickly and stably in both straight-line and curve guidance. In the experiment, it is compared with other algorithms using the PID controller and Qihang USV model. The simulation results show the effectiveness of the adaptive lookahead distance algorithm in both straight-line and curve desired path. Compared with other guidance algorithms, the improved adaptive LOS guidance algorithm reaches the desired path faster for the first time and is closer to the desired path after stabilization.
SynDa: A Novel Synthetic Data Generation Pipeline for Activity Recognition

The task of classifying or predicting the activity performed by hu-mans is called human activity recognition. Many existing models aim to solve the problem of activity recognition in this field, but we recognise the lack of real data can have a great impact on the effectiveness of such models. In this paper, we introduce SynDa, a first-of-its-kind streamlined semi-automated pipeline for synthetic data generation built using photorealistic rendering and AI pose estimation, that harvests existing real-life video datasets to create new large-scale datasets. The synthetic data can augment real data to train models robustly and overcome the lack of and associated costs to acquire more real data. Preliminary work indicates that combining real data and synthetic video data generated using this pipeline to train models presents a mAP of 32.35%, while a model trained on real data presented 29.95%.
Development of a Power Electronics Controller with RISC-V based Core for Security-Critical Applications

Cybersecurity of power hardware is becoming increasingly critical with the emergence of smart and connected devices such as Grid-connected inverters, EVs and their chargers, microgrid controllers, energy storage / energy management controllers, and smart appliances. Cyber-attacks on power hardware have had far-reaching and widespread impacts. For such cyber-physical systems, security must be ensured at all levels in the design - hardware, firmware, software and interfaces. Although previous approaches to cybersecurity have focused mainly on vulnerabilities in the firmware middleware, or software, vulnerabilities in the hardware itself are hard to identify and harder to mitigate, especially when most hardware components are proprietary and not examinable. This paper presents one approach to mitigate this conundrum - a completely open-source implementation of a microcontroller core along with the associated peripherals based on the well-known RISC-V instruction set architecture (ISA). The proof-of-concept architecture presented here uses the “Shakti” E-Class microcontroller core integrated with a fully custom PWM controller implemented in Verilog, and validated on a Xilinx Artix FPGA. For critical applications such designs may be replicated as a custom ASIC thereby guaranteeing total security of the computing hardware.
Ultra-short-term power forecast of photovoltaic power station based on VMD–LSTM model optimised by SSA

Abstract The paper takes the data of a 50 MW photovoltaic power generation system as a sample, divides the weather conditions into two categories according to whether there is a sudden change, optimises the decomposition number K and penalty factor of variational mode decomposition (VMD) by using the sparrow intelligent algorithm, decomposes the power sequence in a power mode by using the optimised VMD decomposition method and sends all sub-components to a long short-term memory (LSTM) network for prediction.
ReaLPrune: ReRAM Crossbar-Aware Lottery Ticket Pruning for CNNs

Training machine learning (ML) models at the edge (on-chip training on end user devices) can address many pressing challenges including data privacy/security, increase the accessibility of ML applications to different parts of the world by reducing the dependence on the communication fabric and the cloud infrastructure, and meet the real-time requirements of AR/VR applications. However, existing edge platforms do not have sufficient computing capabilities to support complex ML tasks such as training large CNNs. ReRAM-based architectures offer high-performance yet energy efficient computing platforms for on-chip CNN training/inferencing. However, ReRAM-based architectures are not scalable with the size of the CNN. Larger CNNs have more weights, which requires more ReRAM cells that cannot be integrated in a single chip. Moreover, training larger CNNs on-chip will require higher power, which cannot be afforded by these smaller devices. Pruning is an effective way to solve this problem. However, existing pruning techniques are either targeted for inferencing only, or they are not crossbar-aware. This leads to sub-optimal hardware savings and performance benefits for CNN training on ReRAM-based architectures. In this paper, we address this problem by proposing a novel crossbar-aware pruning strategy, referred as ReaLPrune, which can prune more than 90% of CNN weights. The pruned model can be trained from scratch without any accuracy loss. Experimental results indicate that ReaLPrune reduces hardware requirements by 77.2% and accelerates CNN training by ∼20× compared to unpruned CNNs. ReaLPrune also outperforms other crossbar-aware pruning techniques in terms of both performance and hardware savings. In addition, ReaLPrune is equally effective for diverse datasets and more complex CNNs.