Reviewerly/exHarmony huggingface dataset:
Infrared Thermal Image Fault Detection based on YOLOV3-L

In the substation scenario, since the substation equipment is exposed to nature for a long time, it is easy to generate heat and cause the failure of the substation equipment. In order to effectively detect potential faults, a thermal infrared image detection method based on improved YOLOV3 (You Only Look Once) is proposed. This method is based on the overall framework of YOLOV3, and uses another lighter convolution structure to replace the standard convolution in Darknet-53, thereby reducing model parameters, improving the detection speed of thermal infrared images, and optimizing the use of The latter NMS algorithm optimizes the detection accuracy. The experimental results show that the proposed YOLOV3-L method has almost a 100% increase in FPS compared with YOLOV3, while the accuracy is almost unchanged, reaching a MAP value of 95.4%.
A High Throughput True Random Number Generator using Metastability and Chaos

A True Random Number Generator based on a differential ring oscillator with two different signal paths, that uses two different entropy sources, i.e., metastability and chaos, is presented. The ring oscillator is made from a differential delay cell that uses two transmission gates along with a base transistor to pull itself into the metastable mode. When the delay cell leaves the metastable mode and enters the oscillation mode, the circuit’s noise causes its initial phase to be random, acting as the first entropy source. During the oscillation mode, the ring oscillator’s frequency is controlled using the output of a chaotic circuit, which acts as the second entropy source. For the said purpose, the delay cell has two differential pairs for input, which allows the oscillator to have two signal paths with different delays. A 3/5 cell ring oscillator was designed and simulated in a 65nm CMOS process. The proposed TRNG passes all 15 of the NIST’s statistical tests without post-processing while generating random numbers up to 4 Gbps with a power consumption of 10.4 mW and efficiency of 2.575 pJ/bit.
Detect the Video Recording Act of UAV through Spectrum Recognition

Spectrum recognition technology could be an important issue to monitor the video recording act of UAV. The existing methods cannot recognize the UAV spectrum if it coexists with other spectra in the same time-frequency map. This paper proposes Safe-UAV, a complex semantic segmentation model, to solve the problem. Safe-UAV utilizes the time-frequency waterfall map(TWM) as input. We adopt a feature extraction module to obtain the convolution of TWM with the different receptive fields. We also propose a feature fusion unit to implement a grading fusion strategy according to the depth of different convolution layers. Once the training process is complete, Safe-UAV can recognize every pixel of the input TWM. Moreover, we collected ten types of civilian UAVs and built a dataset to evaluate the model performance. The experimental results show that our model ensures high recognition accuracy without reducing the efficiency compared with other algorithms such as espnet and fcn8s.
An application case of object detection model based on Yolov3-SPP model pruning

In order to reduce environmental pollution and accelerate the sustainable utilization of resources, it is very important to classify municipal solid waste. The traditional manual sorting method not only has high labor costs, but also can cause physical harm and threaten the health of practitioners when it comes into contact with hazardous waste. Therefore, it is of great research significance to improve the autonomy, intelligence and practical application of waste sorting. In the specific application of garbage classification, it is restricted by the hardware of edge devices, and it is necessary to meet the requirements of detection accuracy and reduction of computing power consumption at the same time. Therefore, this paper adopts the light YOLOv3-SPP network to classify garbage. Specifically, this paper first builds a garbage detection dataset, and then uses the dataset to train the YOLOv3-SPP network to form a YOLOv3-SPP garbage detection model. Next, model compression is performed on the YOLOv3-SPP garbage detection model using channel pruning and layer pruning, and the compressed model can be directly deployed on the low-computing edge computing device carried in the robot that automatically sorts and recycles garbage, so as to realize the miniaturization and large-scale practical application of the garbage sorting robot. The experimental results show that after model compression, the size and inference time of the model drop by 90.77% and 72.93%, respectively, while the mAP of the model only drops by 3.92%. It shows that the constructed garbage detection model meets the application requirements of real-time detection on edge computing devices with low computing power, and has practical application potential.
Assurance information systems design for blockchain - based micro, small and medium enterprises in Indonesia

Abstract Halal Product Guarantee is legal certainty regarding the halalness of a product as evidenced by a halal certificate. Regulations regarding halal certificates are carried out based on Law Number 33 of 2014 concerning Halal Product Assurance (“Law 33/2014”). This provision requires all products to have a halal certificate. This certificate is hoped to add the value of the product to be absorbed by the market more. However, the problem is that not all business actors or industries can implement halal guarantees. Micro, small, and medium enterprises (MSMEs) have weaknesses in the documentation and halal traceability. These two things become one of the criteria for the halal assurance system to get a halal certificate. We need a computerised information system that can help SMEs document and trace. Meanwhile, blockchain technology is needed for documentation transparency, which effectively keeps track of information and transactions. The system is proven to be safe and transparent. Because when the transaction takes place, all parties can see public access.
Autonomous Driving in Highway Scenarios through Artificial Potential Fields and Model Predictive Control

An approach for automated driving in highway scenarios in the context of a two levels hierarchical architecture is proposed. In particular, we define suitable artificial potential functions (APF) combinations that can effectively handle the most relevant maneuvers of highway driving, such as speed and distance tracking, lane keeping, overtaking and returning. Parameters of the APF functions are dynamically tuned according to the acquired scenario. The defined APF are included in the cost function of a Model Predictive Control (MPC) control problem to generate the path trajectory. A behavioral logic described by a finite state machine (FSM), based on sensor acquired data and suitable dynamic conditions is defined to select the most appropriate maneuver to realize. Extensive simulation tests are introduced to show the effectiveness of the proposed approach.
Vehicle mission guidance by symbolic optimal control

Symbolic optimal control is a powerful method to synthesize algorithmically correct-by-design state-feedback controllers for nonlinear plants. Its solutions are (near-)optimal with respect to a given cost function. In this note, it is demonstrated how symbolic optimal control can be used to calculate controllers for an optimized routing guidance of vehicle systems in continuous state space. In fact, the capacitated vehicle routing problem and a variant of travelling salesman problem are investigated. The latter problem has a relevant application in case of loss of vehicles during mission. A goods delivery scenario and a reconnaissance mission, involving bicycle and aircraft dynamics respectively, are provided as examples.
DNN based Learning Algorithm for State Constrained Stochastic Control of a 2D Cartpole System

In this paper we provide a learning based algorithm for solving a state constrained stochastic control problem and apply it to controlling the motion of a 2D cartpole system (2DCPS) i.e. a planar inverted pendulum robot. The goal of the proposed algorithm is to learn to generate an optimal Markov control policies, under the presence of environmental uncertainties, for the task of path following and pole balancing and avoiding obstacles along the prescribed path. To model the environment uncertainties, we apply the standard framework of stochastic differential equations (SDEs). The resulting state constrained stochastic control problem (SCP) is solved statisti-cally via a maximum likelihood estimator (MLE). Relying on universal approximation power of neural-networks (NNs), we build our MLE using gated-recurrent-units (GRUs). Apart from being able to include state constraints, the novel feature of the estimator is the incorporation of ergodic policy generation and step length generation networks. Consequently, the estimator is practically able to handle (i) general SCP with generic (non-quadratic) cost functions, and (ii) deal with heterogeneous and asynchronous sequential data. We apply our algorithm on a model dynamical system i.e. the 2DCPS and evaluate several training configurations. Based on the results we are led to conclude that the direct policy sampling method to control the forward process performs better than using the forward-backward SDEs (FBSDEs) method. Finally, by comparison, we show that the optimally configured estimator outperforms classical optimization method.
UAV trajectory control with rule-based minimum-energy reference generation

This paper deals with the hierarchical real-time control of unmanned aerial vehicles (UAVs) with rule-based strategy for mission time and energetic references generator based on optimal control theory. The objective of this work is to design a control which ensures an energy consumption close to the optimality, and easily implementable thanks to its low computational cost. The first part of the work deals with the extraction of simple and immediate rules for the determination of the optimal mission time, and the generation of 'energetic trajectories' from the analysis of the optimal control strategy results, to minimize the consumption over a high heterogeneous amount of simulations. Then, a hierarchical real-time controller is proposed to track desired energetic trajectories, identified as optimal. The results provided are validated through numerical experiments and compared in terms of energy performance with the optimal solution.
Emotion Detection in Code-Mixed Roman Urdu - English Text

Emotion detection is a widely studied topic in natural language processing due to its significance in a number of application areas. A plethora of studies have been conducted on emotion detection in European as well as Asian languages. However, a large majority of these studies have been conducted in monolingual settings, whereas little attention has been paid to emotion detection in code-mixed text. Specifically, merely one study has been conducted on emotion detection in Roman Urdu (RU) and English (EN) code-mixed text despite the fact that such text is widely used in social media platforms. A careful examination of the existing study has revealed several issues which justify that this area requires attention of researchers. For instance, more than 37% of the messages in the contemporary corpus are monolingual sentences representing that a purely code-mixed emotion analysis corpus is non-existent. To that end, this study has scrapped 400,000 sentences from three social media platforms to identify 20,000 RU-EN code-mixed sentences. Subsequently, an iterative approach is employed to develop emotion detection guidelines. These guidelines have been used to develop a large RU-EN emotion detection (RU-EN-Emotion) corpus in which 20,000 sentences are annotated as Neutral or Emotion-sentence. The sentences having emotions are further annotated with the respective emotions. Subsequently, 102 experiments are performed to evaluate the effectiveness of six classical machine learning techniques and six deep learning techniques. The results show, (a) CNN is the most effective technique when used with GloVe embeddings, and (b) our developed RU-EN-Emotion corpus is more useful than the contemporary corpus, as it employs a two-level classification approach.
Survey Results from Academic Librarians and Professors on Teaching and Using Pirate Websites

Millions of full-text articles are downloaded per month using pirate websites. The purpose of this study was to survey professors and academic librarians on their knowledge, usage and teaching of pirate websites to students. A 21-question survey with adapted Likert scale and free text questions was sent to six academic librarian listservs and primary corresponding authors of top ten evolutionary and bioinformatics and computational biology journals since 2019 from Google Scholar rankings. Academic librarians (48%) and professors (60.5%) are not actively teaching about pirate websites. What is taught is how to ethically and legally access full-text articles, not the existence of pirate websites and how to use them. Concerns about espionage, viruses, and malware for affiliated institutions are concerns for professors and librarians. Sci-Hub continues to be the most known of pirate websites. Librarians and professors are satisfied with the selection of journal title offerings from their affiliated institutions, but desire additional resources for their research. Ease of use continues to be a reason why pirate websites are used, and cost in accessing full-text articles continues to be a concern for librarians and professors.
Spatial and Temporal Audience Behavior of Scrum Practitioners Around Semi-Public Ambient Displays

Exploring spatial and temporal audience behavior around ambient displays is an important area of HCI research. It aids in, for instance, understanding better user appropriation in natural environments. However, there are only a few tools to capture said behavior, and simultaneously, little knowledge of the space around ambient display installations exists. In this research, we report on audience behavior observed during an in-the-wild study where we deployed our custom Ambient Surfaces solution in a professional, large-scale agile software development context for circa 5 years. Across 18 weeks in 2017, we collected skeletal data with two Microsoft Kinect v2 cameras resulting in behavior information of more than 30,000 passersby. Our results indicate, among others, that users did show the highest levels of engagement at quite some distance to the Ambient Surfaces and that people engaging in direct interaction did so rather purposely. Ultimately, this article encapsulates our research's originality in four contributions including an approach to separate passersby from real users and an in-depth exploration of skeletal data. With the tools and methods illustrated, we hope to demonstrate manifold insights for future research on audience behavior tracking.
IFD: An Intelligent Fast Detection for Real-Time Image Information in Industrial IoT

The processing of images by a convolutional neural network will lead to the loss of image information. Downsampling operation within the network is the main reason for the loss. To cut back the loss and reach an acceptable detection speed, this paper proposes an Intelligent Fast Detection for Real-time Image Information in Industrial IoT (IFD). IFD adopts the improved YOLO-Tiny framework and integrates the VaryBlock module. Firstly, we elect a tiny version of YOLO as the backbone and integrate the VaryBlock module into the network structure. Secondly, WGAN is applied to expand the training dataset of small objects. Finally, we use the unsupervised learning algorithm k-means++ to obtain the best-preset boundary box to improve the accuracy of the classification results. IFD optimizes the loss and detection accuracy of image information while meeting the detection speed. The MS-COCO dataset and RGB images in the TUM dataset are used for training and evaluating our model. The upgraded network’s average accuracy is around 8% higher than the YOLO-Tiny series network, according to the experimental data. The increased network’s detection speed in our hardware settings is at least 65 frames per second.
Missing Well Logs Prediction Based on Hybrid Kernel Extreme Learning Machine Optimized by Bayesian Optimization

Geophysical logging is an essential measurement tool in the oil/gas exploration and development field. In practice, predicting missing well logs is an effective way to reduce the exploration expenses. Because of the complexity and heterogeneity of the reservoir, there must be strong nonlinear correlations between the well logs. To improve the accuracy and stability of the missing well logs prediction, a method based on a Bayesian optimized hybrid kernel extreme learning machine (BO-HKELM) algorithm is proposed. Firstly, the LightGBM algorithm is applied to screen out important features related to the missing well logs and reduce the input dimension of the prediction model. Secondly, the hybrid kernel extreme learning machine (HKELM) algorithm is applied to construct the missing well logs prediction model, and the hyperparameters (C0,C1,d,σ,C) of the model are optimized by the Bayesian algorithm. Finally, the BO-HKELM model is applied to the prediction of the missing well logs in a block of the Ordos Basin in China. The results show that the RMSE, MAE, and R-square predicted by the BO-HKELM model are 0.0767, 0.0613, and 0.9029, respectively. It can be found that the BO-HKELM model has better regression accuracy and generalization ability, and can estimate missing logs more accurately than the traditional machine learning methods, which provides a promised method for missing well logs estimation.
A 3DCNN-LSTM Multi-Class Temporal Segmentation for Hand Gesture Recognition

This paper introduces a multi-class hand gesture recognition model developed to identify a set of hand gesture sequences from two-dimensional RGB video recordings, using both the appearance and spatiotemporal parameters of consecutive frames. The classifier utilizes a convolutional-based network combined with a long-short-term memory unit. To leverage the need for a large-scale dataset, the model deploys training on a public dataset, adopting a technique known as transfer learning to fine-tune the architecture on the hand gestures of relevance. Validation curves performed over a batch size of 64 indicate an accuracy of 93.95% (±0.37) with a mean Jaccard index of 0.812 (±0.105) for 22 participants. The fine-tuned architecture illustrates the possibility of refining a model with a small set of data (113,410 fully labelled image frames) to cover previously unknown hand gestures. The main contribution of this work includes a custom hand gesture recognition network driven by monocular RGB video sequences that outperform previous temporal segmentation models, embracing a small-sized architecture that facilitates wide adoption.
PTEENet: Post-Trained Early-Exit Neural Networks Augmentation for Inference Cost Optimization

For many practical applications, a high computational cost of inference over deep network architectures might be unacceptable.A small degradation in the overall inference accuracy might be a reasonable price to pay for a significant reduction in the required computational resources.In this work, we describe a method for introducing ''shortcuts'' into the DNN feedforward inference process by skipping costly feedforward computations whenever possible.The proposed method is based on the previously described BranchyNet (Teerapittayanon et al., 2016) and the EEnet (Demir, 2019) architectures that jointly train the main network and early exit branches.We extend those methods by attaching branches to pretrained models and, thus, eliminating the need to alter the original weights of the network.We also suggest a new branch architecture based on convolutional building blocks to allow enough training capacity when applied on large DNN's.The proposed architecture includes ''confidence'' heads that are used for predicting the confidence level in the corresponding early exits.By defining adjusted thresholds on these confidence extensions, we can control in real-time the amount of data exiting from each branch and the overall tradeoff between speed and accuracy of our model.In our experiments, we evaluate our method using image datasets (SVHN and CIFAR10) and several DNN architectures (ResNet, DenseNet, VGG) with varied depth.Our results demonstrate that the proposed method enables us to reduce the average inference computational cost and further controlling the tradeoff between the model accuracy and the computations cost.
MyBottega: An Environment for the Innovative Production and Distribution of Digital Art

Recent years have seen a number of innovations that have set the stage for a radical expansion of digital art, from the point of view of both the production and the distribution of works. On the one hand, in fact, the advent of generative capabilities in Artificial Intelligence based on Deep Learning methodologies, such as in particular Generative Adversarial Networks (GANs), make radically innovative tools available to support artistic production; on the other hand, the processes of authentication of works of art made possible by blockchain technologies, such as Non-Fungible Tokens (NFTs), have seen the emergence on a large scale of digital art markets on the Web. These new modes of production and distribution need to be tightly integrated to fully express their potential for adoption. Drawing inspiration from the Italian Renaissance workshop (the "bottega"), which was a hugely successful environment for the production and distribution of art, we designed and implemented the MyBottega software architecture, in order to bring these innovations to the stakeholders (artists, curators, exhibitors, etc.) of today's digital art.
Unsupervised Brain Segmentation System Using K-Means and Neural Network

Voxel-based morphometry is an analysis technique used to quantify matter volume in the human brain from magnetic resonance imaging studies. Atrophies and morphologic changes can be signs of neuronal depletion that can lead to various degenerative diseases. Two of the most worldwide used tools for brain volume assessment and segmentation in white matter, grey matter, and cerebrospinal fluid are the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library (FSL) and Statistical Parameter Mapping (SPM). However, the main issue of these tools is related to the choice of the best parameter setting. In this paper, a novel and unsupervised segmentation system, without any user parameter setting, that uses both the k-means clustering algorithm and artificial neural network is proposed. Performance of this system was evaluated on the Internet Brain Segmentation Repository (IBSR) dataset (v.2.0) and results were compared with FSL and SPM results. The dice similarity score was calculated to compare the segmentations obtained with FSL, SPM, and the proposed system with the reference segmentations. The proposed system resulted fast and reliable, and reduced any error (the dice similarity score was greater than 83%, 86%, and 65% for white matter, grey matter, and cerebrospinal fluid, respectively) demonstrating an improvement in the discrimination among white matter, grey matter, and cerebrospinal fluid.