Reviewerly/exHarmony huggingface dataset:
Segmentation of diatoms using edge detection and deep learning

Diatoms are photosynthesizing algae found in almost every aquatic environment.Detecting the number and diversity of diatoms is very important to analyze water quality appropriately.Accurate segmentation of diatoms is therefore crucial for this detection process.In this study, a new and effective model for the automatic segmentation of diatoms based on image processing and deep learning algorithms is proposed.In the proposed model, edge segments of a given image containing diatoms and nondiatom particles are first obtained.These edge segments are then combined, resulting in closed contours representing diatom candidates.In the final step, the diatom candidates are classified as either diatom or nondiatom using a transfer learning approach with different pretrained deep neural network models including AlexNet, ResNet18, VGG16, and their ensemble.Hence, the boundary of a diatom candidate, which is classified as a diatom, is considered to be the final output of the proposed segmentation model.Besides, a new and annotated diatom image dataset is introduced.The dataset is divided into two parts.The first part is used to train and evaluate the classification stage of the proposed model, while the second part is used for evaluating the overall performance of the proposed segmentation model.This dataset is also made publicly available at https: //www.kaggle.com/huseyingunduz/diatom-datasetfor the research community.Through extensive experimental work, the performance of the proposed segmentation model is measured in terms of precision, recall, F-measure, and intersection over union values.Our work is also compared with the previous works based on various aspects such as segmentation method, dataset size, diatom varieties, dataset availability, success metric, and segmentation performance.Considering all these aspects, it is verified that the proposed model surpasses the previous works, and stands out as a well-performing approach to the automatic segmentation of diatoms.
Assistive Technologies and Quadriplegia: A Map Point on the Development and Spread of the Tongue Barbell Piercing

The barbell piercing can be used as an assistive device that allows people with severe disabilities, such as tetraplegia, to control their environments using the movement of the tongue. The human tongue can move rapidly and accurately, such that the tip can touch every tooth. Lingual control systems allow people with disabilities to take advantage of their residual skills for easier communication and to improve the control of mobility and the surrounding environment. The aim of this study was to conduct a narrative review of the development and dissemination of the assistive technologies based on tongue control by means of the barbell piercing. The design of the study was based on: (I) an overview of Pubmed complemented with other databases and Web searches (also institutional); (II) an organization according to a standardized checklist for narrative reviews; (III) an arrangement with four different perspectives: the trends in the scientific literature, technological evolution and categorization, dominant approaches, issues of incorporation into the health domain—such as acceptance, safety, and regulations. The results have highlighted: (1) that the volume of scientific productions, which started in this sector before the smartphone expansion, has not increased; (2) that it is possible to make a map point of the technological evolution and categorization; (3) that these assistive technologies have a high degree of acceptance and performance, especially when integrated with aid tools with mechatronics; (4) and the complexity of the regulatory framework in this area. The study, from a general point of view, highlighted the high potential of these systems and we suggest investing the energy into agreement tools for assistive technologies (AT)s, such as health technology assessment studies, comparative assessment analysis, or consensus conferences that could allow a better diffusion and use of ATs, including these systems.
<i>GeFL</i>: Gradient Encryption-Aided Privacy Preserved Federated Learning for Autonomous Vehicles

Autonomous vehicles (AVs) are getting popular because of their usage in a wide range of applications like delivery systems, self-driving taxis, and ambulances.AVs utilize the power of machine learning (ML) and deep learning (DL) algorithms to improve their self-driving learning experiences.The sudden surge in the number of AVs raises the need for distributed learning ecosystem to optimize their selfdriving experiences at a rapid pace.Toward this goal, federated learning (FL) benefits, which can create a distributed learning environment for AVs.But, the traditional FL transfers the raw input data directly to a server, which leads to privacy concerns among the end-users.The concept of blockchain helps us to protect privacy, but it requires additional computational infrastructure.The extra infrastructure increases the operational cost for the company handling and maintaining the AVs.Motivated by this, in this paper, the authors introduced the concept of gradient encryption in FL, which preserves the users' privacy without the additional computation requirements.The computational power present in the edge devices helps to finetune the local model and encrypt the input data to preserve privacy without any drop in performance.For performance evaluation, the authors have built a German traffic sign recognition system using a convolutional neural network (CNN) algorithm-based classification system and GeFL.The simulation process is carried out over a wide range of input parameters to analyze the performance at scale.Simulation results of GeFL outperform the conventional FL-based algorithms in terms of accuracy, i.e., 2% higher.Also, the amount of data transferred among the devices in the network is nearly three times less in GeFL compared to the traditional FL.
Information extraction pipelines for knowledge graphs

Abstract In the last decade, a large number of knowledge graph (KG) completion approaches were proposed. Albeit effective, these efforts are disjoint, and their collective strengths and weaknesses in effective KG completion have not been studied in the literature. We extend Plumber , a framework that brings together the research community’s disjoint efforts on KG completion. We include more components into the architecture of Plumber to comprise 40 reusable components for various KG completion subtasks, such as coreference resolution, entity linking, and relation extraction. Using these components, Plumber dynamically generates suitable knowledge extraction pipelines and offers overall 432 distinct pipelines. We study the optimization problem of choosing optimal pipelines based on input sentences. To do so, we train a transformer-based classification model that extracts contextual embeddings from the input and finds an appropriate pipeline. We study the efficacy of Plumber for extracting the KG triples using standard datasets over three KGs: DBpedia, Wikidata, and Open Research Knowledge Graph. Our results demonstrate the effectiveness of Plumber in dynamically generating KG completion pipelines, outperforming all baselines agnostic of the underlying KG. Furthermore, we provide an analysis of collective failure cases, study the similarities and synergies among integrated components and discuss their limitations.
Symmetry breaking/symmetry preserving circuits and symmetry restoration on quantum computers

We discuss here some aspects related to the symmetries of a quantum many-body problem when trying to treat it on a quantum computer. Several features related to symmetry conservation, symmetry breaking, and possible symmetry restoration are reviewed. After briefly discussing some of the standard symmetries relevant for many-particle systems, we discuss the advantage of encoding some symmetries directly in quantum ansätze, especially to reduce the quantum register size. It is, however, well-known that the use of symmetry-breaking states can also be a unique way to incorporate specific internal correlations when a spontaneous symmetry breaking occurs. These aspects are discussed in the quantum computing context. Ultimately, an accurate description of quantum systems can be achieved only when the initially broken symmetries are properly restored. We review several methods explored previously to perform symmetry restoration on a quantum computer, for instance, the ones based on symmetry filtering by quantum phase estimation and by an iterative independent set of Hadamard tests. We propose novel methods that pave the new directions to perform symmetry restoration, like those based on the purification of the state employing the linear combination of unitaries (LCU) approach.
Intelligent Diagnosis Method of Data Center Precision Air Conditioning Fault Based on Knowledge Graph

This study first digitizes, rules and structures complex unstructured data such as massive historical operation and maintenance data and fault judgment experience of operation and maintenance engineering based on semi-automatic entity extraction method; annotate the association or indirect relationship between 63,724 types of faults among triads by means of decision trees. Bayesian algorithm is used to further explore the relationship between triples, the realizes knowledge fusion, knowledge reasoning and knowledge update, and completes knowledge graph construction; combines with fault intelligent diagnosis method, realizes fault prediction, fast discovery, locates fault, type and business impact reasoning, and provides solutions to assist decision making.
Entanglement detection with artificial neural networks

Quantum entanglement is one of the essential resources involved in quantum information processing tasks. However, its detection for usage remains a challenge. The Bell-type inequality for relative entropy of coherence serves as an entanglement witness for pure entangled states. However, it does not perform reliably for mixed entangled states. This paper constructs a classifier by employing the relationship between coherence and entanglement for supervised machine learning methods. This method encodes multiple Bell-type inequalities for the relative entropy of coherence into an artificial neural network to detect the entangled and separable states in a quantum dataset.
Blockchain-Based Decentralized Federated Learning Method in Edge Computing Environment

In recent years, federated learning has been able to provide an effective solution for data privacy protection, so it has been widely used in financial, medical, and other fields. However, traditional federated learning still suffers from single-point server failure, which is a frequent issue from the centralized server for global model aggregation. Additionally, it also lacks an incentive mechanism, which leads to the insufficient contribution of local devices to global model training. In this paper, we propose a blockchain-based decentralized federated learning method, named BD-FL, to solve these problems. BD-FL combines blockchain and edge computing techniques to build a decentralized federated learning system. An incentive mechanism is introduced to motivate local devices to actively participate in federated learning model training. In order to minimize the cost of model training, BD-FL designs a preference-based stable matching algorithm to bind local devices with appropriate edge servers, which can reduce communication overhead. In addition, we propose a reputation-based practical Byzantine fault tolerance (R-PBFT) algorithm to optimize the consensus process of global model training in the blockchain. Experiment results show that BD-FL effectively reduces the model training time by up to 34.9% compared with several baseline federated learning methods. The R-PBFT algorithm can improve the training efficiency of BD-FL by 12.2%.
Observation Centric and Central Distance Recovery for Athlete Tracking

Multi-Object Tracking on humans has improved rapidly with the development of object detection and re-identification algorithms. However, multi-actor tracking over humans with similar appearance and non-linear movement can still be very challenging even for the state-of-the-art tracking algorithm. Current motion-based tracking algorithms often use Kalman Filter to predict the motion of an object, however, its linear movement assumption can cause failure in tracking when the target is not moving linearly. And for multi-player tracking over the sports field, because the players on the same team are usually wearing the same color of jersey, making re-identification even harder both in the short term and long term in the tracking process. In this work, we proposed a motion-based tracking algorithm and three post-processing pipelines for three sports including basketball, football, and volleyball, we successfully handle the tracking of the non-linear movement of players on the sports fields. Experimental results achieved a HOTA of 73.968 on the testing set of ECCV DeeperAction Challenge SportsMOT Dataset and a HOTA of 49.97 on the McGill HPTDataset, showing the effectiveness of the proposed framework and its robustness in different sports including basketball, football, hockey, and volleyball.
Strength Learning Particle Swarm Optimization for Multiobjective Multirobot Task Scheduling

Cooperative heterogeneous multirobot systems have attracted increasing attention in recent years. They use multiple heterogeneous robots to execute complex tasks in a coordinated way. The allocation of heterogeneous robots to cooperative tasks is a significant and challenging optimization problem. However, little work has gone into scheduling large-scale cooperative tasks with precedence constraints and multiple conflicting optimization objectives. Existing methods are insufficient to address the issue. We propose a multiobjective model and develop strength learning particle swarm optimization (SLPSO) to optimize multiple objectives. In this article, the problem is converted into a two-step problem of task permutation construction and robot subset selection. In order to coordinate with the time-extended property of the problem, SLPSO utilizes a hybrid encode scheme: an element-based representation for task permutations and a binary representation for robot coalitions. A strength learning strategy with heuristic information guides particles to enhance their best-performing objectives for improving swarm convergence. In addition, an estimation-based local search is developed to improve spare solutions for enhancing swarm diversity, which determines the search direction by estimating fitness improvements. Experimental results on thirty problem instances are elaborated to demonstrate that the proposed SLPSO significantly outperforms the state-of-the-art algorithms in terms of inverted generational distance and hypervolume metrics. The proposed SLPSO can obtain a set of high-quality and diversified solutions.
Skill-Based Hierarchical Reinforcement Learning for Target Visual Navigation

Target visual navigation aims at controlling the agent to find a target object based on a monocular visual RGB image in each step. It is crucial for the agent to adapt to new environments. As target visual navigation is a complex task, understanding the behavior of the agent is beneficial for analyzing the reasons for failure. This work focuses on improving the readability and success rate of navigation policies. In this paper, we propose a framework named Skill-based Hierarchical Reinforcement Learning (SHRL) for target visual navigation. SHRL contains a high-level policy and three low-level skills. The high-level policy accomplishes the task by utilizing or stopping low-level skills at each step. Low-level skills are designed to separately solve three sub-tasks, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Search, Adjustment</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Exploration</i> . In addition, we propose an Abstract Representation and two penalty items to feed robust features to the high-level policy. Abstract Representation is designed to focus on selecting low-level skills rather than the details of navigation. Experimental results in the artificial environment AI2-Thor indicate that the proposed method outperforms state-of-the-art by a large margin in unseen indoor environments. Moreover, we also provide case studies to illustrate the advantages of SHRL.
Three‐dimensional spatial point computation in fringe projection profilometry

The demodulated phase in fringe projection profilometry (FPP) provides information about the (x,y,z)-coordinates of points of the observed object. However, the phase-to-coordinate conversion employing multiple cameras and projectors with different imaging models is not straightforward. This paper presents the following two contributions. First, a theoretical framework to investigate the triangulation problem in FPP is established. Seven representative triangulation methods commonly used in FPP are reviewed. Second, a generalized triangulation method is proposed for single-axis fringe projection supporting multiple cameras and projectors with lens distortion. The performance of the eight investigated triangulation methods is evaluated in terms of noise robustness and computation time by computer simulations and an experimental fringe projection optical setup. The results demonstrate that the mean dispersion approach yields the best performance, but vertical and horizontal fringes must be projected, while the new method is the most appropriate for single-axis fringe projection using multiple devices with lens distortion.
A fully passive transmitter for decoy-state quantum key distribution

Abstract A passive quantum key distribution (QKD) transmitter generates the quantum states prescribed by a QKD protocol at random, combining a fixed quantum mechanism and a post-selection step. By circumventing the use of active optical modulators externally driven by random number generators, passive QKD transmitters offer immunity to modulator side channels and potentially enable higher frequencies of operation. Recently, the first linear optics setup suitable for passive decoy-state QKD has been proposed. In this work, we simplify the prototype and adopt sharply different approaches for BB84 polarization encoding and decoy-state parameter estimation. In particular, our scheme avoids a probabilistic post-selection step that is central to the former proposal. On top of it, we elaborate a simple and tight custom-made security analysis.
FPGA-Based Vehicle Detection and Tracking Accelerator

A convolutional neural network-based multiobject detection and tracking algorithm can be applied to vehicle detection and traffic flow statistics, thus enabling smart transportation. Aiming at the problems of the high computational complexity of multiobject detection and tracking algorithms, a large number of model parameters, and difficulty in achieving high throughput with a low power consumption in edge devices, we design and implement a low-power, low-latency, high-precision, and configurable vehicle detector based on a field programmable gate array (FPGA) with YOLOv3 (You-Only-Look-Once-version3), YOLOv3-tiny CNNs (Convolutional Neural Networks), and the Deepsort algorithm. First, we use a dynamic threshold structured pruning method based on a scaling factor to significantly compress the detection model size on the premise that the accuracy does not decrease. Second, a dynamic 16-bit fixed-point quantization algorithm is used to quantify the network parameters to reduce the memory occupation of the network model. Furthermore, we generate a reidentification (RE-ID) dataset from the UA-DETRAC dataset and train the appearance feature extraction network on the Deepsort algorithm to improve the vehicles’ tracking performance. Finally, we implement hardware optimization techniques such as memory interlayer multiplexing, parameter rearrangement, ping-pong buffering, multichannel transfer, pipelining, Im2col+GEMM, and Winograd algorithms to improve resource utilization and computational efficiency. The experimental results demonstrate that the compressed YOLOv3 and YOLOv3-tiny network models decrease in size by 85.7% and 98.2%, respectively. The dual-module parallel acceleration meets the demand of the 6-way parallel video stream vehicle detection with the peak throughput at 168.72 fps.
Improving Performance, Reliability, and Feasibility in Multimodal Multitask Traffic Classification with XAI

The promise of Deep Learning (DL) in solving hard problems such as network Traffic Classification (TC) is being held back by the severe lack of transparency and explainability of this kind of approaches. To cope with this strongly felt issue, the field of eXplainable Artificial Intelligence (XAI) has been recently founded, and is providing effective techniques and approaches. Accordingly, in this work we investigate interpretability via XAIbased techniques to understand and improve the behavior of state-of-the-art multimodal and multitask DL traffic classifiers. Using a publicly available security-related dataset (ISCX VPNNONVPN), we explore and exploit XAI techniques to characterize the considered classifiers providing global interpretations (rather than sample-based ones), and define a novel classifier, DISTILLER-EVOLVED, optimized along three objectives: performance, reliability, feasibility. The proposed methodology proves as highly appealing, allowing to much simplify the architecture to get faster training time and shorter classification time, as fewer packets must be collected. This is at the expenses of negligible (or even positive) impact on classification performance, while understanding and controlling the interplay between inputs, model complexity, performance, and reliability.
A Taxonomy towards Blockchain based Multimedia content Security

Blockchain can change how media content dispersion, utilization, and installment for music, video, and different means occur. Modified multi-content assistance bundles are past the abilities of current frameworks. Overseeing advanced privileges, eminence assortments, and exchanges among different go-betweens is very troublesome in the present computerized environments. By bringing responsibility, security, and control to the media inventory network, blockchain, with its common record approach, will assist with improving the media production network and diminish copyright encroachments. It may, for instance, decrease copyright encroachments in music streaming, where distributors and lyricists much of the time denounce music web-based features like Spotify, Napster, and Pandora of not paying them anything they are owed, losing up to 25% of streaming eminences.