Reviewerly/exHarmony huggingface dataset:
Blind Modulation Classification Under Uncertain Noise Conditions: A Multitask Learning Approach

Blind modulation classification is widely used in various military and civilian applications. Characterized by its exemption from likelihood calculation and the ability of using well-trained models, feature-based (FB) classifiers are popular with their high computational efficiency. In recent years, Deep Neural Networks based methods have shown significant improvement in the classification accuracy. However, the performance of FB methods decreases rapidly under low signal-to-noise ratios. To overcome this shortcoming, this letter proposes a joint denoising and modulation classification method based on Multitask Learning, where a denoising network and a classification network are simultaneoulsy trained in an end-to-end manner. In addition, the focal loss function is adopted to highlight the importance of hard-to-classify samples during training. Our numerical experiments show that the proposed method can effectively improve the classification accuracy, and outperform state-of-the-art methods. For example, under the 0dB SNR condition, the performance of our proposed Multitask CNN method is 20% higher than that of the traditional CNN method.
Evaluating the added value of blockchains to local energy markets—Comparing the performance of blockchain‐based and centralised implementations

The continuous decentralisation of the energy system due to the expansion of renewable energies requires new coordination mechanisms such as Local Energy Markets (LEMs) that are capable of integrating millions of prosumers as active participants. Since the end of the 2010s, the blockchain technology has been discussed as a potential infrastructure for LEMs and as a potential game-changer in the energy industry. In this work, the authors introduce LEM specific technology-independent infrastructure requirements, present a Solidity and Python toolbox that allows to compute a comparative performance analysis between a blockchain-based and a central LEM and evaluate the added value of a blockchain-based implementation compared to a conventional reference implementation. Simulations of a LEM with a periodic double auction and settlement showed that a blockchain-based LEM operation requires more than 140 times the computation time compared to a centralised implementation and cannot fulfil data security requirements. Thus, the authors find that blockchain technology in its current state of development does not add significant value to LEMs. All implemented programmes are published in the open-source project lemlab as part of the research project RegHEE.
Semantic Segmentation and Defect Detection of Aerial Insulators of Transmission Lines

Abstract Aiming at the problems of low accuracy and poor generalization ability of insulator defect detection in complex aerial images by existing insulator defect detection algorithms, the possibility of using semantic segmentation technology to simplify insulator features in complex images is explored. The semantic segmentation model DeepLabv3 is cascaded with the target detector yolov3 to realize the semantic segmentation of insulators in aerial images and the detection of defects. The experimental results show that the use of the strategy of semantic segmentation and target detection can increase the accuracy of insulator defect detection by 12.58%, which effectively improves the performance of the detection model.
A Systematic Literature Review on Forensics in Cloud, IoT, AI &amp; Blockchain

In the growing diversified software applications, cybersecurity plays a vital role in preserving and avoiding the loss of data in terms of money, knowledge, and assets of businesses and individuals. The Internet of Things and cloud computing are nowadays the integral part of most software applications that assist in acquiring and storing data seamlessly. It provides the convenience of accessibility for the end-user like home automation, storage of huge streams of data, giving elasticity for increasing or decreasing the volume of data. When it comes to decentralized behavior, applications need to be transformed into blockchain technology. Blockchain technology offers value-added features to applications in terms of enhanced security and easier traceability. The blockchain's unchangeable and incorruptible nature protects it from tampering and hacking. Forensics requires the collection, preservation, and analysis of digital evidence. Artificial Intelligence is predominant in many areas and momentum is gaining to utilize it in the field of forensics. This chapter reviews the application of forensics using Artificial Intelligence in the field of Cloud computing, IoT, and Blockchain Technology. To fulfill the study's goal, a systematic literature review (SLR) was done. By manually searching six (6) well-known databases, documents were extracted. Based on the study topic, thirty three (33) primary studies were eventually considered. The study also discovered that (1) highlights several well-known challenges and open-Issues in IoT forensics research, as it is dependent on other technologies and is crucial when considering an end-to-end IoT application as an integrated environment with cloud and other technologies. (2) There has been less research dedicated to the use of AI in the field of forensics. (3) Contributions on forensic analysis of attacks in blockchain-based systems is not found.
MU-TEIR: Traceable Encrypted Image Retrieval in the Multi-User Setting

The encrypted image retrieval technique allows users to retrieve images in an encrypted manner without decrypting images. However, most of the existing schemes still are vulnerable to security threats and inefficiency, caused by malicious users and inefficient feature extraction methods, respectively. To this end, we propose a traceable encrypted image retrieval in the multi-user setting in this article, termed as MU-TEIR. First, MU-TEIR employs a convolutional neural network VGG16 to extract image feature vectors and calculate the mean and variance of the feature vectors to construct the index, then encrypts index with the distributed two trapdoors public-key cryptosystem. After that, MU-TEIR protects image content by encrypting each image pixel with a standard stream cipher. Furthermore, MU-TEIR utilizes a watermark-based mechanism to prevent malicious query users from maliciously distributing images. Detailed security analysis shows that MU-TEIR protects the outsourced images and indexes security as well as query privacy, and can track malicious users. Experimental results verify effectiveness of MU-TEIR.
DR-Unet: Rethinking the ResUnet++ Architecture with Dual ResPath skip connection for Nuclei segmentation

Nuclei segmentation is a crucial stage in the analysis of cell microscope pictures. By identifying nuclei, researchers may identify and characterize each cell in a sample. Some models used techniques based on encoder-decoder pairs, such as U-Net, Multi ResUnet, DoubleUnet, and ResUnet++, which have been implemented and deployed on the Data Science Bowl 2018 dataset and given excellent results. However, there is still a semantics gap between the features that directly connect from encoder to decoder in ResUnet++, and the extraction of information on many different regions is still limited. To improve the performance of ResUnet++ in this segmentation task, in this paper, we propose a new architecture that uses Double ResPath (DR), called Double respath Unet (DR-Unet). The DR-Unet architecture retains some advantages that made Resunet++ successful such as residual block associated with a squeeze and excitation block. Besides that, we also pass the encoder features through Respath, which can bridge the semantic gap instead of combining the encoder with the decoder feature straightforwardly. Moreover, we use Progressive Atrous Spatial Pyramidal Pooling, PASPP, to replace ASPP to capture contextual information more efficiently. Experimental results demonstrate that DR-Unet outperforms ResUnet, DoubleUnet, and other models in the benchmark.
Facial landmark‐guided surface matching for image‐to‐patient registration with an RGB‐D camera

Fiducial marker-based image-to-patient registration is the most common way in image-guided neurosurgery, which is labour-intensive, time consuming, invasive and error prone.We proposed a method of facial landmark-guided surface matching for image-to-patient registration using an RGB-D camera. Five facial landmarks are localised from preoperative magnetic resonance (MR) images using deep learning and RGB image using Adaboost with multi-scale block local binary patterns, respectively. The registration of two facial surface point clouds derived from MR images and RGB-D data is initialised by aligning these five landmarks and further refined by weighted iterative closest point algorithm.Phantom experiment results show the target registration error is less than 3 mm when the distance from the camera to the phantom is less than 1000 mm. The registration takes less than 10 s.The proposed method is comparable to the state-of-the-arts in terms of the accuracy yet more time-saving and non-invasive.
BiRe-ID: Binary Neural Network for Efficient Person Re-ID

Person re-identification (Re-ID) has been promoted by the significant success of convolutional neural networks (CNNs). However, the application of such CNN-based Re-ID methods depends on the tremendous consumption of computation and memory resources, which affects its development on resource-limited devices such as next generation AI chips. As a result, CNN binarization has attracted increasing attention, which leads to binary neural networks (BNNs). In this article, we propose a new BNN-based framework for efficient person Re-ID (BiRe-ID). In this work, we discover that the significant performance drop of binarized models for Re-ID task is caused by the degraded representation capacity of kernels and features. To address the issues, we propose the kernel and feature refinement based on generative adversarial learning (KR-GAL and FR-GAL) to enhance the representation capacity of BNNs. We first introduce an adversarial attention mechanism to refine the binarized kernels based on their real-valued counterparts. Specifically, we introduce a scale factor to restore the scale of 1-bit convolution. And we employ an effective generative adversarial learning method to train the attention-aware scale factor. Furthermore, we introduce a self-supervised generative adversarial network to refine the low-level features using the corresponding high-level semantic information. Extensive experiments demonstrate that our BiRe-ID can be effectively implemented on various mainstream backbones for the Re-ID task. In terms of the performance, our BiRe-ID surpasses existing binarization methods by significant margins, at the level even comparable with the real-valued counterparts. For example, on Market-1501, BiRe-ID achieves 64.0% mAP on ResNet-18 backbone, with an impressive 12.51× speedup in theory and 11.75× storage saving. In particular, the KR-GAL and FR-GAL methods show strong generalization on multiple tasks such as Re-ID, image classification, object detection, and 3D point cloud processing.
Implementation Requirements and System Architecture for Mixed Reality Telepresence Application Scenario

With the advance of technology, human-to-human long distance communication becomes closer to real life-like experience. Cheap and easy to install 3D sensors, affordable virtual and mixed reality Head-Mounted Displays (HMD) are paving the way towards widespread usage. However, the present development in this direction is still infant and the lack of standards and recommendations are the main obstacle for off the shelf availability of such technology. Considering a specific case study, based on a controlled environment, we define the requirements for Mixed Reality Telepresence System (MRTS), propose the system architecture in terms of hardware and software ones and provide implementation details for each of the its components.
Design and implementation of hybrid consensus mechanism for IoT based healthcare system security

Nowadays, blockchain is emerging as a worthwhile technology for managing sensitive data in electronic healthcare system. It plays an important role in healthcare, medical research and insurance sectors. The consensus algorithms used in blockchain technology for the selection of a new block, provide high-level of security to IoT devices. However, the reliability problem still exists. In the present paper, a blockchain based hybrid consensus mechanism (HCM) is implemented in electronic healthcare system (EHS) to overcome trustworthiness issues. The aim of the proposed HCM is to maintain reputation module on the basis of the block activities. In this context, EHS is designed in four layers namely: system layer, inter-network layer, blockchain layer, and cloud layer. Moreover, HCM consists of five algorithms for creation, validation, fork handling (if any), Merkle tree construction and reward/punishment module respectively. Dev C++ software platform is used for the simulation of the above mentioned HCM algorithms excluding Merkle tree construction which is simulated using conditional contrast high itemset tree. It is observed that all the blocks use same methodology to be the part of a new blockchain network. Moreover, the CPU and memory consumption in the implementation of HCM is always below two percent and about fifty percent respectively as shown in the latency graph. The basic security goal (confidentiality, integrity and availability) is guaranteed with the help of the height of the Merkel tree as well. The performances of the proposed HCM and Proof of X-repute (PoXR) blockchain consensus algorithms are compared with respect to various parameters such as final difficulty, reward and punishment provisions etc. HCM shows superior performance as the final earned reputation is calculated for each block along with the reward and punishment modules during the deployment of blockchain network. In addition, a simple concept of Merkle-tree is opted for providing reward and punishment rather than a set of complex mathematical equations as used in PoXR consensus algorithm.
Research on face recognition technology based on an improved LeNet-5 system

With the continuous development of technology, face recognition technology became highly valued in the current society. In this paper, a deep convolutional neural network structure with deconvolution is constructed with the Keras framework based on the theory of deep learning. The LeNet-5 network structure is improved by adding the deconvolution layer. Meanwhile, a face recognition system is also designed. The performed experiment demonstrates that this method exhibits high accuracy and can accurately identify faces presented in videos.
Trusted Decentralized Federated Learning

Federated learning (FL) has received significant attention from both academia and industry, as an emerging paradigm for building machine learning models in a communication-efficient and privacy preserving manner. It enables potentially a massive number of resource constrained agents (e.g. mobile devices and IoT devices) to train a model by a repeated process of local training on agents and centralized model aggregation on a central server. To overcome the single-point-of-failure and scalability issues of the traditional FL frameworks, decentralized (server-less) FL has been proposed. In a decentralized FL setting, agents implement consensus techniques by exchanging local model updates. Despite bypassing the direct exchange of raw data between the collaborating agents, this scheme is still vulnerable to various security and privacy threats such as data poisoning attack.In this paper, we propose trust as a metric to measure the trustworthiness of the FL agents and thereby enhance the security of the FL training. We first elaborate on trust as a security metric by presenting a mathematical framework for trust computation and aggregation within a multi-agent system. We then discuss how this framework can be incorporated within a decentralized FL setup introducing the trusted decentralized FL algorithm. Finally, we validate our theoretical findings by means of numerical experiments.
Supervised Machine Learning Algorithms to Detect Instagram Fake Accounts

Instagram is extremely popular because many celebrities and their fan pages use Instagram as the platform for them to communicate. Instagram offers many media sharing features and has proven to be the most popular social media platform for promoting many brands. As the most popular platform, Instagram also has fake users. Regrettably, some people do malicious activities using fake accounts such as impersonating artists or influencers, hate comments and spread rumors to become viral. Hence, this research aims to detect Instagram fake users based on the user's profile. There are several stages before account authenticity detection is successful, starting from data pre-processing, selecting a classification model, and classification evaluation. The algorithms that are used to create the supervised machine learning model are Logistic Regression, Bernoulli Naive Bayes, Random Forest, Support Vector Machine, and Artificial Neural Network (ANN). This paper tried two experiments. The first is that the default state of the model has no parameters, and no features are added. Second, to improve the accuracy, new features and tuning parameters were added in the experiment. Models that perform better than other models based on the second experiment with new features and parameters are Logistic Regression and Random Forest, with an accuracy of 0.93.
Sampling electronic structure quadratic unconstrained binary optimization problems (QUBOs) with Ocean and Mukai solvers

The most advanced D-Wave Advantage quantum annealer has 5000+ qubits, however, every qubit is connected to a small number of neighbors. As such, implementation of a fully-connected graph results in an order of magnitude reduction in qubit count. To compensate for the reduced number of qubits, one has to rely on special heuristic software such as qbsolv, the purpose of which is to decompose a large quadratic unconstrained binary optimization (QUBO) problem into smaller pieces that fit onto a quantum annealer. In this work, we compare the performance of the open-source qbsolv which is a part of the D-Wave Ocean tools and a new Mukai QUBO solver from Quantum Computing Inc. (QCI). The comparison is done for solving the electronic structure problem and is implemented in a classical mode (Tabu search techniques). The Quantum Annealer Eigensolver is used to map the electronic structure eigenvalue-eigenvector equation to a QUBO problem, solvable on a D-Wave annealer. We find that the Mukai QUBO solver outperforms the Ocean qbsolv with one to two orders of magnitude more accurate energies for all calculations done in the present work, both the ground and excited state calculations. This work stimulates the further development of software to assist in the utilization of modern quantum annealers.
Multi-source weakly supervised salient object detection via boosting weak-annotation source and constraining object structure

Training under multi-source weak annotations can reduce the performance gap between weakly supervised and fully supervised salient object detection (SOD) methods. However, it is challenging to train multiple weak-annotation sources since it may lose important structural information of salient regions and generate unnecessary noise when utilizing weak-annotation sources and fusing multi-source weak annotations. In this study, we propose a novel weakly supervised saliency detection framework which contains a multi-source fused refinement network (MFRN) and a salient region prediction network (SRPN), to make full use of the attributive advantages of diverse weak-annotation sources to generate a refined saliency map. In MFRN, we design a weak-supervision boosting module to integrate pixel-wise boosted annotations into weak annotations, so as to eliminate the risk of missing salient information when using only weak annotation for learning saliency and provided strengthened weak supervision for training SRPN. As unnecessary noise is produced in the training process, we propose a structure-constraint loss for SRPN to eliminate noise from the scope of salient objects. The structural consistency between the detected saliency regions and the real salient objects that comply with human perception can be preserved by the awareness of the structural constraint. Moreover, an edge enhancement branch is designed to generate feature maps with rich structures for SRPN to sharpen the edge of the predicted salient region. Experimental results on several large-scale datasets demonstrate that the proposed method can outperform other weakly supervised SOD methods; furthermore, the proposed method even outperformed some fully supervised methods.
Multimodal face aging framework via learning disentangled representation

Existing face aging (FA) approaches usually concentrate on a universal aging pattern, and produce restricted aging faces from one-to-one mapping. However, the diversity of living environments impact individuals differently in their oldness. To simulate various aging effects, we propose a multimodal FA framework based on face disentanglement technique of age-specific and age-irrelevant information. A Variational Autoencoder (VAE)-based encoder is designed to represent the distribution of the age-specific attributes. To capture the age-irrelevant features, a cycle-consistency loss of unpaired faces is utilized among various age spans. The extensive experimental results demonstrate that the sampled age-specific codes along with an age-irrelevant feature make the multimodal FA diverse and realistic.
An Omnidirectional Platform for Education and Research in Cooperative Robotics

In this paper we present a new, affordable, omnidirectional robot platform which is suitable for research and education in cooperative robotics. We design and implement the platform for the purpose of multi-agent object manipulation and transportation. The design consists of three omnidirectional wheels with two additional traction wheels, making multirobot object manipulation possible. It is validated by performing simple experiments using a setup with one robot and one target object. The execution flow of a simple task (Approach–Press–Lift–Hold–Set) is studied. In addition, we experiment to find the limits of the applied pressure and object orientation under certain conditions. The experiments demonstrate the significance of our inexpensive platform for research and education by proving its feasibility of use in topics such as collaborative robotics, physical interaction, and mobile manipulation.
Out-of-Domain Discourse Dependency Parsing via Bootstrapping: An Empirical Analysis on Its Effectiveness and Limitation

Abstract Discourse parsing has been studied for decades. However, it still remains challenging to utilize discourse parsing for real-world applications because the parsing accuracy degrades significantly on out-of-domain text. In this paper, we report and discuss the effectiveness and limitations of bootstrapping methods for adapting modern BERT-based discourse dependency parsers to out-of-domain text without relying on additional human supervision. Specifically, we investigate self-training, co-training, tri-training, and asymmetric tri-training of graph-based and transition-based discourse dependency parsing models, as well as confidence measures and sample selection criteria in two adaptation scenarios: monologue adaptation between scientific disciplines and dialogue genre adaptation. We also release COVID-19 Discourse Dependency Treebank (COVID19-DTB), a new manually annotated resource for discourse dependency parsing of biomedical paper abstracts. The experimental results show that bootstrapping is significantly and consistently effective for unsupervised domain adaptation of discourse dependency parsing, but the low coverage of accurately predicted pseudo labels is a bottleneck for further improvement. We show that active learning can mitigate this limitation.