Reviewerly/exHarmony huggingface dataset:
A Decentralized Electricity Trading Framework (DETF) for Connected EVs: A Blockchain and Machine Learning for Profit Margin Optimization

Connected electric vehicles (CEVs) can help cities to reduce road congestion and increase road safety. With the technical improvement made to the battery system in terms of capacity and flexibility, CEVs, as mobile power plants can be an important actor for the electricity markets. Especially, they can trade electricity between each other when supply stations are full or temporarily not available. In this article, we propose an advanced decentralized electricity trading framework between CEVs in parking lots based on consortium blockchain, machine learning, and Game theoretic model. We design a distributed smart contract solution based on a stochastic bidding process, which helps CEVs to sell and buy electricity with their maximum profitability. Finally, numerical simulations with MATLAB and Solidity are conducted to prove the effectiveness of our proposed solution. Also, a comparison with another method in terms of CEVs' profitability improvement and energy trading management is provided.
SSS-YOLO: towards more accurate detection for small ships in SAR image

Aiming at the low detection rate and high false alarm in small ship detection in SAR images, we propose a small-scale ship detection algorithm based on convolutional neural network in this paper. First, we redesign the feature extraction network according to the characters of ship targets in SAR images. The modified network can enrich the spatial and semantics information of small ships. Then, we propose the Path Argumentation Fusion Network (PAFN) to improve the fusion of different feature maps. PAFN uses bottom-up and top-down ways to fuse more location information and semantic information. Both these two optimizations can enhance the detection for small ships. We evaluate our model based on the open SAR-Ship-Dataset and Gaofen-3 SAR images. The experiment results show that our method has excellent performance for small ship detection compared with other deep learning models. Our model improves AP by 6.5% and has higher detection efficiency compared with the baseline YOLOv3 model.
Interoperability Solutions for Blockchain

In recent times, Blockchain has become one of the most influential technology. Numerous industry projects are developed and are under the development phase for enhancing blockchain technology in every way possible and making it applicable to various application-domains. Since the inception of Ethereum, many blockchain platforms have been developed using different types of technologies. Blockchain platforms, from its security perspective, don't interact with the outside world and acts as an independent and standalone ledger. But blockchain technology can not meet the needs of today's world if different ledgers don't interact with each other and with the legacy systems. Hence, Blockchain platforms should be able to communicate with each other and provide services to each other. Researchers and developers around the world are already working to achieve blockchain interoperability. In this paper, we survey interoperability solutions provided for blockchain platforms, both by industry people and the research community.
L-SHADE-E: Ensemble of two differential evolution algorithms originating from L-SHADE

For real parameter single objective optimization, Differential Evolution (DE) performs better than other types of population-based metaheuristic. Nevertheless, in the field of DE, it is impossible for a given combination of operators to perform well in all fitness landscapes. Practice has proved that assembling more than one combinations of operators may lead to improvement in solution. However, some DE algorithms with good performance, e.g., one of the best performers in competitions of real parameter single objective optimization among population-based metaheuristics held by the series of IEEE Congress on Evolutionary Computation - L-SHADE-EpSin, are still not involved in ensemble. Furthermore, ensemble based on similar DE algorithms is rarely reported. Based on such a background, we propose L-SHADE-E, ensemble of L-SHADE-EpSin and L-SHADE-RSP in this paper. The two constituent algorithms are both variants of L-SHADE and belong to similar DE algorithms. In our algorithm, L-SHADE-EpSin or L-SHADE-RSP is selected randomly to run at the beginning. If progress on fitness is low for generations, the other constituent algorithm takes over population immediately. Our experiments are based on the CEC 2014 and CEC 2017 benchmark test suites. Altogether, our algorithm is compared with nine DE algorithms and three population-based metaheuristics other than DE. Experimental results show that our algorithm is very competitive.
A Hybrid MCDM Approach of Selecting Lightweight Cryptographic Cipher Based on ISO and NIST Lightweight Cryptography Security Requirements for Internet of Health Things

The most serious challenges currently faced by healthcare environment is the decision making related to the installation of the most suitable and appropriate lightweight authentication cipher that could provide solutions towards the authentication issues prevailing in IoHT devices. This decision making becomes more troublesome and tricky due to the number of factors that are taken into account such as availability of many existing ciphers, complex and multiple numbers of requirements involved and frequent changing of these requirements from one platform to another. This decision making is also hampered by the nature of IoT devices operating in healthcare environment as they come up with limited functionality, processing, bandwidth and memory. In this regard, we present an evaluation framework focuses upon the selection of best light weight cryptographic ciphers by considering the most important parameters or requirements of criteria. The proposed framework considers the requirements like performance, physical and security as suggested by widely accepted standards such as National Institute of Standards and Technology (NIST) and International Standard Organization standard such as ISO/IEC 29192 for building evaluation criteria. This framework evaluates and selects the best lightweight cryptographic among the 10 ciphers i.e. PRESENT-80, Scalable Encryption Algorithm (SEA), HIGHT, Lightweight Encryption Algorithm (LEA) Advanced Encryption Standard (AES-128), mCrypton, NOEKEON, Klein, Camellia and Tiny Encryption Algorithm (TEA) for the purpose of evaluation in IoHT environment. This framework uses two decision making methods such as Criteria Importance Through Inter criteria (CRITIC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). CRITIC assigns weights to alternatives and TOPSIS is used for evaluating alternatives (ciphers) against the defined criteria of evaluation. The proposed work is novel due to number of reasons such as the newly defined criteria adopted in this framework is the first attempt to use the security requirements of International Standard Organization (ISO) and National Institute of Standards and Technology (NIST). Secondly, this is first time that CRITIC and TOPSIS methods have been applied for assessment and decision making in healthcare environment. Similarly, the selected lightweight authentication cryptographic ciphers are used for the first time for assessment in IoHT environment. This approach addresses both hardware and software characteristics for selecting the best security option for lightweight cryptographic security.
Voice Pathology Detection Using Machine Learning Technique

Recent proposed researches have witnessed that voice pathology detection systems can effectively contribute to the voice disorders assessment and provide early detection of voice pathologies. These systems used machine learning techniques which are considered as very promising tools in the detection of voice pathologies. However, most proposed systems in the detection of voice disorder utilized limited database. Furthermore, low accuracy rate is still the one of the most challenging issues for these techniques. This paper presents a voice pathology detection system using Online Sequential Extreme Learning Machine (OSELM) to classify the voice signal into healthy or pathological. In this work, the voice features are extracted by using Mel-Frequency Cepstral Coefficient (MFCC). The voice samples for the vowel /a/ were collected equally from Saarbrücken voice database (SVD). The proposed method is evaluated by three widely used measurements which are accuracy, sensitivity and specificity. The obtained results show that the maximum accuracy, sensitivity and specificity are 85%, 87% and 87%, respectively. According to the experimental results, the performance of OSELM algorithm is able to differentiate healthy and pathological voices effectively.
MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection

Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and application domains. Most of existing methods can be summarized as anomaly object detection-based and reconstruction error-based techniques. However, due to the bottleneck of defining encompasses of real-world high-diversity outliers and inaccessible inference process, individually, most of them have not derived groundbreaking progress. To deal with those imperfectness, and motivated by memory-based decision-making and visual attention mechanism as a filter to select environmental information in human vision perceptual system, in this paper, we propose a Multi-scale Attention Memory with hash addressing Autoencoder network (MAMA Net) for anomaly detection. First, to overcome a battery of problems result from the restricted stationary receptive field of convolution operator, we coin the multi-scale global spatial attention block which can be straightforwardly plugged into any networks as sampling, upsampling and downsampling function. On account of its efficient features representation ability, networks can achieve competitive results with only several level blocks. Second, it's observed that traditional autoencoder can only learn an ambiguous model that also reconstructs anomalies "well" due to lack of constraints in training and inference process. To mitigate this challenge, we design a hash addressing memory module that proves abnormalities to produce higher reconstruction error for classification. In addition, we couple the mean square error (MSE) with Wasserstein loss to improve the encoding data distribution. Experiments on various datasets, including two different COVID-19 datasets and one brain MRI (RIDER) dataset prove the robustness and excellent generalization of the proposed MAMA Net.
Policy-based Chameleon Hash for Blockchain Rewriting with Black-box Accountability

Policy-based chameleon hash is a useful primitive for blockchain rewriting. It allows a party to create a transaction associated with an access policy, while another party who possesses enough rewriting privileges satisfying the access policy can rewrite the transaction. However, it lacks accountability. The chameleon trapdoor holder may abuse his/her rewriting privilege and maliciously rewrite the hashed object in the transaction without being identified. In this paper, we introduce policy-based chameleon hash with black-box accountability (PCHBA). Black-box accountability allows an attribute authority to link modified transactions to responsible transaction modifiers in case of dispute, in which any public user identifies those transaction modifiers from interacting with an access device/blackbox. We first present a generic framework of PCHBA. Then, we present a practical instantiation, showing its practicality through implementation and evaluation analysis.
Commodity anti-counterfeiting decision in e-commerce trade based on machine learning and Internet of Things

Some unscrupulous merchants saw the profitable sale of goods on the Internet. They took the opportunity to introduce fake and inferior goods and infringing goods online for sale. This practice greatly damaged the legitimate rights and interests of online shopping consumers. Therefore, how to effectively manage Internet fake sales is a big problem that needs to be solved urgently. This article mainly studies the decision-making of counterfeit goods in e-commerce trade based on machine learning and Internet of Things. This article defines the core issues of anti-counterfeiting of e-commerce goods, clarifies the purpose of anti-counterfeiting of e-commerce goods, innovates and proposes new ideas and methods of anti-counterfeiting systems from the perspective of machine learning and the Internet of Things Anti-counterfeiting sharing and anti-counterfeiting punishment, effectively deal with the issue of e-commerce goods purchase. The survey results of this paper show that about 89.9% of repurchasing users are in the 0.4-1 prediction score segment, indicating that the model's threshold setting at about 0.4 will have a good prediction effect, and the model's prediction effect is stable. The experimental results of this paper show that the prediction model in this paper can well predict users' purchasing behaviour and can shield and report merchants with fake and shoddy products.
ISD: Self-Supervised Learning by Iterative Similarity Distillation

Recently, contrastive learning has achieved great results in self-supervised learning, where the main idea is to pull two augmentations of an image (positive pairs) closer compared to other random images (negative pairs). We argue that not all negative images are equally negative. Hence, we introduce a self-supervised learning algorithm where we use a soft similarity for the negative images rather than a binary distinction between positive and negative pairs. We iteratively distill a slowly evolving teacher model to the student model by capturing the similarity of a query image to some random images and transferring that knowledge to the student. Specifically, our method should handle unbalanced and unlabeled data better than existing contrastive learning methods, because the randomly chosen negative set might include many samples that are semantically similar to the query image. In this case, our method labels them as highly similar while standard contrastive methods label them as negatives. Our method achieves comparable results to the state-of-the-art models. Our code is available here: https://github.com/UMBCvision/ISD.
RIHOOP: Robust Invisible Hyperlinks in Offline and Online Photographs

In the era of multimedia and Internet, the quick response (QR) code helps people obtain information from offline to online quickly. However, the QR code is often limited in many scenarios because of its random and dull appearance. Therefore, this article proposes a novel approach to embed hyperlinks into common images, making the hyperlinks invisible for human eyes but detectable for mobile devices equipped with a camera. Our approach is an end-to-end neural network with an encoder to hide messages and a decoder to extract messages. To maintain the hidden message resilient to cameras, we build a distortion network between the encoder and the decoder to augment the encoded images. The distortion network uses differentiable 3-D rendering operations, which can simulate the distortion introduced by camera imaging in both printing and display scenarios. To maintain the visual attraction of the image with hyperlinks, a loss function conforming to the human visual system (HVS) is used to supervise the training of the encoder. Experimental results show that the proposed approach outperforms the previous work on both robustness and quality. Based on the proposed approach, many applications become possible, for example, "image hyperlinks" for advertisement on TV, website, or poster, and "invisible watermark" for copyright protection on digital resources or product packagings.
Hybridizing Niching, Particle Swarm Optimization, and Evolution Strategy for Multimodal Optimization

Multimodal optimization problems (MMOPs) are common problems with multiple optimal solutions. In this article, a novel method of population division, called nearest-better-neighbor clustering (NBNC), is proposed, which can reduce the risk of more than one species locating the same peak. The key idea of NBNC is to construct the raw species by linking each individual to the better individual within the neighborhood, and the final species of the population is formulated by merging the dominated raw species. Furthermore, a novel algorithm is proposed called NBNC-PSO-ES, which combines the advantages of better exploration in particle swarm optimization (PSO) and stronger exploitation in the covariance matrix adaption evolution strategy (CMA-ES). For the purpose of demonstrating the performance of NBNC-PSO-ES, several state-of-the-art algorithms are adopted for comparisons and tested using typical benchmark problems. The experimental results show that NBNC-PSO-ES performs better than other algorithms.
Human-Swarm-Teaming Transparency and Trust Architecture

Transparency is a widely used but poorly defined term within the explainable artificial intelligence literature. This is due, in part, to the lack of an agreed definition and the overlap between the connected - sometimes used synonymously - concepts of interpretability and explain ability. We assert that transparency is the overarching concept, with the tenets of interpretability, explainability, and predictability subordinate. We draw on a portfolio of definitions for each of these distinct concepts to propose a human-swarm-teaming transparency and trust architecture (HST3-Architecture). The architecture reinforces transparency as a key contributor towards situation awareness, and consequently as an enabler for effective trustworthy human-swarm teaming (HST).
TGK-Planner: An Efficient Topology Guided Kinodynamic Planner for Autonomous Quadrotors

In this letter, we propose a lightweight yet effective Topology Guided Kinodynamic planner (TGK-Planner) for quadrotor aggressive flights with limited onboard computing resources. The proposed system follows the traditional hierarchical planning workflow, with novel designs to improve the robustness and efficiency in both the pathfinding and trajectory optimization sub-modules. Firstly, we propose the topology guided graph, which roughly captures the topological structure of the environment and guides the state sampling of a sampling-based kinodynamic planner. In this way, we significantly improve the efficiency of finding a safe and dynamically feasible trajectory. Then, we refine the smoothness and continuity of the trajectory in an optimization framework, which incorporates the homotopy constraint to guarantee the safety of the trajectory. The optimization program is formulated as a sequence of quadratic programmings (QPs) and can be iteratively solved in a few milliseconds. Finally, the proposed system is integrated into a fully autonomous quadrotor and validated in various simulated and real-world scenarios. Benchmark comparisons show that our method outperforms state-of-the-art methods with regard to efficiency and trajectory quality. Moreover, we will release our code as an open-source package.
Toward Dynamic User Intention

User intention is an important factor to be considered for recommender systems, which always changes dynamically in different contexts. Recent studies (represented by sequential recommendation) begin to focus on predicting what users want beyond what users like, which are better at capturing user intention and have attracted a surge of interest. However, user intention modeling is non-trivial, because it is generally influenced by various factors, among which item relations and their temporal evolutionary effects are of great importance. For example, consumption of a cellphone will have varying impacts on the demands for its relational items: For complements, the demands are likely to be promoted in the short term; while for substitutes, the long-term effect may take advantage, because users do not need another cellphone immediately. Moreover, the temporal evolutions of different relational effects vary across different domains, which makes it challenging to adaptively take them into consideration. As a result, most existing studies only loosely incorporate item relations by encoding their semantics into embeddings, neglecting fine-grained time-aware effects. In this work, we propose Knowledge-aware Dynamic Attention (KDA) to take both relational effects and their temporal evolutions into consideration. Specifically, to model dynamic impacts of historical relational interactions on user intention, we aggregate the history sequence into relation-specific embeddings, where the attention weight consists of two parts. First, we measure the relational intensity between historical items and the target item to model the absolute degree of influence in terms of each relation. Second, to model how the relational effects drift with time, we innovatively introduce Fourier transform with learnable frequency-domain embeddings to estimate temporal decay functions of different relations adaptively. Subsequently, the self-attention mechanism is leveraged to derive the final representation of the whole history sequence, which reflects the dynamic user intention and will be applied to generate the recommendation list. Extensive experiments in three real-world datasets indicate the proposed KDA model significantly outperforms the state-of-the-art methods on the Top- K recommendation task. Moreover, the proposed Fourier-based method opens up a new avenue to adaptively integrate temporal dynamics into general neural models.
Self-attention binary neural tree for video summarization

In this paper, we address the problem of shot-level video summarization, which aims at selecting a subset of video shots as a summary to represent the original video contents compactly and completely. Most existing methods rely on various network architectures to learn a single score predictor for shot ranking and selection. Different from these methods, we plug network feature learning into a binary neural tree to consider multi-path predictions for each shot, thus enabling the shot evaluation from different aspects. Due to the hierarchical structure of the tree, video shots can be coarse-to-fine encoded by imposing self-attention on them along branches, leading to favorable predictions. Extensive experiments were conducted on two real-world datasets, and the results reveal that the proposed method achieves superior performance in comparison with previous state-of-the-art methods.