Reviewerly/exHarmony huggingface dataset:
Practical Insights to Design a Blockchain-Based Energy Trading Platform

Today, the development of decentralized energy management systems has accelerated due to the daily growth of renewable energy technologies and communications infrastructure. At the distribution system level, this approach has manifested itself with the emergence of the local energy market. In fact, the local energy market is becoming a new operating model to control local generation units. This paper describes the general architecture and elements used to implement a blockchain-based local energy market within a transactive management platform. After an overview of internet of things (IoT) communication technologies and the existing central-authority-based applications, the general structure and elements of peer-to-peer (P2P) networks are reviewed. Next, the concepts of blockchain-based technologies and the required specifications for different building layers are outlined based on the limited relevant literature available. The concepts and requirements are investigated to provide practical insights to design trading platforms.
An improved smart key frame extraction algorithm for vehicle target recognition

The smart extraction of key frames is one of the critical technologies for vehicle target recognition in the video streaming environment. Currently, the common algorithms are scale-invariant feature transform (SIFT) or background-difference method. Both algorithms have their own advantages and shortcomings. In this paper, a smart key frame extraction algorithm is proposed by combining the background difference method and SIFT feature matching algorithm, at the same time, the criterion factor K is introduced. The algorithm absorbs the idea of background difference and SIFT feature matching, and divides video frames into mutation frames and non-mutation frames. Hash algorithm is used to distinguish the similarity of two frames and remove redundant frames. The local feature of video sequence is introduced to solve the problem of sharp increasing calculation for inter frame distance. Experimental results show that the improved smart key frame extraction algorithm has a lower missing frame rate and higher accuracy for vehicle target recognition.
Talk to The Ghost: The Storybox methodology for faster development of storytelling chatbots

This paper presents the Storybox Methodology which combines a novel framework for structuring knowledge and conversations around a story (D-PAF), with a live chatroom-based training approach that builds the conversation knowledge base via live chatroom interactions. Chatbots have achieved success as intelligent interfaces in education, health, sales and support, but their move towards mainstream adoption has been hindered by the large amount of development resources required, in terms of data collection, preparation, user testing and technical knowledge. The complexity of the development task often necessitates both a system author and a domain expert working effectively together, adding further complexity and risk. Overcoming these barriers could increase feasibility of chatbots in a range of expert contexts. In education, there are groups of learners who do not enjoy reading and writing. Storytelling chatbots might be able to introduce these groups to enjoyable new ways to read and write, having a beneficial impact on their education and future prospects. This paper proposes the Storybox Methodology for the rapid development of storytelling chatbots. Storybox is evaluated by creating, training and testing 'The Ghost', a chatbot enacting Hamlet's Ghost character from William Shakespeare's dramatic tragedy. The results showed that after a period of live chatbot training of only 25 training conversations, The Ghost was able to conduct convincing conversations with participants.
MARS: Detecting brain class/method code smell based on metric–attention mechanism and residual network

Abstract Code smell is the structural design defect that makes programs difficult to understand, maintain, and evolve. Existing works of code smell detection mainly focus on prevalent code smells, such as feature envy, god class, and long method. Few works have been done on detecting brain class/method. Furthermore, existing deep‐learning‐based approaches leverage the CNN model to improve accuracy by barely increasing the number of layers, which may cause a problem of gradient degradation. To this end, this paper proposes a novel approach called MARS to detect brain class/method. MARS improves the gradient degradation by employing an improved residual network. It increases the weight value of those important code metrics to label smelly samples by introducing a metric–attention mechanism. To support the training of MARS, a dataset called BrainCode is generated by extracting more than 270,000 samples from 20 real‐world applications. MARS is evaluated on BrainCode and compared to other machine‐learning‐based and deep‐learning‐based approaches. The experimental results demonstrate that the average accuracy of MARS is 2.01 % higher than that of the existing approaches, which improves state‐of‐the‐art.
A classification method to classify bone marrow cells with class imbalance problem

Bone marrow cell morphology has long been used to diagnose blood diseases. However, it requires long-term experience from a suitable person. Furthermore, the outcomes of their recognition are subjective and no quantitative standard has been established yet. Consequently, developing a deep learning automatic system for classifying bone marrow cells is extremely important. However, real-life data sets, such as bone marrow cell data, constantly suffer from a long-tail distribution problem, owing to which the final trained classifier is biased toward a large number of categories. Thus, addressing this issue is crucial. The current research presents a class balance classification method (CBCM) for classifying 15 types of bone marrow cell data sets with a class imbalance problem. CBCM outperforms other balance approaches such as random over-sampling, synthetic minority over-sampling technique (SMOTE), random under-sampling, weighted random forest and weighted cross-entropy function, achieving precision, sensitivity, and specificity values of 84.53%, 84.44% and 99.29% respectively. A more extensive comparison between the baseline and CBCM, as well as the Grad-CAM and Guided Grad-CAM of CBCM, reveals that CBCM is a reliable and effective solution to address the long-tail distribution problem of the bone marrow cell data sets.
MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation

Monocular 3D object detection has recently shown promising results, however there remain challenging problems. One of those is the lack of invariance to different camera intrinsic parameters, which can be observed across different 3D object datasets. Little effort has been made to exploit the combination of heterogeneous 3D object datasets. In contrast to general intuition, we show that more data does not automatically guarantee a better performance, but rather, methods need to have a degree of ’camera independence’ in order to benefit from large and heterogeneous training data. In this paper we propose a category-level pose estimation method based on instance segmentation, using camera independent geometric reasoning to cope with the varying camera viewpoints and intrinsics of different datasets. Every pixel of an instance predicts the object dimensions, the 3D object reference points projected in 2D image space and, optionally, the local viewing angle. Camera intrinsics are only used outside of the learned network to lift the predicted 2D reference points to 3D. We surpass camera independent methods on the challenging KITTI3D benchmark and show the key benefits compared to camera dependent methods.
THF: 3-Way Hierarchical Framework for Efficient Client Selection and Resource Management in Federated Learning

Federated learning (FL) is a promising technique for collaboratively training machine-learning models on massively distributed clients data under privacy constraints. However, the existing FL literature focuses on speeding up the learning process and ignores minimizing the communication cost which is critical for resource-constrained clients. To this end, in this article, we propose a novel 3-way hierarchical framework (THF) to promote communication efficiency in FL. Using the proposed framework, only a cluster head (CH) communicates with the cloud server through edge aggregation in order to minimize the communication cost of clients. In particular, the clients upload their local models to their respective CHs, which are responsible to forward them to the corresponding edge server. The edge server averages the local models and iterates until it achieves the edge accuracy. Afterward, each edge server uploads the edge models to the cloud server for global aggregation. In this way, model downloading and uploading requires less bandwidth due to the short distance from source to destination that makes an efficient 3-way hierarchical network structure. In addition, we formulate a joint communication and computation resource management scheme through efficient client selection in order to achieve global cost minimization in FL. We conduct extensive empirical evaluations on diverse data learning tasks on multiple data sets to signify that THF achieves global cost savings and converges within fewer communication rounds compared to other FL approaches.
Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory

In the medical field, the detection of breast cancer may be a mysterious task. Physicians must deduce a conclusion from a significantly vague knowledge base. A mammogram can offer early diagnosis at a low cost if the breasts' satisfactory mammogram images are analyzed. A multi-decision Intuitionistic Fuzzy Evidential Reasoning (IFER) approach is introduced in this paper to deal with imprecise mammogram classification efficiently. The proposed IFER approach combines intuitionistic trapezoidal fuzzy numbers and inclusion measures to improve representation and reasoning accuracy. The results of the proposed technique are approved through simulation. The simulation is created utilizing MATLAB software. The screening results are classified and finally grouped into three categories: normal, malignant, and benign. Simulation results show that this IFER method performs classification with accuracy almost 95% compared to the already existing algorithms. The IFER mammography provides high accuracy in providing early diagnosis, and it is a convenient diagnostic tool for physicians.
Optimal computational resource pricing in vehicular edge computing: A Stackelberg game approach

Vehicular edge computing (VEC) pushes the computational resources to the logical edge of the networks, thus enabling vehicles to run resource-hungry and time-sensitive applications by outsourcing operations. Many studies revolved around VEC focus on the optimization of response latency, energy consumption, or both of them, assuming that the computational resources in VEC can be utilized for free. However, VEC provisions computational resources on a pay-as-you-go basis, which means VEC can obtain revenues by leasing the computational resources. In this paper, we focus on the real-time computational resource pricing in VEC, in the hope to reach a win–win situation where both VEC and vehicles can optimize their respective utility values. To reach such a mutually satisfactory result, we adopt the Stackelberg game to model the computational resource pricing problem in this paper. In this game, vehicles are followers and the edge server serves as the leader. Furthermore, we have proven that a unique Stackelberg equilibrium exists in the proposed pricing game. A distributed algorithm is put forward to solve our problem, which considers the privacy of vehicles. The distributed approach is evaluated by extensive experiments, in terms of convergence rate, running time and so on. The simulation results demonstrate that the distributed approach can achieve satisfactory results without privacy disclosure compared to the centralized approach.
Multiclass Fuzzily Weighted Adaptive-Boosting-Based Self-Organizing Fuzzy Inference Ensemble Systems for Classification

Adaptive boosting (AdaBoost) is a widely used technique to construct a stronger ensemble classifier by combining a set of weaker ones. Zero-order fuzzy inference systems (FISs) are very powerful prototype-based predictive models for classification, offering both great prediction precision and high user interpretability. However, the use of zero-order FISs as base classifiers in AdaBoost has not been explored yet. To bridge the gap, in this article, a novel multiclass fuzzily weighted AdaBoost (FWAdaBoost)-based ensemble system with a self-organizing fuzzy inference system (SOFIS) as the ensemble component is proposed. To better incorporate the SOFIS, FWAdaBoost utilizes the confidence scores produced by the SOFIS in both sample weight updating and ensemble output generation, resulting in more accurate classification boundaries and greater prediction precision. Numerical examples on a wide range of benchmark classification problems demonstrate the efficacy of the proposed approach.
SiamOAN: Siamese object-aware network for real-time target tracking

Existing Siamese-based tracking algorithms usually utilize local features to represent the object, which lack sufficient discrimination and may degrade tracking performance in challenging situations. To address this issue, we propose a novel object-aware network to improve feature representation and achieve robust object tracking. The proposed object-aware network contains a background filter module (BFM), channel complementary module (CCM), and template adaptive network (TAN). Specifically, by locating the target in the initial frame on the feature maps, BFM suppresses the background interference of the target template. CCM captures the global context by exploring the complementary information of each channel. The lightweight TAN adaptively recognizes valuable features for the target and represents the target template just through a single vector. Benefiting from these three components, the object-aware network enhances the discrimination of feature maps and alleviates background interference to some extent. The proposed object-aware network could be integrated with the Siamese-based backbone network for real-time object tracking, named SiamOAN. Extensive experiments on the six challenging benchmarks including OTB100, UAV123, VOT2016, VOT2018, GOT10k, and LaSOT, show that the proposed SiamOAN outperforms many state-of-the-art trackers and runs at approximately 67 fps on GPU RTX3090.
FakeWake: Understanding and Mitigating Fake Wake-up Words of Voice Assistants

In the area of Internet of Things (IoT), voice assistants have become an important interface to operate smart speakers, smartphones, and even automobiles. To save power and protect user privacy, voice assistants send commands to the cloud only if a small set of preregistered wake-up words are detected. However, voice assistants are shown to be vulnerable to the FakeWake phenomena, whereby they are inadvertently triggered by innocent-sounding fuzzy words. In this paper, we present a systematic investigation of the FakeWake phenomena from three aspects. To start with, we design the first fuzzy word generator to automatically and efficiently produce fuzzy words instead of searching through a swarm of audio materials.We manage to generate 965 fuzzy words covering 8 most popular English and Chinese smart speakers. To explain the causes underlying the FakeWake phenomena, we construct an interpretable tree-based decision model, which reveals phonetic features that contribute to false acceptance of fuzzy words by wake-up word detectors. Finally, we propose remedies to mitigate the effect of FakeWake. The results show that the strengthened models are not only resilient to fuzzy words but also achieve better overall performance on original training datasets.
Novel machine learning approach for solar photovoltaic energy output forecast using extra-terrestrial solar irradiance

The inherently intermittent nature of solar irradiance and other meteorological variables means that accurate forecasting of the photovoltaic power output is essential for planning and balancing photovoltaic power systems. This study proposes a novel approach to predicting one-week-ahead half-hourly photovoltaic power output in the United Kingdom using sloped extra-terrestrial irradiance and weather data (e.g., cloud-cover and temperature) as input parameters. A Non-linear Autoregressive Exogenous Neural Network is trained on a three-year historical dataset from two photovoltaic plants in the United Kingdom with capacities of 53 and 103 MWp. The forecasting model captures huge intra-daily variations of photovoltaic output, which is particularly useful to balance the supply and demand of the electricity system. The result of the study validates the concept of using sloped extra-terrestrial irradiance as an input parameter and suggests that meteorological conditions will dictate the accuracy of predictions. Findings also indicate that the use of sloped extra-terrestrial irradiance in conjunction with cloud-cover presented the optimal combination of input parameters as these provided the simplest and most cost-effective model without reducing accuracy. The approach can have universal value as it only requires coordinates and weather data. There is now a strong imperative to use the model in other locations where the weather is more stable.
On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks

Deploying federated learning (FL) over wireless networks with resource-constrained devices requires balancing between accuracy, energy efficiency, and precision. Prior art on FL often requires devices to train deep neural networks (DNNs) using a 32-bit precision level for data representation to improve accuracy. However, such algorithms are impractical for resource-constrained devices since DNNs could require execution of millions of operations. Thus, training DNNs with a high precision level incurs a high energy cost for FL. In this paper, a quantized FL framework, that represents data with a finite level of precision in both local training and uplink transmission, is proposed. Here, the finite level of precision is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format. In the considered FL model, each device trains its QNN and transmits a quantized training result to the base station. Energy models for the local training and the transmission with the quantization are rigorously derived. An energy minimization problem is formulated with respect to the level of precision while ensuring convergence. To solve the problem, we first analytically derive the FL convergence rate and use a line search method. Simulation results show that our FL framework can reduce energy consumption by up to 53% compared to a standard FL model. The results also shed light on the tradeoff between precision, energy, and accuracy in FL over wireless networks.
Histograms of oriented mosaic gradients for snapshot spectral image description

This paper presents a feature descriptor using Histogram of Oriented Mosaic Gradient (HOMG) that extracts spatial-spectral features directly from mosaic spectral images. Spectral imaging utilizes unique spectral signatures to distinguish objects of interest in the scene more discriminatively. Snapshot spectral cameras equipped with spectral filter arrays (SFAs) capture spectral videos in real time, making it possible to detect/track fast moving targets based on spectral imaging. How to effectively extract the spatial-spectral feature directly from the mosaic spectral images acquired by snapshot spectral cameras is a core issue for detection/tracking. So far, there is a lack of comprehensive and in-depth research on this issue. To this end, this paper proposed a new spatial-spectral feature extractor for mosaic spectral images. The proposed scheme finds two forms of SFA neighborhood (SFAN) to construct a feature extractor suitable for any SFA structure. Exploiting the spatial-spectral correlation in two SFANs, we design six mosaic spatial-spectral gradient operators to compute spatial-spectral gradient maps (SGMs). HOMG descriptors are constructed using the magnitude and orientation of SGMs. The effectiveness and generalizability of the proposed method have been verified with object tracking experiments. Compared to the state-of-the-art feature descriptors, HOMG ranked first on two datasets captured with snapshot spectral camera with different SFAs, achieving a gain of 3.9% and 5.9% in average success rate over the second-ranked feature.
Eluding Secure Aggregation in Federated Learning via Model Inconsistency

Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of secure aggregation prevents the server from learning the value and the source of the individual model updates provided by the users, hampering inference and data attribution attacks.
Attribute disentanglement and registration for occluded person re-identification

Occluded person re-identification is a challenging task which suffers from various obstacles. However, existing occluded Re-ID methods tend to exploit body detectors for pedestrian alignment, which are over-reliant on detection and lack of a flexible matching mechanism. To address this issue, we propose an Attribute Disentanglement and Registration (ADR) network to excavate non-occluded regions via attribute feature disentanglement, which can be matched effectively with a robust and soft attribute registration. The proposed ADR takes full advantages of pedestrian attributes' high-level semantic concepts to alleviate the occlusion problem. First, the Attribute Disentanglement (AD) module obtains the representations of different attributes by localizing their spatial positions. Then the Attribute Registration (AR) module searches and matches these localized regions between different pedestrian images to conduct a registration, which allows the attribute features to be adaptively and efficiently matched. Extensive experiments on occluded, partial, and holistic Re-ID benchmarks demonstrate the effectiveness of the proposed ADR framework as well as its superiority over the existing state-of-the-art methods.
Scene Essence

What scene elements, if any, are indispensable for recognizing a scene? We strive to answer this question through the lens of an exotic learning scheme. Our goal is to identify a collection of such pivotal elements, which we term as Scene Essence, to be those that would alter scene recognition if taken out from the scene. To this end, we devise a novel approach that learns to partition the scene objects into two groups, essential ones and minor ones, under the supervision that if only the essential ones are kept while the minor ones are erased in the input image, a scene recognizer would preserve its original prediction. Specifically, we introduce a learnable graph neural network (GNN) for labelling scene objects, based on which the minor ones are wiped off by an off-the-shelf image inpainter. The features of the inpainted image derived in this way, together with those learned from the GNN with the minor-object nodes pruned, are expected to fool the scene discriminator. Both subjective and objective evaluations on Places365, SUN397, and MIT67 datasets demonstrate that, the learned Scene Essence yields a visually plausible image that convincingly retains the original scene category.