Reviewerly/exHarmony huggingface dataset:
Fast wireless sensor for anomaly detection based on data stream in an edge-computing-enabled smart greenhouse

Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things (IoT) technology, which can monitor the environmental information in real-time and employ the information to contribute to intelligent decision-making. In the process, anomaly detection for wireless sensor data plays an important role. However, the traditional anomaly detection algorithms originally designed for anomaly detection in static data do not properly consider the inherent characteristics of the data stream produced by wireless sensors such as infiniteness, correlations, and concept drift, which may pose a considerable challenge to anomaly detection based on data stream and lead to low detection accuracy and efficiency. First, the data stream is usually generated quickly, which means that the data stream is infinite and enormous. Hence, any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory space. Second, there exist correlations among different data streams, and traditional algorithms hardly consider these correlations. Third, the underlying data generation process or distribution may change over time. Thus, traditional anomaly detection algorithms with no model update will lose their effects. Considering these issues, a novel method (called DLSHiForest) based on Locality-Sensitive Hashing and the time window technique is proposed to solve these problems while achieving accurate and efficient detection. Comprehensive experiments are executed using a real-world agricultural greenhouse dataset to demonstrate the feasibility of our approach. Experimental results show that our proposal is practical for addressing the challenges of traditional anomaly detection while ensuring accuracy and efficiency.
SketchHairSalon

Recent deep generative models allow real-time generation of hair images from sketch inputs. Existing solutions often require a user-provided binary mask to specify a target hair shape. This not only costs users extra labor but also fails to capture complicated hair boundaries. Those solutions usually encode hair structures via orientation maps, which, however, are not very effective to encode complex structures. We observe that colored hair sketches already implicitly define target hair shapes as well as hair appearance and are more flexible to depict hair structures than orientation maps. Based on these observations, we present SketchHairSalon , a two-stage framework for generating realistic hair images directly from freehand sketches depicting desired hair structure and appearance. At the first stage, we train a network to predict a hair matte from an input hair sketch, with an optional set of non-hair strokes. At the second stage, another network is trained to synthesize the structure and appearance of hair images from the input sketch and the generated matte. To make the networks in the two stages aware of long-term dependency of strokes, we apply self-attention modules to them. To train these networks, we present a new dataset containing thousands of annotated hair sketch-image pairs and corresponding hair mattes. Two efficient methods for sketch completion are proposed to automatically complete repetitive braided parts and hair strokes, respectively, thus reducing the workload of users. Based on the trained networks and the two sketch completion strategies, we build an intuitive interface to allow even novice users to design visually pleasing hair images exhibiting various hair structures and appearance via freehand sketches. The qualitative and quantitative evaluations show the advantages of the proposed system over the existing or alternative solutions.
A Real-Time Object Detector for Autonomous Vehicles Based on YOLOv4

Object detection is an important part of autonomous driving technology. To ensure the safe running of vehicles at high speed, real-time and accurate detection of all the objects on the road is required. How to balance the speed and accuracy of detection is a hot research topic in recent years. This paper puts forward a one-stage object detection algorithm based on YOLOv4, which improves the detection accuracy and supports real-time operation. The backbone of the algorithm doubles the stacking times of the last residual block of CSPDarkNet53. The neck of the algorithm replaces the SPP with the RFB structure, improves the PAN structure of the feature fusion module, adds the attention mechanism CBAM and CA structure to the backbone and neck structure, and finally reduces the overall width of the network to the original 3/4, so as to reduce the model parameters and improve the inference speed. Compared with YOLOv4, the algorithm in this paper improves the average accuracy on KITTI dataset by 2.06% and BDD dataset by 2.95%. When the detection accuracy is almost unchanged, the inference speed of this algorithm is increased by 9.14%, and it can detect in real time at a speed of more than 58.47 FPS.
Country-level evaluation of solar radiation data sets using ground measurements in China

Solar radiation is a crucial parameter that affects the thermal environment in buildings. The spatial and temporal distributions of solar radiation data are important for energy-efficient building design. Models that correlate solar radiation with other parameters can address the lack of solar radiation data. Satellite-derived products and reanalysis data sets have been produced using solar radiation models. The accuracy of these products directly affects building thermal environment design. To choose the most appropriate data set, it is necessary to evaluate the deviation in different data sets based on ground measurements. We used data acquired between 2001 and 2016 from 98 solar radiation measurement stations in China to verify two satellite-derived products (SARAH-E and CERES-SYN1deg) and two reanalysis data sets (ERA5 and MERRA-2). The CERES-SYN1deg and SARAH-E products performed better than the ERA5 and MERRA-2 data sets at estimating the daily global solar radiation. The daily global radiation products were more accurate than direct, diffuse, and hourly global solar radiation products. The models merged ground measurements show good performance. Further improvement in solar radiation estimation especially direct and diffuse in areas where there are no ground measurements and taking into account the effect of inadequate weather conditions on the hourly solar radiation is required. These findings may provide the basis for solar radiation models and products, especially applications in the building industry.
Scaling advantage of chaotic amplitude control for high-performance combinatorial optimization

Abstract The development of physical simulators, called Ising machines, that sample from low energy states of the Ising Hamiltonian has the potential to transform our ability to understand and control complex systems. However, most of the physical implementations of such machines have been based on a similar concept that is closely related to relaxational dynamics such as in simulated, mean-field, chaotic, and quantum annealing. Here we show that dynamics that includes a nonrelaxational component and is associated with a finite positive Gibbs entropy production rate can accelerate the sampling of low energy states compared to that of conventional methods. By implementing such dynamics on field programmable gate array, we show that the addition of nonrelaxational dynamics that we propose, called chaotic amplitude control, exhibits exponents of the scaling with problem size of the time to find optimal solutions and its variance that are smaller than those of relaxational schemes recently implemented on Ising machines.
Indoor Human Fall Detection Using Data Augmentation-Assisted Transfer Learning in an Aging Population for Smart Homecare: A Deep Convolutional Neural Network Approach

We provide a one-of-a-kind solution to the problem of detecting human falls in naturalistic environments. This is crucial since falls cause thousands of deaths each year, and vision-based approaches provide a promising and effective way to identify falls. We consider this tough problem to be an example of action detection, and we solve it using the power of deep networks. In this study, the YOLOv3 model, a cutting-edge deep transfer learning object identification approach, is utilized to construct a standing and fall detection model. The detection model, according to the study's findings, has a training and validation accuracy of 97.60% and 92.63%, respectively, with an mAP value of 99.96%. The suggested model is suited for Smart Home Care for the Elderly because of its superior performance over existing algorithms for fall detection. The system has a total testing accuracy of 100%, with detection per frame accuracy ranging from 75% to 99%.
Profiling gas consumption in solidity smart contracts

Nowadays, more and more applications are developed for running on a distributed ledger technology, namely dApps. The business logic of dApps is usually implemented within smart contracts developed through Solidity, a programming language for writing smart contracts on different blockchain platforms, including the popular Ethereum. In Ethereum, the smart contracts run on the machines of miners and the gas corresponds to the execution fee compensating such computing resources. However, the deployment and execution costs of a smart contract depend on the implementation choices done by developers. Unappropriated design choices could lead to higher gas consumption than necessary. In this paper, we (i) identify a set of 19 Solidity code smells affecting the deployment and transaction costs of a smart contract, and (ii) assess the relevance of such smells through a survey involving 34 participants. On top of these smells, we propose GasMet, a suite of metrics for statically evaluating the code quality of a smart contract from the gas consumption perspective. An experiment involving 2186 smart contracts demonstrates that the proposed metrics have direct associations with deployment costs. The metrics in our suite can be used for more easily identifying source code segments that need optimizations.
New goal-oriented requirements extraction framework for e-health services: a case study of diagnostic testing during the COVID-19 outbreak

Purpose This paper aims to introduce the goal-oriented requirements extraction approach (GOREA). It is an elicitation approach that uses, specifically, healthcare business goals to derive the requirements of e-health system to be developed. Design/methodology/approach GOREA consists of two major phases: (1) modelling e-health business requirements phase and (2) modelling e-health information technology (IT) and systems requirements phase. The modelling e-health business requirements phase is divided into two main stages: (1) model e-health business strategy stage and (2) model e-health business environment stage. The modelling e-health IT and systems requirements phase illustrates the process of obtaining requirements of e-health system from the organizational goals that are determined in the previous phase. It consists of four main steps that deal with business goals of e-health system: (1) modelling e-health business process (BP) step; (2) modelling e-health business goals step; (3) analysing e-health business goals step; and (4) eliciting e-health system requirements step. A case study based on the basic operations and services in hospital emergency unit for checking patient against COVID-19 virus and taking its diagnostic testing has been set and used to examine the validity of the proposed approach by achieving the conformance of the developed system to the business goals. Findings The results indicate that (1) the proposed GOREA has a positive influence on the system implementation according to e-health business expectations; and (2) it can successfully fulfil the need of e-health business in order to save the citizens life by checking them against COVID-19 virus. Research limitations/implications The proposed approach has some limitations. For example, it is only validated using one e-health business goal and thus it has to be authenticated with different e-health business goals in order to address different e-health problems. Originality/value Many e-health projects and innovations are not established based on robust system requirements engineering phase. In order to ensure the success delivery of e-health services, all characteristics of e-health systems and applications must be understood in terms of technological perspectives as well as the all system requirements.
Deep image clustering by fusing contrastive learning and neighbor relation mining

Contrastive learning is widely used in deep image clustering due to its ability to learn discriminative representations. However, some studies simply combined contrastive learning with clustering. This line of works often ignores semantic meaningful representations and leads to suboptimal performance. In this paper, we propose a new deep image clustering framework called Nearest Neighbor Contrastive Clustering (NNCC), which fuses contrastive learning with neighbor relation mining. During training, contrastive learning and neighbor relation mining are updated alternately, where the former is conducted in the backward pass, while the latter is employed in the forward pass. Specially, we empirically find that data augmentation is an effective technique for generating nearest neighbors manually. A stronger data augmentation means more nearest neighbors involved for learning powerful discriminative representations in the contrastive learning. Due to effective neighbor relation mining, the proposed framework learns more semantic meaningful representations with contrastive learning and obtains more accurate image clusters. Through experimental results on six image datasets, the proposed framework defeats compared state-of-the-arts clustering methods.
Detecting computer-generated disinformation

Abstract Modern neural language models can be used by malicious actors to automatically produce textual content looking as it has been written by genuine human users. Due to progress in the controllability of computer-generated text, there is a risk that state-sponsored actors may start using such methods for conducting large-scale information operations. Various detection algorithms have been suggested in the research literature to identify texts produced by language model-based generators, but these are often mainly evaluated on test data from the same distribution as they have been trained on. We evaluate promising Transformer-based detection algorithms in a large variety of experiments involving both in-distribution and out-of-distribution test data, as well as evaluation on more realistic in-the-wild data. It is shown that the generalizability of the detectors can be questioned, especially when applied to short social media posts. Moreover, the best performing (RoBERTa-based) detector is shown to be non-robust also to basic adversarial attacks, illustrating how easy it is for malicious actors to avoid detection by the current state-of-the-art detection algorithms.
Identifying Quality Attributes of FODA and DSSA Methods in Domain Analysis using a Case Study

The concept of reuse is addressed in software product line engineering (SPLE) by distinguishing between two types of processes for development: application and domain engineering. In domain engineering (DE), the focus is to define and manage all the valid combinations of reusable artefacts participating in the product line (PL) and the relationships between them. Domain analysis (DA) is the activity in DE, which describes the variability and commonalities in a domain. Instead of being applicable to a single software system, DA is applicable to multiple related software systems. Each DA method supports different quality attributes which are preserved while reuse. In this paper, two DA methods namely domain specific software architecture (DSSA) ad feature oriented domain analysis (FODA) are explained to model common and variable requirements of PL(s). A case study on Automated Teller Machine (ATM) is discussed using these two methods to identify quality attributes supported by these methods. This paper also discuss two different DA methods, which allows reusability for the identification, organization and knowledge modelling regarding the domain solution, so as to provide reuse among each element of domain.
Secure Decentralized Attribute-Based Sharing of Personal Health Records With Blockchain

Personal health records (PHRs) are located in a patient-centered electronic health system in which users can store and share medical information. However, PHRs have recently been plagued by security issues, such as the leakage of personal health information, illegal access to patient data, and data tampering. Recent security developments, such as introducing an access control policy with attribute-based encryption (ABE) or utilizing blockchain, have only been partially successful in solving these issues. Ongoing challenges to PHR sharing include single points of failure, node cheating attacks, and fair keyword search issues. In this article, we tackle these challenges by introducing a distributed PHR-sharing scheme based on blockchain and ciphertext policy ABE (CP-ABE), which allows for fast and efficient encryption and decryption. Blockchain maintains the integrity and the tracing source of the data while also recording all operations on the data in the form of transactions. In addition, the blockchain nodes act as attribute authorities to construct the CP-ABE cryptosystem. The tracing of malicious blockchain nodes is realized by tracing cryptography algorithms. Furthermore, the fair retrieval of ciphertext is achieved by employing smart contracts. To overcome the limited storage capacity of blockchain, we adopt both the on-chain and off-chain storage modes in our new system. Security analysis indicates that our new scheme remains intact when threatened by an indistinguishable chosen plaintext attack (IND-CPA) and an indistinguishable chosen keywords attack (IND-CKA). As such, we conclude that our proposed approach is feasible and efficient.
Federated Learning for Physical Layer Design

Model-free techniques, such as machine learning (ML), have recently attracted much interest toward the physical layer design (e.g., symbol detection, channel estimation, and beamforming). Most of these ML techniques employ centralized learning (CLK) schemes and assume the availability of datasets at a parameter server (PS), demanding the transmission of data from edge devices, such as mobile phones, to the PS. Exploiting the data generated at the edge, federated learning (FL) has been proposed recently as a distributed learning scheme, in which each device computes the model parameters and sends them to the PS for model aggregation, while the datasets are kept intact at the edge. Thus, FL is more communication-efficient and privacy-preserving than CL and applicable to the wireless communication scenarios, wherein the data are generated at the edge devices. This article presents the recent advances in FL-based training for physical layer design problems. Compared to CL, the effectiveness of FL is presented in terms of communication overhead with a slight performance loss in the learning accuracy. The design challenges, such as model, data, and hardware complexity, are also discussed in detail along with possible solutions.
Eye-Tracking in Interactive Virtual Environments: Implementation and Evaluation

Not all eye-tracking methodology and data processing are equal. While the use of eye-tracking is intricate because of its grounding in visual physiology, traditional 2D eye-tracking methods are supported by software, tools, and reference studies. This is not so true for eye-tracking methods applied in virtual reality (imaginary 3D environments). Previous research regarded the domain of eye-tracking in 3D virtual reality as an untamed realm with unaddressed issues. The present paper explores these issues, discusses possible solutions at a theoretical level, and offers example implementations. The paper also proposes a workflow and software architecture that encompasses an entire experimental scenario, including virtual scene preparation and operationalization of visual stimuli, experimental data collection and considerations for ambiguous visual stimuli, post-hoc data correction, data aggregation, and visualization. The paper is accompanied by examples of eye-tracking data collection and evaluation based on ongoing research of indoor evacuation behavior.
Multiscale Object Detection in Remote Sensing Images Combined with Multi-Receptive-Field Features and Relation-Connected Attention

Object detection is an important task of remote sensing applications. In recent years, with the development of deep convolutional neural networks, object detection in remote sensing images has made great improvements. However, the large variation of object scales and complex scenarios will seriously affect the performance of the detectors. To solve these problems, a novel object detection algorithm based on multi-receptive-field features and relation-connected attention is proposed for remote sensing images to achieve more accurate detection results. Specifically, we propose a multi-receptive-field feature extraction module with dilated convolution to aggregate the context information of different receptive fields. This achieves a strong capability of feature representation, which can effectively adapt to the scale changes of objects, either due to various object scales or different resolutions. Then, a relation-connected attention module based on relation modeling is constructed to automatically select and refine the features, which combines global and local attention to make the features more discriminative and can effectively improve the robustness of the detector. We designed these two modules as plug-and-play blocks and integrated them into the framework of Faster R-CNN to verify our method. The experimental results on NWPU VHR-10 and HRSC2016 datasets demonstrate that these two modules can effectively improve the performance of basic deep CNNs, and the proposed method can achieve better results of multiscale object detection in complex backgrounds.
Semi-supervised classification via full-graph attention neural networks

Graph neural networks (GNNs) leverage graph convolutions or their approximations to extract features of nodes from graph-structured data. Nevertheless, these methods only combine information from nodes’ neighborhoods, without taking into account the impact of other nodes outside neighborhoods. To address the shortcomings, we present full-graph attention neural networks (FGANNs), novel neural network architectures that consider the impact of all nodes when performing self-attention, leveraging masked attention to enable (implicitly) specifying different weights to different nodes in a neighborhood. Under such circumstances, we address several important challenges of spectral-based graph neural networks simultaneously, and make FGANN readily available to semi-supervised classification problems. Extensive experiments on citation networks offer evidence that the proposed approach outperforms state-of-the-art methods.