Reviewerly/exHarmony huggingface dataset:
Forensic soil provenancing in an urban/suburban setting: A simultaneous multivariate approach

Soil is a ubiquitous material at the Earth's surface with potential to be a useful evidence class in forensic and intelligence applications. Compositional data from a soil survey over North Canberra, Australian Capital Territory, are used to develop and test an empirical soil provenancing method. Mineralogical data from Fourier Transform InfraRed spectroscopy (FTIR) and geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for both total and aqua regia-soluble trace elements) are obtained from the survey's 268 topsoil samples (0-5 cm depth; 1 sample per km2 ). The simultaneous provenancing approach is underpinned by (i) the calculation of Spearman's correlation coefficients (rS ) between an evidentiary sample and all the samples in the database for all variables generated by each analytical method; and (ii) the preparation of an interpolated raster grid of rS for each evidentiary sample and method resulting in a series of provenance rasters ("heat maps"). The simultaneous provenancing method is tested on the North Canberra soil survey with three "blind" samples representing simulated evidentiary samples. Performance metrics of precision and accuracy indicate that the FTIR (mineralogy) and XRF (geochemistry) analytical methods offer the most precise and accurate provenance predictions. Maximizing the number of analytes/analytical techniques is advantageous in soil provenancing. Despite acknowledged limitations, it is concluded that the empirical soil provenancing approach can play an important role in forensic and intelligence applications.
Reliable and Sustainable Product Evaluation Management System Based on Blockchain

To facilitate the consumers&#x2019; online shopping, it is very necessary to build a reliable and sustainable product evaluation management (PEM) system to store, secure, and retrieve the evaluation data of products. To this end, we propose a novel PEM system based on blockchain technologies. In this PEM system, the evaluation data of products including evaluation scores and comments are stored in the interplanetary file system (IPFS), and then the data hash addresses are returned and stored on the blockchain. Then, the retrieval of evaluation data is implemented by designing and deploying smart contracts on the Ethereum blockchain. Since it is almost impossible to change any data stored in blockchain and IPFS, the proposed PEM system can protect the comment data from intentional or unintentional modifications. Moreover, the proposed PEM system allows users to efficiently and reliably retrieve the relevant evaluation data of a certain product by a given query keyword with the increase of data stored on the blockchain. In addition, to achieve high sustainability, the proposed system also contains an economic incentive mechanism, which is beneficial to form a virtuous circle in online shopping. The proposed system is simulated on the popular blockchain platform, i.e., Ethereum with Solidity programming language. The experimental results and analysis demonstrate that the proposed PEM system achieves desirable usability, reliability, and sustainability.
An Attack Tree Based Risk Analysis Method for Investigating Attacks and Facilitating Their Mitigations in Self-Sovereign Identity

Self-Sovereign Identity (SSI) is a digital identity that is managed in a decentralized manner utilising an underlying blockchain. It allows identity owners to manage and store their digital identities without relying on centralised third-party providers. Providing full control of an identity to its owner seeks to enhance the security and privacy of the individual. The utilisation of the decentralised trust model provided by an underlying blockchain realises this user-centred control. However, this operational change towards greater control and responsibility placed upon identity owners poses new challenges and security threats to the SSI system. Heretofore, there have been no significant research studies performed to assess potential attacks on the SSI system. The SSI model is an emerging Identity Management model, and requires a meticulous study of its potential attack surfaces. Therefore, this paper proposes an attack tree based risk analysis method for investigating potential attacks on the SSI system and their associated risks in facilitating their mitigations. This proposed attack tree based risk analysis method presents a systematic and generalised model to generate attack trees that can be used to perform risk analysis. In this investigation, three potential attacks on the SSI system are focused: faking identity, identity theft and distributed denial of service attacks. For each attack, the attack tree based risk analysis is performed; and subsequently, their mitigations are proposed.
Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight

In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.
S-BDS: An Effective Blockchain-based Data Storage Scheme in Zero-Trust IoT

With the development of the Internet of Things (IoT) , a large-scale, heterogeneous, and dynamic distributed network has been formed among IoT devices. There is an extreme need to establish a trust mechanism between devices, and blockchain can provide a zero-trust security framework for IoT. However, the efficiency of the blockchain is far from meeting the application requirements of the IoT, which has become the biggest resistance to the application of the blockchain in the IoT. Therefore, this paper combines sharding to build an effective Blockchain-based IoT data storage scheme (S-BDS) . Sharding can solve the problem of blockchain capacity and scalability. While the blockchain provides data immutability and traceability for the IoT, it also brings huge demands for data credibility verification. The communication delay in the IoT system seriously affects the security of the system, while the Merkle proof of traditional blockchain occupies a lot of communication resources. This paper constructs Insertable Vector Commitment (IVC) in the bilinear group and replaces the Merkle tree with IVC to store IoT data in the blockchain. The construct has small-sized proof. It also has the ability to record the number of updates, which can prevent replay-attacks. Experiments show that each block processes 1,000 transactions, the proof size of a single data piece is 30% of the original scheme, and proofs from different shards can be aggregated. IVC can effectively reduce communication congestion and improve the stability and security of the IoT system.
Qualitative Analysis of Text Summarization Techniques and Its Applications in Health Domain

For the better utilization of the enormous amount of data available to us on the Internet and in different archives, summarization is a valuable method. Manual summarization by experts is an almost impossible and time-consuming activity. People could not access, read, or use such a big pile of information for their needs. Therefore, summary generation is essential and beneficial in the current scenario. This paper presents an efficient qualitative analysis of the different algorithms used for text summarization. We implemented five different algorithms, namely, term frequency-inverse document frequency (TF-IDF), LexRank, TextRank, BertSum, and PEGASUS, for a summary generation. These algorithms are chosen based on various factors. After reviewing the state-of-the-art literature, it generates good summaries results. The performance of these algorithms is compared on two different datasets, i.e., Reddit-TIFU and MultiNews, and their results are measured using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure to perform analysis to decide the best algorithm among these and generate the summary. After performing a qualitative analysis of the above algorithms, we observe that for both the datasets, i.e., Reddit-TIFU and MultiNews, PEGASUS had the best average F-score for abstractive text summarization and TextRank algorithms for extractive text summarization, with a better average F-score.
A Hierarchical Path Planning Approach with Multi-SARSA Based on Topological Map

In this paper, a novel path planning algorithm with Reinforcement Learning is proposed based on the topological map. The proposed algorithm has a two-level structure. At the first level, the proposed method generates the topological area using the region dynamic growth algorithm based on the grid map. In the next level, the Multi-SARSA method divided into two layers is applied to find a near-optimal global planning path, in which the artificial potential field method, first of all, is used to initialize the first Q table for faster learning speed, and then the second Q table is initialized with the connected domain obtained by topological map, which provides the prior information. A combination of the two algorithms makes the algorithm easier to converge. Simulation experiments for path planning have been executed. The results indicate that the method proposed in this paper can find the optimal path with a shorter path length, which demonstrates the effectiveness of the presented method.
Detection of Lower Body for AGV Based on SSD Algorithm with ResNet

Detection of human lower body provides an implementation idea for the automatic tracking and accurate relocation of automatic vehicles. Based on traditional SSD and ResNet, this paper proposes an improved detection algorithm R-SSD for human lower body detection, which utilizes ResNet50 instead of VGG16 to improve the feature extraction level of the model. According to the application of acquisition equipment, the model input resolution is increased to 448 × 448 and the model detection range is expanded. Six feature maps of the updated resolution network are selected for detection and the lower body image dataset is clustered into five categories for aspect ratio, which are evenly distributed to each feature detection map. The experimental results show that the model R-SSD detection accuracy after training reaches 85.1% mAP. Compared with the original SSD, the detection accuracy is improved by 7% mAP. The detection confidence in practical application reaches more than 99%, which lays the foundation for subsequent tracking and relocation for automatic vehicles.
Students' Attitudes Towards Mobile Learning

The aim of this research is to reveal the attitudes of engineering faculty students towards mobile learning. The study group of the research consisted of 80 students studying at various universities in Kazakhstan and voluntarily agreeing to participate in the research. The research was created in a qualitative design. In this study, a semi-structured interview form developed by the researchers was used in order not to determine the mobile learning attitudes of the students. Students' attitudes towards mobile learning were evaluated based on the content analysis method. The result of the research reveals that the vast majority of students use mobile learning applications frequently. When students were asked about the advantages of mobile learning applications, they stated that it increases motivation, facilitates learning and is useful. The disadvantages of mobile learning were categorized by students as information pollution, need for mobile support and high cost. In order to eliminate the disadvantages of mobile learning applications; Uninterrupted internet and wireless data transportation support can be provided to meet the mobile support needs in universities.
Potential estimation of rooftop photovoltaic with the spatialization of energy self-sufficiency in urban areas

The distribution of rooftop PV is spatial heterogeneity with the location of buildings. With the assistance of geographic information systems, this study proposed an integrated research framework for the geo-spatial potential assessment of PV systems on the roof of urban buildings as well as its feasibility analysis. Furthermore, a geo-spatialization of electricity self-sufficiency rate is also introduced to explore the contribution of rooftop PV on urban energy security. The proposed approach was verified by a case study in the central area of Wuhan, China with a 100 m spatial resolution. The results show that the maximum annual PV power generation potential in the central area of Wuhan is 10,757 GWh, which could satisfy 31.83% of local energy consumption. A 100% deployment of rooftop PV would also lead to a great carbon emission reduction, i.e. 8.62 million tons per year, with an annual total cost of 8.07 bn USD. According to the distribution characteristics of the PV self-sufficiency rate on the map, priority is given to investing in the peripheral areas of the city with the higher return. It is estimated that when the PV conversion rate increases by 5%, the peak self-sufficiency rate to the area will increase by 20% in Wuhan. Moreover, if the conversion rate of PV system reaches 47.13%, a fully utilized rooftop in Wuhan for PV deployment could meet the local electricity demand, which implies a 100% electricity self-sufficiency. This study is helpful to understand the spatial heterogeneity of rooftop PV in urban areas, and could serve as the guidelines for local governments to develop renewable energy in a sustainable way.
If a human can see it, so should your system

Machine Vision Components (MVC) are becoming safety-critical. Assuring their quality, including safety, is essential for their successful deployment. Assurance relies on the availability of precisely specified and, ideally, machine-verifiable requirements. MVCs with state-of-the-art performance rely on machine learning (ML) and training data, but largely lack such requirements.
Recommending good first issues in GitHub OSS projects

Attracting and retaining newcomers is vital for the sustainability of an open-source software project. However, it is difficult for newcomers to locate suitable development tasks, while existing "Good First Issues" (GFI) in GitHub are often insufficient and inappropriate. In this paper, we propose RecGFI, an effective practical approach for the recommendation of good first issues to newcomers, which can be used to relieve maintainers' burden and help newcomers onboard. RecGFI models an issue with features from multiple dimensions (content, background, and dynamics) and uses an XGBoost classifier to generate its probability of being a GFI. To evaluate RecGFI, we collect 53,510 resolved issues among 100 GitHub projects and carefully restore their historical states to build ground truth datasets. Our evaluation shows that RecGFI can achieve up to 0.853 AUC in the ground truth dataset and outperforms alternative models. Our interpretable analysis of the trained model further reveals interesting observations about GFI characteristics. Finally, we report latest issues (without GFI-signaling labels but recommended as GFI by our approach) to project maintainers among which 16 are confirmed as real GFIs and five have been resolved by a newcomer.
Tailor Versatile Multi-Modal Learning for Multi-Label Emotion Recognition

Multi-modal Multi-label Emotion Recognition (MMER) aims to identify various human emotions from heterogeneous visual, audio and text modalities. Previous methods mainly focus on projecting multiple modalities into a common latent space and learning an identical representation for all labels, which neglects the diversity of each modality and fails to capture richer semantic information for each label from different perspectives. Besides, associated relationships of modalities and labels have not been fully exploited. In this paper, we propose versaTile multi-modAl learning for multI-labeL emOtion Recognition (TAILOR), aiming to refine multi-modal representations and enhance discriminative capacity of each label. Specifically, we design an adversarial multi-modal refinement module to sufficiently explore the commonality among different modalities and strengthen the diversity of each modality. To further exploit label-modal dependence, we devise a BERT-like cross-modal encoder to gradually fuse private and common modality representations in a granularity descent way, as well as a label-guided decoder to adaptively generate a tailored representation for each label with the guidance of label semantics. In addition, we conduct experiments on the benchmark MMER dataset CMU-MOSEI in both aligned and unaligned settings, which demonstrate the superiority of TAILOR over the state-of-the-arts.
Realistic Blur Synthesis for Learning Image Deblurring

Training learning-based deblurring methods demands a tre-mendous amount of blurred and sharp image pairs. Unfortunately, existing synthetic datasets are not realistic enough, and deblurring models trained on them cannot handle real blurred images effectively. While real datasets have recently been proposed, they provide limited diversity of scenes and camera settings, and capturing real datasets for diverse settings is still challenging. To resolve this, this paper analyzes various factors that introduce differences between real and synthetic blurred images. To this end, we present RSBlur, a novel dataset with real blurred images and the corresponding sharp image sequences to enable a detailed analysis of the difference between real and synthetic blur. With the dataset, we reveal the effects of different factors in the blur generation process. Based on the analysis, we also present a novel blur synthesis pipeline to synthesize more realistic blur. We show that our synthesis pipeline can improve the deblurring performance on real blurred images.
Siamese Network with Interactive Transformer for Video Object Segmentation

Semi-supervised video object segmentation (VOS) refers to segmenting the target object in remaining frames given its annotation in the first frame, which has been actively studied in recent years. The key challenge lies in finding effective ways to exploit the spatio-temporal context of past frames to help learn discriminative target representation of current frame. In this paper, we propose a novel Siamese network with a specifically designed interactive transformer, called SITVOS, to enable effective context propagation from historical to current frames. Technically, we use the transformer encoder and decoder to handle the past frames and current frame separately, i.e., the encoder encodes robust spatio-temporal context of target object from the past frames, while the decoder takes the feature embedding of current frame as the query to retrieve the target from the encoder output. To further enhance the target representation, a feature interaction module (FIM) is devised to promote the information flow between the encoder and decoder. Moreover, we employ the Siamese architecture to extract backbone features of both past and current frames, which enables feature reuse and is more efficient than existing methods. Experimental results on three challenging benchmarks validate the superiority of SITVOS over state-of-the-art methods. Code is available at https://github.com/LANMNG/SITVOS.
Enhancing Adjoint Optimization-Based Photonic Inverse Design with Explainable Machine Learning

A fundamental challenge in the design of photonic devices, and electromagnetic structures more generally, is the optimization of their overall architecture to achieve a desired response. To this end, topology or shape optimizers based on the adjoint variable method have been widely adopted due to their high computational efficiency and ability to create complex freeform geometries. However, the functional understanding of such freeform structures remains a black box. Moreover, unless a design space of high-performance devices is known in advance, such gradient-based optimizers can get trapped in local minima valleys or saddle points, which limits performance achievable through this inverse design process. To elucidate the relationships between device performance and nanoscale structuring while mitigating the effects of local minima trapping, we present an inverse design framework that combines adjoint optimization, automated machine learning, and explainable artificial intelligence. Integrated with a numerical electromagnetic simulation method, our framework reveals structural contributions toward a figure-of-merit (FOM) of interest. Through an explanation-based reoptimization process, this information is then leveraged to minimize the FOM further than that obtained through adjoint optimization alone, thus overcoming the optimization’s local minima. We demonstrate our framework in the context of waveguide splitter design and achieve between 39 and 74% increases in device performance relative to state-of-the-art adjoint optimization-based inverse design across a range of telecom wavelengths. Our results highlight machine learning strategies that can substantially extend and enhance the capabilities of a conventional, optimization-based inverse design algorithm while revealing deeper insights into the algorithm’s designs.
Progress on deep learning in digital pathology of breast cancer: a narrative review

Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis. However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology.A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied.DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches.Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.