Reviewerly/exHarmony huggingface dataset:
Attentional feature pyramid network for small object detection

Recent state-of-the-art detectors generally exploit the Feature Pyramid Networks (FPN) due to its advantage of detecting objects at different scales. Despite significant advances in object detection owing to the design of feature pyramids, it is still challenging to detect small objects with low resolution and dense distribution in complex scenes. To address these problems, we propose Attentional Feature Pyramid Network, a new feature pyramid architecture named AFPN which consists of three components to enhance the small object detection ability, specifically: Dynamic Texture Attention, Foreground-Aware Co-Attention, and Detail Context Attention. First, Dynamic Texture Attention augments the texture features dynamically by filtering out redundant semantics to highlight small objects in lower layers and amplifying credible details to emphasize large objects in higher layers. Then, Foreground-Aware Co-Attention is explored to detect densely arranged small objects by enhancing the objects feature via foreground-correlated contexts and suppressing the background noise. Finally, to better capture the features of small objects, Detail Context Attention adaptively aggregates detail cues of RoI features with different scales for a more accurate feature representation. By substituting FPN with AFPN in Faster R-CNN, our method performs on par with the state-of-the-art performance on Tsinghua-Tencent 100K. Furthermore, we achieve highly competitive results on small category of both PASCAL VOC and MS COCO.
Phishing URL detection using machine learning methods

Phishing is among the most concerning issues in a constantly changing world. The increasing use of the Internet has led to a new way of stealing data, known as cybercrime. Cybercrime refers to stealing private information and violating privacy through computers. The primary technique used is phishing. Phishing via URLs (Uniform Resource Locators) is one of the most common types, and its primary goal is to steal the data from the user when the user accesses the malicious website. Detecting a malicious URL is a significant challenge. This work aims to provide a solution for detecting such websites with the help of machine learning algorithms focused on the behaviors and qualities of the suggested URL. The web security community has created blacklisting services to identify malicious websites. A variety of methods, such as manual reporting, and site analysis heuristics are used to create these blacklists. Due to their recentness, lack of evaluation, or incorrect evaluation, many malicious websites inadvertently escape blacklisting. To create a machine learning model for detecting whether a URL is malicious or not, algorithms such as Random Forests, Decision Trees, Light GBM, Logistic Regression, and Support Vector Machine (SVM) are used. Extracting features is the first step, and applying the model is the next step.
Multi-label feature selection via robust flexible sparse regularization

Multi-label feature selection is an efficient technique to deal with the high dimensional multi-label data by selecting the optimal feature subset. Existing researches have demonstrated that l1-norm and l2,1-norm are promising roles for multi-label feature selection. However, two important issues are ignored when existing l1-norm and l2,1-norm based methods select discriminative features for multi-label data. First, l1-norm can enforce sparsity on each feature across all instances while numerous selected features lack discrimination due to the generated zero weight values. Second, l2,1-norm not only neglects label-specific features but also ignores the redundancy among features. To this end, we design a Robust Flexible Sparse Regularization norm (RFSR), furthermore, proposing a global optimization framework named Robust Flexible Sparse regularized multi-label Feature Selection (RFSFS) based on RFSR. Finally, an efficient alternating multipliers based optimization scheme is developed to iteratively optimize RFSFS. Empirical studies on fifteen benchmark multi-label data sets demonstrate the effectiveness and efficiency of RFSFS.
Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging

Forgery and tampering continue to provide unnecessary economic burdens. Although new anti-forgery and counterfeiting technologies arise, they inadvertently lead to the sophistication of forgery techniques over time, to a point where detection is no longer viable without technological aid. Among the various optical techniques, one of the recently used techniques to detect counterfeit products is HSI, which captures a range of electromagnetic data. To aid in the further exploration and eventual application of the technique, this study categorizes and summarizes existing related studies on hyperspectral imaging and creates a mini meta-analysis of this stream of literature. The literature review has been classified based on the product HSI has used in counterfeit documents, photos, holograms, artwork, and currency detection.
Quantum computing of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mi>Li</mml:mi><mml:mprescripts /><mml:none /><mml:mn>6</mml:mn></mml:mmultiscripts></mml:math> nucleus via ordered unitary coupled clusters

The variational quantum eigensolver (VQE) is an algorithm to compute ground and excited state energy of quantum many-body systems. A key component of the algorithm and an active research area is the construction of a parametrized trial wave function---a so-called variational ansatz. The wave function parametrization should be expressive enough, i.e., represent the true eigenstate of a quantum system for some choice of parameter values. On the other hand, it should be trainable, i.e., the number of parameters should not grow exponentially with the size of the system. Here, we apply VQE to the problem of finding ground and excited state energies of the odd-odd nucleus $^{6}\mathrm{Li}$. We study the effects of ordering fermionic excitation operators in the unitary coupled clusters ansatz on the VQE algorithm convergence by using only operators preserving the ${J}_{z}$ quantum number. The accuracy is improved by two orders of magnitude in the case of descending order. We first compute optimal ansatz parameter values using a classical state-vector simulator with arbitrary measurement accuracy and then use those values to evaluate energy eigenstates of $^{6}\mathrm{Li}$ on a superconducting quantum chip from IBM. We post-process the results by using error mitigation techniques and are able to reproduce the exact energy with an error of $3.8%$ and $0.1%$ for the ground state and for the first excited state of $^{6}\mathrm{Li}$, respectively.
D-former: a U-shaped Dilated Transformer for 3D medical image segmentation

Computer-aided medical image segmentation has been applied widely in diagnosis and treatment to obtain clinically useful information of shapes and volumes of target organs and tissues. In the past several years, convolutional neural network (CNN)-based methods (e.g., U-Net) have dominated this area, but still suffered from inadequate long-range information capturing. Hence, recent work presented computer vision Transformer variants for medical image segmentation tasks and obtained promising performances. Such Transformers modeled long-range dependency by computing pair-wise patch relations. However, they incurred prohibitive computational costs, especially on 3D medical images (e.g., CT and MRI). In this paper, we propose a new method called Dilated Transformer, which conducts self-attention alternately in local and global scopes for pair-wise patch relations capturing. Inspired by dilated convolution kernels, we conduct the global self-attention in a dilated manner, enlarging receptive fields without increasing the patches involved and thus reducing computational costs. Based on this design of Dilated Transformer, we construct a U-shaped encoder–decoder hierarchical architecture called D-Former for 3D medical image segmentation. Experiments on the Synapse and ACDC datasets show that our D-Former model, trained from scratch, outperforms various competitive CNN-based or Transformer-based segmentation models at a low computational cost without time-consuming per-training process.
Cognitive psychology-based artificial intelligence review

Most of the current development of artificial intelligence is based on brain cognition, however, this replication of biology cannot simulate the subjective emotional and mental state changes of human beings. Due to the imperfections of existing artificial intelligence, this manuscript summarizes and clarifies that artificial intelligence system combined with cognitive psychology is the research direction of artificial intelligence. It aims to promote the development of artificial intelligence and give computers human advanced cognitive abilities, so that computers can recognize emotions, understand human feelings, and eventually achieve dialog and empathy with humans and other artificial intelligence. This paper emphasizes the development potential and importance of artificial intelligence to understand, possess and discriminate human mental states, and argues its application value with three typical application examples of human-computer interaction: face attraction, affective computing, and music emotion, which is conducive to the further and higher level of artificial intelligence research.
Modified Remora Optimization Algorithm with Multistrategies for Global Optimization Problem

Remora Optimization Algorithm (ROA) is a metaheuristic optimization algorithm, proposed in 2021, which simulates the parasitic attachment, experiential attack, and host feeding behavior of remora in the ocean. However, the performance of ROA is not very good. Considering the habits of the remora that rely on the host to find food, and in order to improve the performance of the ROA, we designed a new host-switching mechanism. By adding new a host-switching mechanism, joint opposite selection, and restart strategy, a modified remora optimization algorithm (MROA) is proposed. We use 23 standard benchmark and CEC2020 functions to test the performance of MROA and compare them with eight state-of-art optimization algorithms. The experimental results show that MROA has better-optimized performance and robustness. Finally, the ability of MROA to solve practical problems is demonstrated by five classical engineering problems.
How the Internet of Things reshapes the organization of innovation and entrepreneurship

• The Internet of Things (IoT) is one of the key digital technologies. • IoT transforms traditional value chains into digitalized ecosystems. • IoT introduces new technological and social features of organizational design. • We highlight seven articles covering trends/implementation, applications, and busines models. • Future research could address cybersecurity, data privacy, inequality, and digital busines models.
Small-Object Detection for UAV-Based Images Using a Distance Metric Method

Object detection is important in unmanned aerial vehicle (UAV) reconnaissance missions. However, since a UAV flies at a high altitude to gain a large reconnaissance view, the captured objects often have small pixel sizes and their categories have high uncertainty. Given the limited computing capability on UAVs, large detectors based on convolutional neural networks (CNNs) have difficulty obtaining real-time detection performance. To address these problems, we designed a small-object detector for UAV-based images in this paper. We modified the backbone of YOLOv4 according to the characteristics of small-object detection. We improved the performance of small-object positioning by modifying the positioning loss function. Using the distance metric method, the proposed detector can classify trained and untrained objects through object features. Furthermore, we designed two data augmentation strategies to enhance the diversity of the training set. We evaluated our method on a collected small-object dataset; the proposed method obtained 61.00% mAP50 on trained objects and 41.00% mAP50 on untrained objects with 77 frames per second (FPS). Flight experiments confirmed the utility of our approach on small UAVs, with satisfying detection performance and real-time inference speed.
A renewable energy microgrids trading management platform based on permissioned blockchain

This paper presents an energy trading platform based on the permissioned blockchain for managing renewable energy microgrids. To accomplish low-cost and efficient renewable energy trading, a series of automated trading processes is designed for the trading platform, including the token trading mechanism and account management mechanism. Meanwhile, we actualise set of trading processes in the trading platform using smart contract and use the Internet of Things technology to facilitate the automatic execution of trading. Besides, we devised a homomorphic encryption scheme that provides an exceptional level of privacy protection without compromising trading efficiency to better safeguard users' privacy in the trading platform. Therefore, utilising the trading management platform for renewable energy we developed can increase user participation in trading. Finally, the proposed trading management platform has been constructed and deployed utilising the permissioned blockchain Hyperledger Fabric, and the effect of the homomorphic encryption scheme and the trading process has been tested. The simulation results demonstrate the practicability of the designed renewable energy trading management platform.
A survey on XAI and natural language explanations

The field of explainable artificial intelligence (XAI) is gaining increasing importance in recent years. As a consequence, several surveys have been published to explore the current state of the art on this topic. One aspect that seems to be overlooked by these works is the applied presentation methods and, specifically, the role of natural language in generating the final explanations. This survey reviews 70 XAI papers published between 2006 and 2021 and evaluates their readiness with respect to natural language explanations. Thus, together with a set of hierarchical criteria, we define a multi-criteria decision-making model. Finally, we conclude that only a handful of recent XAI works either considered natural language explanations to approach final users (see, e.g.,( Bennetot et al., 2021 )) or implemented a method capable of generating such explanations. • Review of current XAI methods that provide explanations through natural language. • A novel set of hierarchical criteria (taxonomy) to compare the state of the art. • A multi-criteria decision-making model to compare XAI works according to user needs. • A ranking model, publicly available to the community to keep the survey updated.
Global contextually guided lightweight network for RGB-thermal urban scene understanding

Recent achievements in scene understanding have benefited considerably from the rapid development of convolutional neural networks. However, improvements of scene understanding methods have been restricted in terms of practical deployment, especially in mobile devices, owing to their high computational costs and memory consumption. Existing networks can integrate RGB and thermal (RGB-T) cues for sample fusion, resulting in insufficient exploitation of the complicated correlations between the two image modalities. Moreover, some of these methods do not consider the influence of global features on the interactions between low- and high-level features. Hence, in this study, we introduce a novel network named the global contextually guided lightweight network (GCGLNet), which has fewer parameters and higher speed, ensuring accuracy. Specifically, secondary cross-modal integration is introduced to remove redundant information while fusing and propagating effective modal information. A hybrid feature-cascaded aggregation module is also introduced to emphasize the global context along with complementation and calibration between the high- and low-level features. Extensive experiments were conducted on two benchmark RGB-T datasets to demonstrate that the proposed GCGLNet yields an accuracy comparable with those of state-of-the-art approaches when operated at 51.89 FPS for 480 × 640 pixel inputs with only 7.87 M parameters. Thus, GCGLNet is expected to open new avenues for research on urban scene understanding via RGB-T sensors.
VulRepair: a T5-based automated software vulnerability repair

As software vulnerabilities grow in volume and complexity, researchers proposed various Artificial Intelligence (AI)-based approaches to help under-resourced security analysts to find, detect, and localize vulnerabilities. However, security analysts still have to spend a huge amount of effort to manually fix or repair such vulnerable functions. Recent work proposed an NMT-based Automated Vulnerability Repair, but it is still far from perfect due to various limitations. In this paper, we propose VulRepair, a T5-based automated software vulnerability repair approach that leverages the pre-training and BPE components to address various technical limitations of prior work. Through an extensive experiment with over 8,482 vulnerability fixes from 1,754 real-world software projects, we find that our VulRepair achieves a Perfect Prediction of 44%, which is 13%-21% more accurate than competitive baseline approaches. These results lead us to conclude that our VulRepair is considerably more accurate than two baseline approaches, highlighting the substantial advancement of NMT-based Automated Vulnerability Repairs. Our additional investigation also shows that our VulRepair can accurately repair as many as 745 out of 1,706 real-world well-known vulnerabilities (e.g., Use After Free, Improper Input Validation, OS Command Injection), demonstrating the practicality and significance of our VulRepair for generating vulnerability repairs, helping under-resourced security analysts on fixing vulnerabilities.
NatGen: generative pre-training by “naturalizing” source code

Pre-trained Generative Language models (e.g., PLBART, CodeT5, SPT-Code) for source code yielded strong results on several tasks in the past few years, including code generation and translation. These models have adopted varying pre-training objectives to learn statistics of code construction from very large-scale corpora in a self-supervised fashion; the success of pre-trained models largely hinges on these pre-training objectives. This paper proposes a new pre-training objective, "Naturalizing" of source code, exploiting code's bimodal, dual-channel (formal & natural channels) nature. Unlike natural language, code's bimodal, dual-channel nature allows us to generate semantically equivalent code at scale. We introduce six classes of semantic preserving transformations to introduce unnatural forms of code, and then force our model to produce more natural original programs written by developers. Learning to generate equivalent, but more natural code, at scale, over large corpora of open-source code, without explicit manual supervision, helps the model learn to both ingest & generate code. We fine-tune our model in three generative Software Engineering tasks: code generation, code translation, and code refinement with limited human-curated labeled data and achieve state-of-the-art performance rivaling CodeT5. We show that our pre-trained model is especially competitive at zero-shot and few-shot learning, and better at learning code properties (e.g., syntax, data flow)
Artificial intelligence based medical decision support system for early and accurate breast cancer prediction

Feature selection, which picks the optimal subset of characteristics related to the target data by deleting unnecessary data, is one of the most important aspects of the machine learning area. A major part of big data preprocessing is feature selection (reduction). There are 2n alternative feature subsets for every n features, making it difficult to choose the best set of features from a dataset using typical feature selection techniques. Consequently, the present study proposes and suggests a unique feature selection method based on the Eagle Strategy(ESO) Optimization, Gravitational Search Optimization (GSO) algorithm, and their hybrid algorithm. We chose this infection as our subject of investigation since the number of women with breast cancer is increasing rapidly on a global scale. After lung cancer, which affects more women than any other kind of cancer, breast cancer is the second leading cause of cancer mortality. The goal of this study is to categorize breast cancer into two groups using the benchmark feature set (Wisconsin Diagnostic Breast Cancer (WDBC)) and to choose the fewest features (feature selection) to achieve maximum accuracy. This work also provides a hybrid technique for finding important features that combines two algorithms, ESO and the GSO algorithm, while reducing insignificant characteristics (features) and complexity. Soft computing technologies and machine learning algorithms provide a framework for prognostic research by classifying data instances as relevant or irrelevant depending on cancer severity. Thus, this work presented a new approach for classifying breast cancer tumors. In this research, we coupled soft computing methodologies—our implemented algorithms are applied for the first time to this problem—with artificial intelligence-based machine learning strategies to create a prediction model. The efficacy of our suggested technique was evaluated using WDBC breast cancer data sets, and the findings show that our proposed hybrid algorithm performs very well in breast cancer classification. We have been able to attain astonishing results with accuracy up to 98.9578%, sensitivity up to 0.9705, specificity up to 1.000, precision up to 1.000, F1-score up to 0.9696, and an AUC up to 0.9980 (close to maximum, i.e., 1.0000). Our study's goal is to incorporate our findings into a valid clinical prediction system, allowing visual science specialists to make more accurate and effective judgments in the future. Furthermore, our suggested technology might be used to detect a wide range of diseases.
Blockchain technologies for sustainability in the agrifood sector: A literature review of academic research and business perspectives

In the realm of digital transformation, Blockchain technologies are considered prominent for managing agrifood supply chains due to their contribution to traceability, safety, quality, transparency, and scalability. Both academia and businesses are paying increased attention to the application of Blockchain technologies in the agrifood sector, also highlighting their potential to address sustainable issues. Aiming to deepen the understanding of how Blockchain technology is being applied in the agrifood sector for sustainable supply chain management, this paper investigates the link between Blockchain, agrifood, and sustainability, focusing on both professional and scientific sources. The article employs a content-based analysis methodology of both academic literature and professional documents like patents and business cases to delineate an up-to-date picture of the current scenario as well as to provide integrated insights regarding the opportunities and potentialities of Blockchain technologies for agrifood sustainability. Our findings show that Blockchain technologies are playing a crucial role in speeding up the digital transformation process of the agrifood supply chains and are being considered essential for fostering the development of new sustainable business models. New research avenues and practical implications for businesses and policy-makers are also presented and discussed.