Reviewerly/exHarmony huggingface dataset:
The development of phishing during the COVID-19 pandemic: An analysis of over 1100 targeted domains

To design preventive policy measures for email phishing, it is helpful to be aware of the phishing schemes and trends that are currently applied. How phishing schemes and patterns emerge and adapt is an ongoing field of study. Existing phishing works already reveal a rich set of phishing schemes, patterns, and trends that provide insight into the mechanisms used. However, there seems to be limited knowledge about how email phishing is affected in periods of social disturbance, such as COVID-19 in which phishing numbers have quadrupled. Therefore, we investigate how the COVID-19 pandemic influences the phishing emails sent during the first year of the pandemic. The email content (header data and html body, excl. attachments) is evaluated to assess how the pandemic influences the topics of phishing emails over time (peaks and trends), whether email campaigns correlate with momentous events and trends of the COVID-19 pandemic, and what hidden content revealed. This is studied through an in-depth analysis of the body of 500.000 phishing emails addressed to Dutch registered top-level domains collected during the start of the pandemic. The study reveals that most COVID-19 related phishing emails follow known patterns indicating that perpetrators are more likely to adapt than to reinvent their schemes.
An Autoencoder-Based I/Q Channel Interaction Enhancement Method for Automatic Modulation Recognition

This article proposes an autoencoder-based method to enhance the information interaction between in-phase/quadrature (I/Q) channels of the input data for automatic modulation recognition (AMR). The proposed method utilizes an autoencoder built by fully-connected layers to correlate the features of I/Q data and obtain the interaction feature from the intermediate layer, which is concatenated together with the original I/Q data as model inputs. To accommodate the new data dimensions, a modification scheme for the existing representative deep learning based AMR (DL-AMR) models is presented. Experimental results show that our method can improve the recognition accuracy of the state-of-the-art baseline models, and has a smaller time overhead compared with complex-valued neural networks.
An Efficient Low Complexity Region-of-Interest Detection for Video Coding in Wireless Visual Surveillance

Following its use in several applications, including video coding in wireless surveillance, moving object detection (MOD) has become a popular video analysis topic.Despite the considerable progress in the accuracy of MOD for video coding, its implementation in constrained sensors is a real challenge owing to their high complexity and energy consumption.Therefore, there is a great need to address the trade-off between the accuracy and the energy efficiency of MOD approaches for video coding in constrained systems.In this work, an energy-efficient region-of-interest (ROI) detection algorithm as a pre-encoder for wireless visual surveillance (WVS) is proposed.The algorithm ensures a trade-off between detection accuracy and computational complexity.To this end, we propose constructing an activity map by measuring each block activity between successive frames.The map scores are processed using a combination of a fast Gaussian smoother and a rank-order filter to improve accuracy.Only the blocks in motion are coded and counted for transmission.The accuracy of our approach has been evaluated on a large dataset using key performance metrics.It has been found that our algorithm outperforms other state-of-theart techniques in terms of true positive rate (TPR), with 80.84% on sensitivity metric, while exhibiting a well-balanced accuracy for all categories.A careful examination of the computational complexity confirms the low overhead.The energy and bitrate savings could achieve nearly 90% and 98%, respectively.
Comparing Interactive Retrieval Approaches at the Lifelog Search Challenge 2021

The Lifelog Search Challenge (LSC) is an interactive benchmarking evaluation workshop for lifelog retrieval systems. The challenge was first organised in 2018 aiming to find the system that can quickly retrieve relevant lifelog images for a given semantic query. This paper provides an analysis of the performance of all 17 systems participating in the 4th LSC workshop held at the 2021 Annual ACM International Conference on Multimedia Retrieval (ICMR). LSC’21 was the largest effort at comparing different approaches to interactive lifelog retrieval systems seen thus far. Findings from the challenge suggest that many different interactive factors contribute to the success (or otherwise) of participating teams. In this paper, we provide an overview of the LSC’21 challenge, introduce each team’s approach and explore these factors in depth and offer clues on how to develop a high-performing interactive lifelog search engine.
Online transportation network cyber-attack detection based on stationary sensor data

Penetration of connected vehicles and crowdsourced mapping applications give rise to security vulnerabilities in transportation networks. Accurate detection of cyber-attacks on transportation networks is critical to minimize impacts on transportation systems. This task is particularly challenging because the impacts of regional cyber-attacks can be invisible on aggregated traffic data, especially when only sensor data is accessible to transportation agencies. We propose an analytical framework that leverages real-time road link sensory data to conduct online data-driven transportation network anomaly detection using non-parametric long short-term memory (LSTM) and parametric Gaussian process model. The online anomaly detection models can continuously update model coefficients as real-time sensory data arrives. We utilize a city-scale microscopic traffic simulation to validate our cyber-attack detecting framework. The cyber-attack detection model achieves a F1 score, which is a harmonic mean of the precision and recall of classifiers, between 84% to 96% considering different initial training data sizes. We compare with major offline models to demonstrate the effectiveness and robustness of online models. In addition, we devised a meta-heuristic method to solve the multi-objective sensor location problem to simultaneously enhance anomaly detection efficiency and maximize traffic information gain. This study demonstrates a systematic approach to address the emerging concerns of cyber-security in transportation networks with minimum requirements for infrastructure upgrades. Our results can help transportation security authorities identify potential cyber-attacks and protect transportation infrastructure from malicious cyber-hackers.
Lidar-Based 3D Obstacle Detection Using Focal Voxel R-CNN for Farmland Environment

With advances in precision agriculture, autonomous agricultural machines can reduce human labor, optimize workflow, and increase productivity. Accurate and reliable obstacle-detection and avoidance systems are essential for ensuring the safety of automated agricultural machines. Existing LiDAR-based obstacle detection methods for the farmland environment process the point clouds via manually designed features, which is time-consuming, labor-intensive, and weak in terms of generalization. In contrast, deep learning has a powerful ability to learn features autonomously. In this study, we attempted to apply deep learning in LiDAR-based 3D obstacle detection for the farmland environment. In terms of perception hardware, we established a data acquisition platform including LiDAR, a camera, and a GNSS/INS on the agricultural machine. In terms of perception method, considering the different agricultural conditions, we used our datasets to train an effective 3D obstacle detector, known as Focal Voxel R-CNN. We used focal sparse convolution to replace the original 3D sparse convolution because of its adaptable ability to extract effective features from sparse point cloud data. Specifically, a branch of submanifold sparse convolution was added to the upstream of the backbone convolution network; this adds weight to the foreground point and retains more valuable information. In comparison with Voxel R-CNN, the proposed Focal Voxel R-CNN significantly improves the detection performance for small objects, and the AP in the pedestrian class increased from 89.04% to 92.89%. The results show that our model obtains an mAP of 91.43%, which is 3.36% higher than the base model. The detection speed is 28.57 FPS, which is 4.18 FPS faster than the base model. The experiments show the effectiveness of our model, which can provide a more reliable obstacle detection model for autonomous agricultural machines.
Compliance with HIPAA and GDPR in Certificateless-Based Authenticated Key Agreement Using Extended Chaotic Maps

Electronically protected health information is held in computerized healthcare records that contain complete healthcare information and are easily shareable or retrieved by various health care providers via the Internet. The two most important concerns regarding their use involve the security of the Internet and the privacy of patients. To protect the privacy of patients, various regions of the world maintain privacy standards. These are set, for example, by the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe. Most recently developed authenticated key agreement schemes for HIPAA and GDPR privacy/security involve modular exponential computations or scalar multiplications on elliptic curves to provide higher security, but they are computationally heavy and therefore costly to implement. Recent studies have shown that cryptosystems that use modular exponential computation and scalar multiplication on elliptic curves are less efficient than those based on Chebyshev chaotic maps. Therefore, this investigation develops a secure and efficient non-certificate-based authenticated key agreement scheme that uses lightweight operations, including Chebyshev chaotic maps and hash operations. The proposed scheme overcomes the limitations of alternative schemes, is computationally more efficient, and provides more functionality. The proposed scheme complies with the privacy principles of HIPAA and GDPR.
AI business models and its impact on business strategic framework

Recently, a lot of research was done on how to evolve the artificial intelligence business model framework. However, one issue was still undeveloped, which was immature to understand. This has been vital for budding and progressive managers, policy decision makers, and academics alike, namely, how businesses transform and develop their AI business models framework to accomplish continuous value formation. Businesses, which achieve to make value over prolonged stages of time effectively, figure, acclimate and recommence their business models with AI technology to fuel such value formation. Sketch on verdicts from a research program on uninterruptedly budding businesses is given. This paper categorizes three perilous competences, namely, an alignment towards investigating with and manipulating innovative business openings, a well-adjusted use of properties, as well as attaining lucidity between management, ethos, and member of staff obligations, together they all decisive key maneuvering schedules. We conclude the paper by signifying the consequences for AI business model framework research and experts and by providing a tool for executives which permits them to reproduce and classify perilous problems relevant for shifting and emerging their business model to sustain value creation. In this paper, we try to explore the keys of Business Model Framework, which will revolutionize business market ecosystem.
HRRNet: Hierarchical Refinement Residual Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of high-resolution remote sensing images plays an important role in many practical applications, including precision agriculture and natural disaster assessment. With the emergence of a large number of studies on convolutional neural networks, the performance of the semantic segmentation model of remote sensing images has been dramatically promoted. However, many deep convolutional network models do not fully refine the segmentation result maps, and, in addition, the contextual dependencies of the semantic feature map have not been adequately exploited. This article proposes a hierarchical refinement residual network (HRRNet) to address these issues. The HRRNet mainly consists of ResNet50 as the backbone, attention blocks, and decoders. The attention block consists of a channel attention module (CAM) and a pooling residual attention module (PRAM) and residual structures. Specifically, the feature map output by the four blocks of Resnet50 is passed through the attention block to fully explore the contextual dependencies of the position and channel of the semantic feature map, and, then, the feature maps of each branch are fused step by step to realize the refinement of the feature maps, thereby improving the segmentation performance of the proposed HRRNet. Experiments show that the proposed HRRNet improves segmentation result maps compared with various state-of-the-art networks on Vaihingen and Potsdam datasets.
Suitability of syllable-based modeling units for end-to-end speech recognition in Sanskrit and other Indian languages

Most Indian languages are spoken in units of syllables. However, speech recognition systems developed so far for Indian languages have generally used characters or phonemes as modeling units. This work evaluates the performance of syllable-based modeling units in end-to-end speech recognition for several Indian languages. The text is represented in 3 different forms: native script, Sanskrit library phonetics (SLP1) encoding, and syllables, and tokenized with sub-word units like character, byte-pair encoding (BPE), and unigram language modeling (ULM). The performances of these tokens in monolingual training and cross-lingual transfer learning are compared. Syllable-based BPE/ULM subword units give promising results in the monolingual setup if the dataset is sufficiently diverse to represent the syllable distribution in the language. For the Vāksañcayaḥ dataset in Sanskrit, syllable-BPE tokens achieve state-of-the-art results. The capability of syllable-BPE units to complement SLP1-character models through a pretraining–finetuning setup is also evaluated. For Sanskrit, syllable-BPE achieves better word error rates (WER) than the pretraining–finetuning approaches. For Tamil and Telugu, both result in comparable WERs. SLP1-character units are largely better than syllable-based units for cross-lingual transfer learning.
Bangla Text Emotion Classification using LR, MNB and MLP with TF-IDF &amp; CountVectorizer

Emotions have a significant role in human contact. Written material, vocal discourse, and facial expressions can all be used to convey emotion. The habit of showing emotion on digital platforms or blogs has grown considerably in recent years. Bangla is a widely spoken language throughout the globe, with billions of people speaking it. Bangla is used by these folks to express their emotions. It will be wonderful to have a means to identify these emotions outside of the text. In order to achieve this goal, we tested three algorithms for detecting emotion in Bangla texts. Logistic Regression, Multinomial Naive Bayes, and Multi-layer Perceptron have all been used to determine six identical emotion-related categories. TF-IDF, count vectorizer and their combination is used as features on two blended datasets to evaluate the performance of these three algorithms. It is found that he LR with TF-IDF approach gives the best overall accuracy, precision, recall, and F1-measure score among all of the results.
Monitoring System of Wearable Sensor Signal in Rehabilitation Using Efficient Deep Learning Approaches

Recognition of human activity has utilized inputs from wearable sensors, which has significant implications for rehabilitative medicine and cognitive neuroscience. Unfortunately, some crucial dynamic data on upper-limb movements need to be included in the feature extraction procedure for wearable sensor data. The issue is that only a few rehabilitative motions can be recognized, and classification precision is readily compromised. We study several convolution neural networks to extract valuable characteristics from multichannel wearable sensor inputs automatically and precisely identify rehabilitation operations. We gathered wearable sensor signal data for six physiotherapy exercises to assess identification effectiveness using the SPARS9x standard rehabilitation dataset. Experiments showed that the PyramidNet18 model had the highest F1-score on the benchmark dataset, 99.15%.
X-MODE: Extended Multi-operator Differential Evolution algorithm

During past years, multi-operator optimization techniques gained vast attention due to their flexible structure and self-adaptation. They yielded promising results when compared with the single-operator based methods. However, the existing multi-operator models can be further improved regarding exploration–exploitation equilibrium. Therefore, we propose a novel multi-operator algorithm for solving single-objective optimization problems that categorize differential evolution mutation strategies according to the purpose each of them fulfills. A new mutation scheme is also presented to promote the mutation across search space boundaries. A novel extended crossover technique is proposed in this study to retain the properties of applied mutation strategies, as well as develop the potential of the standard crossover function. The model is afterward evaluated on the CEC2020 benchmark test-suite. The proposed algorithm has overwhelming results while comparing single-operator and multi-operator algorithms for several classes of functions. It has been observed from the study that proposed mutation and crossover techniques help improve the efficiency of MODE variants.
IGT: Illumination-guided RGB-T object detection with transformers

Multispectral object detection for autonomous driving has gotten significant attention in recent years. Since the complementing visual information provided by multispectral (i.e., RGB and thermal) could produce a more reliable detection result, which is crucial for the safety of autonomous driving. The previous studies have mainly focused on leveraging the illumination information in the decision-level fusion and suffered from generating reasonable illumination weights. In this paper, we propose IGT, an illumination-guided RGB-T object detection framework based on transformers, to effectively generate appropriate illumination weights and use them to guide multispectral feature rectification and fusion processes. Specifically, an illumination-based sub-network (IBSN) is first proposed to produce reasonable illumination weights from the mid-level features of the RGB stream. Then, we design an illumination-guided feature rectification module (IFRM) to determine the utilization level of the thermal modality’s illumination invariance according to the illumination intensity of the RGB image during the rectification process. With the rectified feature pairs, we deploy the illumination-guided feature fusion module (IFFM), which uses a novel illumination-based loss function to promote multispectral feature fusion and generate illumination-invariant fused features. Extensive experiments reveal that IGT achieves state-of-the-art performance on the FLIR dataset. Detailed analyses show that our network is robust under varying illumination conditions.
DPGNN: Dual-perception graph neural network for representation learning

Graph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks, especially in semi-supervised learning on graphs. However, most existing GNNs are based on the message-passing paradigm to iteratively aggregate neighborhood information in a single topology space. Despite their success, the expressive power of GNNs is limited by some drawbacks, such as inflexibility of message source expansion, negligence of node-level message output discrepancy, and restriction of single message space. To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction. To verify its validity, we instantiate the new message-passing paradigm as a Dual-Perception Graph Neural Network (DPGNN), which applies a node-to-step attention mechanism to aggregate node-specific multi-step neighborhood information adaptively. Our proposed DPGNN can capture the structural neighborhood information and the feature-related information simultaneously for graph representation learning. Experimental results on six benchmark datasets with different topological structures demonstrate that our method outperforms the latest state-of-the-art models, which proves the superiority and versatility of our method. To our knowledge, we are the first to consider node-specific message passing in the GNNs.
Examining the role of financial innovation on economic growth: Fresh empirical evidence from developing and developed countries

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Semi-supervised non-negative matrix tri-factorization with adaptive neighbors and block-diagonal learning

Graph-regularized non-negative matrix factorization (GNMF) is proved to be effective for the clustering of nonlinear separable data. Existing GNMF variants commonly improve model performance by adding different additional constraints or refining the model factorization form, which can lead to problems such as increased algorithm complexity or insufficient performance release. In this paper, we propose semi-supervised non-negative matrix tri-factorization with adaptive neighbors and block-diagonal (ABNMTF). Different from existing methods, in ABNMTF the similarity graph matrix is learned from the original data by adaptive neighbors k-nearest model, and a block diagonal matrix is constructed based on a few labeled data to update the similarity matrix. Our approach reconstructs the block diagonal structure into the adaptive similarity matrix, which enables simultaneous learning of the similarity matrix and label binding during factorization, engendering a distinguishable subspace representation matrix and therefore improving the clustering performance without significantly increasing the complexity of the algorithm. We also represent an optimization method to solve the ABNMTF and provide analyses of convergence and computational complexity. Extensive experiments on 8 real image datasets show that the proposed algorithm reports superior performance against several state-of-the-art approaches. Code has been made available at: https://github.com/LstinWh/ABNMTF.