Reviewerly/exHarmony huggingface dataset:
Jamming Detection and Classification in OFDM-Based UAVs via Feature- and Spectrogram-Tailored Machine Learning

In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers with applications to unmanned aerial vehicles (UAVs). Using software-defined radio (SDR), four types of jamming attacks; namely, barrage, protocol-aware, single-tone, and successive-pulse are launched and investigated. Each type is qualitatively evaluated considering jamming range, launch complexity, and attack severity. Then, a systematic testing procedure is established by placing an SDR in the vicinity of a UAV (i.e., drone) to extract radiometric features before and after a jamming attack is launched. Numeric features that include signal-to-noise ratio (SNR), energy threshold, and key OFDM parameters are used to develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature-based</i> classification model via conventional ML algorithms. Furthermore, spectrogram images collected following the same testing procedure are exploited to build a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spectrogram-based</i> classification model via state-of-the-art deep learning algorithms (i.e., convolutional neural networks). The performance of both types of algorithms is analyzed quantitatively with metrics including detection and false alarm rates. Results show that the spectrogram-based model classifies jamming with an accuracy of 99.79% and a false-alarm of 0.03%, in comparison to 92.20% and 1.35%, respectively, with the feature-based counterpart.
Wrapping based curvelet transform approach for ECG watermarking in telemedicine application

Patient information is currently maintained separately from the physiological signals received in telemedicine. Furthermore, in recent years, the usage of portable devices to obtain such medical information has become more common. This form of storing can lead to several other authentication issues and compromise the accuracy of diagnostics. To address this issue, we propose a watermarking method for embedding patient information in the ECG signal in this study. In our technique, the watermark is a QR code that contains the patient's information as well as the Electrocardiogram signal. The 1D ECG signal is transformed to a 2D signal in the proposed technique. A fast discrete curvelet transform is used to extract the signal's frequency content, and then Schur decomposition is used to the frequency sub-bands obtained. Finally, the watermark bits are integrated by modifying the Eigen values produced. According to imperceptibility experiments, this method produces a watermarked signal that is identical to the originals, keeping the diagnostic content of the signal. Robustness test indicated that the watermark could overcome the most popular watermarking attacks.
Designing with Alganyl

We explore designing with Alganyl, a highly customizable and sustainable biomaterial made of food-grade ingredients. To make Alganyl, we optimized existing bioplastics for flexibility and strength, resulting in a biomaterial that acts and feels similar to vinyl fabric. Moreover, Alganyl is a zero-waste material that can be fully recycled or biodegraded in 60 days. We further explored designing with Alganyl by embedding color, texture, and sensing pigments. We showcase a wide range of applications made entirely of Alganyl including a sculpture, doll clothing, environment-sensing accessories, and a recycled concert bag. We conducted a user study with 12 participants that made Alganyl at home, fabricated their own applications, and then speculated on future applications for Alganyl. Overall, participants found Alganyl to be an accessible biomaterial that can be used for many applications. We hope designers and HCI researchers will embrace Alganyl as a sustainable biomaterial for making recyclable and biodegradable artifacts.
MR brain segmentation based on DE-ResUnet combining texture features and background knowledge

The segmentation of the brain Magnetic Resonance (MR) images plays an essential role in neuroimaging research and clinical settings. Currently, deep learning combined with prior knowledge and attention mechanism is intensively implemented to solve the brain tissue segmentation task because of its superior performance. However, there are still two problems: firstly, some prior knowledge is difficult to obtain; secondly, incorrect attention is easy to produce in self-attention mechanism. To address these two issues, a novel dual encoder residual U-Net based on texture features and background knowledge, namely DE-ResUnet, is proposed in this work. In DE-ResUnet, the dual encoders for T1-weighted image and texture features are combined to learn hidden additional information. The introduction of channel attention mechanism (CAM) into two encoder and decoder paths facilitates the model to extract more useful informative features. Moreover, we design a strengthen module to refine the coarse segmentation, which can focus on brain tissue regions guided by background knowledge. We evaluate our proposed method on BrainWeb, OASIS-1 and CANDI datasets. The experimental results show that the proposed DE-ResUnet network achieves the accurate segmentation superior to that of several state-of-the-art methods. We also evaluate DE-ResUnet on the BraTS 2020 dataset and achieve good segmentation results. These experiments demonstrate that DE-ResUnet can not only segment normal brain MR images accurately, but also locate the area of the lesion in abnormal images. Our code is freely available at https://github.com/LiangWUSDU/DE-ResUnet.
Approaches to Federated Computing for the Protection of Patient Privacy and Security Using Medical Applications

Computing model may train on a distributed dataset using Medical Applications, which is a distributed computing technique. Instead of a centralised server, the model trains on device data. The server then utilizes this model to train a joint model. The aim of this study is that Medical Applications claims no data is transferred, thereby protecting privacy. Botnet assaults are identified through deep autoencoding and decentralised traffic analytics. Rather than enabling data to be transmitted or relocated off the network edge, the problem of the study is in privacy and security in Medical Applications strategies. Computation will be moved to the edge layer to achieve previously centralised outcomes while boosting data security. Study Results in our suggested model detects anomalies with up to 98 percent accuracy utilizing MAC IP and source/destination/IP for training. Our method beats a traditional centrally controlled system in terms of attack detection accuracy.
Remote-Sensing Image Captioning Based on Multilayer Aggregated Transformer

Remote-sensing image (RSI) captioning aims to automatically generate sentences describing the content of RSIs. The multiscale information of RSIs contains attributes and complex relationships of objects of different sizes. However, current methods still have some weaknesses in efficiently utilizing multiscale information to generate accurate and detailed sentences. In this letter, we propose a new model based on the “encoder–decoder” framework to address the problem. In the encoder, we fuse the features of different layers in ResNet-50 to extract multiscale information. In the decoder, we propose multilayer aggregated transformer (MLAT) to utilize the extracted information to generate sentences sufficiently. Specially, as the transformer encoding layer goes deeper, the extracted features will be more similar. To sufficiently utilize the features from different transformer encoding layers, compress redundant information, and extract important information, long short-term memory (LSTM) in MLAT aggregates the features to obtain better feature representations. The self-attention mechanism and the aggregation strategy enable MLAT to utilize the features sufficiently. The experimental results show that MLAT as the decoder can help the model address the multiscale problem, significantly improve the model performance on sentence accuracy and diversity, and show that our proposed method performs better than other current methods. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Chen-Yang-Liu/MLAT</uri> .
Deep Object Detector With Attentional Spatiotemporal LSTM for Space Human–Robot Interaction

Global temporal information and local semantic information are essential cues for high-performance online object detection in videos. However, despite their promising detection accuracy in most cases, most state-of-the-art approaches have following two limitations: invalid background/scale suppression and inadequate temporal information mining between frames. Many jobs currently focus on temporal information learning based on a single frame. In this article, we propose an attentional global–local information learning network; this is one of the first attempts to fully use both types of information between frames. Attention maps are creatively utilized to transfer temporal contexts between frames. This also effectively alleviates the adverse effects of scale changes. Furthermore, empowered by a detailed framework, a proposed detector effectively uses multilevel feature extraction. Given these contributions, the proposed detector achieves state-of-the-art performance on challenging benchmarks. Finally, practical experiments are conducted on a space human–robot interaction platform.
Fast LDP-MST: An Efficient Density-Peak-Based Clustering Method for Large-Size Datasets

Recently, a new density-peak-based clustering method, called clustering with local density peaks-based minimum spanning tree (LDP-MST), was proposed, which has several attractive merits, e.g., being able to detect arbitrarily shaped clusters and not very sensitive to noise and parameters. Nevertheless, we also found the limitation of LDP-MST in efficiency. Specifically, LDP-MST has <inline-formula><tex-math notation="LaTeX">$O(N\log N+M^{2})$</tex-math></inline-formula> time, where <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> denotes the dataset size and <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> is an intermediate variable denoting the number of local density peaks. As our experimental results reveal, when processing large-size datasets, the value of <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> could be very large and consequently those steps of LDP-MST involving <inline-formula><tex-math notation="LaTeX">$O(M^{2})$</tex-math></inline-formula> time term would be time-consuming. And in the worst case, the value of <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> could be very close to that of <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> , which means that the time complexity of LDP-MST could be <inline-formula><tex-math notation="LaTeX">$O(N^{2})$</tex-math></inline-formula> in the worst case of <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> . In this study, we use more efficient algorithms to implement those steps of LDP-MST that involve the <inline-formula><tex-math notation="LaTeX">$O(M^{2})$</tex-math></inline-formula> time term such that the proposed method, Fast LDP-MST, has <inline-formula><tex-math notation="LaTeX">$O(N\log N)$</tex-math></inline-formula> time complexity even if <inline-formula><tex-math notation="LaTeX">$M\approx N$</tex-math></inline-formula> . Our experiments demonstrate that Fast LDP-MST is overall more efficient than LDP-MST on large-size datasets, without sacrificing the merits of LDP-MST in effectiveness, robustness, and user-friendliness.
The evolution of Foresight: What evidence is there in scientific publications?

This study examines the recent evolution of Foresight as a well-recognized tool for long-term policy and strategy making and discusses the future directions the activity might take. The science mapping approach is used for the analysis. Drawing on big data, science mapping provides an evolutionary overview of scientific domains across time and helps to identify patterns showing the research agenda and directions of development of those domains. In the present study, we demonstrate the use of the science mapping approach with the case of the Foresight field. The results of our analysis confirm earlier academic studies on the evolution of Foresight; hence show the validity of the science mapping approach. Besides demonstrating the evolution of the field with the scientific publications in the last couple of decades, the study seeks scientific evidence for the next generations of Foresight to emerge towards the 2030s.
Detection of local and clustered outliers based on the density–distance decision graph

Outlier detection tasks refer to identifying the objects that have different characteristics from the normal observations. Most existing approaches detect outliers from the global perspective, which can effectively detect global outliers and most clustered outliers but cannot detect local outliers when the normal samples form clusters with different densities. The methods based on local outlier factors can effectively detect local outliers, but when the number of outliers increases, the more occurrences of clustered outliers will lead to the degeneration of the detection performance. We proposed an outlier detection method based on density–distance decision graph to detect local, global and clustered outliers simultaneously. Firstly, kernel density estimation and local reachable distance are combined to calculate the local density. The density ratio of the neighbors of an instance to itself is calculated as the degree of local outliers. Then, we propose a metric named density lifting distance as the degree of global outliers, which is calculated by the distance between k nearest neighbors with higher density of the instance and itself. The density ratio and density lift distance are combined to draw the density–distance decision graph, and the product of two metrics is calculated as the final outlier score. Comprehensive experiments were conducted on 8 synthetic datasets and 16 real-world datasets compared with 12 state-of-the-art methods. The results show that the proposed method works well when the samples form clusters with different densities as well as the percentage of outliers varies, and outperforms the state-of-the-art methods tested in terms of AUC.
Studying key antecedents of disruptive technology adoption in the digital supply chain: an Indian perspective

Purpose The study examines the key drivers of the adoption of disruptive technologies (DTs) in the digital supply chain (DSC) in developing nations. Design/methodology/approach The data were collected using well-established measures grounded in the diffusion of innovation (DOI) theory. The hypotheses were tested using the structural equation modeling (SEM) approach using SmartPLS. The authors control for the demographics and apply the required statistical diagnostics for robust findings. Findings The compatibility and IT expertise were the two key factors in adopting the DTs in DSC in developing nations. The organizations with higher compatibility and internal IT expertise and competence witness a higher level of adoption of DT. The perceived cost and complexity were not found statistically significant. This may be probably because developing nations such as India do not perceive the technology adoption complex. Research limitations/implications The research enhances DTs adoption, assuming it is organizational innovation. This study makes a theoretical contribution to the DOI literature. Practical implications The practicing managers should pay attention to addressing the existing technology compatibility issues and spend efforts on training employees to increase the IT expertise to improve the adoption of DT. Social implications The greater adoption of the DTs in DSCs can reduce wastages in supply chains by a faster sense and response and greater technological flexibility with transparency and information sharing. Originality/value The key antecedent to the acceptance of the DTs in developing nations is compatibility than complexity and IT expertise than the cost. The study's originality lies in the fact that most studies on technology adoption study a single technology, but this study captures a holistic view on a group of technologies under industry 4.0.
Point-Level Region Contrast for Object Detection Pre-Training

In this work we present point-level region contrast, a self-supervised pre-training approach for the task of object detection. This approach is motivated by the two key factors in detection: localization and recognition. While accurate localization favors models that operate at the pixel- or point-level, correct recognition typically relies on a more holistic, region-level view of objects. Incorporating this perspective in pre-training, our approach performs contrastive learning by directly sampling individual point pairs from different regions. Compared to an aggregated representation per region, our approach is more robust to the change in input region quality, and further enables us to implicitly improve initial region assignments via online knowledge distillation during training. Both advantages are important when dealing with imperfect regions encountered in the unsupervised setting. Experiments show point-level region contrast improves on state-of-the-art pre-training methods for object detection and segmentation across multiple tasks and datasets, and we provide extensive ablation studies and visualizations to aid understanding. Code will be made available.
On Image Registration using The Radon Transform: Review-and-Improvement

In this paper, we review the problem of identifying a Linear Transformation applied on an image. Three major parts are presented, all involved the use of Radon Transform: First, recovering a sequence of basic transformations on an image; namely, reflection, rotation, dilation, and translation. Second, recovering a transformation on reference Image and an inspected image, where is obtained from by a general Linear Transformation. In doing so, we review our Analysis using the Singular Value Decomposition of the Transformation's Matrix. Third, we present an alternative efficient method of obtaining a matrix of transformation by testing a well-defined class of potential matrices using only the two projections of the inspected image.
Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and Survey

Transfer learning is a pervasive technology in computer vision and natural language processing fields, yielding exponential performance improvements by leveraging prior knowledge gained from data with different distributions. However, while recent works seek to mature machine learning and deep learning techniques in applications related to wireless communications, a field loosely termed radio frequency machine learning, few have demonstrated the use of transfer learning techniques for yielding performance gains, improved generalization, or to address concerns of training data costs. With modifications to existing transfer learning taxonomies constructed to support transfer learning in other modalities, this paper presents a tailored taxonomy for radio frequency applications, yielding a consistent framework that can be used to compare and contrast existing and future works. This work offers such a taxonomy, discusses the small body of existing works in transfer learning for radio frequency machine learning, and outlines directions where future research is needed to mature the field.
A Graph Convolution Network-Based Bug Triage System to Learn Heterogeneous Graph Representation of Bug Reports

Many bugs and defects occur during software testing and maintenance. These bugs should be resolved as soon as possible, to improve software quality. However, bug triage aims to solve these bugs by assigning the reported bugs to an appropriate developer or list of developers. It is an arduous task for a human triager to assign an appropriate developer to a bug report, when there are several developers with different skills, and several automated and semi-automated triage systems have been proposed in the last decade. Some recent techniques have suggested possibilities for the development of an effective triage system. However, these techniques require improvement. In previous work, we proposed a heterogeneous graph representation for bug triage, using word&#x2013;word edges and word-bug document co-occurrences to build a heterogeneous graph of bug data. Cosine similarity is used to weight the word&#x2013;word edges. Then, a graph convolution network is used to learn a heterogeneous graph representation. This paper extends our previous work by adopting different similarity metrics and correlation metrics for weighting word&#x2013;word edges. The method was validated using different small and large datasets obtained from large-scale open-source projects. The top-k accuracy metric was used to evaluate the performance of the bug triage system. The experimental results showed that the point-wise mutual information of the proposed model was better than that of other word&#x2013;word weighting methods, and our method had better accuracy for large datasets than other recent state-of-the-art methods. The proposed method with point-wise mutual information showed 3&#x0025; to 6&#x0025; higher top-1 accuracy than state-of-the-art methods for large datasets.
Profiling the Design Space for Graph Neural Networks based Collaborative Filtering

In recent years, Graph Neural Networks (GNNs) have been widely used in Collaborative Filtering (CF), one of the most popular methods in recommender systems. However, most existing works focus on designing an individual model architecture given a specific scenario, without studying the influences of different design dimensions. Thus, it remains a challenging problem to quickly obtain a top-performing model in a new recommendation scenario. To address the problem, in this work, we make the first attempt to profile the design space of GNN-based CF methods to enrich the understanding of different design dimensions as well as provide a novel paradigm of model design. Specifically, a unified framework of GNN-based CF is proposed, on top of which a design space is developed and evaluated by extensive experiments. Interesting findings on the impacts of different design dimensions on recommendation performance are obtained. Guided by the empirical findings, we further prune the design space to obtain a compact one containing a higher concentration of top-performing models. Empirical studies demonstrate its high quality and strong generalization ability.