Reviewerly/exHarmony huggingface dataset:
Blind Super-Resolution for SAR Images with Speckle Noise Based on Deep Learning Probabilistic Degradation Model and SAR Priors

As an active microwave coherent imaging technology, synthetic aperture radar (SAR) images suffer from severe speckle noise and low-resolution problems due to the limitations of the imaging system, which cause difficulties in image interpretation and target detection. However, the existing SAR super-resolution (SR) methods usually reconstruct the images by a determined degradation model and hardly consider multiplicative speckle noise, meanwhile, most SR models are trained with synthetic datasets in which the low-resolution (LR) images are down-sampled from their high-resolution (HR) counterparts. These constraints cause a serious domain gap between the synthetic and real SAR images. To solve the above problems, this paper proposes an unsupervised blind SR method for SAR images by introducing SAR priors in a cycle-GAN framework. First, a learnable probabilistic degradation model combined with SAR noise priors was presented to satisfy various SAR images produced from different platforms. Then, a degradation model and a SR model in a unified cycle-GAN framework were trained simultaneously to learn the intrinsic relationship between HR–LR domains. The model was trained with real LR and HR SAR images instead of synthetic paired images to conquer the domain gap. Finally, experimental results on both synthetic and real SAR images demonstrated the high performance of the proposed method in terms of image quality and visual perception. Additionally, we found the proposed SR method demonstrates the tremendous potential for target detection tasks by reducing missed detection and false alarms significantly.
A modified YOLOv5 for object detection in UAV-captured scenarios

Object detection in UAV image processing has gradually become a hot research topic in recent years. The performance of general object detection algorithms tends to degrade significantly when applied to UAV scenes. This is due to the fact that UAV images are taken from high altitude with high resolution and a large proportion of small objects. In order to improve the precision of UAV object detection while satisfying the lightweight feature, we modify the YOLOv5s model. To address the small object detection problem, a prediction head is added to better retain small object feature information. The CBAM attention module is also integrated to better find attention regions in dense scenes. The original IOU-NMS is replaced by NWD-NMS in post-processing to alleviate the sensitivity of IOU to small objects. Experiments show that our method has good performance on the dataset Visdrone-2020, and the mAP is significantly improved from the original.
Scalable In-Memory Clustered Annealer with Temporal Noise of FinFET for the Travelling Salesman Problem

We propose a scalable in-memory annealer for solving the large-scale travelling salesman problem (TSP), based on a Hopfield neural network (HNN) implemented with a crossbar array of FinFET, using its intrinsic temporal noise of the drain current caused by trapping/detrapping to realize the annealing process. A hierarchical clustered approach is adopted to overcome the scalability challenge of solving large-scale TSP. We speed up the system convergence by only allowing feasible permutation states for the spins update, and updating non-adjacent clusters in parallel with equal size k-means clustering. The proposed hardware is tested with up to 150-city problems and evaluated by the system-level simulation. It shows benefits on fast convergence and ultra-low energy compared with state-of-the-art annealers.
Enhancing aspect-category sentiment analysis via syntactic data augmentation and knowledge enhancement

The goal of aspect-category sentiment analysis (ACSA) is to predict the sentiment polarity toward a specific aspect category from reviewers’ expressed opinions in a sentence. With the boom of pretrained language models, various relative methods have achieved significant improvements in ACSA. However, two major issues still remain to be solved. First, most of these studies usually follow the canonical method of fine-tuning on limited labeled data, neglecting to leverage external knowledge to further enhance ACSA performance. Second, aspect categories are usually abstract concepts that are mentioned explicitly or implicitly, and the corresponding different sentiment polarities are not easy to accurately recognize. To address these issues, we first transform the ACSA task into a sentence-pair classification task with natural language inference, constructing synthetic sentences as hypotheses based on the predefined aspect categories and the prompt-generation sentence template. Then the model applies a passivization transformation to the synthetic sentences and generates more syntactic data to augment the limited training data. Furthermore, we enhance ACSA with curated knowledge from a common sense knowledge graph. Finally, different representations are synergistically fused with a gating mechanism to output richer sentiment features and enable context-, syntax-, and knowledge-aware predictions. Experimental results on three challenging benchmark datasets show that the proposed model outperforms some competitive baselines.
Meta-path fusion based neural recommendation in heterogeneous information networks

As a powerful data modeling tool, Heterogeneous Information Network (HIN) has been successfully used in auxiliary information exploitation to boost recommendation performance. For HIN based recommendation, it is challenging to extract and fuse useful features of user preferences and item attributes under different semantic paths in HINs. Existing methods leverage a pre-defined fusion function to integrate different semantics for recommendation, which cannot characterize the complex nonlinear interactions between users and items. In this paper, we present a general framework named MNRec, short for Meta-path fusion based Neural Recommendation, to extract and fuse user and item embeddings under different meta-paths for recommendation. Under the framework, we propose an instantiation of MNRec with Multi-Layer Perceptron (MLP) structure. It consists of two major steps, i.e., meta-path based heterogeneous network embedding and deep learning based rating prediction. Concretely, appropriate meta-paths are first designed according to domain knowledge. Then the embeddings of users and items are obtained through a meta-path and commuting matrix based heterogeneous network embedding method. Finally, in light of the powerful nonlinear modeling capabilities of deep neural networks, the learned embeddings under different meta-paths are integrated into a two-pathway MLP structure for rating prediction. Experimental results on three real-world datasets demonstrate the superiority and effectiveness of MNRec compared with state-of-the-art baselines in rating prediction.
Enhanced Bi-directional Motion Estimation for Video Frame Interpolation

We propose a simple yet effective algorithm for motion-based video frame interpolation. Existing motion-based interpolation methods typically rely on an off-the-shelf optical flow model or a U-Net based pyramid network for motion estimation, which either suffer from large model size or limited capacity in handling various challenging motion cases. In this work, we present a novel compact model to simultaneously estimate the bi-directional motions between input frames. It is designed by carefully adapting the ingredients (e.g., warping, correlation) in optical flow research for simultaneous bi-directional motion estimation within a flexible pyramid recurrent framework. Our motion estimator is extremely lightweight (15x smaller than PWC-Net), yet enables reliable handling of large and complex motion cases. Based on estimated bi-directional motions, we employ a synthesis network to fuse forward-warped representations and predict the intermediate frame. Our method achieves excellent performance on a broad range of frame interpolation benchmarks. Code and trained models are available at https://github.com/srcn-ivl/EBME.
Chimp Optimization Algorithm Influenced Type-2 Intuitionistic Fuzzy C-Means Clustering-Based Breast Cancer Detection System

In recent years, breast cancer detection is an important area of concentration towards curative image dispensation and exploration. Detection of a disease at an early stage is an important factor in taking it to the next level of treatment. Accuracy plays an important role in the detection of disease. COA-T2FCM (Chimp Optimization Algorithm Based Type-2 Intuitionistic Fuzzy C-Means Clustering) is constructed for detection of such malignancy with the highest accuracy in this paper. The proposed detection process is designed with the combination of type-2 intuitionistic fuzzy c-means clustering in addition to oppositional function. In the type-2 intuitionistic fuzzy c-means clustering, the efficient cluster center can be preferred using the chimp optimization algorithm. Initially, the objective function of the type-2 intuitionistic fuzzy c-means clustering is considered. The chimp optimization algorithm is utilized to optimize the cluster center and fuzzifier in the clustering method. The projected technique is implemented, and in addition, performance metrics such as specificity, sensitivity, accuracy, Jaccard Similarity Index (JSI), and Dice Similarity Coefficient (DSC) are assessed. The projected technique is compared with the conventional technique such as fuzzy c means clustering and k mean clustering methods. The resulting method was also compared with existing methods to ensure the accuracy in the proposed method. The proposed algorithm is tested for its effectiveness on the mammogram images of the three different datasets collected from the Mini–Mammographic Image Analysis Society (Mini–MIAS), the Digital Database for Screening Mammography (DDSM), and Inbreast. The accuracy and Jaccard index score are generally used to measure the similarity between the proposed output and the actual cancer affected regions from the image considered. On an average the proposed method achieved an accuracy of 97.29% and JSI of 95%
Breaking the chain with individual gain? Investigating the moral intensity of COVID-19 digital contact tracing

During the COVID-19 pandemic, contact tracing apps such as the German Corona-Warning-App (CWA) were introduced to facilitate contact tracing of infected individuals with the aim of breaking chains of infection. Therefore, using a contact tracing app is beneficial to society as a whole. Even though this is a good cause, the rather reluctant use of the CWA in the beginning indicated that the pains (e.g., privacy concerns) obviously outweighed the gains (helping others) at the level of the individual user. Thus, in order to identify what lies behind the gain of this app and how it can be promoted, we were interested in the individual's moral perspective (helping others) on the app. We expected a positive relation between CWA download and moral intensity derived from (i) the magnitude or seriousness of consequences, (ii) social norms about app use, (iii) the individual proximity to COVID-19 cases, and (iv) the probability of the app's positive effect. Using a heterogeneous German sample of N = 1,454, we found a strong influence of moral intensity on app download. Furthermore, a manipulation of moral intensity among non-users led to a higher number of downloads in a follow-up study (N = 662) as compared to the population. Our results show possibilities to enhance the adoption of contact tracing apps and potentially other apps for the common good in the population.
CYBERCRIME AND THE NATURE OF INSIDER THREAT COMPLEXITIES IN HEALTHCARE AND BIOTECHNOLOGY ENGINEERING ORGANIZATIONS

This article explores the nature of cybersecurity professionals being insider threats to their own organization, as well as the general increase in harder-to-detect threats coming from an ever-widening acceptance of third-party insiders, which organizations, biotechnology engineering, and other healthcare organizations rely on. After examining the current and emerging literature on how individuals are motivated to engage in problematic workplace behaviors as a means of gaining their specific goal or need, the paper articulates malicious cybersecurity insider threat indicators, then provides best practices for reducing the risk of these threats in healthcare and biotechnology engineering organizations.
STC: Spatio-Temporal Contrastive Learning for Video Instance Segmentation

Video Instance Segmentation (VIS) is a task that simultaneously requires classification, segmentation, and instance association in a video. Recent VIS approaches rely on sophisticated pipelines to achieve this goal, including RoI-related operations or 3D convolutions. In contrast, we present a simple and efficient single-stage VIS framework based on the instance segmentation method CondInst by adding an extra tracking head. To improve instance association accuracy, a novel bi-directional spatio-temporal contrastive learning strategy for tracking embedding across frames is proposed. Moreover, an instance-wise temporal consistency scheme is utilized to produce temporally coherent results. Experiments conducted on the YouTube-VIS-2019, YouTube-VIS-2021, and OVIS-2021 datasets validate the effectiveness and efficiency of the proposed method. We hope the proposed framework can serve as a simple and strong baseline for other instance-level video association tasks.
Vulnerability-Aware Task Scheduling for Edge Intelligence Empowered Trajectory Analysis in Intelligent Transportation Systems

In order to fulfill the requirements of Intelligent Transportation Systems (ITS) on ultra-delay service response, task scheduling for trajectory analysis is being shifted from the data center into the network edge of ITS. Such a decentralized paradigm motivates the computing power of the edge device and makes traditional analysis tasks open to the users around ITS. However, since these ITS users have differentiated identities and roles with differentiated security demands and privacy protection, assigning tasks for different users requires identifying and assessing the vulnerability of edge intelligence entities (EIEs). Otherwise, sensitive tasks assigned to the vulnerable EIEs will extremely increase the security risks of industrial control networks. To solve these problems, this paper proposes a vulnerability-aware task scheduling (VATS) mechanism, which integrates vulnerability assessment and access control. With VATS, secure EIEs can obtain more permissions and join in the privacy-sensitive trajectory analysis task, which is essential to enhance privacy protection at edges and ultimately improve the efficiency of task scheduling. The simulation results demonstrate the validity of the proposed scheme to defend insecure task scheduling like trajectory analysis.
A Generalized Deep Learning Clustering Algorithm Based on Non-Negative Matrix Factorization

Clustering is a popular research topic in the field of data mining, in which the clustering method based on non-negative matrix factorization (NMF) has been widely employed. However, in the update process of NMF, there is no learning rate to guide the update as well as the update depends on the data itself, which leads to slow convergence and low clustering accuracy. To solve these problems, a generalized deep learning clustering (GDLC) algorithm based on NMF is proposed in this article. Firstly, a nonlinear constrained NMF (NNMF) algorithm is constructed to achieve sequential updates of the elements in the matrix guided by the learning rate. Then, the gradient values corresponding to the element update are transformed into generalized weights and generalized biases, by inputting the elements as well as their corresponding generalized weights and generalized biases into the nonlinear activation function to construct the GDLC algorithm. In addition, for improving the understanding of the GDLC algorithm, its detailed inference procedure and algorithm design are provided. Finally, the experimental results on eight datasets show that the GDLC algorithm has efficient performance.
Attention-based neural networks for trust evaluation in online social networks

Trust relationship prediction has gained a lot of interest in online social networks as it allows users to escape complex analysis in decision-making. However, most existing work fail to consider two kinds of complementary crucial user data, users' trusted neighbors and temporal continuity of user behaviors, simultaneously. In this paper, we analyze the complementarity between the crucial user data mentioned above and design an attention-based neural networks model, GainTrust, to aggregate the two kinds of user data for trust relationship prediction task. Specifically, we firstly map the users' trust neighbors and behavioral records into the same feature space by designing heterogeneous network embedding layer. Then, we develop multi-layer Long Short-Term Memory (LSTM) network to further learn the time series features of users over multiple time slots. Finally, after integrating time series features with trusted neighborhood features, we design two-level multi-head attention mechanism to obtain global trust features for trust evaluation among users. A series of extensive experiments are conducted on two real-world datasets and the results demonstrate the effectiveness of the proposed approach.
A sentiment analysis driven method based on public and personal preferences with correlated attributes to select online doctors

This paper proposes a method to assist patients in finding the most appropriate doctor for online medical consultation. To do that, it constructs an online doctor selection decision-making method that considers the correlation attributes, in which the measure of attribute correlation is derived from the history real decision data. To combine public and personal preference with correlated attributes, it proposes a Choquet integral based comprehensive online doctor ranking method. In detail, a two stage classification model based on BERT (Bidirectional Encoder Representations from Transformers) is used to extract service features from unstructured text reviews. Then, 2-additive fuzzy measure is adopted to represent the patient public group aggregated attribute preference. Next, a novel optimization model is proposed to combine the public preference and personal preference. Finally, a case study of dxy.com is carried out to illustrate the procedure of the method. The comparison result between proposed method and other traditional MADM (multi-attribute decision-making) methods prove its rationality.
Classifying Malignant and Benign Tumors of Breast Cancer

Breast cancer is the second major cause of cancer deaths in women. Machine learning classification techniques can be used to increase the precision of diagnosis and bring it closer to 100%, thus saving the lives of many people. This paper proposed four different models, built using different combinations of selected features and applying five ML classification techniques to all the models to identify the best model with the highest accuracy. It analyzes five machine learning techniques, namely logistic regression (LR), support vector machines (SVM), naive bayes (NB), decision trees (DT), and k-nearest neighbor (KNN), for prediction of breast cancer using the Wisconsin Diagnostic Breast Cancer Dataset on these four models. The objective of the paper is to find the best ML algorithm that can most accurately predict breast cancer for a particular model. The outcome of this paper helps the doctors to improvise the diagnosis by knowing the effect of combinations of symptoms with the growth of breast cancer.
Comparison of Using an Augmented Reality Learning Tool at Home and in a Classroom Regarding Motivation and Learning Outcomes

The recent pandemic brought on considerable changes in terms of learning activities, which were moved from in-person classroom-based lessons to virtual work performed at home in most world regions. One of the most considerable challenges faced by educators was keeping students motivated toward learning activities. Interactive learning environments in general, and augmented reality (AR)-based learning environments in particular, are thought to foster emotional and cognitive engagement when used in the classroom. This study aims to compare the motivation and learning outcomes of middle school students in two educational settings: in the classroom and at home. The study involved 55 middle school students using the AR application to practice basic chemistry concepts. The results suggested that students’ general motivation towards the activity was similar in both settings. However, students who worked at home reported better satisfaction and attention levels compared with those who worked in the classroom. Additionally, students who worked at home made fewer mistakes and achieved better grades compared with those who worked in the classroom. Overall, the study suggests that AR can be exploited as an effective learning environment for learning the basic principles of chemistry in home settings.
DEVELOPING ALGORITHMIC THINKING BY EDUCATIONAL COMPUTER GAMES

Basics of algorithmic thinking should not be limited to create right solutions and express them by a computer program, but should also be used a suitable methodology based on problem solving, preferably in a playful way. In many cases at school most of the learners consider the topic of algorithms as hard and not very attractive. For beginners in programming the knowledge of specific algorithms is not so important. The ability to understand principles of algorithms, as well as to find own algorithms for new problems are more desirable. One main educational objective is to know that an algorithm prescribes exactly what to do in the possible situations. The educational computer games based on the use of basic control structures do a good service to pupils can understand correctly how to get to the solution, using clearly defined steps with immediate feedback, with the possibility of visualizing the sequence of steps (with the possibility of corrections). Students gain new knowledge based on their own observation and discovery. The games also motivate the students to improve their algorithms to find more efficient solutions in the strategy of games. The aim is for pupils to acquire new knowledge by exploring and learning by doing. The main aim of the paper is to show a way of learning principles and concepts of algorithms by using computer game that is much easier to comprehend by the learners and makes them more fun. During the creation of the game, which was inspired by the well-known programmable toy Bee-bot, we tried to comply with the didactic principles of illustrationality, appropriaty and individual approach.