Reviewerly/exHarmony huggingface dataset:
Efficient guided evolution for neural architecture search

Neural Architecture Search methods have been successfully applied to image tasks with excellent results. However, NAS methods are often complex and tend to quickly converge for local minimas. In this paper, we propose G-EA, a novel approach for guided NAS. G-EA guides the evolution by exploring the search space by generating and evaluating several architectures in each generation at initialisation stage using a zero-proxy estimator, where only the highest-scoring architecture is trained and kept for the next generation. By generating several off-springs from an existing architecture at each generation, G-EA continuously extracts knowledge about the search space without added complexity. More, G-EA forces exploitation of the most performant architectures by descendant generation while at the same time forcing exploration by parent mutation and favouring younger architectures to the detriment of older ones. Experimental results demonstrate the effectiveness of the proposed method. Results show that G-EA achieves state-of-the-art results in NAS-Bench-101 and in all NAS-Bench-201 search space data sets: CIFAR-10, CIFAR-100 and ImageNet16-120, with mean accuracies of 93.99%, 72.62% and 46.04% respectively.
A Radar Jamming Recognition Method based on Hybrid Dilated Convolution

Radar anti-jamming technology is developing with the development of radar jamming, and jamming recognition is the premise of anti-jamming. This paper proposed a radar jamming recognition method based on hybrid dilated convolution. Firstly jamming data set and pre-processing are introduced, then hybrid dilated convolution was analyzed; finally, HDC was used to recognize jamming, including suppressive jamming, intermittent sampling repeating jamming, smeared spectrum jamming, and smart noise jamming. Simulation results verified the proposed method.
Dynamic Knowledge Graph Embeddings via Local Embedding Reconstructions

Knowledge graph embeddings and successive machine learning models represent a topic that has been gaining popularity in recent research. These allow the use of graph-structured data for applications that, by definition, rely on numerical feature vectors as inputs. In this context, the transformation of knowledge graphs into sets of numerical feature vectors is performed by embedding algorithms, which map the elements of the graph into a low-dimensional embedding space. However, these methods mostly assume a static knowledge graph, so subsequent updates inevitably require a re-run of the embedding process. In this work the Navi Approach is introduced which aims to maintain advantages of established embedding methods while making them accessible to dynamic domains. Relational Graph Convolutional Networks are adapted for reconstructing node embeddings based solely on local neighborhoods. Moreover, the approach is independent of the original embedding process, as it only considers its resulting embeddings. Preliminary results suggest that the performance of successive machine learning tasks is at least maintained without the need of relearning the embeddings nor the machine learning models. Often, using the reconstructed embeddings instead of the original ones even leads to an increase in performance.
A discrete wavelet transform-based audio watermarking technique for digital security

This work proposes a model using discrete wavelet transform (DWT) to eliminate the attacks on digital security. Semi-fragile watermarking techniques strive towards establishing the confidentiality of data content. Minutiae algorithm finds its application in various fields to safeguard and protect the data content ensuring integrity of data security, authenticity, copyright proofs and data ownership. This work is based on the usage of the combined technique of employing the minutiae algorithm along with DWT for the secured retrieval of audio data to protect network security. Experimental results are shown for the proposed technique based on minutiae algorithm and DWT by considering the four parameters - signal-to-noise ratio (SNR), computation time, entropy and IQI. DWT ensures high secured digital network in areas like defense, satellite and related fields.
Learning Quality Evaluation of Course Implementation Supported by Online Teaching Platform

With the rapid development of science and technology, the use of network information technology means more and more widely; network teaching platform has become an important measure to promote the development of education and improve the quality of teaching. The network teaching platform has been widely used and plays a vital role in the construction of teaching informationization and modernization. However, the study of student learning quality evaluation under the network teaching platform has become a huge challenge. In order to solve this problem, this paper takes the teaching of basic Python course as an example, adopts data-driven theory, and combs the teaching process of network teaching platform. Curriculum can be divided into class leading learning stage, stage of studies in class, and after class extension stage and uses the panel method, literature query method, mathematical statistics, performance appraisal, and questionnaire survey method to analyze the factors influencing the quality of learning curriculum implementation, and compared with the traditional classroom teaching, build more indicators of curriculum implementation under the network teaching platform to support students’ learning quality evaluation scheme. The experimental results show that, through the statistics of the evaluation index data, using the analysis method of the KMO coefficient and the KMO test, the three stages of the curriculum implementation of the KMO coefficient and the KMO test prove that the constructed learning quality evaluation scheme has high credibility and good structural validity. Based on class leading learning stage, stage of inquiry learning in the class, after class extension stage, and students’ learning quality correlation analysis, using Pearson correlation test, it is concluded that different index correlations with students’ academic performance prove students’ input and participation, learning ability and innovation ability, self-study ability and students’ learning achievement of great relevance. It has a direct impact on students’ learning quality. The results of this study provide powerful data and theoretical support for educators to carry out network teaching.
Enhancing students’ speaking skill through a game-based learning innovation of family game show

Gamification and game-based learning (GBL) is the newly introduced learning strategies considered fit for English fun learning. . This study aims to investigate the implementation of teaching Speaking in Foreign Language for Specific Purposes (FLSP) at Universitas Muhammadiyah Malang. The employed research design was Classroom Action Research (CAR) with two cycles. The GBL employed was modified from the Family 100 and Trivia game show. Subjects of the study were the Psychology students 2021/2022. Data collection methods were a survey and observation checklist, and the obtained data were analyzed using Activity Theory. The data triangulation was obtained by comparing researchers’ observation notes and questionnaire responses. . Results show that students were not enthusiastic when learning about Speaking through games. Their enthusiasm increased when the game level, rules, and topics were adjusted. The findings of this study imply that GBL enhanced students’ understanding, motivated their learning, and increased their positive behaviours
Protecting sensitive data in the cloud-to-edge continuum: The FogProtect approach

Data produced by end devices like smartphones, sensors or IoT devices can be stored and processed across a continuum of compute resources, from end devices via fog nodes to the cloud, enabling reduced latency, increased processing speed and energy savings. However, the data may be sensitive (e.g., personal data or confidential commercially sensitive information), with regulatory or other requirements for its protection. Protecting sensitive data in the dynamic, heterogeneous, and decentralized cloud-to-edge continuum is very challenging. This paper describes a solution: FogProtect, an integrated set of four technologies to protect data in the cloud-to-edge continuum. Fog-Protect addresses four concerns: (i) control and enforcement of distributed data access and usage; (ii) management of distributed data protection policies; (iii) risk assessment for data assets in the cloud-to-edge continuum; (iv) automated optimisation and adaptation to address identified risks. FogProtect operates dynamically, reacting to system changes or detected vulnerabilities to keep the data secure across the cloud- to-edge continuum. This paper describes an overview of the FogProtect concept, discusses each of the four approaches, and illustrates their usage for the protection of data in three real-world use cases.
Experience report of the contributions of telehealth in riverside communities of Amazonas in the pandemic

to report on the creation and implementation of telehealth activities developed by the Forest Health Program in communities in conservation areas, in the state of Amazonas, during the COVID-19 pandemic.this is an experience report on the creation and implementation of the program.the project began in June 2021 with medical and nursing services and currently has 63 points of connectivity installed. Dermatology was the most requested specialty (30.1%), with dermatitis being the main grievance diagnosed. For nursing, the most requested specialty was obstetrics, followed by pediatrics. Rio Madeira and the Catuá-Ipixema Extractive Reserve requested more than half of all consultations.this project showed a differentiated performance of telehealth in riverside communities in Amazonas, especially in the pandemic context, expanding health care in remote areas, such as these.
Adaptive Target Tracking With Interacting Heterogeneous Motion Models

Multiple motion estimators such as an interacting multiple model (IMM) have been utilized to track target objects such as cars and pedestrians with diverse motion patterns. However, the standard IMM has limitations in combining motion models with different state definitions, so it cannot contain a complementary set of models that accurately work for all motion patterns. In this paper, we propose IMM-based adaptive target tracking with heterogeneous velocity representations and linear/curvilinear motion models. It can integrate four motion models with different state definitions and dimensions to be completely complimentary for all types of motions. We experimentally demonstrate the effectiveness of the proposed method with accuracy for various motion patterns using two types of datasets: synthetic datasets and real datasets. Experimental results show that the proposed method achieves the adaptive target tracking for diverse types of motion and also for various objects such as cars, pedestrians, and drones in the real world.
Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures

Data glove devices, apart from being widely used in industry and entertainment, can also serve as a means for communication with the environment. This is possible thanks to the advancement in electronic technology and machine learning algorithms. In this paper, the results of the study using a designed data glove equipped with 10 piezoelectric sensors are reported, and the designed glove is validated on a recognition task of hand gestures based on 16 static signs of the Polish Sign Language (PSL) alphabet. The main result of the study is that recognition of 16 PSL static gestures is possible with a reduced number of piezoelectric sensors. This result has been achieved by applying the decision tree classifier that can rank the importance of the sensors for the recognition performance. Other machine learning algorithms were also tested, and it was showed that for the Support Vector Machines, k-NN and Bagged Trees classifiers, a recognition rate of the signs exceeding 90% can be achieved just for three preselected sensors. Such a result is important for a reduction in design complexity and costs of such a data glove with sustained reliability of the device.
“Make Your Best Use of This”

User-Experience Design (UX) is a contemporary design discipline that creates products, interfaces and intuitive approaches to information architecture based on the needs and perceptions of the intended user(s). This chapter is a case study of a project where the author co-designed a digital interface for a new critical edition of a Shakespeare play using a UX approach. The chapter explains the process and results of this experiment. It contrasts it with the implicit assumptions of the print interface about who the users are, what it is they want and need, and whose agendas are being served.
Distilling Hypernymy Relations from Language Models: On the Effectiveness of Zero-Shot Taxonomy Induction

In this paper, we analyze zero-shot taxonomy learning methods which are based on distilling knowledge from language models via prompting and sentence scoring.We show that, despite their simplicity, these methods outperform some supervised strategies and are competitive with the current state-of-the-art under adequate conditions.We also show that statistical and linguistic properties of prompts dictate downstream performance 1 .
SemEval-2022 Task 4: Patronizing and Condescending Language Detection

This paper presents an overview of Task 4 at SemEval-2022, which was focused on detecting Patronizing and Condescending Language (PCL) towards vulnerable communities.Two sub-tasks were considered: a binary classification task, where participants needed to classify a given paragraph as containing PCL or not, and a multi-label classification task, where participants needed to identify which types of PCL are present (if any).The task attracted 77 teams.We provide an overview of how the task was organized, discuss the techniques that were employed by the different participants, and summarize the main resulting insights about PCL detection and categorization.
Uncovering inclusivity gaps in design pedagogy through the digital design marginalization framework

Designers play a key role in the design of inclusive and socially conscious interfaces. Thus, it is imperative for designers to be thoughtful of the ethical and social implications of design. However, gaps in the foundational training that designers receive (e.g., as university students) can negatively impact their ability to consider the social implications of their design practice. This can result in consequences such as digital marginalization, which, as defined by the Digital Design Marginalization (DDM) framework, is the “pushing away”, whether intentional or not, of a defined group of users from a digital or online service or system, where the exclusion has additional, indirect, and long-lasting social consequences on that particular user group. Designers can contribute, even unintentionally, to digital marginalization through their design practices and the design choices they make. We argue that our role as educators includes ensuring not only that our design pedagogy is inclusive, but that the designers we train now are prepared to conduct their future design practice in a manner that is inclusive to all users. As such, we propose to use the Digital Design Marginalization as a lens to guide a reflection-based approach to identify gaps in our pedagogy that may lead to designers becoming ill-equipped to identify how their designs may lead to digital marginalization. Through seven case studies from our own teaching practice, we demonstrate the use of the DDM framework to guide marginalization-focused introspective reflections of curricula. These reflections through the DDM lens revealed gaps in our pedagogy with respect to providing future designers with training that enables them to consider the broader societal and individual implications of the design choices they will make in future practice. Based on our experience using the DDM framework, we then discuss in greater depth how reflection of social consequences of design pedagogy can be operationalized within institutions to reduce educational gaps that may be associated with design-mediated digital marginalization. Finally, we comment on avenues for further development of pedagogical reflection using DDM.
Failure mode and effects analysis for errors detected during pretreatment physics plan and chart review in external beam radiotherapy

Background: Quality assurance (QA) plays a critical role in patient safety during radiation therapy. Among various QA checks, pretreatment physics plan review is sensitive at detecting errors before beam delivery. Therefore, we aimed to assess the errors detected during pretreatment physics plan and chart review (PTPCR) through a failure mode and effects analysis (FMEA) and to evaluate the quality and effectiveness of the PTPCR performed at our clinic. Methods: Five qualified medical physicists used a single checklist to perform a total of 1,406 PTPCRs in 2019. Five major PTPCR process steps were reviewed: (I) planning parameters, (II) plan quality, (III) image parameters, (IV) MOSAIQ management, and (V) chart documents. Errors detected during the PTPCR were identified to failure modes (FMs) in each process step. A risk priority number (RPN) was assigned to each FM based on tabulated scores for the severity (S), frequency of occurrence (O), and detectability (D) of errors, each on a scale of 1 to 10. The single error rate, multiple error rate, and overall error rate were calculated to evaluate the quality of the treatment planning. The PTPCR compliance rate was used to quantify the effectiveness of the PTPCR. Results: In total, 201 errors were identified from the 1,406 plans. From the FMEA results, image parameters had the highest mean RPN, and the planning parameters had the highest RPN from the FM of skin flash. The chart documents had the highest number of FMs and highest occurrence rate, followed by MOSAIQ management and planning parameters. The average single, multiple, and overall error rates were 10.6%, 1.3%, and 11.9%, respectively. The PTPCR compliance rate was 90.3%. Conclusions: The FMEA provided a systematic and useful method for evaluating the errors detected in the PTPCR. The compliance rate could help us understand the effectiveness of our PTPCR. The FMEA results and the PTPCR compliance rate could help us improve our PTPCR to ensure the safety of treatment and the efficiency of the clinical workflow.
Breast Cancer Detection using Principal Component Analysis and Machine Learning Models

Breast cancer is one of the major problems in women. It accounts at least 14% in Indian women. It is a most common form of cancer in women. Breast cancer is spreading in rural as well as urban areas. Approximately, 87,090 deaths and 1,62,468 new cases are reported in the year 2018. Breast cancer detection at an early stage and diagnosis can increase the lives of women. Our work deals with a breast cancer detection system that facilitates user to detect breast cancer using principal component analysis (PCA) and machine learning models (MLMs) based on the features of the data. Five different models are trained and tested to determine which model gave the highest accuracy on the data. The obtained accuracy, precision, sensitivity, and F-Measure are 97.53%, 97.59%, 95.75%, and 96.66%, respectively. The proposed method gave accurate and effective results compared to existing methods. Thus, this provides an effective application of machine learning models in the field of healthcare.
Temporally Stable Real-Time Joint Neural Denoising and Supersampling

Recent advances in ray tracing hardware bring real-time path tracing into reach, and ray traced soft shadows, glossy reflections, and diffuse global illumination are now common features in games. Nonetheless, ray budgets are still limited. This results in undersampling, which manifests as aliasing and noise. Prior work addresses these issues separately. While temporal supersampling methods based on neural networks have gained a wide use in modern games due to their better robustness, neural denoising remains challenging because of its higher computational cost. We introduce a novel neural network architecture for real-time rendering that combines supersampling and denoising, thus lowering the cost compared to two separate networks. This is achieved by sharing a single low-precision feature extractor with multiple higher-precision filter stages. To reduce cost further, our network takes low-resolution inputs and reconstructs a high-resolution denoised supersampled output. Our technique produces temporally stable high-fidelity results that significantly outperform state-of-the-art real-time statistical or analytical denoisers combined with TAA or neural upsampling to the target resolution.
Subnetwork-Lossless Robust Watermarking for Hostile Theft Attacks in Deep Transfer Learning Models

Recently, considerable progress has been made in providing solutions to prevent intellectual property (IP) theft for deep neural networks (DNNs) in ideal classification or recognition scenarios. However, little work has been dedicated to protecting the IP of DNN models in the context of transfer learning. Moreover, knowledge transfer is usually achieved through knowledge distillation or cross-domain distribution adaptation techniques, which will easily lead to the failure of the IP protection due to the high risk of the underlying DNN watermark being corrupted. To address this issue, we propose a subnetwork-lossless robust DNN watermarking (SRDW) framework, which can exploit out-of-distribution (OOD) guidance data augmentation to boost the robustness of watermarking. Specifically, we accurately seek the most rational modification structure (i.e., core subnetwork) using the module risk minimization, and then calculate the contrastive alignment error and the corresponding hash value as the reversible compensation information for the restoration of carrier network. Experimental results show that our scheme has superior robustness against various hostile attacks, such as fine-tuning, pruning, cross-domain matching, and overwriting. In the absence of malicious jamming attacks, the core subnetwork can be recovered without any loss. Besides that, we investigate how embedding watermarks in batch normalization (BN) layers affect the generalization performance of the deep transfer learning models, which reveals that reducing the embedding modifications in BN layers can further promote the robustness to resist hostile attacks.