Reviewerly/exHarmony huggingface dataset:
Automated Lesion Segmentation

Studies investigating the effects of brain lesions on cognition and behavior have played a key role in shaping modern theories of brain function. While early studies in this domain typically involved post-mortem dissections of selected patients who presented with cognitive and/or behavioral deficits following brain injuries or neurological disease, modern lesion studies typically involve analyzing noninvasive neuroimaging data acquired from large numbers of patients using techniques such as computed tomography (CT) or magnetic resonance imaging (MRI). Modern studies therefore depend critically on the accurate segmentation of the lesion from healthy brain tissue in neuroimaging scans. Lesion segmentation has traditionally been performed manually by expert raters, which is a time-intensive procedure that poses obstacles to reproducibility. There is growing interest in automated lesion segmentation techniques that can be applied to large datasets collected from patients with brain lesions. Here, we provide an overview of the state of the art with respect to semiautomated and fully automated lesion segmentation methods that have been implemented in publicly available software packages. We discuss considerations for selecting an automated lesion segmentation method and highlight the potential advantages and disadvantages of different approaches. The second appendix at booknnnn’s end provides hands-on tutorials for using some of the selected approaches.
Physics exam in distance learning in the context of formative and summative assessment

In Slovakia, the school year 2020/2021 began in the classrooms. This changed in the last week of October, when lower secondary school pupils switched to distance learning, as part of the government’s response to the second wave of COVID-19. In an online setting we verified the knowledge and skills of pupils in the 8th grade through an exam covering the topics of force and torque. We use inquiry-based instruction and thus we consider it appropriate to verify not only pupils’ theoretical knowledge but also their practical skills acquired during Physics lessons and how they put these skills to use when solving a problem. As part of the exam, the pupils therefore had to individually perform an experiment and use the data measured in the experiment to answer a series of questions. Additionally, pupils had to record videos where they described the course of their experiments and presented their results. The completed exam, the video presentation and an online session to discuss the exam with each pupil were used to assess the pupil’s performance. Here we give an analysis of our approach to the grading of the exams with respect to formative and summative assessment. Since the shift to distance learning, there has been a lot of discussion on online cheating. We therefore also examine cheating in the light of the type of assessment used in this work.
Dynamics Modeling and Motion Simulation of a Segway Robotic Transportation System

Simulation is an essential interactive computer tool that can be applied to monitor systems graphically. A simulation of motion is presented in this study to support the control of Segway Robot Transportation Platform (SRTP). The introduced system provides simulation of the SRTP using MATLAB. The nonlinear dynamic equations of the motion for the Segway system are determined using the Lagrange approach. The derived nonlinear equations are then arranged in state space model format. The SRTP is simulated using the derived state space model using MATLAB. The introduced modeling and simulation processes have shown their ability to produce and plot the motion of the Segway parts with monitoring the effect of the Segway physical parameters such as mass of cart.
Accelerators &amp; Security: The Socket Approach

We propose the concept of security socket to protect the integration of loosely-coupled accelerators in system-on-chip (SoC) architectures. The socket is placed between an accelerator and the rest of the SoC and provides security services that are completely decoupled from the accelerator implementation. Any accelerator can be plugged into the socket and transparently benefit from all the security services.
An Approach to Medical Diagnosis Using Smart Chatbot

People avoid initial hospital treatment that could turn into a major disease in the future. With rapid changes, remote or home-stationed diagnosis systems are becoming exponentially popular, featuring ample number of advantages like cost-efficiency, quick and authentic decision support for medical diagnostics, treatment and prevention of any physical or mental damages. Our proposed idea is to create an affordable and accessible all-day chatbot- DiagZone for people to check on their health in contrast to the conventional way of higher time investments. Since it is free and provides mobility with the capability of being accessible anywhere irrespective of the user's location, it encourages practical utilization. It saves on unnecessary time required in specialist consultations. With the help of AWS Lex, AWS Lambda and channel Twilio, we have integrated our Flask based Chatbot to Whatsapp. Text entered by the user is processed with the pivotal implementation of NLP. After DiagZone has gathered adequate keywords from the original messages, it will start to lead the conversation by interrogating the user and trying to list a few diseases that the user may suffer from. Once the Chatbot has detected the probable disease, it works effectively by suggesting redressal measures and medications to the user or by referring them to a doctor.
Secure Data Transfer and Provenance for Distributed Healthcare

The rise of the Internet of Things (IoT) has enabled a shift to a smart, remote, and more distributed healthcare ecosystem supported by learning-based secure Internet of Medical Things (IoMT). Infrastructure availability is a barrier. The distributed and layered architecture of IoMT another. Trust must be addressed across the full stack and involves challenges in security, privacy, and edge intelligence. This chapter’s objective is to examine the state-of-the-art in security, privacy preservation and provenance of data generated by the IoMT and identify challenges and opportunities. The chapter highlights the existing security and challenges and how they can be addressed from the incorporation of blockchain technologies. Also, it discusses the challenges generated by infrastructure availability and suggests edge computing and federated learning as opportunities to address IoMT service provision where infrastructure is lacking. To demonstrate the feasibility of the proposed solutions a state-of-the-art exemplar system is examined. The system is designed with trustworthy Artificial Intelligence (AI) principles in mind and the results demonstrate not only the benefit for remote diagnostics but also the improvements in security, privacy preservation and provenance when transferring and processing data. The chapter proposes future directions of research to enhance transfer and provenance in distributed healthcare data.
Perceptual Hashing Based on Salient Region and NMF

Wu, Xujun Cui, Chen Wang, ShenWe propose a novel perceptual hashing based on salient region and a nonnegative matrix factorization (NMF) in this paper. Firstly, the input image is standardized and processed with low-pass filtering. Secondly, the salient region is extracted from the obtained preprocessed image. The minimum bounding rectangle of each salient region is extracted, and the pixels in all rectangles are rearranged to form a secondary image, then the preprocessed image is decomposed by NMF to obtain the coefficient matrix as the final image hash. The algorithm is robust to general content-preserving manipulations through the experiment. The proposed algorithm outflanks some best in the performances of perceptual robustness and discrimination indicated by identification accuracy performances.
Medical Gesture Recognition Method Based on Improved Lightweight Network

Surgical procedures are a compelling application area for collaborative control robots, and this paper proposes a gesture recognition method applied to a medical assistant robot delivering instruments to collaborate with surgeons to complete surgeries. The key to assisting the surgeon in passing instruments in the operating room is the ability to recognize the surgeon's hand gestures accurately and quickly. Existing gesture recognition techniques suffer from poor recognition accuracy and low rate. To address the existing shortcomings, we propose an improved lightweight convolutional neural network E-MobileNetv2. The ECA module is added to the original MobileNetv2 network model to obtain more useful features by computing the information interactions between the current channel and the adjacent channels and between the current channel and the distant channels in the feature map. Add R6-SELU activation function to enhance the network's ability to extract features. By adjusting the shrinkable hyper-parameters, the number of parameters of the network is reduced to improve the recognition speed. The improved network model achieves excellent performance on both the self-built dataset Gesture\_II and the public dataset Jester, realizing the improvement in accuracy and recognition speed.
The Time-Course of Real-World Scene Perception: Spatial and Semantic Processing

Real-world scene perception unfolds remarkably quickly, yet the underlying visual processes are poorly understood. Space-centered theory – an influential account of early visual processing – maintains that a scene’s spatial structure (e.g., openness, roughness, mean depth) can be rapidly recovered from low-level image statistics. In turn, the statistical relationship between a scene’s spatial properties and its semantic content allows the scene’s semantic identity to be inferred from its layout. We tested this theory by investigating (i) the temporal dynamics of semantic and spatial structure perception in real-world scenes, and (ii) dependencies between spatial and semantic judgements. Participants viewed images for 13.3 to 106.7 msecs (with backward masking), and identified the semantic (e.g., beach, road) or spatial structure (e.g., open, closed off) category. Performance in both tasks was above chance after only 13.3 msecs, and improved with presentation time. Critically, there was no temporal precedence for spatial discrimination relative to semantic discrimination. The availability of binocular disparity and colour information improved both semantic and spatial structure discrimination. Computational analyses of these data suggest that, instead of using spatial layout to infer semantic categories, as predicted by space-centered theory, the brain does the opposite, exploiting semantic information to discriminate spatial structure categories. These findings challenge traditional ‘bottom-up’ views of scene perception, suggesting an earlier role for semantic processing of visual scenes.
Machine Reading Comprehension Model Based on Multi-head Attention Mechanism

Natural language processing is the core technology of artificial intelligence. Machine reading comprehension, as a hotspot in the field of natural language processing, has made a great breakthrough under the rapid development of deep learning technology. In this paper, starting from the DuReader dataset, we propose a machine reading comprehension model based on a multi-head attention mechanism for the newly released baseline model BiDAF in DuReader 2.0 for the problems of time-consuming training in terms of large data volume and complex tasks, and analyze two evaluation indexes of poor model performance, such as rouge lipid L and BLEU-4, through continuous experiments. baseline model. The model uses a multi-head attention mechanism to replace the BiLSTM technology used in the coding layer and modeling layer of the DuReader baseline model to encode and model. In addition, it uses pre-training word vectors instead of randomly generated word vectors to embed words into the model, which effectively solves the shortcomings of the above baseline model. As a result, the model proposed in this paper has a ROUGE-L score of 39.76 and a BLEU-4 score of 35.66 on the Search test set of DuReader2.0, which is about 2.8 and 2.1% higher than the DuReader baseline model BiDAF. In the meanwhile, the ROUGE-L score and BLEU-4 score on the Zhidao test set are 48.61 and 43.63, respectively. Compared with the baseline model DiDAF, it is increased by about 2.4% and 2.0%, respectively. In the meanwhile, the training time of an epoch model in this paper is only about 2 beat 5 of the baseline model BiDAF. The results show that the model proposed in this paper is a good model not only in terms of time cost, but also in terms of accuracy of the model prediction results.
Big Data Analytics

Big data analytics, in the context of geospatial data, employs distributed computing using advanced tools that support spatiotemporal analysis, spatial statistics, and machine learning algorithms and techniques (e. g., classification, clustering, and prediction) on very large spatiotemporal datasets to visualize, detect patterns, gain deeper understandings, and answer questions. In this chapter, the key definitions, domain-specific problems, analysis concepts, current technologies and tools, and remaining challenges are discussed.
Applying Time-Constraints Using Ontologies to Sensor Data for Predictive Maintenance

Predictive maintenance depends on sources of data analysed through machine learning and data mining algorithms to identify and predict potential fault states. However, sources of raw data lack a layer of semantic abstraction providing a consistent way of interpreting data. The use of ontologies to describe data from structured and unstructured sources has been employed to provide a semantic layer delivering a consistent interpretation and meaning to data that can be exchanged between different entities. Data supplied by sensors is time-sensitive, as variations and fluctuations occur over periods and must be analysed regarding the period they occur. Transforming raw data and applying time constraints to fit the data to the semantic concepts is a process not frequently documented. In this paper, we present an architecture for the transformation of data acquired through different sources – from sensors to contextual information supplied by management software – from JSON to ontology instances that can be used in a predictive maintenance scenario, alongside with machine learning and data mining solutions. Here, the different data sources are transformed semantic time-sensitive data, represented through means of an ontology and stored in a triple store.
Models for Classifying AI Systems: The Switch, the Ladder, and the Matrix

Organisations that design and deploy systems based on artificial intelligence (AI) increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical challenges. Nevertheless, pragmatic problem-solving demands that things should be sorted so that their grouping will promote successful actions for some specific end. In this article, we review and compare previous attempts to classify AI systems for the purpose of implementing AI governance in practice. We find that attempts to classify AI systems found in previous literature use one of three mental models: the Switch, i.e., a binary approach according to which systems either are or are not considered AI systems depending on their characteristics; the Ladder, i.e., a risk-based approach that classifies systems according to the ethical risks they pose; and the Matrix, i.e., a multi-dimensional classification of systems that take various aspects into account, such as context, data input, and decision-model. Each of these models for classifying AI systems comes with its own set of strengths and weaknesses. By conceptualising different ways of classifying AI systems into simple mental models, we hope to provide organisations that design, deploy, or regulate AI systems with the conceptual tools needed to operationalise AI governance in practice.
Locality-Aware Discriminative Subspace Learning for Image Classification

Projection learning is an effective and widely used technique for extracting discriminative features for pattern recognition and classification. In projection learning, it is essential to preserve the global and local structure of the data while extracting discriminative features. However, transforming the source data directly to a target i.e., the strict binary label matrix, using a projection matrix may result in the loss of some intrinsic information. We propose a locality-aware discriminative subspace learning (LADSL) method to address these limitations. In LADSL, the original data is transformed into a latent space instead of a restrictive label space. The latent space seamlessly integrates the original visual features and the class labels to improve the classification performance. The projection matrix and classification parameters are jointly optimized to supervise the discriminative subspace learning. Additionally, LADSL exploits the adaptive local structure to preserve the nearest neighbor relationship among the data samples while learning more projections to achieve superior classification performance. Experiments have been carried out on various data sets for face and object recognition, and the results achieved are compared with the state-of-the-art methods to validate the effectiveness of the proposed LADSL method.
SURREY-CTS-NLP at WASSA2022: An Experiment of Discourse and Sentiment Analysis for the Prediction of Empathy, Distress and Emotion

This paper summarises the submissions our team, SURREY-CTS-NLP has made for the WASSA 2022 Shared Task for the prediction of empathy, distress and emotion.In this work, we tested different learning strategies, like ensemble learning and multi-task learning, as well as several large language models, but our primary focus was on analysing and extracting emotion-intensive features from both the essays in the training data and the news articles, to better predict empathy and distress scores from the perspective of discourse and sentiment analysis.We propose several text feature extraction schemes to compensate the small size of training examples for fine-tuning pretrained language models, including methods based on Rhetorical Structure Theory (RST) parsing, cosine similarity and sentiment score.Our best submissions achieve an average Pearson correlation score of 0.518 for the empathy prediction task and an F1 score of 0.571 for the emotion prediction task 1 , indicating that using these schemes to extract emotion-intensive information can help improve model performance.
Clustering Analysis for Newsgroup Classification

There is humongous amount of data on the internet. One such category of data is the text-based newsgroup documents that can be clustered based on recurring patterns of words. Similar news articles when clustered together can provide for a better reading experience to the user. Unsupervised clustering algorithms can be used for grouping together similar text documents. In this work, we apply five popular clustering algorithms:—K-means, Fuzzy C-Means (FCM), Fuzzy Co-clustering of Documents and Keywords (FCoDoK), Agglomerative Hierarchical clustering, and DBSCAN for classifying the newsgroup data, and compare their performance using accuracy and F1-score metrics. The bag-of-words model is used to generate vector representations of the text documents. The vectors comprise of 0's and 1's, where a 1 indicates the presence of a word in a text document. Our paper presents our findings and results for the various unsupervised clustering algorithms on three categories of the 20 Newsgroups dataset, namely, alt.atheism, rec.sport.baseball, and sci.space. The results prove that FCoDoK achieves the best score, whereas, agglomerative hierarchical clustering and DBSCAN did not give good results.
Pyramid Feature Aggregation for Hierarchical Quality Prediction of Stitched Panoramic Images

Panoramic image quality assessment (PIQA) is crucial to the successful application of technologies that can provide immersive visual experience. Stitching distortions are one of the main types of distortions that result in panoramic image degradation. However, most existing PIQA methods are general-purpose ones, which ignore the special characteristics of the stitching distortions caused by imperfect stitching algorithms. This results in unsatisfactory performance. To this end, we propose an effective stitched PIQA method, which consists of an imaginary reference generation (IRG) module and a hierarchical quality prediction (HQP) module. Among them, the IRG module is proposed to mimic the capability of the human visual system in imagining the raw version in the face of a degraded image. For the IRG module learning, we construct a large-scale database. The HQP module is presented to adapt to the particularity and complexity of stitching distortions, which is achieved by the pyramid feature aggregation. Extensive experiments and comparisons have been performed on the stitched PIQA database and the experimental results demonstrate the superiority of the proposed method in evaluating the quality of stitched panoramic images.
Reduction of the Position Bias via Multi-level Learning for Activity Recognition

The relative position of sensors placed on specific body parts generates two types of data related to (1) the movement of the body part w.r.t. the body and (2) the whole body w.r.t. the environment. These two data provide orthogonal and complementary components contributing differently to the activity recognition process. In this paper, we introduce an original approach that separates these data and abstracts away the sensors’ exact on-body position from the considered activities. We learn for these two totally orthogonal components (i) the bias that stems from the position and (ii) the actual patterns of the activities abstracted from these positional biases. We perform a thorough empirical evaluation of our approach on the various datasets featuring on-body sensor deployment in real-life settings. Obtained results show substantial improvements in performances measured by the f1-score and pave the way for developing models that are agnostic to both the position of the data generators and the target users.
Lattice-Based Public Key Encryption with Multi-Ciphertexts Equality Test in Cloud Computing

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