Reviewerly/exHarmony huggingface dataset:
Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning (Invited Paper)

Deep learning models have been deployed in an increasing number of edge and mobile devices to provide healthcare. These models rely on training with a tremendous amount of labeled data to achieve high accuracy. However, for medical applications such as dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients, and each device only has a limited amount of data. Directly learning from limited data greatly deteriorates the performance of learned models. Federated learning (FL) can train models by using data distributed on devices while keeping the data local for privacy. Existing works on FL assume all the data have ground-truth labels. However, medical data often comes without any accompanying labels since labeling requires expertise and results in prohibitively high labor costs. The recently developed self-supervised learning approach, contrastive learning (CL), can leverage the unlabeled data to pre-train a model for learning data representations, after which the learned model can be fine-tuned on limited labeled data to perform dermatological disease diagnosis. However, simply combining CL with FL as federated contrastive learning (FCL) will result in ineffective learning since CL requires diverse data for accurate learning but each device in FL only has limited data diversity. In this work, we propose an on-device FCL framework for dermatological disease diagnosis with limited labels. Features are shared among devices in the FCL pre-training process to provide diverse and accurate contrastive information without sharing raw data for privacy. After that, the pre-trained model is fine-tuned with local labeled data independently on each device or collaboratively with supervised federated learning on all devices. Experiments on dermatological disease datasets show that the proposed framework effectively improves the recall and precision of dermatological disease diagnosis compared with state-of-the-art methods.
Question Answering Model Based Conversational Chatbot using BERT Model and Google Dialogflow

Contemporary conversational chatbots are user-friendly and possess the capabilities to simulate human conversations. However, they cannot evaluate large comprehensive datasets to provide an answer to the user. In contrast, the state-of-the-art Question Answering Model (QAM) trained on a large dataset can answer questions in the given context, and sometimes without context. This research designed a QAM to improve the customer's experience while using a chatbot for reading comprehension tasks using the BERT model and Google Dialogflow. QAM analyses and provides an accurate response using a comprehensive dataset and simulates human-like conversation. BERT model is used to predict accurate answers using the reading comprehension Conversational Question Answering (CoQA) dataset and Google Dialogflow to simulate human-like interactions. QAM extends the conventional way of using Google Dialogflow. A user-friendly Question Answering Model (QAM) reaps the benefits of Google Dialogflow and BERT Model integration. The BERT model and the chatbot interacts with each other using webhook and API. When a user interacts with the Dialogflow chatbot, it matches intents and sends the request to the BERT model. Finally, the BERT model provides an answer to the chatbot and respond to the end-user. The QAM provides accurate responses to end-users for questions based on large datasets.
The unified system for storing and accessing geophysical data. Traditions and new approaches

The features of the development of data collection systems within the Information Processing Center (IPC) of the Geophysical Survey of the Russian Academy of Sciences (GS RAS) are presenting. Historical information is given, including technical details related to the systematization of the archive and the evolution of geophysical data formats. The historical, territorial features, as well as the experience of deploying such information systems within the Federal Research Center of the Unified State Social Service of the Russian Academy of Sciences and abroad are taken into account. A new concept of building an information system is proposed, which takes into account the requirements for scalability, reproducibility at various objects and the use of standard software. A lot of work was done to form a homogeneous archive of waveforms and an inventory of metadata for seismic stations, which resulted in the possibility of including the GS RAS in the international centers for processing geophysical information based on FDSN. Creation of a distributed collection and processing system using a cloud service allows abstracting from the territorial features of collecting and storing geophysical information, which increases the performance of the data access service and the degree of technical readiness of key system nodes.
An OpenPose-Based Exercise and Performance Learning Assistant Design for Self-Practice Yoga

In this paper, a performance learning assistant design based on OpenPose is studied for self-practice Yoga. Firstly, the skeleton information of the human body is extracted through OpenPose. Secondly, to calculate the angle values of the selected keypoints, the vectors of the user and the instructor are obtained based on the center points. Next, by using the angle values, the angle differences between the user and the instructor are calculated. Finally, the scoring system is developed to calculate the total scores for evaluation of the user’s Yoga posture. The experimental results show that the proposed design effectively detects the posture differences between the user and the instructor. Moreover, the proposed design performs up to 5.5 frames per second (FPS) on the GPU-based embedded platform.
Determination of inert and labile copper on GEOTRACES samples using a novel solvent extraction method

Copper, in seawater, is predominantly bound by organic ligands of unknown composition. Complexation has been thermodynamically characterized using synthetic ligand competition experiments which assumes equilibrium among all chelators within the system. However, equilibration times are constrained by wall loss issues with the synthetic ligands. Here, a solvent extraction methodology, was utilized to avoid the wall-loss problems. Using an exceptionally high concentration of a strong copper chelator, oxine (8-hydroxyquinoline), at least six hours of equilibration time is required to reach steady state between the competing ligand and the labile copper in seawater. This is much longer than equilibration times used in previously published works. Our method was optimized by using samples from GEOTRACES expeditions in the North Pacific and North Atlantic Oceans. Surprisingly, 60–90% of the copper was not exchangeable with oxine under these conditions. We define this fraction as "inert", and these data, which include profiles as deep as 1000 m in the North Pacific, suggest that this is a widespread feature. Our results suggest that there are two distinct pools of labile and inert copper, rather than an assemblage of similar complexes with incremental differences in stability constants. The results have important implications for the marine geochemistry of copper and its bioavailability. Complexation has been shown to limit copper bioavailability and influences scavenging and residence time. Moreover, a basic paradigm of copper speciation methodologies, that even strong Cu complexes are relatively labile, is likely incorrect.
Tight Bounds on the Expected Runtime of a Standard Steady State Genetic Algorithm

Abstract Recent progress in the runtime analysis of evolutionary algorithms (EAs) has allowed the derivation of upper bounds on the expected runtime of standard steady-state genetic algorithms (GAs). These upper bounds have shown speed-ups of the GAs using crossover and mutation over the same algorithms that only use mutation operators (i.e., steady-state EAs) both for standard unimodal (i.e., OneMax ) and multimodal (i.e., Jump ) benchmark functions. The bounds suggest that populations are beneficial to the GA as well as higher mutation rates than the default 1/ n rate. However, making rigorous claims was not possible because matching lower bounds were not available. Proving lower bounds on crossover-based EAs is a notoriously difficult task as it is hard to capture the progress that a diverse population can make. We use a potential function approach to prove a tight lower bound on the expected runtime of the (2+1) GA for OneMax for all mutation rates c / n with $$c &lt; 1.422$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>c</mml:mi> <mml:mo>&lt;</mml:mo> <mml:mn>1.422</mml:mn> </mml:mrow> </mml:math> . This provides the last piece of the puzzle that completes the proof that larger population sizes improve the performance of the standard steady-state GA for OneMax for various mutation rates, and it proves that the optimal mutation rate for the (2+1) GA on OneMax is $$(\sqrt{97}-5)/(4n) \approx 1.2122/n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mo>(</mml:mo> <mml:msqrt> <mml:mn>97</mml:mn> </mml:msqrt> <mml:mo>-</mml:mo> <mml:mn>5</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>/</mml:mo> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>4</mml:mn> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>≈</mml:mo> <mml:mn>1.2122</mml:mn> <mml:mo>/</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> .
Accuracy versus reliability-based modelling approaches for medical decision making

Forecasting in the medical domain is critical to the quality of decisions made by physicians, patients, and health planners. Modeling is one of the most important components of decision support systems, which are frequently used to simulate and analyze under-studied systems in order to make more appropriate decisions in medical science. In the medical modeling literature, various approaches with varying structures and characteristics have been proposed to cover a wide range of application categories and domains. Regardless of the differences between modeling approaches, all of them aim to maximize the accuracy or reliability of the results in order to achieve the most generalizable model and, as a result, a higher level of profitability decisions. Despite the theoretical significance and practical impact of reliability on generalizability, particularly in high-risk decisions and applications, a significant number of models in the fields of medical forecasting, classification, and time series prediction have been developed to maximize accuracy in mind. In other words, given the volatility of medical variables, it is also necessary to have stable and reliable forecasts in order to make sound decisions. The quality of medical decisions resulting from accuracy and reliability-based intelligent and statistical modeling approaches is compared and evaluated in this paper in order to determine the relative importance of accuracy and reliability on the quality of made decisions in decision support systems. For this purpose, 33 different case studies from the UCI in three categories of supervised modeling, namely causal forecasting, time series prediction, and classification, were considered. These cases were chosen from various domains, such as disease diagnosis (obesity, Parkinson's disease, diabetes, hepatitis, stenosis of arteries, orthopedic disease, autism) and cancer (lung, breast, cervical), experiments, therapy (immunotherapy, cryotherapy), fertility prediction, and predicting the number of patients in the emergency room and ICU. According to empirical findings, the reliability-based strategy outperformed the accuracy-based strategy in causal forecasting cases by 2.26%, classification cases by 13.49%, and time series prediction cases by 3.08%. Furthermore, compared to similar accuracy-based models, the reliability-based models can generate a 6.28% improvement. As a result, they can be considered an appropriate alternative to traditional accuracy-based models for medical decision support systems modeling purposes.
SkinKit

The emergence of on-skin interfaces has created an opportunity for seamless, always-available on-body interactions. However, developing a new fabrication process for on-skin interfaces can be time-consuming, challenging to incorporate new features, and not available for quick form-factor preview through prototyping. We introduce SkinKit, the first construction toolkit for on-skin interfaces, which enables fast, low-fidelity prototyping with a slim form factor directly applicable to the skin. SkinKit comprises modules consisting of skin-conformable base substrates and reusable Flexible Printed Circuits Board (FPCB) blocks. They are easy to attach and remove under tangible plug-and-play construction but still offer robust conductive connections in a slim form. Further, SkinKit aims to lower the barrier to entry in building on-skin interfaces without demanding technical expertise. It leverages a variety of preprogrammed modules connected in unique sequences to achieve various function customizations. We describe our iterative design and development process of SkinKit, comparing materials, connection mechanisms, and modules reflecting on its capability. We report results from single- and multi- session workshops with 34 maker participants spanning STEM and design backgrounds. Our findings reveal how diverse maker populations engage in on-skin interface design, what types of applications they choose to build, and what challenges they faced.
Insulator defect detection: A detection method of target search and cascade recognition

Aiming at the difficulties of few real samples for defective insulators and complex background of aerial images, this paper proposes a detection method based on target search and cascade recognition. Using the SINet framework, we apply fine-grained texture enhancement to different sizes of receptive fields. Through nearest-neighbor decoding and grouping reverse attention, the more recognizable features are guided to aggregate and generate a refined location area map by performing cascading purification operations. Additionally, we integrate the classification network to complete the solution. Experimental results show that the AUC value is up to 99.82%, which demonstrates the effectiveness and superiority of the proposed method on insulator defect detection.
Deepfake attribution: On the source identification of artificially generated images

Abstract Synthetic media or "deepfakes" are making great advances in visual quality, diversity, and verisimilitude, empowered by large‐scale publicly accessible datasets and rapid technical progress in deep generative modeling. Heralding a paradigm shift in how online content is trusted, researchers in digital image forensics have responded with different proposals to reliably detect AI‐generated images in the wild. However, binary classification of image authenticity is insufficient to regulate the ethical usage of deepfake technology as new applications are developed. This article provides an overview of the major innovations in synthetic forgery detection as of 2020, while highlighting the recent shift in research towards ways to attribute AI‐generated images to their generative sources with evidence. We define the various categories of deepfakes in existence, the subtle processing traces and fingerprints that distinguish AI‐generated images from reality and each other, and the different degrees of attribution possible with current understanding of generative algorithms. Additionally, we describe the limitations of synthetic image recognition methods in practice, the counter‐forensic attacks devised to exploit these limitations, and directions for new research to assure the long‐term relevance of deepfake forensics. Reliable, explainable, and generalizable attribution methods would hold malicious users accountable for AI‐enabled disinformation, grant plausible deniability to appropriate users, and facilitate intellectual property protection of deepfake technology. This article is categorized under: Commercial, Legal, and Ethical Issues &gt; Security and Privacy Algorithmic Development &gt; Multimedia
The effect of repertoire, routinization and enacted complexity: Explaining task performance through patterns of action

We use pattern mining tools from computer science to engage a classic problem in organizational theory: the relation between routinization and task performance. We develop and operationalize new measures of two key characteristics of organizational routines: repertoire and routinization. Repertoire refers to the number of recognizable patterns within a stream of actions used to perform the task, and routinization refers to the degree to which a stream of actions follows recognizable patterns. We use these measures to develop a novel theory that predicts task performance based on the size of repertoire, the degree of routinization, and enacted complexity. We test this theory in two settings that differ in their programmability: crisis management and invoice management. We find that repertoire and routinization are important determinants of task performance in both settings, but with opposite effects. In both settings, however, the effect of repertoire and routinization is mediated by enacted complexity. This theoretical contribution is enabled by the methodological innovation of pattern mining, which allows us to treat routines as a collection of sequential patterns or paths. This innovation also allows us to clarify the relation of routinization and complexity, which are often confused because the terms routine and routinization connote simplicity. We demonstrate that routinization and enacted complexity are distinct constructs, conceptually and empirically. It is possible to have a high degree of routinization and complex enactments that vary each time a task is performed. This is because enacted complexity depends on the repertoire of patterns and how those patterns are combined to enact a task.
Graphical integrity issues in open access publications: Detection and patterns of proportional ink violations

Academic graphs are essential for communicating complex scientific ideas and results. To ensure that these graphs truthfully reflect underlying data and relationships, visualization researchers have proposed several principles to guide the graph creation process. However, the extent of violations of these principles in academic publications is unknown. In this work, we develop a deep learning-based method to accurately measure violations of the proportional ink principle (AUC = 0.917), which states that the size of shaded areas in graphs should be consistent with their corresponding quantities. We apply our method to analyze a large sample of bar charts contained in 300K figures from open access publications. Our results estimate that 5% of bar charts contain proportional ink violations. Further analysis reveals that these graphical integrity issues are significantly more prevalent in some research fields, such as psychology and computer science, and some regions of the globe. Additionally, we find no temporal and seniority trends in violations. Finally, apart from openly releasing our large annotated dataset and method, we discuss how computational research integrity could be part of peer-review and the publication processes.
Improved whale optimization algorithm based on variable spiral position update strategy and adaptive inertia weight

In this paper, a whale optimization algorithm based on adaptive inertia weight and variable spiral position updating strategy is proposed. The improved algorithm is used to solve the problem that the whale optimization algorithm is more dependent on the randomness of the parameters, so that the algorithm’s convergence accuracy and convergence speed are insufficient. The adaptive inertia weight, which varies with the fitness of individual whales, is used to balance the algorithm’s global search ability and local exploitation ability. The variable spiral position update strategy based on the collaborative convergence mechanism is used to dynamically adjust the search range and search accuracy of the algorithm. The effective combination of the two can make the improved whale optimization algorithm converge to the optimal solution faster. It had been used 18 international standard test functions, including unimodal function, multimodal function, and fixed-dimensional function to test the improved whale optimization algorithm in this paper. The test results show that the improved algorithm has faster convergence speed and higher algorithm accuracy than the original algorithm and several classic algorithms. The algorithm can quickly converge to near the optimal value in the early stage, and then effectively jump out of the local optimal through adaptive adjustment, and has a certain ability to solve large-scale optimization problems.
Zero-Shot Learning for IMU-Based Activity Recognition Using Video Embeddings

The Activity Recognition Chain generally precludes the challenging scenario of recognizing new activities that were unseen during training, despite this scenario being a practical and common one as users perform diverse activities at test time. A few prior works have adopted zero-shot learning methods for IMU-based activity recognition, which work by relating seen and unseen classes through an auxiliary semantic space. However, these methods usually rely heavily on a hand-crafted attribute space which is costly to define, or a learnt semantic space based on word embedding, which lacks motion-related information crucial for distinguishing IMU features. Instead, we propose a strategy to exploit videos of human activities to construct an informative semantic space. With our approach, knowledge from state-of-the-art video action recognition models is encoded into video embeddings to relate seen and unseen activity classes. Experiments on three public datasets find that our approach outperforms other learnt semantic spaces, with an additional desirable feature of scalability, as recognition performance is seen to scale with the amount of data used. More generally, our results indicate that exploiting information from the video domain for IMU-based tasks is a promising direction, with tangible returns in a zero-shot learning scenario.
Fuzzy Cognitive Map-Driven Comprehensive Time-Series Classification

This article presents a comprehensive approach for time-series classification. The proposed model employs a fuzzy cognitive map (FCM) as a classification engine. Preprocessed input data feed the employed FCM. Map responses, after a postprocessing procedure, are used in the calculation of the final classification decision. The time-series data are staged using the moving-window technique to capture the time flow in the training procedure. We use a backward error propagation algorithm to compute the required model hyperparameters. Four model hyperparameters require tuning. Two are crucial for the model construction: 1) FCM size (number of concepts) and 2) window size (for the moving-window technique). Other two are important for training the model: 1) the number of epochs and 2) the learning rate (for training). Two distinguishing aspects of the proposed model are worth noting: 1) the separation of the classification engine from pre- and post-processing and 2) the time flow capture for data from concept space. The proposed classifier joins the key advantage of the FCM model, which is the interpretability of the model, with the superior classification performance attributed to the specially designed pre- and postprocessing stages. This article presents the experiments performed, demonstrating that the proposed model performs well against a wide range of state-of-the-art time-series classification algorithms.