Reviewerly/exHarmony huggingface dataset:
Cyber-physical spatial temporal analytics for digital twin-enabled smart contact tracing

Purpose Physical gatherings at social events have been found as one of the main causes of COVID-19 transmission all over the world. Smartphone has been used for contact tracing by exchanging messages through Bluetooth signals. However, recent confirmed cases found in venues indicated that indirect transmission of the causative virus occurred, resulting from virus contamination of common objects, virus aerosolization in a confined space or spread from inadequate ventilation environment with no indication of human direct or close contact observed. Design/methodology/approach This paper presents a novel cyber-physical architecture for spatial temporal analytics (iGather for short). Locations with time windows are modeled as digital chromosomes in cyberspace to represent human activity instances in the physical world. Findings Results show that the high spatial temporal correlated but indirect tracing can be realized through the deployment of physical hardware and spatial temporal analytics including mobility and traceability analytics. iGather is tested and verified in different spatial temporal correlated cases. From a management perspective of mobilizing social capacity, the venue plays not only a promotion role in boosting the utilization rates but also a supervision-assisted role for keeping the venue in a safe and healthy situation. Social implications This research is of particular significance when physical distancing measures are being relaxed with situations gradually become contained. iGather is able to help the general public to ease open questions: Is a venue safe enough? Is there anyone at a gathering at risk? What should one do when someone gets infected without raising privacy issues? Originality/value This study contributes to the existing literature by cyber-physical spatial temporal analytics to trace COVID-19 indirect contacts through digital chromosome, a representation of digital twin technology. Also, the authors have proposed a venue-oriented management perspective to resolve privacy-preserving and unitization rate concerns.
Open Science: Open Data, Open Models, …and Open Publications?

Abstract This commentary explores the challenges and opportunities associated with a possible transition of Water Resources Research to a publication model where all articles are freely available upon publication (“Gold” open access). It provides a review of the status of open access publishing models, a summary of community input, and a path forward for AGU leadership. The decision to convert to open access is framed by a mix of finances and values. On the one hand, the challenge is to define who pays, and how, and what we can do to improve the affordability of publishing. On the other hand, the challenge is to increase the extent to which science is open and accessible. The next steps for the community include an incisive analysis of the financial feasibility of different cost models, and weighing the financial burden for open access against the desire to further advance open science.
Quality Assessment of Screen Content Images in Wavelet Domain

Objective image quality assessment (IQA) aims to predict human judgment on an image’s visual quality. This computational model is applicable in various scenarios, such as monitoring, image compression and image restoration. However, the research in this area began with natural scenes while image contents has become much more diverse. Remote computing and screen sharing applications led to the prevalence of screen content images (SCIs), which are full of texts, flat areas, and also natural scenes. Due to their different characteristics, traditional methods for IQA are insufficient for SCIs. In this paper, we show that capturing the distortions in the horizontal and vertical structures of an SCI is effective for predicting its quality. We also take into account the human visual system’s trend to analyze a scene in a multi-scale fashion. Based on these observations, we propose wavelet analysis as a means to accomplish the mentioned strategies. Experimental results on three SCI quality datasets show that plausible predictions can be made by the proposed full-reference method, while having an acceptable time complexity.
Deep Anomaly Detection with Ensemble-Based Active Learning

Many anomaly detection solutions are domain specific, which requires assumptions and knowledge on the data and problem before a project starts. Often, finding the best single model to use creates a long development cycle. In addition, existing unsupervised anomaly detection models, although they do not depend on the existence of labels to start training, can suffer from high false positive rates, and it is difficult to incorporate human experts' feedback. In this paper, we introduce a domain-agonistic, end-to-end, ensemble-based deep active learning framework for anomaly detection. By leveraging unsupervised and semi-supervised anomaly detection models, our framework does not rely on labeled data to start training. By using active learning, our framework incorporates human feedback in the workflow to reduce false positive rates, which is essential in many real-world applications. In addition, we use a neural network to ensemble various anomaly detectors for further performance improvement and leverage a reference score generator to enable an intuitive explanation of anomaly scores. Our framework showed better performance characteristics during experiments than the benchmarks using data across different domains.
Top-Down Attention in End-to-End Spoken Language Understanding

Spoken language understanding (SLU) is the task of inferring the semantics of spoken utterances. Traditionally, this has been achieved with a cascading combination of Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) modules that are optimized separately, which can lead to a suboptimal overall performance. More recently, End-to-End SLU (E2E SLU) was proposed to perform SLU directly from speech through a joint optimization of the modules, addressing some of the traditional SLU shortcomings. A key challenge of this approach is how to best integrate the feature learning of the ASR and NLU sub-tasks to maximize their performance. While it is known that in general, ASR models focus on low-level features, and NLU models need higher-level contextual information, ASR models can nonetheless also leverage top-down syntactic and semantic information to improve their recognition. Based on this insight, we propose Top-Down SLU (TD-SLU), a new transformer-based E2E SLU model that uses top-down attention and an attention gate to fuse high-level NLU features with low-level ASR features, which leads to a better optimization of both tasks. We have validated our model using the public FluentSpeech set, and a large custom dataset. Results show TD-SLU is able to outperform selected baselines both in terms of ASR and NLU quality metrics, and suggest that the added syntactic and semantic high-level information can improve the model’s performance.
A Hybrid Newton–Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing

Bearing-only target motion analysis (BO-TMA) by batch processing remains a challenge due to the lack of information on underwater target maneuvering and the nonlinearity of sensor measurements. Traditional batch estimation for BO-TMA is mainly performed based on deterministic algorithms, and studies performed with heuristic algorithms have recently been reported. However, since the two algorithms have their own advantages and disadvantages, interest in a hybrid method that complements the disadvantages and combines the advantages of the two algorithms is increasing. In this study, we proposed Newton–Raphson particle swarm optimization (NRPSO): a hybrid method that combines the Newton–Raphson method and the particle swarm optimization method, which are representative methods that utilize deterministic and heuristic algorithms, respectively. The BO-TMA performance obtained using the proposed NRPSO was tested by varying the measurement noise and number of measurements for three targets with different maneuvers. The results showed that the advantages of both methods were well combined, which improved the performance.
Class label altering fuzzy min-max network and its application to histopathology image database

Hyperbox classifier is efficiently implemented using fuzzy min max neural network, where the input patterns present in the training phase place a vital role. In the training phase, a set of hyperboxes are constructed which are used to classify a testing pattern. A better selection of input patterns of the training set will generate an efficient set of hyperboxes. Inappropriate selection of training samples leads to an erroneous set of hyperboxes, which will degrade the accuracy rate. Therefore, instead of using a single training set an extra training set, secondary training set, may be used to reshuffle the hyperboxes generated during the primary training set. In this paper, we have used a secondary training set to update the hyperboxes generated during the primary training phase. When a secondary training set is used, two cases are arised. First, change the class label of some inefficient hyperboxes created during the primary training phase. Second, fix the class label of efficient hyperboxes to the class allotted during the primary training set. By using the above secondary training set mechanism, a novel class label altering fuzzy min max (CLAFMM) network is proposed to alter the class labels depending on the secondary training set. Experimental results prove that the proposed approach provides more accuracy rate than the FMM, Enhanced FMM and K-nearest FMM. Simultaneously, the proposed approach reduces the complexity of the network by reducing the number of hyperboxes generated by the above said state-of-the-art methods. The proposed method is also applied to the breast cancer histopathological images to identify the best magnifying factor for classification.
<scp>MOSQUITO‐NET</scp>: A deep learning based <scp>CADx</scp> system for malaria diagnosis along with model interpretation using <scp>GradCam</scp> and class activation maps

Abstract Malaria is considered one of the deadliest diseases in today's world, which causes thousands of deaths per year. The parasites responsible for malaria are scientifically known as Plasmodium, which infects the red blood cells in human beings. Diagnosis of malaria requires identification and manual counting of parasitized cells in microscopic blood smears by medical practitioners. Its diagnostic accuracy is primarily affected by extensive scale screening due to the unavailability of resources. State of the art Computer‐Aided Diagnostic techniques based on deep learning algorithms such as CNNs, which perform an end to end feature extraction and classification, have widely contributed to various image recognition tasks. In this paper, we evaluate the performance of Mosquito‐Net, a custom made convnet to classify the infected and uninfected blood smears for malaria diagnosis. The CADx system can be deployed on IoT and mobile devices due to its fewer parameters and computation power, making it wildly preferable for diagnosis in remote and rural areas that lack medical facilities. Statistical analysis demonstrates that the proposed model achieves greater accuracy than the previous SOTA architectures for malaria diagnosis despite being 10 times lighter in parameters and inference time. Mosquito‐Net achieves an AUC of 99.009% and an F‐1 score of 96.7% on the validation set.
Route-Planning Method for Plant Protection Rotor Drones in Convex Polygon Regions

Aiming at the problem of low operating efficiency due to the poor endurance of plant protection rotor drones and the small volume of pesticide carried, this paper proposes a route-planning algorithm for convex polygon regions based on the initial heading angle. First, a series of coordinate conversion methods ranging from the Earth coordinate system to the local plane coordinate system are studied. Second, in the local plane coordinate system, a route generation method based on subregion is proposed; therefore, multiple routes can be generated with different initial heading angles. Lastly, the optimal route and the best initial heading angle can be obtained after the comparison according to the three evaluation criteria: number of turns, route distance, and pesticide waste rate. The simulation results show that, compared with the common grid method, the route generation method based on subregion reduces the route distance and pesticide waste rate by 2.27% and 13.75%, respectively. Furthermore, it also shows that, compared with the route generated by the initial heading angle of 0°, the optimal route reduces the number of turns, route distance, and pesticide waste rate by 60%, 17.65%, and 38.18%, respectively. The route was optimized in three aspects and reached the best overall result using this method, which in turn proved its feasibility.
Europe Unites for the Digital Transformation of Pathology: The Role of the New ESDIP

The European Society for Digital and Integrative Pathology (ESDIP) was formally founded in 2016 in Berlin. After a well-participated annual general meeting, ESDIP members elected a new active structure for the next term of office. The priority goals of this new and highly motivated team will be to support the digital transformation in the pathology laboratories, to build inter-institutional bridges for cooperation, to establish a solid educational program, and to increase the collaboration with industry partners.
Unsupervised Drift Detection on High-speed Data Streams

Changes in data distribution of streaming data (i.e., concept drifts), constitute a central issue in online data mining. The main reason is that these changes are responsible for outdating stream learning models, reducing their predictive performance over time. A common approach adopted by real-time adaptive systems to deal with concept drifts is to employ detectors that indicate the best time for updates. However, an unrealistic assumption of most detectors is that the labels become available immediately after data arrives. In this paper, we introduce an unsupervised and model-independent concept drift detector suitable for high-speed and high-dimensional data streams in realistic scenarios with the scarcity of labels. We propose a straightforward two-dimensional representation of the data aiming faster processing for detection. We develop a simple adaptive drift detector on this visual representation that is efficient for fast streams with thousands of features and is accurate as existing costly methods that perform various statistical tests. Our method achieves better performance measured by execution time and accuracy in classification problems for different types of drifts, including abrupt, oscillating, and incremental. Experimental evaluation demonstrates the versatility of the method in several domains, including astronomy, entomology, public health, political science, and medical science.
Evolution of Complex Combinational Logic Circuits Using Grammatical Evolution with SystemVerilog

Scalability problems have hindered the progress of Evolvable Hardware in tackling complex circuits. The two key issues are the amount of testing (for example, a 64-bit $$\times $$ 64-bit add-shift multiplier problem has $$2^{64 + 64}$$ test cases) and low level that hardware works at: a circuit to implement 64-bit $$\times $$ 64-bit add-shift multiplier would require approximately 33,234 gates when synthesized using the powerful Yosys Open SYnthesis Suite tool. We use Grammatical Evolution and SystemVerilog, a Hardware Description Language (HDL), to evolve fully functional parameterized adder, multiplier and selective parity circuits with default input bit-width sizes of 64-bit + 64-bit, 64-bit $$\times $$ 64-bit and 128-bit respectively. These are substantially larger than the current state of the art for evolutionary approaches, specifically, 6.4 $$\times $$ (adder), 10.7 $$\times $$ (multiplier), and 6.7 $$\times $$ (parity). We are able to scale so dramatically because our use of an HDL permits us to operate at a far higher level of abstraction than most other approaches. This has the additional benefit that no further evolutionary experiments are needed to design different input bit-width sizes of the same circuit as is the case for existing EHW approaches. Thus, one can evolve once and reuse multiple times, simply by specifying the newly desired input/output bit-width sizes during module instantiation. For example, 32-bit $$\times $$ 32-bit and 256-bit $$\times $$ 256-bit multipliers can be instantiated from an evolved parameterized multiplier. We also adopt a method for reducing testing from Digital Circuit Design known as corner case testing, well-known technique heavily relied upon by circuit designers to avoid time-consuming exhaustive testing; we demonstrate a simple way to identify and use corner cases for evolutionary testing and show that it enables the generation of massively complex circuits with a huge number of inputs. We obtain successful results (ranging from 72% to 100%) on each benchmark and all three problems were tackled without resorting to the use of any standard decomposition methods due to our ability to use high-level programming constructs and operators available in SystemVerilog.
From Reductionism to Reintegration: Solving society’s most pressing problems requires building bridges between data types across the life sciences

Decades of reductionist approaches in biology have achieved spectacular progress, but the proliferation of subdisciplines, each with its own technical and social practices regarding data, impedes the growth of the multidisciplinary and interdisciplinary approaches now needed to address pressing societal challenges. Data integration is key to a reintegrated biology able to address global issues such as climate change, biodiversity loss, and sustainable ecosystem management. We identify major challenges to data integration and present a vision for a “Data as a Service”-oriented architecture to promote reuse of data for discovery. The proposed architecture includes standards development, new tools and services, and strategies for career-development and sustainability.
A novel optimal data fusion algorithm and its application for the integrated navigation system of missile

For Inertial Navigation System (INS)/Celestial Navigation System (CNS)/Global Navigation Satellite System (GNSS) integrated navigation system of the missile, the performance of data fusion algorithms based on the Cubature Kalman Filter (CKF) is seriously degraded when there are non-Gaussian noise and process-modeling errors in the system model. Therefore, a novel method is proposed, which is called Optimal Data Fusion algorithm based on the Adaptive Fading maximum Correntropy generalized high-degree CKF (AFCCKF-ODF). First, the Adaptive Fading maximum Correntropy generalized high-degree CKF (AFCCKF) is proposed and used as the local filter for the INS/GNSS and INS/CNS subsystems to improve the robustness of local state estimation. Then, the local state estimation is fused based on the minimum variance principle and high-degree cubature criterion to get the globally optimal state. Finally, the experimental results verify that the proposed algorithm can significantly improve the robustness of the missile-borne INS/CNS/GNSS integrated navigation system to non-Gaussian noise and process modeling error and obtain the global optimal navigation information.
The Next Billion Users of Visualization

We argue that visualization research has overwhelmingly focused on users from the economically developed world. However, billions of people around the world are rapidly emerging as new users of information technology. Most of the next billion users of visualization technologies will come from parts of the world that are extremely populous but historically ignored by the visualization research community. Their needs may be different to the types of users that researchers have targeted in the past, but, at the same time, they may have even more to gain in terms of access to data potentially affecting their quality of life. We propose a call to action for the visualization community to identify opportunities and use cases where users can benefit from visualization; develop universal design principles; extend evaluations by including the general population; and engage with a wider global population.
The Future of E-Commerce Systems: 2030 and Beyond

The sophistication and efficiency of systems is undeniably advancing. As businesses evolve, questions on its past performance is beside the point but its anticipated functions and relevance in the future. With the use of software in commerce at the core of every business today, the overlook of how omnichannel transactions operates is an awe with the 4th industrial revolution and its societal impact. This chapter explains how commerce has currently evolved in the advent of technology elaborating on the current state and challenges of systems, its architecture, and the innovations of cyber physical systems in electronic commerce. It further expounds on the application of omnichannel systems in communication through the fifth-generation network, in transaction through blockchain and in composition through Social Internet of Things. We believe that this study will benefit all stakeholders in commerce from governments, supply chain organizations and consumers to understanding the forthcoming drivers of omnichannel systems in the 4th industrial revolution, its prospects, and its anticipated challenges.
Research on Credit Card Default Prediction Based on k-Means SMOTE and BP Neural Network

Aiming at the problem that the credit card default data of a financial institution is unbalanced, which leads to unsatisfactory prediction results, this paper proposes a prediction model based on k-means SMOTE and BP neural network. In this model, k-means SMOTE algorithm is used to change the data distribution, and then the importance of data features is calculated by using random forest, and then it is substituted into the initial weights of BP neural network for prediction. The model effectively solves the problem of sample data imbalance. At the same time, this paper constructs five common machine learning models, KNN, logistics, SVM, random forest, and tree, and compares the classification performance of these six prediction models. The experimental results show that the proposed algorithm can greatly improve the prediction performance of the model, making its AUC value from 0.765 to 0.929. Moreover, when the importance of features is taken as the initial weight of BP neural network, the accuracy of model prediction is also slightly improved. In addition, compared with the other five prediction models, the comprehensive prediction effect of BP neural network is better.