Reviewerly/exHarmony huggingface dataset:
Block Chain Based Internet of Medical Things for Uninterrupted, Ubiquitous, User-Friendly, Unflappable, Unblemished, Unlimited Health Care Services (BC IoMT U<sup>6</sup> HCS)

The most burning topic of today, calls for a holistic solution that is reliable, secure, privacy preserved, cost effective Cloud storage that can tide over the turbulent conditions of the rapidly budding digital storage technologies. This send an outcry for a devoted solution, in the form of an individualized, patient-centric care - IoMT that augments precise disease identifications, decrease in errors, reduction in costs of care through the support of technology, allows patients to direct health information data to doctors,manage drugs, keep Personal Health Records, caters to remote medical supports Care, provides proactive approach to preserving Good Health, improves and Accelerates Clinician Workflows, empowers extreme connectivity due to better automation and perceptions in the DNA of IoMT functions. But IoMT adoption is like a rose with thorns like constraints of increased administrative costs, deficiency of universal data access, present-day electronic medical records. The BCT is used in the framework to overcome the security issues of IoMT through the use of latest encryptions. Furthermore, this framework harnesses the benefits of Block Chain like reduced cost, speed, automation, immutability, near-impossible loss of data, permanence, removal of intermediaries, decentralization of consensus, legitimate access to health data, data safekeeping, accrual-based imbursement mechanisms, and medical supply chain efficacy. The outcomes in this paper are (i)A systematic investigation of the current IoMT, Block Chain and Cloud Storage in Health Care;(ii) Explore the challenges and necessities for the confluence of Block Chain (BC), Internet of Medical Things (IoMT), Cloud Computing (CC);(iii)Formulate the requirements necessary for the real-time remote Health Care of one-to-one care structure, which, supports the vital functions that are critical to the Patient Centric Health Care;(iv) Design and develop a novel BC IoMT U6 HCS (Block Chain based Internet of Medical Things for Uninterrupted, Ubiquitous, User-friendly, Unflappable, Unblemished, Unlimited Health Care Services) Layered Architecture, to support the vital functions critical for Patient Centric Health Care and (v) Implement and test with the previous established and proven techniques. The integrity of the Layered Architecture is validated with the already existing ones in terms of audit performances. The results from the Layered Architecture are validated and are proven to be competent in achieving safe auditing and surpass the former ones. The technology is in the sprouting phases, it is perilous that affiliates of the Health Care community realize the rudimentary ideas behind Block Chain, and detect its feasible impact on the future of patient centric medical care. Finally, and most importantly, this paper also gives a panoramic view on the current research status, and imminent directions of Secure Internet of Medical Things Using Block Chain.
How can organizations develop situation awareness for incident response: A case study of management practice

Organized, sophisticated and persistent cyber-threat-actors pose a significant challenge to large, high-value organizations. They are capable of disrupting and destroying cyber infrastructures, denying organizations access to IT services, and stealing sensitive information including intellectual property, trade secrets and customer data. Past research points to Situation Awareness as critical to effective response. However, most research has focused on the technological perspective with comparatively less focus on the practice perspective. We therefore present an in-depth case study of a leading financial organization with a well-resourced and mature incident response capability that has evolved as a result of experiences with past attacks. Our contribution is a process model that explains how organizations can practice situation awareness of the cyber-threat landscape and the broad business context in incident response.
Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments

We develop a language-guided navigation task set in a continuous 3D environment where agents must execute low-level actions to follow natural language navigation directions. By being situated in continuous environments, this setting lifts a number of assumptions implicit in prior work that represents environments as a sparse graph of panoramas with edges corresponding to navigability. Specifically, our setting drops the presumptions of known environment topologies, short-range oracle navigation, and perfect agent localization. To contextualize this new task, we develop models that mirror many of the advances made in prior settings as well as single-modality baselines. While some transfer, we find significantly lower absolute performance in the continuous setting – suggesting that performance in prior ‘navigation-graph’ settings may be inflated by the strong implicit assumptions. Code at jacobkrantz.github.io/vlnce .
Improving Vision-and-Language Navigation with Image-Text Pairs from the Web

Following a navigation instruction such as ‘Walk down the stairs and stop at the brown sofa’ requires embodied AI agents to ground referenced scene elements referenced (e.g. ‘stairs’) to visual content in the environment (pixels corresponding to ‘stairs’). We ask the following question – can we leverage abundant ‘disembodied’ web-scraped vision-and-language corpora (e.g. Conceptual Captions) to learn the visual groundings that improve performance on a relatively data-starved embodied perception task (Vision-and-Language Navigation)? Specifically, we develop VLN-BERT, a visiolinguistic transformer-based model for scoring the compatibility between an instruction (‘...stop at the brown sofa’) and a trajectory of panoramic RGB images captured by the agent. We demonstrate that pretraining VLN-BERT on image-text pairs from the web before fine-tuning on embodied path-instruction data significantly improves performance on VLN – outperforming prior state-of-the-art in the fully-observed setting by 4 absolute percentage points on success rate. Ablations of our pretraining curriculum show each stage to be impactful – with their combination resulting in further gains.
NAS-Count: Counting-by-Density with Neural Architecture Search

Most of the recent advances in crowd counting have evolved from hand-designed density estimation networks, where multi-scale features are leveraged to address the scale variation problem, but at the expense of demanding design efforts. In this work, we automate the design of counting models with Neural Architecture Search (NAS) and introduce an end-to-end searched encoder-decoder architecture, Automatic Multi-Scale Network (AMSNet). Specifically, we utilize a counting-specific two-level search space. The encoder and decoder in AMSNet are composed of different cells discovered from micro-level search, while the multi-path architecture is explored through macro-level search. To solve the pixel-level isolation issue in MSE loss, AMSNet is optimized with an auto-searched Scale Pyramid Pooling Loss (SPPLoss) that supervises the multi-scale structural information. Extensive experiments on four datasets show AMSNet produces state-of-the-art results that outperform hand-designed models, fully demonstrating the efficacy of NAS-Count.
Curriculum Manager for Source Selection in Multi-source Domain Adaptation

The performance of Multi-Source Unsupervised Domain Adaptation depends significantly on the effectiveness of transfer from labeled source domain samples. In this paper, we proposed an adversarial agent that learns a dynamic curriculum for source samples, called Curriculum Manager for Source Selection (CMSS). The Curriculum Manager, an independent network module, constantly updates the curriculum during training, and iteratively learns which domains or samples are best suited for aligning to the target. The intuition behind this is to force the Curriculum Manager to constantly re-measure the transferability of latent domains over time to adversarially raise the error rate of the domain discriminator. CMSS does not require any knowledge of the domain labels, yet it outperforms other methods on four well-known benchmarks by significant margins. We also provide interpretable results that shed light on the proposed method.
The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement

Existing disentanglement methods for deep generative models rely on hand-picked priors and complex encoder-based architectures. In this paper, we propose the Hessian Penalty, a simple regularization term that encourages the Hessian of a generative model with respect to its input to be diagonal. We introduce a model-agnostic, unbiased stochastic approximation of this term based on Hutchinson’s estimator to compute it efficiently during training. Our method can be applied to a wide range of deep generators with just a few lines of code. We show that training with the Hessian Penalty often causes axis-aligned disentanglement to emerge in latent space when applied to ProGAN on several datasets. Additionally, we use our regularization term to identify interpretable directions in BigGAN’s latent space in an unsupervised fashion. Finally, we provide empirical evidence that the Hessian Penalty encourages substantial shrinkage when applied to over-parameterized latent spaces. We encourage readers to view videos of our disentanglement results at www.wpeebles.com/hessian-penalty , and code at https://github.com/wpeebles/hessian_penalty .
Decentralized vs. Distributed Organization: Blockchain, Machine Learning and the Future of the Digital Platform

The terms decentralized organization and distributed organization are often used interchangeably, despite describing two distinct phenomena. I propose distinguishing decentralization, as the dispersion of organizational communications, from distribution, as the dispersion of organizational decision-making. Organizations can be distributed without being decentralized (and vice versa), and having multiple management layers directly affects only distribution – not decentralization. This proposed distinction has implications for understanding the growth of digital platforms (e.g. amazon.com ), which dominate the global economy in the 21 st century. While prominent platforms typically use machine learning as their core technology to transform inputs (e.g. data) into outputs (e.g. matchmaking services), blockchain has emerged as an alternative technological blueprint. I argue that blockchain enables platforms that are both decentralized and distributed (e.g. Bitcoin), whereas machine learning fosters centralized communications and the concentration of decision-making (e.g. Facebook Inc.). This distinction has crucial implications for antitrust policy, which, I contend, should shift both its analysis and its target of action away from the corporate level and focus instead on the data level. Based on this essay’s framework, I make several predictions regarding the future of competition between centralized and decentralized platforms, the evolution of government regulation, and broader implications for managers in the digital economy and for the business schools charged with their education. I conclude with reflections on the opportunity to revive cybernetic thinking for preventing a dystopian future dominated by a handful of platform behemoths.
Affine Transformation-Enhanced Multifactorial Optimization for Heterogeneous Problems

Evolutionary multitasking (EMT) is a newly emerging research topic in the community of evolutionary computation, which aims to improve the convergence characteristic across multiple distinct optimization tasks simultaneously by triggering knowledge transfer among them. Unfortunately, most of the existing EMT algorithms are only capable of boosting the optimization performance for homogeneous problems which explicitly share the same (or similar) fitness landscapes. Seldom efforts have been devoted to generalize the EMT for solving heterogeneous problems. A few preliminary studies employ domain adaptation techniques to enhance the transferability between two distinct tasks. However, almost all of these methods encounter a severe issue which is the so-called degradation of intertask mapping. Keeping this in mind, a novel rank loss function for acquiring a superior intertask mapping is proposed in this article. In particular, with an evolutionary-path-based representation model for optimization instance, an analytical solution of affine transformation for bridging the gap between two distinct problems is mathematically derived from the proposed rank loss function. It is worth mentioning that the proposed mapping-based transferability enhancement technique can be seamlessly embedded into an EMT paradigm. Finally, the efficacy of our proposed method against several state-of-the-art EMTs is verified experimentally on a number of synthetic multitasking and many-tasking benchmark problems, as well as a practical case study.
The show must go on - virtualisation of sport events during the COVID-19 pandemic

Due to the high risk of COVID-19 transmission, many sport events, including organised cycling competitions, have been cancelled. Nevertheless, staying active is important for individual well-being and professional athletes need to compete to earn their living. We propose that mixed-reality applications like ZWIFT allow people to conduct virtually mediated sports competitions, which resemble traditional sport realistically and could serve as a remedy for this contradiction. To test this proposition, we compare professional cyclists' performance data from virtual competitions with data obtained from traditional cycling races and find that the physical activities are comparable. Thus, we contribute to the discussion on the distinctiveness of sports and eSport. Moreover, we interview 11 professional cyclists, who participated in virtual race series regarding their evaluation of the virtualisation of cycling events. Accordingly, we contribute to the literature on mixed-reality applications by focusing on user perceptions of a novel and physical dimension of virtually mediated interaction. Thereby, we apply the sports digitalisation framework proposed by Xiao et al. (2017). Professional athletes mainly perceive the virtualisation of sport as positive, but also mention some specific risks. Consequently, we derive implications for practice that include the supportive role of virtual sport to help master pandemics such as COVID-19.
How human users engage with consumer robots? A dual model of psychological ownership and trust to explain post-adoption behaviours

Consumer robots are technically evolving and have a growing presence in our daily lives with enhanced interactive capabilities. While there is insightful literature on robot adoption, so far, research has done less to examine the post-adoption interaction of human-consumer robots. Drawing on trust in technology model and psychological ownership theory, this study proposes a conceptual dual model to explain robot users’ post-adoption behaviours, while considers the moderating roles of anthropomorphism and social presence. We empirically corroborated our model by asking from 403 current robot owners to illustrate theoretical paths to their post-adoption behaviours including cognitive absorption and intention to explore. This study contributes to the extant literature of human-robot interaction by proposing a theoretically grounded and empirically tested framework that contextualizes psychological ownership theory, uncertainty reduction theory, and trust in technology model. We also highlight the implications for practitioners to leverage trust and psychological ownership mechanisms together for encouraging users to actively engage with robots.
Self-organized operational neural networks for severe image restoration problems

Discriminative learning based on convolutional neural networks (CNNs) aims to perform image restoration by learning from training examples of noisy-clean image pairs. It has become the go-to methodology for tackling image restoration and has outperformed the traditional non-local class of methods. However, the top-performing networks are generally composed of many convolutional layers and hundreds of neurons, with trainable parameters in excess of several million. We claim that this is due to the inherently linear nature of convolution-based transformation, which is inadequate for handling severe restoration problems. Recently, a non-linear generalization of CNNs, called the operational neural networks (ONN), has been shown to outperform CNN on AWGN denoising. However, its formulation is burdened by a fixed collection of well-known non-linear operators and an exhaustive search to find the best possible configuration for a given architecture, whose efficacy is further limited by a fixed output layer operator assignment. In this study, we leverage the Taylor series-based function approximation to propose a self-organizing variant of ONNs, Self-ONNs, for image restoration, which synthesizes novel nodal transformations on-the-fly as part of the learning process, thus eliminating the need for redundant training runs for operator search. In addition, it enables a finer level of operator heterogeneity by diversifying individual connections of the receptive fields and weights. We perform a series of extensive ablation experiments across three severe image restoration tasks. Even when a strict equivalence of learnable parameters is imposed, Self-ONNs surpass CNNs by a considerable margin across all problems, improving the generalization performance by up to 3 dB in terms of PSNR.
Intelligent Diagnosis of Incipient Fault in Power Distribution Lines Based on Corona Detection in UV-Visible Videos

As one of the non-contact methods for incipient fault diagnosis of power distribution lines, UV-Visible imaging has become popular due to its good performance and robustness against environmental parameters. This paper presents a deep learning-based method for UV-Visible video processing. First, for preparing the dataset, some videos are acquired from distribution lines using CoroCam 6D2 considering observation distance, the imager's gain, air pressure, and humidity as effective parameters on the discharge area in the images. Then, based on the logged information of unplanned power outages in the system and also planned inspections over a year, the incipient fault type and the severity level of the incipient fault are determined for each video. For processing each video, frames are extracted with the rate according to the nominal voltage of the line. Power devices are detected in each frame using Faster R-CNN and are tracked through the whole video frames to compensate the camera movement. Then, color thresholding is used for each device to identify corona discharges in the frames. In-depth median filtering is also used through the video to eliminate noises in the UV channel. Finally, based on the ratio of the detected discharge area to the area of the equipment, the severity level of the incipient fault is determined. The proposed method not only performs better than the state-of-the-art but also is a practical method, and with the least dependence on environmental conditions, can automatically identify incipient faults in distribution lines, even in videos containing several possible defective devices.
CNN-based multivariate data analysis for bitcoin trend prediction

Bitcoin is the most widely known blockchain, a distributed ledger that records an increasing number of transactions based on the bitcoin cryptocurrency. New bitcoins are created at a predictable and decreasing rate, which means that the demand must follow this level of inflation to keep the price stable. Actually, the price is highly volatile, because it is affected by many factors including the supply of bitcoin, its market demand, the cost of the mining process, as well as economic and political world-class news. In this work, we illustrate a novel approach for bitcoin trend prediction, based on the One-Dimensional Convolutional Neural Network (1D CNN). First, we propose a methodology for building useful datasets that take into account social media data, the full blockchain transaction history, and a number of financial indicators. Moreover, we present a cloud-based system characterized by a highly efficient distributed architecture, which allowed us to collect a huge amount of data in order to build thousands of different datasets, using the aforementioned methodology. To the best of our knowledge, this is the first work that uses 1D CNN for bitcoin trend prediction. Remarkably, an efficient and low-cost implementation is feasible due to the simple and compact configuration of 1D CNN models that perform one-dimensional convolutions (i.e., scalar multiplications and additions). We show that the 1D CNN model we implemented, trained, validated and tested using the aforementioned datasets, allow one to predict the bitcoin trend with higher accuracy compared to LSTM models. Last but not least, we introduce and simulate a trading strategy based on the proposed 1D CNN model, which increases the profit when the bitcoin trend is bullish and reduces the loss when the trend is bearish.
Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes

Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality.We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations.The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients.UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.