Reviewerly/exHarmony huggingface dataset:
Insuring Crypto: The Birth of Digital Asset Insurance

In December 2020, Bitcoin hit an all-time high of over $28,000…and rising. New digital assets (also referred to as “crypto assets”) are being launched seemingly every day, and more people and investors hold crypto assets than ever before. Yet, the same characteristics, such as immutability and decentralization, that make crypto assets innovative can contribute to them being particularly susceptible to costly hacks. Digital asset insurance is a new product that provides coverage against such loss or theft. Security-oriented crypto custody providers and crypto wallets have greatly reduced the risk of hacks, but these solutions are all still relatively new, untested, and unavailable to many crypto holders. In 2019 and 2020, billions of dollars of crypto assets were stolen or lost through hacks and scams, many of which were high-profile and highly publicized. The threat of a hack, real or perceived, still acts as a strong deterrent to broader adoption and usage of crypto assets. Digital asset insurance provides a necessary backstop for holders of crypto assets while also lending legitimacy in an industry often perceived as unsafe or even nefarious.In this paper, I provide an overview of the digital asset industry. I outline which insurers are providing digital asset insurance, how insurers are overcoming the challenges of underwriting this new insurance product, and which companies in the crypto ecosystem are obtaining coverage. I discuss several shortcomings in the new industry including the problems associated with the murky regulatory landscape, the lack of transparency for consumers, and the significant amount of bias around the crypto industry. I also propose ways in which digital asset insurance could be more efficient and effective including insurers creating industry standards and serving as a de facto regulator in digital asset storage. In just a few short years, this insurance product has gone from nonexistent to a multi-hundred-million-dollar premium market that is growing faster than cyber insurance. Despite the insurance product’s progress or importance to the broader crypto ecosystem, this paper is the first scholarly analysis of the digital asset insurance industry.
Hierarchical Distillation Network for Biomedical Event Extraction

Biomedical event extraction is a challenging task in biomedical information extraction. There exist two main problems in previous works: (1) Existing methods are insufficient to capture the indicative information from distant context. (2) Existing methods are not skilled at generating the hierarchical representations for each sentence, which is important for the downstream classification task. In this paper, we propose a novel Hierarchical Distillation Network (HDN) for biomedical event extraction. Firstly, HDN encodes a given sentence from multiple perspectives: a bidirectional GRU (BI-GRU) is employed for sequential encoding and several graph convolution networks (GCN) are employed for multi-order syntactic encodings. Second, HDN integrates the distillation module which can emphasize the difference among all levels of encodings to reduce the redundant information, then HDN adopts residual connection to obtain more expressive sentence representation. Finally, we perform biomedical event extraction on the commonly used Multi-Level Event Extraction (MLEE) corpus and achieve an F1-score of 62.74% which is 3.13% higher than the previous best.
Certify or Predict: Boosting Certified Robustness with Compositional Architectures

A core challenge with existing certified defense mechanisms is that while they improve certified robustness, they also tend to drastically decrease natural accuracy, making it difficult to use these methods in practice. In this work, we propose a new architecture which addresses this challenge and enables one to boost the certified robustness of any state-of-the-art deep network, while controlling the overall accuracy loss, without requiring retraining. The key idea is to combine this model with a (smaller) certified network where at inference time, an adaptive selection mechanism decides on the network to process the input sample. The approach is compositional: one can combine any pair of state-of-the-art (e.g., EfficientNet or ResNet) and certified networks, without restriction. The resulting architecture enables much higher natural accuracy than previously possible with certified defenses alone, while substantially boosting the certified robustness of deep networks. We demonstrate the effectiveness of this adaptive approach on a variety of datasets and architectures. For instance, on CIFAR-10 with an l∞ perturbation of 2/255, we are the first to obtain a high natural accuracy (90.1%) with non-trivial certified robustness (27.5%). Notably, prior state-of-the-art methods incur a substantial drop in accuracy for a similar certified robustness.
Smart Multipurpose Robotic Car

In this modern digital world, everyone is moving towards automation. Robotics allows automation where machines perform a well-defined step safely and productively, in autonomous or partial autonomous manners. Many articles have been published in recent times to present automatic/robotic cars which can perform some process such as, smart surveillance, obstacle detection, and collision aversion, etc. The design of the smart robotic car is presented through this paper and demonstrates its operations. The proposed developed model can perform multiple operations such as smart irrigation, obstacle detection, line-following, grass cutting, and vacuum cleaning. All these operations are operated or managed through the smartphone. The proposed smart model work as the expert system which utilized the features of Artificial Intelligence. The smart robotic car can sense the environment and take the proper decision without human guidance.
Detection of Abnormal Data Flow at Network Boundary of Renewable Energy Power System

As more and more renewable energy stations are built and access the power grid, the network security risks of renewable energy power systems are increasing. This paper analyzes the typical network attacks that renewable energy plants face and researches the network boundary abnormal data flow detection technology to realize the identification of potential threats and improve the level of network security protection for renewable energy power system.
Estimating the Unique Information of Continuous Variables.

The integration and transfer of information from multiple sources to multiple targets is a core motive of neural systems. The emerging field of partial information decomposition (PID) provides a novel information-theoretic lens into these mechanisms by identifying synergistic, redundant, and unique contributions to the mutual information between one and several variables. While many works have studied aspects of PID for Gaussian and discrete distributions, the case of general continuous distributions is still uncharted territory. In this work we present a method for estimating the unique information in continuous distributions, for the case of one versus two variables. Our method solves the associated optimization problem over the space of distributions with fixed bivariate marginals by combining copula decompositions and techniques developed to optimize variational autoencoders. We obtain excellent agreement with known analytic results for Gaussians, and illustrate the power of our new approach in several brain-inspired neural models. Our method is capable of recovering the effective connectivity of a chaotic network of rate neurons, and uncovers a complex trade-off between redundancy, synergy and unique information in recurrent networks trained to solve a generalized XOR task.
Comparative cognition: Perspectives, challenges, and prospects.

The publication of the centennial year of the Journal of Comparative Psychology is an occasion to reflect on the state of our discipline. In this article, I focus on one aspect of comparative psychology, namely, comparative cognition. This focus stems from my long-standing interest in comparative cognition. The trends and challenges in comparative cognition share many of the trends and challenges in the broader field of comparative psychology. In the first part of this article, I outline my perspective on the field. Next, I consider challenges. I end with a section on prospects for the future. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
AUXILIARY TASK UPDATE DECOMPOSITION: THE GOOD, THE BAD AND THE NEUTRAL

While deep learning has been very beneficial in data-rich settings, tasks with smaller training set often resort to pre-training or multitask learning to leverage data from other tasks. In this case, careful consideration is needed to select tasks and model parameterizations such that updates from the auxiliary tasks actually help the primary task. We seek to alleviate this burden by formulating a model-agnostic framework that performs fine-grained manipulation of the auxiliary task gradients. We propose to decompose auxiliary updates into directions which help, damage or leave the primary task loss unchanged. This allows weighting the update directions differently depending on their impact on the problem of interest. We present a novel and efficient algorithm for that purpose and show its advantage in practice. Our method leverages efficient automatic differentiation procedures and randomized singular value decomposition for scalability. We show that our framework is generic and encompasses some prior work as particular cases. Our approach consistently outperforms strong and widely used baselines when leveraging out-of-distribution data for Text and Image classification tasks.
Research on network vulnerability assessment based on attack graph and security metrics

Abstract In recent years, the information security environment is undergoing great changes. On one hand, with the development of technology and the advancement of network integration, network involves in a larger and larger scale, and its structure is becoming more and more complex. On the other hand, the development of attack technology has also taken a qualitative leap. The ability of violent cracking and the scale of botnets are no longer the primary factors that determine the attack effect. On the contrary, attackers are more likely to adopt information-driven complex combination attacks with clear attack targets. These changes have brought great challenges to network security defense. Therefore, from the perspective of building a secure network environment, this paper studies network vulnerability assessment in the field of computer network security, and proposes a method of network vulnerability assessment based on attack graph and security metrics. This method models and constructs the attack graph for the possible network attack behaviors. Besides, taking it as the analysis model, the risk assessment technology is taken to determine the indicators and calculating methods of security metrics so as to realize the evaluation of network vulnerability. Finally, the discussion was conducted based on the results, and a follow-up study was suggested.
On problems of quality of higher professional education

Examined is the problem of achieving the quality of education of university graduates in implementation of competence-based learning. Shown is that when assessing quality, it is necessary to focus on achieved level of compliance with requirements. The project of the higher education reform of the Ministry of Education and Science is analyzed from the standpoint of implementation of the competence-based approach in vocational education. Attention is drawn to the need to make adjustments to the formulation of competencies of graduates, and to develop real mechanisms for achievement of such by university graduates.
Weakly Supervised Instance Segmentation of SEM Image via Synthetic Data

Instance segmentation of scanning electron microscope images provides useful information for quantitative analysis of particle morphometry and distribution that contributes to various biomedical research such as the phenotyping of drug delivery systems. Compared to the conventional segmentation methodologies, the learning-based approaches stand out, benefiting from the prosperous development of artificial intelligence. However, most of the current learning-based segmentation methods require sufficient manually annotated training data, which is considered to be laborious. To alleviate this problem, we present a novel weakly supervised framework for instance segmentation on scanning electron microscope images. In the proposed framework, only one instance from each raw image is manually labeled to generate a synthesized dataset, which will be further used to select the training set. With the weakly annotated training dataset, the instance segmentation network is trained and applied to segment the particles of raw testing images. Based on our experimental results, the trained network gains 75% recall and 74% average precision on the tested images, which is seen as a reasonable performance considering the data complexity in our research. The overall experiments demonstrate that the proposed weakly supervised framework is able to provide an efficient solution to the instance segmentation of biomedical images.
SegBlocks: Towards Block-Based Adaptive Resolution Networks for Fast Segmentation

We propose a method to reduce the computational cost and memory consumption of existing neural networks, by exploiting spatial redundancies in images. Our method dynamically splits the image into blocks and processes low-complexity regions at a lower resolution. Our novel BlockPad module, implemented in CUDA, replaces zero-padding in order to prevent the discontinuities at patch borders of which existing methods suffer, while keeping memory consumption under control. We demonstrate SegBlocks on Cityscapes semantic segmentation, where the number of floating point operations is reduced by 30% with only 0.2% loss in accuracy (mIoU), and an inference speedup of 50% is achieved with 0.7% decrease in mIoU.
Cybercrime and Punishment

This paper models cybercrime by adding an active victim to the seminal Becker model of crime. The victim invests in security that may protect her from a cybercrime and, if the cybercrime is thwarted, generate evidence that can be used for prosecution. Successful crimes leave insufficient evidence for apprehension and conviction and, thus, cannot be punished. Results show that increased penalties for cybercriminals lead them to exert more effort and make cybercrimes more likely to succeed. Above a threshold they also lead victims to invest less in security. It may be impossible to deter cybercriminals by punishing them. Deterrence is possible, but not necessarily optimal, through punishing victims, such as data controllers or processors that fail to protect their networks.
Image Style Transfer Method Based on Improved Style Loss Function

In order to improve the quality of composite image in the process of image style transfer. This paper proposes an image style transfer method based on an improved style loss function: the improved Gram matrix calculates the inner product of the feature map and the spatial transformation map, and then calculates the new style loss function. At the same time, combined with the content loss function, the weighted algebraic sum of the two loss functions is used as the total loss function of the neural network. The gradient descent algorithm is used to iteratively optimize to generate the style-transferred image. Experimental results show that the Peak Signal to Noise Ratio and Structural Similarity values of this method are better than other style transfer algorithms, and the image texture details and spatial arrangement are more complete.
Enhanced Compression Model for Brain MRI Images Using Genetic Algorithm and Clustering Techniques

In general, analyzing the information present in biomedical images is a very challenging task in the wireless communication domain. Image retrieval is one of the most emerging technologies for telemedicine to handle medical data present in the images (MRI and CT). The core intention of this process is to utilize a genetic algorithm (GA) and clustering algorithm in order to perform the transformed computing of vector quantizer (VQ) design in biomedical image compression such as brain MRI images. In this proposed work, the genetic algorithm and clustering is applied to the optimal design of codebook generation in VQ, where a momentous improvement is observed in the decompression performance. The proposed method uses PSNR for evaluation method as fitness function in the system. The proposed method has used a combined scheme that makes complete use of the stochastic searching capability in GA and the computational complexity reduction in clustering to compute the codebook.
A Magnetic Resonance Coupling Based Touchless Pad for Human-Computer Interfacing

In this paper, an artificial human interface based on magnetic resonance coupling is proposed to detect and sense the direction of movement of a fingertip. The finger movement is analyzed by recognizing the change in magnetic coupling observed between a static transmitter coil and a static receiver coil array due to presence of a small coil resonator placed at the fingertip. By measuring induce voltage responses in the spatially distributed receiver coil array, the movement of the resonator coil is characterized. A commercial software Ansys EM suite 19.1 is utilized to demonstrate applicability of the proposed design. The fingertip movement is examined by moving the resonator in various directions and the observed results corroborate the claims. Therefore, the proposed touchless pad is found well suitable for infection free accessibility of the computer systems.
A Two-stage Training Mechanism for the CNN with Trainable Activation Function

Activation function design is critical in the convolutional neural network (CNN) because it affects the learning speed and the precision of classification. In a hardware implementation, using traditional activation function may cause large hardware area overhead due to its complicated calculation such as exponential. To reduce the hardware overhead, Taylor series expansion is a popular way to approximate the traditional activation function. However, this approach brings some approximation errors, which reduce the accuracy of the involved CNN model. Therefore, the trainable activation function and a two-stage training mechanism are proposed in this paper to compensate for the accuracy loss due to the Taylor series expansion. After initializing the involved trainable activation function, the coefficients of the trainable activation function according to different neural network layer will be adjusted properly along with the neural network training process. Compared with the conventional approach, the proposed trainable activation function can involve fewer Taylor expansion terms to improve the classification accuracy by 2.24% to 53.96%. Therefore, CNN with trainable activation functions can achieve better classification accuracy with less area cost.
ANN with 3 hidden layers

The recent implementation built in Chapter 7 works with a network of any number of inputs and any number of hidden neurons within just 2 hidden layers. In this chapter, there are 2 major changes: Introducing a third hidden layer where any number of neurons can be used. Using a generic way to calculate the sum of products (SOP) and activation function outputs in the forward pass. The chapter discusses architecture with 3 inputs, 1 output, and 3 hidden layers with 4, 3, and 2 hidden neurons, respectively.
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers

We propose a simple, practical, and intuitive approach for domain adaptation in reinforcement learning. Our approach stems from the idea that the agent's experience in the source domain should look similar to its experience in the target domain. Building off of a probabilistic view of RL, we achieve this goal by compensating for the difference in dynamics by modifying the reward function. This modified reward function is simple to estimate by learning auxiliary classifiers that distinguish source-domain transitions from target-domain transitions. Intuitively, the agent is penalized for transitions that would indicate that the agent is interacting with the source domain, rather than the target domain. Formally, we prove that applying our method in the source domain is guaranteed to obtain a near-optimal policy for the target domain, provided that the source and target domains satisfy a lightweight assumption. Our approach is applicable to domains with continuous states and actions and does not require learning an explicit model of the dynamics. On discrete and continuous control tasks, we illustrate the mechanics of our approach and demonstrate its scalability to high-dimensional~tasks.
Engaging Children’s Reading with Reflective Augmented Reality

Abstract This study explored the integration of mobile augmented reality (AR) into reading in elementary schools. With the reflective messages designed using AR (termed as reflective AR), children were guided to process the story content through preview, reading, and review of the content. The study focused on the enhancement of children’s reading comprehension using reflective AR and the implementation processes of reading with reflective AR experienced by teachers and children. A blended research approach was used for data collection. Via the quantitative approach, the reading comprehension scores of the children in the reflective AR and control group (without reflective AR) were compared. Via the qualitative approach, observations and interviews with the teachers from four elementary schools were conducted. It was observed that students achieved better reading scores when using the reflective AR approach ( p &lt; 0.001). Both teachers and students enjoyed the AR reading exploration. Teachers were devoted to planning the settings required in the reading exploration. Reading via the AR approach of preview–reading–review engaged children in the reading content. From the study, the implementation of AR reflective reading provides a model to guide children’s reading. However, the research is preliminary and might have sampling limitations for generalization. Future research on diverse learning settings is needed.
Automatic model-based 3-D reconstruction of the teeth from five photographs with predefined viewing directions

Misalignment of teeth or jaws can impact the ability to chew or speak, increase the risk of gum disease or tooth decay, and potentially influence a person’s (psychological) well-being. Orthodontic treatments of misaligned teeth are complex procedures that employ dental braces to apply forces in order to move the teeth or jaws to their correct position. Photographs are typically used to document the treatment. An automatic analysis of those photographs could support the decision making and monitoring process. In this paper, we propose an automatic model-based end-to-end 3-D reconstruction approach of the teeth from five photographs with predefined viewing directions (i.e. the photographs used in orthodontic treatment documentation). It uses photo- or view-specific 2-D coupled shape models to extract the teeth contours from the images. The shape reconstruction is then carried out by a deformation-based reconstruction approach that utilizes 3-D coupled shape models and minimizes a silhouette-based loss. The optimal model parameters are determined by an optimization which maximizes the overlaps between the projected 2-D outlines of individual teeth of the 3-D model and the contours extracted from the corresponding photograph. After that the point displacements between the projected outline and segmented contour are used to iteratively deform the 3-D shape model of each tooth for all five views. Back-projection into shape space ensures that the 3-D coupled shape model consists of (statistically) valid teeth. Evaluation on 22 data sets shows promising results with an average symmetric surface distance of 0.848mm and an average DICE coefficient of 0.659.