Reviewerly/exHarmony huggingface dataset:
A Representation Scheme for Managing Complex Professional Knowledge

A representation scheme called CKR-1 is introduced to deal with the challenges of representing complex knowledge.&nbsp; Complex knowledge is defined as deep knowledge concerning a complex object, event, situation or process.&nbsp; The nature of complex knowledge is identified from samples of professional knowledge drawn from the insurance industry.&nbsp; These examples highlight efficacy of the scheme as well as its limitations.&nbsp; CKR-1 is a type of semantic network that supports simple and abstract concepts, events, activities and situations.&nbsp; It also supports several types of relationships including business, logical, causal, process etc.&nbsp; It derives its expressivity from ability to support abstractions, elaborations, assertions and alternative points of view.&nbsp; Usability was tested with a randomized selection of fifty knowledge items from a book for insurance professionals.&nbsp; The scheme handled 39 of these well.&nbsp; Despite this, the scheme suffers from limitations stemming from the inherent difficulty in expressing concepts involving several other vaguely defined abstract concepts. &nbsp
3M-AI: A Multi-task and Multi-core Virtualization Framework for Multi-FPGA AI Systems in the Cloud

With the ever-growing demands for online Artificial Intelligence (AI), the hardware virtualization support for deep learning accelerators is vital for providing AI capability in the cloud. Three basic features, multi-task, dynamic workload, and remote access, are fundamental for hardware virtualization. However, most of the deep learning accelerators do not support concurrent execution of multiple tasks. Besides, the SOTA multi-DNN scheduling algorithm for NN accelerators neither consider the multi-task concurrent execution and resources allocation for the multi-core DNN accelerators. Moreover, existing GPU virtualized solutions could introduce a huge remote access latency overhead, resulting in a severe system performance drop.
Providing guaranteed network performance across tenants: Advances, challenges and opportunities

The varied network performance in the cloud hurts application performance. This increases the tenant's cost and becomes the key hindrance to cloud adoption. It is because virtual machines (VMs) belonging to one tenant can reside in multiple physical servers and communication interference across tenants occasionally occurs when encountering network congestion. In order to prevent such unpredictability, it is critical for cloud providers to offer the guaranteed network performance at tenant level. Such a critical issue has drawn increasing attention in both academia and industry. Many elaborate mechanisms are proposed to provide guaranteed network performance, such as guaranteed bandwidth or bounded message delay across tenants. However, due to the intrinsic complexities and limited capabilities of commodity hardware, the deployment of these mechanisms still faces great challenges in current cloud data centers. Moreover, with the rapid development of new technologies, there are new opportunities to improve the performance of existing works, but these possibilities are not under full discussion yet. Therefore, in this paper, we survey the latest development of the network performance guarantee approaches and summarize them based on their features. Then, we explore and discuss the possibilities of using emerging technologies as knobs to upgrade the performance or overcome the inherent shortcomings of existing advances. We hope this article will help readers quickly understand the causes of the problems and serve as a guide to motivate researchers to develop innovative algorithms and frameworks.
A Block Minifloat Representation for Training Deep Neural Networks

Training Deep Neural Networks (DNN) with high efficiency can be difficult to achieve with native floating point representations and commercially available hardware. Specialized arithmetic with custom acceleration offers perhaps the most promising alternative. Ongoing research is trending towards narrow floating point representations, called minifloats, that pack more operations for a given silicon area and consume less power. In this paper, we introduce Block Minifloat (BM), a new spectrum of minifloat formats capable of training DNNs end-to-end with only 4-8 bit weight, activation and gradient tensors. While standard floating point representations have two degrees of freedom, via the exponent and mantissa, BM exposes the exponent bias as an additional field for optimization. Crucially, this enables training with fewer exponent bits, yielding dense integer-like hardware for fused multiply-add (FMA) operations. For ResNet trained on ImageNet, 6-bit BM achieves almost no degradation in floating point accuracy with FMA units that are 4.1×(23.9×) smaller and consume 2.3×(16.1×) less energy than FP8 (FP32). Furthermore, our 8-bit BM format matches floating-point accuracy while delivering a higher computational density and faster expected training times.
Limitation of acyclic oriented graphs matching as cell tracking accuracy measure when evaluating mitosis

Multi-object tracking (MOT) in computer vision and cell tracking in biomedical image analysis are two similar research fields, whose common aim is to achieve instance level object detection/segmentation and associate such objects across different video frames. However, one major difference between these two tasks is that cell tracking also aim to detect mitosis (cell division), which is typically not considered in MOT tasks. Therefore, the acyclic oriented graphs matching (AOGM) has been used as de facto standard evaluation metrics for cell tracking, rather than directly using the evaluation metrics in computer vision, such as multiple object tracking accuracy (MOTA), ID Switches (IDS), ID F1 Score (IDF1) etc. However, based on our experiments, we realized that AOGM did not always function as expected for mitosis events. In this paper, we exhibit the limitations of evaluating mitosis with AOGM using both simulated and real cell tracking data.
Digital Financial Transaction Security Based on Blockchain Technology

Abstract Blockchain technology is currently recognized as the most potential new key technology, it can bring earth shaking changes, it is expected to trigger a new round of technological innovation and industrial change, and cause market attention. The purpose of this paper is to study the security of digital financial transactions based on blockchain technology. Firstly, the security of sdte is analyzed, and the DoS attacks that each role may launch, the attacks that a single role may send, and the attacks that can be launched by multiple roles in collusion are analyzed. It shows that sdte can resist these attacks and has strong security. Then, the related environment of the system test is described. Then, the performance test and analysis are carried out from the key security transmission, the execution of smart contract in the trusted environment SGX and the total running time. The experimental results show that the extra time consumed by using the k-nearest neighbor (KNN) algorithm to process data is less than 0.45s, At the same time, the additional cost brought by the system is also acceptable.
Analysis of the Formation of Conflictological Competence of a Modern Teacher

This article examines the problem of the formation of conflictological competence, which is fundamental in the teacher’s professiogram. Namely, the reasons for the high conflict potential of the educational environment, the possibility of its optimization. The article describes the analysis of the teacher’s conflictological competence from the standpoint of its essence and structure, as well as the possibility of its development.
Does Standard Backpropagation Forget Less Catastrophically Than Adam

Catastrophic forgetting remains a severe hindrance to the broad application of artificial neural networks (ANNs), however, it continues to be a poorly understood phenomenon. Despite the extensive amount of work on catastrophic forgetting, we argue that it is still unclear how exactly the phenomenon should be quantified, and, moreover, to what degree all of the choices we make when designing learning systems affect the amount of catastrophic forgetting. We use various testbeds from the reinforcement learning and supervised learning literature to (1) provide evidence that the choice of which modern gradient-based optimization algorithm is used to train an ANN has a significant impact on the amount of catastrophic forgetting and show that--surprisingly--in many instances classical algorithms such as vanilla SGD experience less catastrophic forgetting than the more modern algorithms such as Adam. We empirically compare four different existing metrics for quantifying catastrophic forgetting and (2) show that the degree to which the learning systems experience catastrophic forgetting is sufficiently sensitive to the metric used that a change from one principled metric to another is enough to change the conclusions of a study dramatically. Our results suggest that a much more rigorous experimental methodology is required when looking at catastrophic forgetting. Based on our results, we recommend inter-task forgetting in supervised learning must be measured with both retention and relearning metrics concurrently, and intra-task forgetting in reinforcement learning must--at the very least--be measured with pairwise interference.
Implementation of sequential real-time waveform generator for high-quality vocoder

We describe an implementation of real-time waveform generation from vocoded speech parameters. High-quality vocoders such as STRAIGHT and WORLD have been used for voice conversion and statistical parametric speech synthesis. The current implementation of such vocoders has a function for generating the whole waveform from the speech parameters in all frames at one time. To sequentially generate a shortperiod waveform, implementations such as realtime STRAIGHT have been proposed. However, the generated speech waveform is inferior in sound quality to that of the original vocoder. To achieve sequential real-time waveform generation, a struct named WorldSynthesizer (WS struct) and six functions were implemented. The implementation is based on the WORLD vocoder, and it can generate the completely same waveform as the original except for the several points such as random seed used for generating the white noise. We therefore evaluated its processing speed by using the real time factor (RTF). The results showed that the processing speed of the proposed implementation decreased by 14.5% compared with the original WORLD. On the other hand, the RTF of the proposed implementation calculated from female speech was below 0.1, which suggests that the implementation is able to carry out real-time synthesis.
Graphical Glitch Detection in Video Games Using CNNs

This work addresses the following research question: Can we detect videogame glitches using Convolutional Neural Networks? Focusing on the most common types of glitches, texture glitches (Stretched ...
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning

Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeling a decision-maker’s policy is challenging—with no access to underlying states, no knowledge of environment dynamics, and no allowance for live experimentation. We desire learning a data-driven representation of decision- making behavior that (1) inheres transparency by design, (2) accommodates partial observability, and (3) operates completely offline. To satisfy these key criteria, we propose a novel model-based Bayesian method for interpretable policy learning (“Interpole”) that jointly estimates an agent’s (possibly biased) belief-update process together with their (possibly suboptimal) belief-action mapping. Through experiments on both simulated and real-world data for the problem of Alzheimer’s disease diagnosis, we illustrate the potential of our approach as an investigative device for auditing, quantifying, and understanding human decision-making behavior.
A constrained Bregman framework for unsupervised convolutional denoising of multi-channel x-ray CT data

Deep learning (DL) approaches to image denoising have advanced real-world performance in one of the most thoroughly studied areas of digital signal processing. Keys to the success of DL are data-driven approximation of an underlying signal model and non-linear domain mapping. By contrast, “classic” denoising approaches rely on prior assumptions about the structure of data and noise and generally penalize deviations through convex optimization. Despite these apparent differences, denoising with CNNs can be viewed as an extension of older sparse coding models, and even state-of-the-art DL models commonly employ cost terms like mean-squared-error and total variation. In this work, we adapt the split Bregman optimization method (SBM) for use with CNN-based regularizers. We show how the operator splitting of the SBM lends itself to splitting cost terms for CNN-based denoising, simplifying the selection of regularization hyperparameters and the hybridization of classic and DL-specific regularizers (bilateral TV, patch rank, discriminator loss). Furthermore, we demonstrate a simple, data-driven method to rebalance cost terms during network training, while limiting the potential to “invent” features. Using preclinical photon-counting micro-CT data of the mouse heart, our proposed framework performs 2D denoising of the time (PSNR, 27.7; SSIM, 0.62) and energy (PSNR, 35.0; SSIM, 0.81) dimensions which compares favorably with both 3D, multi-channel iterative reconstruction (reference) and the BM3D denoising algorithm (time: PSNR, 26.6; SSIM, 0.58. energy: PSNR, 27.2; SSIM, 0.76). The time and energy networks evaluate in under 2 minutes each vs. ~1-2 hours for our iterative reconstructions using similar hardware.
Analysis on types of spelling errors in true Tibetan characters

Spelling error checking is a challenging research topic with a wide range of applications such as text editing, word processing, spell checking, teaching, etc. As an alphabetic language, spelling errors in Tibetan could be categorized into three types, namely, non-true type, true type, and punctuation misuse. In order to study true Tibetan syllable spelling error in much more depth, the article analyses the types of Tr u e Tibetan syllable spelling errors based on Tibetan word formation rules, grammar and semantic features laying a foundation for Tibetan spelling error checking research.
Categorical Normalizing Flows via Continuous Transformations

Despite their popularity, to date, the application of normalizing flows on categorical data stays limited. The current practice of using dequantization to map discrete data to a continuous space is inapplicable as categorical data has no intrinsic order. Instead, categorical data have complex and latent relations that must be inferred, like the synonymy between words. In this paper, we investigate Categorical Normalizing Flows, that is normalizing flows for categorical data. By casting the encoding of categorical data in continuous space as a variational inference problem, we jointly optimize the continuous representation and the model likelihood. Using a factorized decoder, we introduce an inductive bias to model any interactions in the normalizing flow. As a consequence, we do not only simplify the optimization compared to having a joint decoder, but also make it possible to scale up to a large number of categories that is currently impossible with discrete normalizing flows. Based on Categorical Normalizing Flows, we propose GraphCNF a permutation-invariant generative model on graphs. GraphCNF implements a three step approach modeling the nodes, edges, and adjacency matrix stepwise to increase efficiency. On molecule generation, GraphCNF outperforms both one-shot and autoregressive flow-based state-of-the-art.
Not-So-Simple Pendulums

Figure 1 shows the starting positions of two point masses on a pair of semi-elliptical tracks. Each track hangs vertically in a uniform gravitational field, and the masses are released simultaneous...
Inshore Ship Detection Based on Multi-Information Fusion Network and Instance Segmentation

Inshore ship detection is a challenging task due to the complex background and object placement in remote sensing port images. To address this problem, we propose a detection method based on semantic and instance segmentation. First, a multi-information fusion network is designed to segment ship objects by taking edge and global information into account, which comprises a multi-task network, a global network and a fusion network. Then a simple but effective instance segmentation method based on line scanning and interval analysis is presented to separate multiple ships within one semantic region. Experimental results on an inshore ship data set collected from Google Earth validate the state-of-the-art performance of the proposed method.
Black-Box Expectation–Maximization Algorithm for Estimating Latent States of High-Speed Vehicles

Tracking an object in a noisy environment is difficult, especially when unknown parameters affect the object’s behavior. In the case of a high-speed ballistic object, its trajectory is affected by changes in atmospheric conditions as well as various parameters of the object itself. To filter these latent factors of the dynamics model, this paper proposes a black-box expectation–maximization algorithm to estimate the latent parameters and enhance the accuracy of object tracking. The expectation step involves calculating the likelihood of observations through extended Kalman smoothing, which reflects the forward–backward probability combination. The maximization step involves optimizing the unknown parameter, which is the object mass, to maximize the likelihood through Bayesian optimization with Gaussian process regressions. Our simulation experiments show that our algorithm reduces the error of tracking a ballistic object’s position given noisy observations with unknown parameters.
Learning to Generate Noise for Multi-Attack Robustness

Adversarial learning has emerged as one of the successful techniques to circumvent the susceptibility of existing methods against adversarial perturbations. However, the majority of existing defense methods are tailored to defend against a single category of adversarial perturbation (e.g. l∞-attack). In safety-critical applications, this makes these methods extraneous as the attacker can adopt diverse adversaries to deceive the system. Moreover, training on multiple perturbations simultaneously significantly increases the computational overhead during training. To address these challenges, we propose a novel meta-learning framework that explicitly learns to generate noise to improve the model's robustness against multiple types of attacks. Its key component is Meta Noise Generator (MNG) that outputs optimal noise to stochastically perturb a given sample, such that it helps lower the error on diverse adversarial perturbations. By utilizing samples generated by MNG, we train a model by enforcing the label consistency across multiple perturbations. We validate the robustness of models trained by our scheme on various datasets and against a wide variety of perturbations, demonstrating that it significantly outperforms the baselines across multiple perturbations with a marginal computational cost.
Wavelets in Multi-Scale Time Series Analysis: An Application to Seismic Data

Forecasting earthquakes is one of the most important problems in Earth science because of their devastating consequences. Current scientific studies related to earthquake forecasting focus on three key points: when the event will occur, where it will occur, and how large it will be. In this work we investigate the possibility to determine when the earthquake will take place. We formulate the problem as a multiple change-point detection in the time series. In particular, we refer to the multi-scale formulation described in Fryzlewicz (Ann Stat 46(6B): 3390–3421, 2018). In that paper a bottom-up hierarchical structure is defined. At each stage, multiple neighbor regions which are recognized to correspond to locally constant underlying signal are merged. Due to their multi-scale structure, wavelets are suitable as basis functions, since the coefficients of the representation contain local information. The preprocessing stage involves the discrete unbalanced Haar transform, which is a wavelet decomposition of one-dimensional data with respect to an orthonormal Haar-like basis, where jumps in the basis vectors do not necessarily occur in the middle of their support. The algorithm is tested on data from a well-characterized laboratory system described in Rouet-Leduc et al. (Geophys Res Lett 44(18): 9276–9282, 2017).
Oculomotor response to images in primary school children with mild intellectual disability

Oculomotor activity (eye movements) is an essential component of visual data acquisition, analysis and use. The aim of this study was to determine the characteristics of oculomotor response to static images in primary school children with mild intellectual disability (ID). Our sample included a total of 49 schoolers (23 children with mild ID and 26 typically developing children). Oculomotor activity was evaluated using a GP3 Gazepoint eye tracker. The participants were presented with 15 visual stimuli: 10 pictorial and 5 mixed (pictures + text) static color images. Children with mild ID generated significantly fewer fixations (р = 0.038) than typically developing children. So, learning materials containing both pictorial and textual images are ineffective because textual elements are completely ignored by children with mild ID. The total duration of gaze fixations was significantly longer (р = 0.029) in typically developing children than in children with mild ID. However, the average duration of a single gaze fixation was longer in children with mild ID. The identified features of oculomotor response can help to optimize the format of instructional materials for primary school children with mild ID.
Design and Implementation of Information Service System of Hunan Regional Seismic Network

Abstract In this paper, the framework of one-stop application service system of Hunan Regional Seismological Network is introduced, taking the fast earthquake reporting of Hunan Regional Seismological Network as an example, in order to realize the automatic, one-stop application service of graphic and text data display, which integrates "obtaining earthquake parameters-issuing earthquake message-completing website earthquake situation update-automatically sending fax".
Localization of two dimensional quantum walks defined by generalized Grover coins

Abstract Localization phenomena of quantum walks makes the propagation dynamics of a walker strikingly different from that corresponding to classical random walks. In this paper, we study the localization phenomena of four-state discrete-time quantum walks on two-dimensional lattices with coin operators as one-parameter orthogonal matrices that are also permutative, a combinatorial structure of the Grover matrix. We show that the proposed walks localize at its initial position for canonical initial coin states when the coin belongs to classes which contain the Grover matrix that we consider in this paper, however, the localization phenomena depends on the coin parameter when the class of parametric coins does not contain the Grover matrix.