Reviewerly/exHarmony huggingface dataset:
Transformation of business processes in the conditions of manufacturing digitalization

There are considered the features, advantages and problems of modeling as a basis and a link for the transformation of business processes. The role of business process models in the business architecture of a manufacturing enterprise is shown. Approaches to the transformation of business processes in the context of increasing their efficiency in the context of manufacturing digitalization are proposed. The technique of eliminating “bottlenecks” in business processes based on the system and process approaches to management and theory of constraints is considered. The criteria and methodology for assessing the economic efficiency of transforming business processes are considered.
Moving Toward the Implementation of Precision Medicine Needs Highly Discriminatory, Validated, Inexpensive, and Easy-to-Use Prediction Models

Diabetes is one of the most challenging global health problems, affecting more than 400 million people (1). Although 10% of current global health care spending is devoted to diabetes (1), patients with diabetes remain at high risk of morbidity and mortality (2) mainly due to cardiovascular disease (3). This extremely heavy burden is likely to increase in the coming decades, especially considering the epidemic nature of the most common form, type 2 diabetes (4). It is, therefore, mandatory to address vigorously the negative impact of type 2 diabetes on vascular health and life expectancy. To this end, the availability of well-performing risk prediction models capable of identifying high-risk patients to be targeted with the most aggressive and most burdensome prevention strategies would play a pivotal role.The study of Aminian et al. (5), published in this issue of Diabetes Care , presents several models (called Individualized Diabetes Complications [IDC] Risk Scores) able to estimate in obese patients with type 2 diabetes the risk of mortality and of long-term vascular complications, including coronary artery events, heart failure, and estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. Twenty-six baseline variables as potential predictors were modeled by time-to-event regression and random forest machine learning, an ensemble of survival regression trees grown on bootstrap resampling of the observations. The left-out data were then used to predict the error rate and, after permutation, to estimate the importance of a given predictor. In addition to the time-dependent area under the receiver operating characteristic curve (AUROC) and the calibration plot, a recently described index of prediction accuracy, which combines discrimination and calibration in a single value, was also used (6).A total of 13,722 patients from the Cleveland Clinic database were analyzed retrospectively (i.e., 2,287 who underwent metabolic surgery and 11,435 propensity-matched nonsurgical individuals, …
CNN-based hierarchical coarse-to-fine segmentation of pelvic CT images for prostate cancer radiotherapy

Accurate segmentation of organs-at-risk is important in prostate cancer radiation therapy planning. However, poor soft tissue contrast in CT makes the segmentation task very challenging. We propose a deep convolutional neural network approach to automatically segment the prostate, bladder, and rectum from pelvic CT. A hierarchical coarse-to-fine segmentation strategy is used where the first step generates a coarse segmentation from which an organ-specific region of interest (ROI) localization map is produced. The second step produces detailed and accurate segmentation of the organs. The ROI localization map is generated using a 3D U-net. The localization map helps adjusting the ROI of each organ that needs to be segmented and hence improves computational efficiency by eliminating irrelevant background information. For the fine segmentation step, we designed a fully convolutional network (FCN) by combining a generative adversarial network (GAN) with a U-net. Specifically, the generator is a 3D U-net that is trained to predict individual pelvic structures, and the discriminator is an FCN which fine-tunes the generator predicted segmentation map by comparing it with the ground truth. The network was trained using 100 CT datasets and tested on 15 datasets to segment the prostate, bladder and rectum. The average Dice similarity (mean±SD) of the prostate, bladder and rectum are 0.90&plusmn;0.05, 0.96&plusmn;0.06 and 0.91&plusmn;0.09, respectively, and Hausdorff distances of these three structures are 5.21&plusmn;1.17, 4.37&plusmn;0.56 and 6.11&plusmn;1.47 (mm), respectively. The proposed method produces accurate and reproducible segmentation of pelvic structures, which can be potentially valuable for prostate cancer radiotherapy treatment planning.
National Cybersecurity Education: Bridging Defense to Offense

Abstract Defense Secretary Robert Gates approved the creation of a unified cyber command under the Obama Administration that was focused on cyber operations. This organization was to oversee the protection of government networks against cyber threats known and unknown. Coupled with growing attacks on national infrastructure, digital theft of intellectual property, and election meddling has the United States government actively working to develop cybersecurity talent. Some of the changes that have come as a result are more specialized degree program accreditation, technical frameworks, and policies to help usher this realization of the need to address the shortage of talent for today’s mission.
Document Ranking with a Pretrained Sequence-to-Sequence Model

This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the task of document ranking. Our approach is fundamentally different from a commonly-adopted classification-based formulation of ranking, based on encoder-only pretrained transformer architectures such as BERT. We show how a sequence-to-sequence model can be trained to generate relevance labels as "target words", and how the underlying logits of these target words can be interpreted as relevance probabilities for ranking. On the popular MS MARCO passage ranking task, experimental results show that our approach is at least on par with previous classification-based models and can surpass them with larger, more-recent models. On the test collection from the TREC 2004 Robust Track, we demonstrate a zero-shot transfer-based approach that outperforms previous state-of-the-art models requiring in-dataset cross-validation. Furthermore, we find that our approach significantly outperforms an encoder-only model in a data-poor regime (i.e., with few training examples). We investigate this observation further by varying target words to probe the model's use of latent knowledge.
Klotski: Efficient Obfuscated Execution against Controlled-Channel Attacks

Intel Software Guard eXtensions (SGX) provides a hardware-based trusted execution environment for security-sensitive computations. A program running inside the trusted domain (an enclave) is protected against direct attacks from other software, including privileged software like the operating system (OS), the hypervisor, and low-level firmwares. However, recent research has shown that the SGX is vulnerable to a set of side-channel attacks that allow attackers to compromise the confidentiality of an enclave's execution, such as the controlled-channel attack. Unfortunately, existing defenses either provide an incomplete protection or impose too much performance overhead. In this work, we propose Klotski, an efficient obfuscated execution technique to defeat the controlled-channel attacks with a tunable trade-off between security and performance. From a high level, Klotski emulates a secure memory subsystem. It leverages an enhanced ORAM protocol to load code and data into two software caches with configurable size, which are re-randomized for after a configurable interval. More importantly, Klotski employs several optimizations to reduce the performance overhead caused by software-based address translation and software cache replacement. Evaluation results show that Klotski is secure against controlled-channel attacks and its performance overhead much lower than previous solutions.
Synthesis of naturalistic vehicle driving cycles using the Markov Chain Monte Carlo method

Simulation methods commonly used throughout the design and verification process of various types of motor vehicles require development of naturalistic driving cycles. Optimization of parameters, testing and gradual increase in the degree of autonomy of vehicles is not possible based on standard driving cycles. Ensuring representativeness of synthesized time series based on collected databases requires algorithms using techniques based on stochastic and statistical models. A synthesis technique combining the MCMC method and multifractal analysis has been proposed and verified. The method allows simple determination of the speed profile compared to classic frequency analysis.
Towards a new model for inclusive education based on virtual social inclusion and mobile openness

The political and social instability in many parts of the world has led to large numbers of refugees forced to find a haven in Europe, and the social and political pressure is felt in these countries, as they see the situation worsening dramatically. In this article, an analysis of the geo-sociological assumptions within the conventional conception of social inclusion is undertaken and substituted by a new one based upon the knowledge-based, network-oriented nature of modern European society. This is referred to as Virtual Social Inclusion (VSI) and it aims primarily at two fundamental dimensions of human activity: education and employment. The article focuses on the nature and implications of VSI for inclusive education and its underlying principles, particularly mobility and openness. What VSI requires to turn from a theoretical framework into a practical reality and the foreseeable implications of its implementation are explored.&#x0D; Keywords: Virtual social inclusion, social inclusion, MOOC, open education.
Variational Transformers for Diverse Response Generation

Despite the great promise of Transformers in many sequence modeling tasks (e.g., machine translation), their deterministic nature hinders them from generalizing to high entropy tasks such as dialogue response generation. Previous work proposes to capture the variability of dialogue responses with a recurrent neural network (RNN)-based conditional variational autoencoder (CVAE). However, the autoregressive computation of the RNN limits the training efficiency. Therefore, we propose the Variational Transformer (VT), a variational self-attentive feed-forward sequence model. The VT combines the parallelizability and global receptive field of the Transformer with the variational nature of the CVAE by incorporating stochastic latent variables into Transformers. We explore two types of the VT: 1) modeling the discourse-level diversity with a global latent variable; and 2) augmenting the Transformer decoder with a sequence of fine-grained latent variables. Then, the proposed models are evaluated on three conversational datasets with both automatic metric and human evaluation. The experimental results show that our models improve standard Transformers and other baselines in terms of diversity, semantic relevance, and human judgment.
Human Action Recognition based on Simple Deep Convolution Network PCANet

Human action recognition is an essential topic in computer vision research that has received a wide concern. In the previous decade, deep Convolutional Neural Networks (CNN) have offered an effective execution for several action recognition datasets. However, utilizing an immense amount of training data and using backpropagation algorithm in the training stage have a negative influence on its efficiency for action recognition problem. To help alleviate these limitations, we present a novel technique for human action recognition based on Principal Component Analysis Network (PCANet), which is a simple deep learning network. A subset of frames is selected from each action while for each frame a feature vector is calculated from the previously trained PCANet. All feature vectors are then fused and their dimensionality are reduced using Whitening Principal Component Analysis algorithm (WPCA). Finally, Support Vector Machines (SVM) classifier is employed for action recognition. We assess the proposed approach on the challenging dataset, KTH Human Action Dataset. Our experimental results using the leave-one-out evaluation strategy show the efficiency of the proposed method.
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning

The Information Bottleneck principle offers both a mechanism to explain how deep neural networks train and generalize, as well as a regularized objective with which to train models. However, multiple competing objectives are proposed in the literature, and the information-theoretic quantities used in these objectives are difficult to compute for large deep neural networks, which in turn limits their use as a training objective. In this work, we review these quantities and compare and unify previously proposed objectives, which allows us to develop surrogate objectives more friendly to optimization without relying on cumbersome tools such as density estimation. We find that these surrogate objectives allow us to apply the information bottleneck to modern neural network architectures. We demonstrate our insights on MNIST, CIFAR-10 and Imagenette with modern DNN architectures (ResNets).
Adiabatic Quantum Computing for Max-Sum Diversification

The combinatorial problem of max-sum diversification asks for a maximally diverse subset of a given set of data. Here, we show that it can be expressed as an Ising energy minimization problem. Given this result, max-sum diversification can be solved on adiabatic quantum computers and we present proof of concept simulations which support this claim. This, in turn, suggests that quantum computing might play a role in data mining. We therefore discuss quantum computing in a tutorial like manner and elaborate on its current strengths and weaknesses for data analysis.
Voice Activation Systems for Embedded Devices: Systematic Literature Review

A large number of modern mobile devices, embedded devices and smart home devices are equipped with a voice control.Automatic recognition of the entire audio stream, however, is undesirable for the reasons of the resource consumption and privacy.Therefore, most of these devices use a voice activation system, whose task is to find the specified in advance word or phrase in the audio stream (for example, Ok, Google) and to activate the voice request processing system when it is found.The voice activation system must have the following properties: high accuracy, ability to work entirely on the device (without using remote servers), consumption of a small amount of resources (primarily CPU and RAM), noise resistance and variability of speech, as well as a small delay between the pronunciation of the key phrase and the system activation.This work is a systematic literature review on voice activation systems that satisfy the above properties.We describe the principle of various voice activation systems' operation, the characteristic representation of sound in such systems, consider in detail the acoustic modelling and, finally, describe the approaches used to assess the models' quality.In addition, we point to a number of open questions in this problem.
An Effective Cybersecurity Exercises Platform CyExec and its Training Contents

Recently the threats of cyberattacks, especially of targeted attacks are increasing rapidly and a large number of cybersecurity incidents are occurring frequently.On the other hand, capable personnel are greatly lacking, and strengthen the systematic human resource development cultivating capabilities for cybersecurity activities is becoming an urgent issue.However, only a few parts of academia and private sector in Japan can carry out the cybersecurity exercises because of high cost and inflexibility of commercial or existing training software.On this account, in order to enforce cybersecurity practical exercises cost-effectively and flexibly, we developed a virtual environment Cybersecurity Exercises (CyExec) system utilizing VirtualBox and Docker.We also implemented an open source vulnerability scanner tool WebGoat and our original cyberattack and defense training contents on CyExec.
Creation of One Excavator as an Obstacle in C-Space for Collision Avoidance during Remote Control of the Two Excavators Using Pose Sensors

Many solutions about the teleoperation of unmanned robotic excavators have been studied continuously. However, if excavators that are remotely controlled were employed, the probability of an accident would be higher. For this reason, there have been many ways proposed to attempt to reduce accidents in these dangerous situations. In this paper, a novel methodology will be introduced with a focus on the multiple excavators themselves. The proposed method details how one excavator can be generated or expressed as an obstacle in the Configuration-Space (C-space) with respect to another excavator’s side for collision avoidance each other. This method is based on kinematics information which is measurable and given. Therefore, this method can be used or applied independently by using widely used pose sensors and wireless communication devices. The phase of mathematical derivation about obstacle sets is described in detail. The results are shown in accompanying figures and tables for demonstrating that the proposed method detected the proximity and found the collision-free zone accurately. The proposed system can be powerful when applied to a teleoperation system, where it would be particularly useful and helpful to the operator.
China takes blockchain national: The state-sponsored platform will launch in 100 cities

This month, an alliance of Chinese government groups, banks, and technology companies will publicly launch the Blockchain-based Service Network (BSN). It will be among the first blockchain networks to be built and maintained by a central government. Think of it like an operating system, where participants can use existing blockchain programs, or build their own bespoke tools, without having to design a framework from the ground up.
Image-Based Rendering of Cars using Semantic Labels and Approximate Reflection Flow

Image-Based Rendering (IBR) has made impressive progress towards highly realistic, interactive 3D navigation for many scenes, including cityscapes. However, cars are ubiquitous in such scenes; multi-view stereo reconstruction provides proxy geometry for IBR, but has difficulty with shiny car bodies, and leaves holes in place of reflective, semi-transparent windows on cars. We present a new approach allowing free-viewpoint IBR of cars based on an approximate analytic reflection flow computation on curved windows. Our method has three components: a refinement step of reconstructed car geometry guided by semantic labels, that provides an initial approximation for missing window surfaces and a smooth completed car hull; an efficient reflection flow computation using an ellipsoid approximation of the curved car windows that runs in real-time in a shader and a reflection/background layer synthesis solution. These components allow plausible rendering of reflective, semi-transparent windows in free viewpoint navigation. We show results on several scenes casually captured with a single consumer-level camera, demonstrating plausible car renderings with significant improvement in visual quality over previous methods.
Machine learning for scene 3D reconstruction using a single image

Image-based scene 3D reconstruction is one of the key tasks for many machine vision applications such as scene understanding, object pose estimation, autonomous navigation. A set of reliable and accurate methods for multi-view scene 3D reconstruction has been developed last decades. But a significant drawback of such 3D reconstruction technique is the need for acquiring a large number of images in the processed sequence to obtain an acceptable 3D scene representation. Recently modern convolutional neural network (CNN) models achieve the best quality for object recognition, image segmentation, image translation and some other challenging computer vision problems. The paper proposes a convolutional neural network architecture and a technique for training data preparation which provide a prediction of voxel model of a 3D scene with several objects. For CNN training and evaluation a special dataset was collected and annotated. It contains image sequences of several scenes and corresponding depth images and 3D models of these scenes. The image sequence serves as the primary data used for further scene 3D reconstruction by SfM technique. Structure from Motion processing results in surface 3D models of all objects in the scene and camera positions and orientation for every image in a sequence. Then surface 3D model is transformed into voxel 3D model and segmented into separate objects. Conditional generative adversarial network architecture was developed for 3D reconstruction by single image. Its generative part translates an input color image into an output voxel model. The discriminative part distinguishes the correct output (close to real voxel model) from false output (wrong output voxel model). Both parts are trained simultaneously on the prepared dataset. Evaluation on the testing part of the prepared dataset has demonstrated the ability of prediction 3D models of previously unobserved complex scenes containing several objects. The proposed neural network architecture provides high generalization ability and improved resolution of predicted voxel 3D models.
Email Spam Classification Using Gated Recurrent Unit and Long Short-Term Memory

High numbers of spam emails have led to an increase in email triage, causing losses amounting to USD 355 million per year.One way to reduce this loss is to classify spam email into categories including fraud or promotions made by unwanted parties.The initial development of spam email classification was based on simple methods such as word filters.Now, more complex methods have emerged such as sentence modeling using machine learning.Some of the most well-known methods for dealing with the problem of text classification are networks with Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU).This study focuses on the classification of spam emails, so both the LTSM and GRU methods were used.The results of this study show that, under the scenario without dropout, the LSTM and GRU obtained the same accuracy value of 0.990183, superior to XGBoost, the base model.Meanwhile, in the dropout scenario, LSTM outperformed GRU and XGboost with each obtaining an accuracy of 98.60%, 98.58% and 98.52%, respectively.The GRU recall score was better than that of LSTM and XGBoost in the scenario with dropouts, each obtaining values of 98.98%, 98.92% and 98.15% respectively.In the scenario without dropouts, LSTM was superior to GRU and XGBoost, with each obtaining values of 98.39%, 98.39% and 98.15% respectively.
Augmented Intelligence with Ontology of Semantic Objects

The most recent AI winter began to thaw several years ago with the advent of deep learning neural networks. Headline grabbing successes propel renewed interests and investments in AI research. Rather than trying to imitate human intelligence and behaviors or excelling in a narrowly defined task, researchers are aiming for general AI. In this paper, we take a different approach, one that aims to multiply human cognitive power through augmented intelligence. This is achieved through ontological reasoning using semantic objects as the atomic level of knowledge representation of real-world concepts and phenomena. Through preliminary experiments, we validate the applicability and efficacy of our approach.