Reviewerly/exHarmony huggingface dataset:
The case for classes and instances - a response to representing instances: the case for reengineering conceptual modelling grammars

In “Representing instances: The case for reengineering conceptual modelling grammars”, Lukyanenko et al. (2019) argue that conceptual modelling has been biased towards a focus on knowledge about general phenomena (classes) rather than about specific instances. While we agree that more attention needs to be paid to instances, we critically reflect on their underlying assumptions about instances and classes. Lukyanenko et al. (2019) assume that instances are mainly material things, and also assume that class-based modelling typically requires that class definitions include comprehensive attribute structures that are expected to be stable over time. Based on these assumptions, they conclude that classes are not needed for modelling instances. As an alternative to these assumptions, we suggest that instances can be viewed as language constructs, i.e., as objects that may be anything that is uniquely referred to and identified in human communication. Based on this assumption, we introduce an identity-oriented view of classes, implying that classes are required for modelling objects (instances). We agree with Lukyanenko et al. (2019) that a reengineering of conceptual modelling grammars is required. This reengineering would benefit from approaches such as an identity-oriented view of classes and a class-instance modelling grammar.
AI-based Safety Analysis for Collaborative Mobile Robots

This paper presents implementation details of AI methods used for safety in human robot collaborative scenarios, based on fuzzy logic and state-of-the-art deep learning-based perception. For semantic representation of the environment, a scene graph encodes the environment surrounding the robots with information from camera, which is then fed to risk management system for safety analysis purpose. Transfer learning effects have been observed when starting training based on weights pre-trained on Image Net and COCO data-sets. A fuzzy logic solution for risk analysis, evaluation and mitigation has been compared to a neuro-fuzzy approach. Experiments have been performed in a physically realistic 3D simulation of a warehouse environment to evaluate which configuration presents the best performance for robotic perception and risk mitigation.
Avoid Phishing Traps

The Dickinson College User Services team has been working diligently to educate members of the campus community on ways to identify and avoid phishing traps. This paper and presentation will highlight our steps in the fight against phishing. We have used annual phishing quizzes with prizes, created a phishing alerts page, hosted Cyber Security Awareness Month events, had an “Avoid Phishing Alerts” presentation, added a warning banner to external incoming emails and started implementing Microsoft Multi-Factor Authentication (MFA) utilizing, primarily, the Microsoft Authenticator app. Success in this initiative involves various members from our entire division. These efforts and techniques have been evolving for years.
Hierarchical Overlap Graph

Given a set of finite words, the Overlap Graph (OG) is a complete weighted digraph where each word is a node and where the weight of an arc equals the length of the longest overlap of one word onto the other (Overlap is an asymmetric notion). The OG serves to assemble DNA fragments or to compute shortest superstrings which are a compressed representation of the input. The OG requires a space is quadratic in the number of words, which limits its scalability. The Hierarchical Overlap Graph (HOG) is an alternative graph that also encodes all maximal overlaps, but uses a space that is linear in the sum of the lengths of the input words. We propose the first algorithm to build the HOG in linear space for words of equal length.
Bisimulation and Coinduction Enhancements: A Historical Perspective

Abstract Bisimulation is an instance of coinduction. Both bisimulation and coinduction are today widely used, in many areas of Computer Science, as well as outside Computer Science. Over, roughly, the last 25 years, enhancements of the principles and methods related to bisimulation and coinduction (i.e., techniques to make proofs shorter and simpler) have become a research topic on its own. In the paper the origins and the developments of the topic are reviewed.
Object Detection and Analysis of Human Body Postures Based on TensorFlow

Human body posture recognition has been an important concern of research in many fields. In this field, a human pose estimation algorithm called OpenPose has been more widely used. But its efficiency is very low. We used deep learning methods based on TensorFlow to recognize human body postures. And we applied it to the judgment of the teacher's teaching states. In order to choose the algorithm that works best for our scenario, we designed eight sets of experimental schemes through combining the classification model and the detection algorithm. Then we used these groups of schemes to detect and classify the teacher's teaching states, including standing, sitting, etc. At last, we analyzed the experimental results in depth and selected the most suitable algorithm for our scenario.
A Temporal Attentive Approach for Video-Based Pedestrian Attribute Recognition

In this paper, we first tackle the problem of pedestrian attribute recognition by video-based approach. The challenge mainly lies in spatial and temporal modeling and how to integrating them for effective and dynamic pedestrian representation. To solve this problem, a novel multi-task model based on the conventional neural network and temporal attention strategy is proposed. Since publicly available dataset is rare, two new large-scale video datasets with expanded attribute definition are presented, on which the effectiveness of both video-based pedestrian attribute recognition methods and the proposed new network architecture is well demonstrated. The two datasets are published on http://irip.buaa.edu.cn/mars_duke_attributes/index.html .
Thermal infrared pedestrian tracking via fusion of features in driving assistance system of intelligent vehicles

Driving in nighttime or poor illumination is much more dangerous than in daytime or rich illumination conditions so it is important to develop driver assistance systems to support safe driving in nighttime. This paper presents a pedestrian tracking algorithm using a thermal infrared camera. The proposed algorithm first computes response maps with normalized grayscale and Felzenszwalb's histogram of oriented gradient in the framework of correlation filtering. Subsequently, according to response maps, this paper proposes an approach to estimate weight ratios in fusion. The fused response map is ultimately used to complete target detection in the framework of correlation filtering. In order to test the performance of the proposed tracker, extensive comparison and tests have been performed using open-source data sets. By testing through 10 different challenges in image processing and tracking, it is evident that the performance of proposed algorithm is superior to seven representative tracking algorithms with a quite promising real-time performance.
Multimodal and Multi-Output Deep Learning Architectures for the Automatic Assessment of Voice Quality Using the GRB Scale

This article addresses the automatic assessment of voice quality according to the GRB scale, based on the use of a variety of deep learning architectures for prediction purposes. The proposed architectures are multimodal, because they employ multiples sources of information; and also multi-output, because they simultaneously predict all the traits of the GRB scale. A feature engineering approach is followed, based on the use of deep neural networks and a set of well-established features such as MFCC, perturbation and complexity characteristics. Likewise, a representation learning is considered, using convolutional neural networks feed on modulation spectra extracted from voices. Finally, diverse loss functions are also investigated, including two surrogate ordinal classification, a conventional weighed categorical cross-entropy, and a mean square error function. Experiments are carried out in a dataset containing registers of the sustained phonation of three vowels. The best deep learning architecture provides a relative performance improvement of 6.25% for G, 14.1% for R and 18.1% for B, in comparison with recently published results using the same dataset.
Optimal filtering of multidimensional random fields generated by autoregressions with multiple roots of characteristic equations

The use of mathematical models allows to compare the theoretical expressions and simulation results. Autoregressive random fields can be used for description of the images, however, such models have pronounced anisotropy, and the simulated images are too sharp. The elimination of this drawback is possible through the use of models with multiple roots of characteristic equations. The analysis shows that using models with multiple roots in filtering images with smoothly varying brightness provides smaller errors than the use of autoregressive random fields. However, studies of the dependences of filtering efficiency on various model parameters and signal-to-noise ratios for multidimensional autoregressive random fields were almost not carried out. The article discusses the solution of the problem of optimal filtering of images based on models with multiple roots of characteristic equations. Theoretical dependences of the relative variance of the filtering error on the dimension of random fields are obtained. Furthermore, it was presented some results of filtering real images by such model in comparison with autoregressive model.
The trace of 2-primitive elements of finite fields

Let $q$ be a prime power and $n, r$ integers such that $r\,|\, q^n-1$. An element of $\mathbb F _{q^n}$ of multiplicative order $(q^n-1)/r$ is called <em>$r$-primitive</em>. For any odd prime power $q$, we show that there exists a $2$-primitive element of
RGB-D Image-Based Object Detection: From Traditional Methods to Deep Learning Techniques

Object detection from RGB images is a long-standing problem in image processing and computer vision. It has applications in various domains including robotics, surveillance, human–computer interaction, and medical diagnosis. With the availability of low- cost 3D scanners, a large number of RGB-D object detection approaches have been proposed in the past years. This chapter provides a comprehensive survey of the recent developments in this field. We structure the chapter into two parts; the focus of the first part is on techniques that are based on hand-crafted features combined with machine learning algorithms. The focus of the second part is on the more recent work, which is based on deep learning. Deep learning techniques, coupled with the availability of large training datasets, have now revolutionized the field of computer vision, including RGB-D object detection, achieving an unprecedented level of performance. We survey the key contributions, summarize the most commonly used pipelines, discuss their benefits and limitations and highlight some important directions for futureObject detection researchDeep learning.
Probabilistic modeling and analysis of sequential cyber‐attacks

Security is one of the major challenges for promoting the computer industry. Existing models for assessing security have mostly assumed that different hazards causing the security breach are independent of each other. Dependencies however can exist among different hazardous actions and they may affect the system security attribute greatly. This paper advances the state of the art in quantitative security risk assessment by modeling one such dependency, where multiple sequence‐dependent hazardous actions are performed to launch a successful security cyber‐attack. Continuous‐time Markov chain and semi‐Markov process–based methods are proposed to estimate the occurrence probability of a security risk for systems undergoing the sequential cyber‐attacks. While the CTMC method is limited to the exponential state transition time, the proposed semi‐Markov process–based approach is applicable to analyzing attacks with any arbitrary types of transition time distributions. Both methods are illustrated using case studies where Trojan attacks in the banking application are modeled and analyzed.
Mitigating Overfitting on Financial Datasets with Generative Adversarial Networks

Overfitting is an inevitable phenomenon when applying deep learning techniques to financial data, given the relative scarcity of available historical data and the ever-changing nature of financial series. This seemingly unavoidable pitfall has heavily impaired the application of many machine learning techniques, such as deep or reinforcement learning, to financial settings. Data augmentation can be an option to circumvent this, specifically generative adversarial networks (GANs). GANs are a type of neural network architecture that focuses on sample generation. Through adversarial training, the GAN can implicitly learn the underlying structure inherent to the dynamics of financial series and acquire the capacity to generate scenarios that share many similarities to those seen in the historic time series. In this article, the authors propose a data augmentation technique using the Wasserstein GAN with gradient penalty and show how training deep models on synthetic data mitigates overfitting, improving their performance on test data when compared to models trained solely on real data. <b>TOPICS:</b>Statistical methods, simulations, big data/machine learning <b>Key Findings</b> • Generative adversarial networks can capture the complex dynamics that govern many financial assets and produce realistic synthetic scenarios based on historic data. • Synthetic financial scenarios can be used to enlarge training datasets in order to improve the accuracy and robustness of other deep learning models. • Synthetic financial series may also be applied in other tasks such as model backtesting, risk analysis, or option pricing.
MIFM: Multi-Granularity Information Fusion Model for Chinese Named Entity Recognition

Chinese Named Entity Recognition (Chinese NER) is an important task in Chinese natural language processing field. It is difficult to identify the boundary of entities because Chinese texts lack natural delimiters to separate words. For this task, two major methods can be distinguished by the model inputs, i.e., word-based model and character-based model. However, the word-based model relies on the result of the Chinese Word Segmentation (CWS), and the character-based model cannot utilize enough word-level information. In this paper, we propose a multi-granularity information fusion model (MIFM) for the Chinese NER task. We introduce a novel multi-granularity embedding layer that utilizes the attention mechanism and an information gate to fuse the character and word level features. The results of this embedding method are dynamic and data-specific because they are calculated based on different contexts. Moreover, we apply the reverse stacked LSTM layer to gain deep semantic information for a sequence. Experiments on two benchmark datasets, MSRA and ResumeNER, show that our approach can effectively improve the performance of Chinese NER.
Reinforced active learning for image segmentation

Learning-based approaches for semantic segmentation have two inherent challenges. First, acquiring pixel-wise labels is expensive and time-consuming. Second, realistic segmentation datasets are highly unbalanced: some categories are much more abundant than others, biasing the performance to the most represented ones. In this paper, we are interested in focusing human labelling effort on a small subset of a larger pool of data, minimizing this effort while maximizing performance of a segmentation model on a hold-out set. We present a new active learning strategy for semantic segmentation based on deep reinforcement learning (RL). An agent learns a policy to select a subset of small informative image regions -- opposed to entire images -- to be labeled, from a pool of unlabeled data. The region selection decision is made based on predictions and uncertainties of the segmentation model being trained. Our method proposes a new modification of the deep Q-network (DQN) formulation for active learning, adapting it to the large-scale nature of semantic segmentation problems. We test the proof of concept in CamVid and provide results in the large-scale dataset Cityscapes. On Cityscapes, our deep RL region-based DQN approach requires roughly 30% less additional labeled data than our most competitive baseline to reach the same performance. Moreover, we find that our method asks for more labels of under-represented categories compared to the baselines, improving their performance and helping to mitigate class imbalance.
Facebook MySQL Performance vs MySQL Performance

MySQL is an open-source, free and relational database management system that is growing rapidly over clouds. All hosting servers support the MySQL database. However, it has its pros and cons, strengths and weaknesses, but this research focuses on its performance speed, a weak point of MySQL and describes how to deal with this issue and how to speed up a MySQL table which has more than a million records. Many organizations and companies are using MySQL database to store their data online whether it is a database or website. In addition, this paper presents how Facebook retrieve its big data from MySQL database with high performance speed and its reason.
Unsupervised Deep Learning via Affinity Diffusion

Convolutional neural networks (CNNs) have achieved unprecedented success in a variety of computer vision tasks. However, they usually rely on supervised model learning with the need for massive labelled training data, limiting dramatically their usability and deployability in real-world scenarios without any labelling budget. In this work, we introduce a general-purpose unsupervised deep learning approach to deriving discriminative feature representations. It is based on self-discovering semantically consistent groups of unlabelled training samples with the same class concepts through a progressive affinity diffusion process. Extensive experiments on object image classification and clustering show the performance superiority of the proposed method over the state-of-the-art unsupervised learning models using six common image recognition benchmarks including MNIST, SVHN, STL10, CIFAR10, CIFAR100 and ImageNet.
A Study on Utilization of Spatial Information in Heterogeneous System Based on Apache NiFi

Recent interest in smart space such as smart city and digital twin has increased, and various types of spatial information are being generated, and studies on the utilization of spatial information are actively underway. Smart space is presented by Gartner as the top 10 strategic technology trends in 2019. However, in order to deal with different types of spatial information, separate software for each spatial information format is required, which degrades the performance of a single computer and causes a large cost. In this paper, we propose a method of using a processor library which can easily access various spatial information producers (Geo-IoT sensor, SNS information, unstructured spatial information, etc). The use of the processor library presented in this paper is based on the Apache NiFi platform. The Apache NiFi Platform is an Open Source that provides powerful ETL functionality based on graphs and has the advantage of easy accessibility for general users.
Software Development Artifacts in Large Agile Organizations: A Comparison of Scaling Agile Methods

Agile frameworks or methods such as Scrum, Kanban and Extreme Programming are widely adopted and used by small teams within large organizations and start-ups. Established Enterprises, banks, and technology giants are aiming to introduce and implement agile methods that helps organize process in high complexity organizations. Main challenges due to higher complexity relates but are not limited to multiple value streams development pipelines orchestration, communications among many distributed teams, inter team dependencies management, information flow between teams. It can be said that large organizations need to deliver, adapt or introduce new methods to manage development organizations and deliver needed artifacts. Purpose—This paper aims to compare possible agile frameworks for scaling development organizations working in an agile culture, and their outcomes materialized as artifacts.
“Less is more”

Purpose This work studies automated user classification on Twitter in the public health domain, a task that is essential to many public health-related research works on social media but has not been addressed. The purpose of this paper is to obtain empirical knowledge on how to optimise the classifier performance on this task. Design/methodology/approach A sample of 3,100 Twitter users who tweeted about different health conditions were manually coded into six most common stakeholders. The authors propose new, simple features extracted from the short Twitter profiles of these users, and compare a large set of classification models (including state-of-the-art) that use more complex features and with different algorithms on this data set. Findings The authors show that user classification in the public health domain is a very challenging task, as the best result the authors can obtain on this data set is only 59 per cent in terms of F1 score. Compared to state-of-the-art, the methods can obtain significantly better (10 percentage points in F1 on a “best-against-best” basis) results when using only a small set of 40 features extracted from the short Twitter user profile texts. Originality/value The work is the first to study the different types of users that engage in health-related communication on social media, applicable to a broad range of health conditions rather than specific ones studied in the previous work. The methods are implemented as open source tools, and together with data, are the first of this kind. The authors believe these will encourage future research to further improve this important task.
Control power of high-dimensional controlled teleportation

To quantitatively analyze the control power of arbitrary $d$-dimensional controlled teleportation (CT), this paper investigates channels of entangled high-dimensional three-qudit standard Greenberger-Horne-Zeilinger (GHZ) and GHZ-type states. The upper bound on the controller's power is established in the $d$-dimensional CT scheme. We identify a trade-off between the control power and the capacity of the quantum channels. The control power can be enhanced by reducing the dimension of the teleported information in a suitably high-dimensional quantum channel, or by increasing the dimension of the qudit held by the controller. Under controlled teleporting of a single qubit in a perfect CT scheme, the control power is maximized in the three-dimensional GHZ-type state channel. This maximum control power exceeds the highest predicted value in previous works and the upper bounds of control power in a two-dimensional quantum channel. Our studies provide a feasible method for achieving higher control power, and it may provide approaches to define high-dimensional entanglement of multibody systems.