Reviewerly/exHarmony huggingface dataset:
A Nonlinear System State Estimation Method Based on Adaptive Fusion of Multiple Kernel Functions

With the development of the industry, the physical model of controlled object tends to be complicated and unknown. It is particularly important to estimate the state variables of a nonlinear system when the model is unknown. This paper proposes a state estimation method based on adaptive fusion of multiple kernel functions to improve the accuracy of system state estimation. First, a dynamic neural network is used to build the system state model, where the kernel function node is constructed by a weighted linear combination of multiple local kernel functions and global kernel functions. Then, the state of the system and the weight of the kernel functions are put together to form an augmented state vector, which can be estimated in real time by using high-degree cubature Kalman filter. The high-degree cubature Kalman filter performs adaptive fusion of the kernel function weights according to specific samples, which makes the neural network function approximate the real system model, and the state estimate follows the real value. Finally, the simulation results verify the feasibility and effectiveness of the proposed algorithm.
An Automatic and Unified Consistency Verification Rule and Method of SG-CIM Model

The current enterprise-level data model-based knowledge resource classification, intelligent search, and cross-domain knowledge integration and representation are still in their infancy. At present, the biggest constraint on the use of existing data models is lack of unified consistency analysis and verification rules. Now based on the data of the existing SG-CIM model of the State Grid Internet Department, an automatic and unified consistency verification rule and method is proposed. It mainly verifies the consistency between logical model and physical model by calculating the similarity both on grammatical level and semantic level, which has made up for the shortcomings of model design, and strengthened the control of data model consistency verification and application implementation. Finally, it can be used to fully support the company's various model analysis and recommendation applications based on the unified verification rule.
Sensitivity – Local index to control chaoticity or gradient globally –

Here, we introduce a fully local index named "sensitivity" for each neuron to control chaoticity or gradient globally in a neural network (NN). We also propose a learning method to adjust it named "sensitivity adjustment learning (SAL)". The index is the gradient magnitude of its output with respect to its inputs. By adjusting its time average to 1.0 in each neuron, information transmission in the neuron changes to be moderate without shrinking or expanding for both forward and backward computations. That results in moderate information transmission through a layer of neurons when the weights and inputs are random. Therefore, SAL can control the chaoticity of the network dynamics in a recurrent NN (RNN). It can also solve the vanishing gradient problem in error backpropagation (BP) learning in a deep feedforward NN or an RNN. We demonstrate that when applying SAL to an RNN with small and random initial weights, log-sensitivity, which is the logarithm of RMS (root mean square) sensitivity over all the neurons, is equivalent to the maximum Lyapunov exponent until it reaches 0.0. We also show that SAL works with BP or BPTT (BP through time) to avoid the vanishing gradient problem in a 300-layer NN or an RNN that learns a problem with a lag of 300 steps between the first input and the output. Compared with manually fine-tuning the spectral radius of the weight matrix before learning, SAL's continuous nonlinear learning nature prevents loss of sensitivities during learning, resulting in a significant improvement in learning performance.
On the algebraic immunity of direct sum constructions

In this paper, we study sufficient conditions to improve the lower bound on the algebraic immunity of a direct sum of Boolean functions. We exhibit three properties on the component functions such that satisfying one of them is sufficient to ensure that the algebraic immunity of their direct sum exceeds the maximum of their algebraic immunities. These properties can be checked while computing the algebraic immunity and they allow to determine better the security provided by functions central in different cryptographic constructions such as stream ciphers, pseudorandom generators, and weak pseudorandom functions. We provide examples for each property and determine the exact algebraic immunity of candidate constructions.
“Calculating biotite formula from electron microprobe analysis data using a machine learning method based on principal components regression” by Li et al. (Lithos 356–357 (2020) 105371) - A discussion

The Rio Ceará-Mirim magmatism produced Mesozoic giant arcuate dike swarms during the initial stage of the West Gondwana breakup near the Equatorial Atlantic Ocean (NE Brazil), where they assemble tholeiites and subordinate intermediate rocks (SiO2 > 57 wt%). A multidata approach based on petrography, mineral chemistry, whole-rock geochemistry, and Sr-Nd-Pb isotopes enables the discrimination of four geochemical groups (two high-Ti and two low-Ti). High-Ti groups are composed of (1) basalts and basaltic andesites with TiO2 generally higher than 2.0 wt%, MgO > ~3 wt% (named the HT group) and rich in incompatible elements and (2) (trachy)andesites and trachytes with lower TiO2 (<2.5 wt%; Mg < 3 wt%) interpreted as fractionated magmas from the HT group, therefore named evolved HT. Both these groups have similar 87Sr/86Sr(127) (~0.706) and εNd(127) (−2.90 to −2.56) and moderately radiogenic 206Pb/204Pb(m) ratios between 18.66 and 18.21. Conversely, low-Ti tholeiites (TiO2 < 2 wt%) are (3) LT1 composed of evolved basaltic andesites, with ~Ti/Zr < 70, MgO <6 wt%, and Al2O3 > 15 wt%; and (4) LT2 composed of subalkaline (to transitional) basalts and basaltic andesites but ~Ti/Zr >70, and MgO > 6 wt%. These two low-Ti groups are, respectively, less radiogenic (LT1 εNd(127) -4.65 to −4.40) and slightly more radiogenic (LT2 εNd(127) -1.44 to −1.08) in Nd relative to the broad HT group. The geochemical groups present different degrees of enrichment in large ion lithophile and light rare-earth elements coupled with depletion in high-field-strength elements (NbTa) combined with isotopic (Sr-Nd-Pb) signatures compatible with enriched mantle sources. We showed that the EMI (Gough-like) isotope compositions of Rio Ceará-Mirim magmatism may be explained by mixing DMM with enriched SCLM-derived melts plus minor crustal assimilation or by the involvement of OIBs derived from mantle plumes of the South Atlantic area mixed with lithospheric sources. In a geodynamical perspective, given the narrow similarity of HT Rio Ceará-Mirim isotope compositions with EMI-Gough type and high-Ti magmas of Transminas swarm, a genetic connection between Equatorial Atlantic and Paraná-Etendeka provinces must be considered.
Making the most of small Software Engineering datasets with modern machine learning

This paper provides a starting point for Software Engineering (SE) researchers and practitioners faced with the problem of training machine learning models on small datasets. Due to the high costs associated with labeling data, in Software Engineering, there exist many small (< 5,000 samples) and medium-sized (<100,000 samples) datasets. While deep learning has set the state of the art in many machine learning tasks, it is only recently that it has proven effective on small-sized datasets, primarily thanks to pre-training, a semi-supervised learning technique that leverages abundant unlabelled data alongside scarce labelled data. In this work, we evaluate pre-trained Transformer models on a selection of 13 smaller datasets from the SE literature, covering both, source code and natural language. Our results suggest that pre-trained Transformers are competitive and in some cases superior to previous models, especially for tasks involving natural language; whereas for source code tasks, in particular for very small datasets, traditional machine learning methods often has the edge. In addition, we experiment with several techniques that ought to aid training on small datasets, including active learning, data augmentation, soft labels, self-training and intermediate-task fine-tuning, and issue recommendations on when they are effective. We also release all the data, scripts, and most importantly pre-trained models for the community to reuse on their own datasets.
Living without Beth and Craig: Definitions and Interpolants in the Guarded and Two-Variable Fragments

In logics with the Craig interpolation property (CIP) the existence of an interpolant for an implication follows from the validity of the implication. In logics with the projective Beth definability property (PBDP), the existence of an explicit definition of a relation follows from the validity of a formula expressing its implicit definability. The two-variable fragment, FO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and the guarded fragment, GF, of first-order logic both fail to have the CIP and the PBDP. We show that nevertheless in both fragments the existence of interpolants and explicit definitions is decidable. In GF, both problems are 3EXPTIME-complete in general, and 2EXPTIME-complete if the arity of relation symbols is bounded by a constant c ≥ 3. In FO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , we prove a CON2EXPTIME upper bound and a 2EXPTIME lower bound for both problems. Thus, both for GF and FO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> existence of interpolants and explicit definitions are decidable but harder than validity (in case of FO <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> under standard complexity assumptions).
Object Detection in Hazy Environment Enhanced by Preprocessing Image Dataset with Synthetic Haze

Object detection in hazy environment has always been a difficult task in the autonomous driving field. Huge breakthrough is hard to achieve due to the lack of large-scale hazy image dataset with detailed labels. In this work, we present a simple and flexible algorithm to generate synthetic haze to MS COCO training dataset, which aims to enhance the performance of object detection in haze when taking the new synthesized hazy images as training dataset. Our algorithm is inspired by the Multiple Linear Regression Dark Channel Prior (MLDCP), and we obtain a general model that can add synthetic haze to haze-free images by implementing Stochastic Gradient Descent (SGD) to the reversed MLDCP model. We further evaluate the mean average precision (mAP) of Mask R-CNN when we train the network with the Hazy-COCO training dataset and preprocessing test hazy dataset with existing single image dehazing algorithms.
Trade Distance and Price Model for Electric Vehicle Charging using Blockchain-based Technology

Due to the demand for Electric Vehicles (EV) has been continuously increased nowadays, electric vehicle users must make their decision on the selection of charging stations (CS) by using both price and distance for effective performance. This article presents optimization for model trading using Blockchain (BC) which has decentralization, security and trustworthiness. According to the result, the price, distance, as well as charging waiting time and driving time to CS are the important factors for EV because all factors affect to make decision. The lowest price and the shortest distance are the best to make decisions to select CS and if considering both price and distance factor, the total cost is the most important for EV. However, to achieve maximum efficiency, the researcher pays attention to the time factor for the decision on the selection of charging stations. Therefore, in order to make decision to select CS, EV need to focus on price, distance and times because these factors cause costs. It will select the appropriate route and costs of CS by BC because it is secure and reliable that makes the best of efficiency.
Understanding, Promoting, and Designing for Sustainable Appropriation of Technologies by Grassroots Communities. Towards a new wave of technological activism

Technological development and adoption are characterized by historical waves, reflecting both technical advancements and social transformations in mutually constitutive relation. Today, we are in the middle of another of these waves characterized, for instance, by the widespread focus on AI and other emerging technologies. In this context, activists and designers are constructively appropriating these emerging technologies, thus showing how socio-technical aspects of technological design, development, and implementation, contribute to ongoing transformations of power relations, life conditions, and our collective future.
Repopulating Street Scenes

We present a framework for automatically reconfiguring images of street scenes by populating, depopulating, or repopulating them with objects such as pedestrians or vehicles. Applications of this method include anonymizing images to enhance privacy, generating data augmentations for perception tasks like autonomous driving, and composing scenes to achieve a certain ambiance, such as empty streets in the early morning. At a technical level, our work has three primary contributions: (1) a method for clearing images of objects, (2) a method for estimating sun direction from a single image, and (3) a way to compose objects in scenes that respects scene geometry and illumination. Each component is learned from data with minimal ground truth annotations, by making creative use of large-numbers of short image bursts of street scenes. We demonstrate convincing results on a range of street scenes and illustrate potential applications.
Reactions to Abusive Supervision: Neutralization and IS Misuse

The relationship between abusive supervision and employee workplace deviance has received much attention. Conceptualizing information systems (IS) misuse as a form of workplace deviance, the literature on IS security lacks a theoretical and empirical investigation of the relationship between abusive supervision and employee IS misuse. Drawing on retaliation, displaced aggression, and neutralization, we develop and empirically test a research model of the relationships among abusive supervision, neutralization, and two types of employees' IS misuse. Results of a two-stage survey of 203 responses showed that when individuals perceived abusive supervision, they were likely to invoke the metaphor of the ledger as a neutralization technique to rationalize their engagement in IS misuse. Our results also reveal that employees' reactions to abusive supervision could be influenced by their tenure with supervisor. These findings contribute to the IS security field and provide important guidance for learning how leaders influence employees' IS security behaviors.
Optimal Axes for Data Value Estimation in Star Coordinates and Radial Axes Plots

Abstract Radial axes plots are projection methods that represent high‐dimensional data samples as points on a two‐dimensional plane. These techniques define mappings through a set of axis vectors, each associated with a data variable, which users can manipulate interactively to create different plots and analyze data from multiple points of view. However, updating the direction and length of an axis vector is far from trivial. Users must consider the data analysis task, domain knowledge, the directions in which values should increase, the relative importance of each variable, or the correlations between variables, among other factors. Another issue is the difficulty to approximate high‐dimensional data values in the two‐dimensional visualizations, which can hamper searching for data with particular characteristics, analyzing the most common data values in clusters, inspecting outliers, etc. In this paper we present and analyze several optimization approaches for enhancing radial axes plots regarding their ability to represent high‐dimensional data values. The techniques can be used not only to approximate data values with greater accuracy, but also to guide users when updating axis vectors or extending visualizations with new variables, since they can reveal poor choices of axis vectors. The optimal axes can also be included in nonlinear plots. In particular, we show how they can be used within RadViz to assess the quality of a variable ordering. The in‐depth analysis carried out is useful for visualization designers developing radial axes techniques, or planning to incorporate axes into other visualization methods.
Distant supervised relation extraction with position feature attention and selective bag attention

Distant supervision greatly reduces manual consumption by automatically labeling data. The relation extraction methods under distant supervision divide sentences with the same entity pair into a bag, and perform training and testing on these sentence bags. The existing distant supervised relation extraction methods ignore two facts. First, there are many sentences where the target entity pairs appear multiple times. Second, the noise between sentence bags is different, and the sentences of some bags are even all mislabeled. To solve these two problems, we propose a novel relation extraction method with position feature attention and selective bag attention. The position feature attention is employed to obtain the weighted sentence representation with different position features by calculating all position combinations of the target entity pair. A bag with large noise and a bag with small noise are selected through the selective bag attention mechanism to form a bag pair, and training is performed at the level of the bag pair, which denoises at the bag level and at the same time balances the noise between different bag pairs. The experimental results show that our method is effective and outperforms several competitive baseline methods.
Myco-kit

This paper explores the design of tools and pedagogies for bio-design understood as a space for children to create and challenge the nature-culture dualism that underpins the environmental crisis. By blurring the boundaries between the learning practices of making and growing, bio-design allows learners to imagine what it means to produce artifacts at the frontier of natural and artificial. We propose the concept of interspecies creative learning to advance in the understanding of learning experiences that engage humans and other living systems in a joint space of creative discovery. Using design-based research, we share the work-in-progress design of Myco-kit, a bio-design toolkit to support interspecies creative learning and advance in the understanding of this concept and its implications for ecologically conscious education.
Autonomous Driving Solution Based on Traffic Sign Detection

One of the main goals of the automotive industry is to develop a fully autonomous vehicle. Among the tasks which the fully autonomous vehicle should perform is traffic sign (TS) detection, which has a great impact on the behavior of the autonomous vehicle on the road. In this article, a solution for controlling a vehicle based on detected TSs is presented. For the TS detection, the possibility of using state-of-the-art algorithms based on machine learning, specifically you only look once (YOLOv3) and YOLOv4, was examined. The part of the solution for controlling the vehicle is developed using robot operating system (ROS), where the complete solution is tested in the car learning to act (CARLA) open-source simulator. TS detectors are evaluated on a real-world dataset and synthetic dataset, after which the proposed solution is tested on different scenarios created in the CARLA simulator in different weather conditions (sunny, rainy, foggy, night). The results show that the proposed solution achieved the highest correct actions rate of 95.83% when using the TS detector based on YOLOv4 trained on both real and synthetic data.
Applied Algorithmic Machine Learning for Intelligent Project Prediction: Towards an AI Framework of Project Success

A growing number of emerging studies have been undertaken to examine the mediating dynamics between intelligent agents, activities, and cost within allocated budgets, in order to predict the outcomes of complex projects in dint of their significant uncertain nature in achieving a successful outcome. For example, prior studies have used machine learning models to calculate and perform predictions. Artificial neural networks are the most frequently used machine learning model with support vector machine, and genetic algorithm and decision trees are sometimes used in several related studies. Furthermore, most machine learning algorithms used in prior studies generally assume that inputs and outputs are independent of each other, which suggests that a project's success is expected to be independent of other projects. As the datasets used to train in prior studies often contain projects from different clients across industries, this theoretical assumption remains tenable. However, in practice projects are often interrelated across several different dimensions, for example through distributed overlapping teams. An ongoing ethnographic study at a leading project management artificial intelligence consultancy, referred to in this research as Company Alpha, suggests that projects within the same portfolio frequently share overlapping characteristics. To capture the emergent inter-project relationships, this study aims to compare two specific types of artificial neural network prediction performances; (i) multilayer perceptron and; (ii) recurrent neural networks. The multilayer perceptron has been found to be one of the most widely used artificial neural networks in the project management literature, and recurrent networks are distinguished by the memory they take from prior inputs to influence input and output. Through this comparison, this research will examine whether recurrent neural networks can capture the potential inter-project relationship towards achieving improved performance in contrast to multilayer perceptron. Our empirical investigation using ethnographic practice-based exploration at Company Alpha will contribute to project management knowledge and support developing an intelligent project prediction AI framework with future applications for project practice.
Deep Image Comparator: Learning to Visualize Editorial Change

We present a novel architecture for comparing a pair of images to identify image regions that have been subjected to editorial manipulation.We first describe a robust near-duplicate search, for matching a potentially manipulated image circulating online to an image within a trusted database of originals.We then describe a novel architecture for comparing that image pair, to localize regions that have been manipulated to differ from the retrieved original.The localization ignores discrepancies due to benign image transformations that commonly occur during online redistribution.These include artifacts due to noise and recompression degradation, as well as out-of-place transformations due to image padding, warping, and changes in size and shape.Robustness towards out-of-place transformations is achieved via the end-to-end training of a differentiable warping module within the comparator architecture.We demonstrate effective retrieval and comparison of benign transformed and manipulated images, over a dataset of millions of photographs.