Reviewerly/exHarmony huggingface dataset:
Compensation for Time Delays in the Navigation of Unmanned Aerial Vehicles

This paper presents a new method of compensating for time delays in the navigation of UAVs when the minimum and maximum time delay experienced by the UAV in receiving command signals can be estimated. The aerial vehicles are controlled with a coordinated lateral controller. The operator signals are delayed and a bank of recursive least squares (RLS) filters are used to identify the delay and the target waypoint. Hypothesis testing is implemented to select the filter that most closely matches the delay. Once a filter is selected, the UAV then computes its heading to the estimated target waypoint and self-navigates its way to the waypoint while keeping track of the operator commands.
SOM-Based Analysis of Volcanic Rocks: An Application to Somma–Vesuvius and Campi Flegrei Volcanoes (Italy)

Algorithms based on artificial intelligence (AI) have had a strong development in recent years in different research fields of earth science such as seismology and volcanology. In particular, they have been applied to the study of the volcanic eruptive products of the recent activity of Mount Etna volcano. This work presents an application of the self-organizing map (SOM) neural networks to perform a clustering analysis on petrographic patterns of rocks of Somma–Vesuvius and Campi Flegrei volcanoes, in the Neapolitan area. The goal is to highlight possible affinity between the magmatic reservoirs of these two volcanic complexes. The SOM is known for its ability to cluster data by using intrinsic similarity measures without any previous information about their distribution. Moreover, it allows an easy understandable data visualization by using a two-dimensional map. The SOM has been tested on a geochemical dataset of 271 samples, consisting of 134 samples of Campi Flegrei eruptions (named CF), 24 samples of Somma–Vesuvius effusive eruptions (VF), 73 samples of Somma–Vesuvius explosive eruptions (VX), and finally 40 samples of “foreign” eruptions (ET), included to verify the neural net classification capability. After a pre-processing phase, applied to have a more appropriate data representation as input for the SOM, each sample has been encoded through a vector of 23 features, containing information about major bulk components, trace elements, and Sr isotopic ratio. The resulting SOM identifies three main clusters, and in particular, the foreign patterns (ET) are well separated from the other ones being mainly grouped in a single node. In conclusion, the obtained results suggest the ability of SOM neural network to associate volcanic rock suites on the basis of their geochemical imprint and can be consistent with the hypothesis that there might be a common magma source beneath the whole Neapolitan area.
Logical Explanations for Deep Relational Machines Using Relevance Information

Our interest in this paper is in the construction of symbolic explanations for predictions made by a deep neural network. We will focus attention on deep relational machines (DRMs, first proposed by H. Lodhi). A DRM is a deep network in which the input layer consists of Boolean-valued functions (features) that are defined in terms of relations provided as domain, or background, knowledge. Our DRMs differ from those proposed by Lodhi, which use an Inductive Logic Programming (ILP) engine to first select features (we use random selections from a space of features that satisfies some approximate constraints on logical relevance and non-redundancy). But why do the DRMs predict what they do? One way of answering this is the LIME setting, in which readable proxies for a black-box predictor. The proxies are intended only to model the predictions of the black-box in local regions of the instance-space. But readability alone may not enough: to be understandable, the local models must use relevant concepts in an meaningful manner. We investigate the use of a Bayes-like approach to identify logical proxies for local predictions of a DRM. We show: (a) DRM's with our randomised propositionalization method achieve state-of-the-art predictive performance; (b) Models in first-order logic can approximate the DRM's prediction closely in a small local region; and (c) Expert-provided relevance information can play the role of a prior to distinguish between logical explanations that perform equivalently on prediction alone.
The ASLERD Pyramid of Smartness: A Study on the Stability of Indices and Indicators in Schools

In this paper, we present the outcomes of a participatory evaluation of the smartness of a sample of Rome’s schools conducted over two years. The analysis of the results allowed:
Fast and simple dataset selection for machine learning

Abstract The task of data reduction is discussed and a novel selection approach which allows to control the optimal point distribution of the selected data subset is proposed. The proposed approach utilizes the estimation of probability density functions (pdfs). Due to its structure, the new method is capable of selecting a subset either by approximating the pdf of the original dataset or by approximating an arbitrary, desired target pdf. The new strategy evaluates the estimated pdfs solely on the selected data points, resulting in a simple and efficient algorithm with low computational and memory demand. The performance of the new approach is investigated for two different scenarios. For representative subset selection of a dataset, the new approach is compared to a recently proposed, more complex method and shows comparable results. For the demonstration of the capability of matching a target pdf, a uniform distribution is chosen as an example. Here the new method is compared to strategies for space-filling design of experiments and shows convincing results.
GRESNET: Graph Residuals for Reviving Deep Graph Neural Nets from Suspended Animation

The existing graph neural networks (GNNs) based on the spectral graph convolutional operator have been criticized for its performance degradation, which is especially common for the models with deep architectures. In this paper, we further identify the suspended animation problem with the existing GNNs. Such a problem happens when the model depth reaches the suspended animation limit, and the model will not respond to the training data any more and become not learnable. Analysis about the causes of the suspended animation problem with existing GNNs will be provided in this paper, whereas several other peripheral factors that will impact the problem will be reported as well. To resolve the problem, we introduce the GResNet (Graph Residual Network) framework in this paper, which creates extensively connected highways to involve nodes' raw features or intermediate representations throughout the graph for all the model layers. Different from the other learning settings, the extensive connections in the graph data will render the existing simple residual learning methods fail to work. We prove the effectiveness of the introduced new graph residual terms from the norm preservation perspective, which will help avoid dramatic changes to the node's representations between sequential layers. Detailed studies about the GResNet framework for many existing GNNs, including GCN, GAT and LoopyNet, will be reported in the paper with extensive empirical experiments on real-world benchmark datasets.
DCTD: Deep Conditional Target Densities for Accurate Regression

While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidence-based regression, which entails predicting a confidence value for each input-target pair (x,y). While this approach has demonstrated impressive results, it requires important task-dependent design choices, and the predicted confidences lack a natural probabilistic meaning. We address these issues by proposing a general and conceptually simple regression method with a clear probabilistic interpretation. In our proposed approach, we create an energy-based model of the conditional target density p(y|x), using a deep neural network to predict the un-normalized density from (x,y). This model of p(y|x) is trained by directly minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. We perform comprehensive experiments on four computer vision regression tasks. Our approach outperforms direct regression, as well as other probabilistic and confidence-based methods. Notably, our model achieves a 2.2% AP improvement over Faster-RCNN for object detection on the COCO dataset, and sets a new state-of-the-art on visual tracking when applied for bounding box estimation. In contrast to confidence-based methods, our approach is also shown to be directly applicable to more general tasks such as age and head-pose estimation. Code is available at this https URL.
Speckle Suppression and Enhancement Approaches for Processing of SAR Images: A Technical Review

Synthetic Aperture Radar (SAR) images are mostly damaged by signal dependent speckle noise. It reduces potential image abstraction and perception. Therefore speckle suppression is considered as a major step to overcome these problems. However, consequences of despeckling are blurred image and weak edges which is therefore followed by the application of enhancement methods on SAR images in order to increase the contrast level of the images. This paper gives an insight technical review of different speckle filtering and enhancement approaches for speckled remote sensing images; conclusion and recommendations are drawn eventually.
Towards Static Verification of Clojure Contract-Based Programs

Detecting possible weaknesses in a dynamically typed functional programming language at compile time plays an important role in the development of correct Software. Unfortunately, this is still an open problem for some functional programming languages. This paper proposes a translation of Clojure programs into Boogie. Thus, users can write formal specifications of Clojure programs, using pre- and postconditions that are supported by the language, translate the code to Boogie, and use Boogie’s automated theorem provers to formally check the correctness of the code w.r.t. its specifications. This enables users to formally prove Clojure programs enriched with pre- and post-conditions. This paper shows the translation rules, its implementation and discusses some of the challenges faced due to differences between the source and the target languages.
Privacy-aware digital forensics

Digital forensics and privacy are the two naturally conflicting concepts. While privacy can be defined as the desire of people to decide for themselves when, how and to what extent their personal information is shared with others, digital forensics is aimed at acquiring and analysing relevant data from devices in the scope of digital forensic investigations, following a set of procedures to comply legal proceedings. Digital forensic investigations are usually carried out after seizing the devices from investigated suspects or third parties, who consequently lose control over the data being accessed by the investigator. Moreover, digital forensic tools are even capable of retrieving information which is apparently no longer present in the device because the user decided to delete it. These tools also have the ability of correlating information from different sources giving rise to new actors in the investigation whose privacy can be affected. Also, the lack of context to determine when and why the user intentionally deleted some of the contents may result in wrong accusations. All things considered, even when digital investigations are conducted by responsible professionals, the data collected from personal devices may result in dreadful invasions to individual privacy. Inevitably, this leads to a controversial debate on the need for strong privacy guarantees in the context of digital forensics. This chapter aims to shed some light into this imperative and highly demanded debate given the fundamental role that the user and his/her personal data play in current and future digital investigations.
Automatic Handwritten Statics Solution Classification and its Applications in Predicting Student Performance

In previous research, we found that the spatial and temporal organization of students' solutions to engineering Statics problems correlates with the correctness of their work.Our technique utilized Livescribe TM Smartpens to capture digital records of students' handwritten solutions.Analysis of the temporal and spatial organization of a solution can, for example, enable an instructor to inexpensively identify students who may be struggling in the course and need extra assistance.This requires, however, that the pen strokes be labeled according to the type of content they represent: free body diagrams, equations, or cross-outs of incorrect work (the ink of the Smartpens cannot be erased).For our initial analyses, we manually labeled the pen strokes, but it is too time consuming to do this for the entire data set.This led to our current work on a technique for automatically determining the content type of the pen stoke in a solution.Our approach correctly classified 93.30% of pen strokes in one experiment.We have used these labeled sketches to detect potentially poor-performing students by comparing the equation drawing time and total length of strokes for each student to the mean from all students.
Unsupervised Topic Extraction from Twitter: A Feature-pivot Approach

Extracting topics from textual data has been an active area of research with many applications in our daily life. The digital content is increasing every day, and recently it has become the main source of information in all domains. Organizing and categorizing related topics from this data is a crucial task to get the best benefit out of this massive amount of information. In this paper we are presenting a feature-pivot based approach to extract topics from tweets. The approach is applied on a Twitter dataset in Egyptian dialect from four different domains. We are comparing our results to a document-pivot based approach and investigate which approach performs better to extract the topics in the underlying datasets. By applying t-test on recall, precision, and F1 measure values for both approaches on different datasets from different domains we confirmed our hypothesis that feature-pivot approach performs better in extracting topics from Egyptian dialect tweets in the datasets in question.
Vision based Indoor Obstacle Avoidance using a Deep Convolutional Neural Network

A robust obstacle avoidance control program was developed for a mobile robot in the context of tight, dynamic indoor environments. Deep Learning was applied in order to produce a refined classifier for decision making. The network was trained on low quality raw RGB images. A fine-tuning approach was taken in order to leverage pre-learned parameters from another network and to speed up learning time. The robot successfully learned to avoid obstacles as it drove autonomously in a tight classroom/laboratory setting.
REET: Joint Relation Extraction and Entity Typing via Multi-task Learning

Relation Extraction (RE) and Entity Typing (ET) are two important tasks in natural language processing field. Existing methods for RE and ET usually handle them separately. However, relation extraction and entity typing have strong relatedness with each other, since entity types are informative for inferring relations between entities, and the relations can provide important information for predicting types of entities. Exploiting the relatedness between relation extraction and entity typing has the potential to improve the performance of both tasks. In this paper, we propose a neural network based approach to jointly train relation extraction and entity typing models using a multi-task learning framework. For relation extraction, we adopt a piece-wise Convolutional Neural Network model as sentence encoder. For entity typing, since there are multiple entities in one sentence, we design a couple-attention model based on Bidirectional Long Short-Term Memory network to obtain entity-specific representation of sentences. In our MTL frame, the two tasks share not only the low-level input embeddings but also the high-level task-specific semantic representations with each other. The experiment results on benchmark datasets demonstrate that our approach can effectively improve the performance of both relation extraction and entity typing.
Distribution-free conditional predictive bands using density estimators.

Conformal methods create prediction bands that control average coverage under no assumptions besides i.i.d. data. Besides average coverage, one might also desire to control conditional coverage, that is, coverage for every new testing point. However, without strong assumptions, conditional coverage is unachievable. Given this limitation, the literature has focused on methods with asymptotical conditional coverage. In order to obtain this property, these methods require strong conditions on the dependence between the target variable and the features. We introduce two conformal methods based on conditional density estimators that do not depend on this type of assumption to obtain asymptotic conditional coverage: Dist-split and CD-split. While Dist-split asymptotically obtains optimal intervals, which are easier to interpret than general regions, CD-split obtains optimal size regions, which are smaller than intervals. CD-split also obtains local coverage by creating a data-driven partition of the feature space that scales to high-dimensional settings and by generating prediction bands locally on the partition elements. In a wide variety of simulated scenarios, our methods have a better control of conditional coverage and have smaller length than previously proposed methods.
An adaptive algorithm for restoring image corrupted by mixed noise

Image denoising is one of the fundamental problems in image processing. Digital images are often contaminated by noise due to the image acquisition process under poor conditions. In this paper, we propose an effective approach to remove mixed Poisson-Gaussian noise in digital images. Particularly, we propose to use a spatially adaptive total variation regularization term in order to enhance the ability of edge preservation. We also propose an instance of the alternating direction algorithm to solve the proposed denoising model as an optimization problem. The experiments on popular natural images demonstrate that our approach achieves superior accuracy than other recent state-of-the-art techniques.
The impulse pattern formulation (IPF) as a model of musical instruments—Investigation of stability and limits

The Impulse Pattern Formulation (IPF) is an analytical modeling approach for synergetic systems in general, motivated by research on musical instruments. Based on the assumption that the coupling of system components can be described by the interaction between individually propagating and exponentially damped impulse trains, the IPF describes tone generation in musical instruments as self-organized systems. In contrast to other nonlinear recursive functions such as the Logistic Map, system predictions cannot be derived easily from knowledge on other theories, neither can the system be completely examined analytically. Therefore, several numerical simulations are performed to explore stable regions and limits of the IPF.
Dynamic Term-Modal Logic for Epistemic Social Network Dynamics

Logics for social networks have been studied in recent literature. This paper presents a framework based on dynamic term-modal logic ( $$\mathsf {DTML}$$ ), a quantified variant of dynamic epistemic logic ( $$\mathsf {DEL}$$ ). In contrast with $$\mathsf {DEL}$$ where it is commonly known to whom agent names refer, $$\mathsf {DTML}$$ can represent dynamics with uncertainty about agent identity. We exemplify dynamics where such uncertainty and de re/de dicto distinctions are key to social network epistemics. Technically, we show that $$\mathsf {DTML}$$ semantics can represent a popular class of hybrid logic epistemic social network models. We also show that $$\mathsf {DTML}$$ can encode previously discussed dynamics for which finding a complete logic was left open. As complete reduction axioms systems exist for $$\mathsf {DTML}$$ , this yields a complete system for the dynamics in question.
General Video Game Artificial Intelligence

Research on general video game playing aims at designing agents or content generators that can perform well in multiple video games, possibly without knowing the game in advance and with little to no
Socio-legal approaches to online hate speech *

Researching how hate speech emerges and spreads and how it is regulated offers a unique lens to understand how different traditions understand speech and its limits. Earlier studies have focused on the deep divergence separating European and American approaches towards regulation, but the realization that the most dire consequences of speech inciting hatred are felt not in relatively stable democracies, but in fragile and transitioning societies has led to calls for more empirically grounded research, trying to understand how hate speech emerges and spreads in different sociocultural contexts, and how online speech can provoke offline violence. By focusing on online hate speech, this chapter first offers an overview of the challenges of defining and responding to hate speech, as further exacerbated by the proliferation of social media and by the opportunities they provide to myriad users to post anonymously, across jurisdictions, and in ways that tend to make hateful remarks more visible and permanent. In the second half, the chapter provides concrete examples of how empirical research can help address some definitional challenges, leading to a better understanding of how different online practices are connected to specific cultures of communication, and to more grounded, and possibly more effective, responses to speech that can lead to violence.
The Internet of Things Game: Illuminating data interactions within the Internet of Things

This paper examines the use of game design as a way to aid developing a better understanding of complexity of data interactions which occur within the Internet of Things (IoT) amongst a non-technical audience. We present findings from the playing a game designed and themed around interactions within the IoT whereby players are introduced to its underlying operations and data flows through the procedural rhetoric of the game. The data associated with IoT interactions is often invisible and/or unintelligible to the general user and we therefore use the rhetoric presented through playing the game to foreground the complexity of both seen and unseen activities within IoT augmented spaces. This allows players to gain a greater understanding of the connection between the digital world which increasingly augments our physical world.
Feature Selection and Classification of Big Data Using MapReduce Framework

The Feature selection (FS) plays an imperative role in Machine Learning (ML) but it is really demanding when we apply feature selection to voluminous data. The conventional FS methods are not competent in handling big datasets. This leads to the need of a technology that processes the data in parallel. MapReduce is a new programming framework used for processing massive data by using the “divide and conquer” approach. In this paper, a novel parallel BAT algorithm is proposed for feature selection of big datasets and finally classification is applied to the set of known classifiers. The proposed parallel FS technique is highly scalable for big datasets. The experimental results have shown improved efficacy of the proposed algorithm in terms of the accuracy and comparatively lesser execution time when the number of parallel nodes is increased.