Reviewerly/exHarmony huggingface dataset:
Bayesian Image Denoising with Multiple Noisy Images

In this paper, we propose a fast image denoising method based on discrete Markov random fields and the fast Fourier transform. The purpose of the image denoising is to infer the original noiseless image from a noise corrupted image. We consider the case where several noisy images are available for inferring the original image and the Bayesian approach is adopted to create the posterior probability distribution of the denoised image. In the proposed method, the estimation of the denoised image is achieved using belief propagation and an expectation–maximization algorithm. We numerically verified the performance of the proposed method using several standard images.
Symbolic regression in dynamic scenarios with gradually changing targets

Symbolic regression is a machine learning task: given a training dataset with features and targets, find a symbolic function that best predicts the target given the features. This paper concentrates on dynamic regression tasks, i.e. tasks where the goal changes during the model fitting process. Our study is motivated by dynamic regression tasks originating in the domain of reinforcement learning: we study four dynamic symbolic regression problems related to well-known reinforcement learning benchmarks, with data generated from the standard Value Iteration algorithm. We first show that in these problems the target function changes gradually, with no abrupt changes. Even these gradual changes, however, are a challenge to traditional Genetic Programming-based Symbolic Regression algorithms because they rely only on expression manipulation and selection. To address this challenge, we present an enhancement to such algorithms suitable for dynamic scenarios with gradual changes, namely the recently introduced type of leaf nodes called Linear Combination of Features. This type of leaf node, aided by the error backpropagation technique known from artificial neural networks, enables the algorithm to better fit the data by utilizing the error gradient to its advantage rather than searching blindly using only the fitness values. This setup is compared with a baseline of the core algorithm without any of our improvements and also with a classic evolutionary dynamic optimization technique: hypermutation. The results show that the proposed modifications greatly improve the algorithm ability to track a gradually changing target.
LiRUL: A Lightweight LSTM Based Model for Remaining Useful Life Estimation at the Edge

Unexpected downtime and sudden breakdown of IoT devices can be extremely destructive most especially for safety-critical systems. Condition monitoring and health state estimation are vital techniques for maintaining high reliability and availability. Many data-driven approaches to Remaining Useful Life (RUL) estimation using deep learning algorithms are computationally-intensive and often not feasible on resource-constrained devices. Time lag and network cost associated with massive data transfer to a centralized cloud for processing can be minimized by adopting edge computing. In this paper, we propose a lightweight Long Short-Term Memory (LSTM) based model called LiRUL, suitable for RUL estimation on an edge device. We also implement a tagging function and evaluate our approach on publicly available Turbofan Engine datasets.
Rough sets based span and its application to extractive text summarization

Rough Sets provide a mathematical tool to handle decision making under uncertainty. One major domain that can be characterized with inherent ambiguity is natural language texts which often leads to uncertainty in understanding the intent and relative importance of a sentence with respect to its con text in the whole text. As a consequence, the process of sentence selection for generation of extractive summary can logically be considered as a process of decision making under uncertainty. In this paper we use rough set based techniques to deal with this uncertainty. This paper’s contribution is two-fold. Firstly, this paper proposes a novel Rough Set based uncertainty measure called span and define special Rough subsets of universe called spanning sets. Span is Rough Set based measure for salience of a subset of universe and spanning set is the subset that maximizes the span. This corresponds to the key elements representing a problem and can be used to solve various real-life applications. Secondly, the concepts are applied to determine extracts of text documents. The idea behind the present work is to determine the most suitable subset(s) of the universe of sentences under consideration. An optimization problem is formulated to generate the extract for the text under consideration using the proposed uncertainty measure of span and is solved using Particle Swarm Optimization. The experimental results on DUC2001, DUC2002 single document data sets and Enron Email datasets establish the effectiveness of the proposed technique. There has been substantial work on Rough Sets though considering a stochastic Rough-subset of the universe and determining its aptness as a representative of the universe is still unexplored. The proposed technique is a novel effort to fill this gap.
Integrating agent actions with genetic action sequence method

Reinforcement learning in general is suitable for putting actions in a specific order within a short sequence, but in the long run its greedy nature leads to eventual incompetence. This paper presents a brief description and implementative analysis of Action Sequence which was designed to deal with such a "penny-wise and pound-foolish" problem. Based on a combination of genetic operations and Monte-Carlo tree search, our proposed method is expected to show improved computational efficiency especially on problems with high complexity in which situational difficulties are often troublesome to resolve with naive behaviors. We tested the method on a video game environment to validate its overall performance.
Weighted correlation filters guidance with spatial-temporal attention for online multi-object tracking

In recent years, discriminative correlation filters based trackers have made remarkable achievements for single object tracking, while directly applying these trackers for multi-object tracking may encounter some problem in drifted results caused by occlusion and missing detection from the detector. Thus, we propose a weighted-correlation-filters framework with spatial-temporal attention mechanism for online multi-object tracking to solve the above problems. First, we use the weighted correlation filters with dynamic updating scheme to pre-track each object in the current frame, which helps to filter out the improper detection according to the position of pre-tack for each object and is capable of tracking objects of the false negative. Then, we introduce a spatial-temporal attention mechanism to produce a discriminative appearance model and calculate reliable similarity scores for data association. The proposed online algorithm achieves 48.4% in MOTA on challenging MOT17 benchmark dataset and better performance on MT and ML than some offline methods.
Graph Database Approach for Data Storing, Presentation and Manipulation

An increasing number of IoT systems and a generation of unstructured data make it difficult to choose and apply a suitable database model. Using a NoSQL database on a test data model shows the advantages of storing, manipulating, and presenting the required data. A test data model was created on a relational database and on a graph database to compare the options of manipulating and presenting the same data. A comparison of these two models was made on a small sample, and the results were displayed on the same query as the default for both types of databases. Simpler query syntax and better visualization of data are some of the benefits presented by this model on the graph database.
Fairness, Accountability and Transparency in Artificial Intelligence

Machine learning -- the part of artificial intelligence aimed at eliciting knowledge from data and automated decision making without explicit instructions -- is making great strides, with new algorithms being invented every day. These algorithms find myriads of applications, but their ubiquity often comes at the expense of limited interpretability, hidden biases and unexpected vulnerabilities. Whenever one of these factors is a priority, the learning algorithm of choice is often a method considered to be inherently interpretable, e.g. logical models such as decision trees. In my research I challenge this assumption and highlight (quite common) cases when the assumed interpretability fails to deliver. To restore interpretability of logical machine learning models (decision trees and their ensembles in particular) I propose to explain them with class-contrastive counterfactual statements, which are a very common type of explanation in human interactions, well-grounded in social science research. To evaluate transparency of such models I collate explainability desiderata that can be used to systematically assess and compare such methods as an addition to user studies. Given contrastive explanations, I investigate their influence on the model's security, in particular gaming and stealing the model. Finally, I evaluate model fairness, where I am interested in choosing the most fair model among all the models with equal performance.
Empowering Instead of Hindering – Challenges in Participatory Development of Cognitively Accessible Software

Participative design is a method of involving users in the design and development process through a collaborative approach. This method allows to involve the potential users with their needs and peculiarities from the beginning of the development. In agile software development, the potential user of the software plays a central role as a stakeholder, but the rapid development cycles force users to deal with incomplete and faulty software. With the example of two agile projects for the development of cognitively accessible software, the challenges of participative development with people with cognitive impairments are demonstrated and discussed.
Identifying and interpreting subgroups in health care utilization data with count mixture regression models

Inpatient care is a large share of total health care spending, making analysis of inpatient utilization patterns an important part of understanding what drives health care spending growth. Common features of inpatient utilization measures such as length of stay and spending include zero inflation, overdispersion, and skewness, all of which complicate statistical modeling. Moreover, latent subgroups of patients may have distinct patterns of utilization and relationships between that utilization and observed covariates. In this work, we apply and compare likelihood‐based and parametric Bayesian mixtures of negative binomial and zero‐inflated negative binomial regression models. In a simulation, we find that the Bayesian approach finds the true number of mixture components more accurately than using information criteria to select among likelihood‐based finite mixture models. When we apply the models to data on hospital lengths of stay for patients with lung cancer, we find distinct subgroups of patients with different means and variances of hospital days, health and treatment covariates, and relationships between covariates and length of stay.
Self-adaptive loop for CPSs: is the Dynamic Partial Reconfiguration profitable?

Nowadays, Cyber-Physical Systems play an important role in the context of several large industries; the need for interaction with a changeable physical world leads the system adapting itself to physical changes. Adaptivity, dependability and reducing communication overheads then appear as the most wanted requirements that are moving on the adoption of the edge-computing. In turn, in this world, the demand for HW platforms able to manage increasing requirements is leading to the use of FPGAs, due to their inherent run-time reconfigurability. However, the dynamic partial reconfiguration process of an FPGA has a timing performance impact that cannot be neglected. This impact, if not well considered, can nullify the advantage obtained using a Dynamic Partial Reconfiguration. Therefore, when exploiting FPGAs with dynamic partial reconfiguration, a crucial problem is to understand whether is profitable or not to dynamically reconfigure them. In this paper, an innovative run-time manager adopting a metric to evaluate the impact of reconfiguration time is introduced, together with its validation through its usage on a basic application implemented on FPGA.
Helper and equivalent objective different evolution for constrained optimisation

A novel multiobjective evolutionary algorithm is proposed for constrained optimisation in this paper. It transforms a constrained optimisation problem into a two objective optimisation problem. One objective is equivalent to solving the original constrained problem, and the other is the degree of constraint violation which only plays a helper role. This multiobjective problem is decomposed into several single objective optimisation problems using a dynamical weighted sum approach. Each single objective eventually tends to an equivalent objective. A decomposition-based multiobjective optimisation differential evolution algorithm is designed for solving these single objective problems simultaneously.
Toward Design and Evaluation Framework for Interpretable Machine Learning Systems

The need for interpretable and accountable intelligent system gets sensible as artificial intelligence plays more role in human life. Explainable artificial intelligence systems can be a solution by self-explaining the reasoning behind the decisions and predictions of the intelligent system. My research supports the design and evaluation methods and interpretable machine learning systems and leverages knowledge and experience in the fields of machine learning, human-computer interactions, and data visualization. My research objectives are to present a design and evaluation framework for explainable artificial intelligence systems, propose new methods and metrics to better evaluate the benefits of transparent machine learning systems, and apply interpretability methods for model reliability verification.
Does constraining the search space of GA always help?

In this paper, we undertake an investigation on the effect of balanced and unbalanced crossover operators against the problem of finding non-linear balanced Boolean functions: we consider three different balanced crossover operators and compare their performances with classic one-point crossover. The statistical comparison shows that the use of balanced crossover operators gives GA a definite advantage over one-point crossover.
Smart Tools in Software Engineering: A Systematic Mapping Study

Software development processes such as waterfall development model have been around for over fifty years, but still, even modern software development approaches, such as DevOps or Test-driven development, fundamentally rely on the same principles and phases as everything before them. Yet, the modern world imposes new challenges for software businesses, and new ways of digital distribution require new ways of resource provisioning and ability to reduce the time-to-market to its absolute minimum. In this study, we identify and analyze the technologies which may be useful for software companies to ease the development and maintenance work by assisting the experts to collect relevant information and observe issues before they cause process disturbances. As a result, we describe a mapping study, which identifies different approaches to developing a smart software engineering tools applying potential technologies such as artificial intelligence, cloud-based service models, adaptive measurement, and other approaches, which could offer significant benefits to the software development process.
InNervate AR: Dynamic Interaction System for Motor Nerve Anatomy Education in Augmented Reality

Augmented reality applications for anatomy education have seen a large growth in its literature presence for 3D model education technology. However, the majority of these new anatomy applications limit their educational scope to the labelling of anatomical structures and layers, and simple identification interactions. There is a strong need for expansion of augmented reality applications, in order to give the user more interactive control of the anatomy education material. To meet this need, the mobile augmented reality application, InNervate AR, was created. This application allows the user to scan a marker for two distinct learning modules; one for labelling and identification of anatomy structures, the other one for interacting with the radial nerve of the canine forelimb. The first module matches other existing anatomy augmented reality structures. The second module is unique, because it allows the user to play an animation of the anatomy models, to show what the normal range of motion for the muscles of the limb is, based on the motor innervation of radial nerve. Next, the user can select where to make a cut along the length of the radial nerve, to cause a nerve deficit to one or more of the muscles of the limb. Based on this user input, the application will then play a new animation of the changed range of motion of the canine thoracic limb. A formal user study was run with this new application, which including pre- and post- knowledge assessments. Our initial data analysis showed that qualitative students’ responses and quantitative data were significantly positive. This implies that the application may prove to be educationally effective. We are going to expand the scope of the application based on the analyses of user data and feedback, and develop educational modules for all of the motor nerves of the canine forelimb.
Multi‐feature radar signal modulation recognition based on improved PSO algorithm

In order to solve the problem that radar emitter signal modulation recognition under low signal-to-noise ratio (SNR) has difficulty in selecting the parameters of the classifier and the problem of low recognition rate caused by the improper selection of features, a multi-feature fusion modulation recognition algorithm based on improved particle swarm optimisation (PSO) algorithm is proposed. Firstly, the algorithm uses Choi–Williams distribution time–frequency analysis to transform the radar signals into time–frequency images, and combines transfer learning and principal component analysis (PCA) to extract the transfer features. Then it extracts the Renyi entropy and AR model coefficients of the signals and combines with the image features to realise multi-feature fusion. Finally, the improved PSO algorithm optimises support vector machine parameters so that population converge quickly. The simulation results show that the recognition rate of this algorithm is 95.3% when the SNR is 0 dB. The number of iterations of this algorithm is small, the signal recognition rate of the system is improved, and the effectiveness of the system is improved by applying PCA.
Parallel and Distributed Machine Learning Algorithms for Scalable Big Data Analytics

This editorial is for the Special Issue of the journal Future Generation Computing Systems, consisting of the selected papers of the 6th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning 2017). In this editorial, we have given a high-level overview of the 4 papers contained in this special issue, along with references to some of the related works.
Designing correlation immune boolean functions with minimal hamming weight using various genetic programming methods

In this paper, we search for Boolean functions with properties useful in cryptography for preventing side-channel attacks. We use three genetic programming methods including linear genetic programming, which has not been used to design these functions before. Our results aim to provide a fair comparison by performing parameter optimization for each individual method, and deliver an insight into how well they cope with the demand for growing number of function inputs and higher degrees of correlation immunity.
Lightweight Distributed Attribute Based Keyword Search System for Internet of Things

Internet of Things (IoT) is a promising networking paradigm that connects various kinds of sensors and exchanges data from smart devices. Since IoT always related to user’s daily life, the problems of security and privacy are of great importance. Presently, the attribute based encryption (ABE) is a popular solution to guarantee the fine-grained sharing of encrypted data in IoT. In this paper, an attribute based keyword search with lightweight decryption in multi-authority (ABKS-LD-MA) is proposed. Our system supports multi-keyword search in cloud by using searchable attribute based encryption. We also integrate the lightweight decryption to searchable ABE scheme that largely reduces the computing overhead for users. Furthermore, our ABKS-LD-MA scheme supports multi-authority scenario, which is more adaptive to the real IoT environment. The experiment analysis shows that our scheme has relatively lowered the communication cost on IoT devices.
Implicit JSON Schema Versioning Driven by Big Data Evolution in the τJSchema Framework

In JSON-based NoSQL data stores, Big Data instance documents and their JSON schemas must evolve over time to reflect changes in the real world. When a JSON instance document, valid with respect to a JSON schema, is updated giving rise to a new document no longer valid with respect to the schema, the update is usually rejected also resulting in user frustration. In such a case, the JSON schema has to be explicitly changed by an administrator in order to become compliant with the new Big Data format before the update can be effected by the user. The different approach we propose in this work is to privilege the user actions and accept in a transparent way any update he/she wants to apply to the instance documents: violation of the validity of an updated instance document with respect to its JSON schema is automatically detected and schema changes necessary to produce a new schema version compliant with the new Big Data format are automatically applied by the system, producing a new JSON schema version. Hence, in this work, we deal with implicit JSON schema versioning driven by updates to JSON-based Big Data instance documents. Our proposed solution consists in an extension of the τJSchema (Temporal JSON Schema) framework we previously introduced to create and validate temporal JSON documents and to allow classical temporal JSON schema versioning, to also support implicit JSON schema versioning.
Optimal Search Space Strategy for Infrared Facial Image Recognition Using Capsule Networks

In this paper, we propose a highly accurate method for person identification in surveillance using infrared cameras. Our model performs well when faced with the challenges of variation in view, pose, expression, scale and lighting. It outperforms the Convolutional Neural Network in scenarios where there is a continuous change in the position and translation of the targeted individual. Our error rates were 1.5 times lower than the error rates of CNNs when tested on some standard infrared and thermal datasets. We have used Local Quantized Patterns to partition people based on their genders. The people in each gender group are identified by dynamic routing between capsules. Our contribution in this paper is a new approach to filter people based on their gender and classify them using the Capsule Network. The method was tested on two infrared datasets and four visible-light-based datasets and the average error rate converged between 1%–3%.