Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Histogram Equalization and Color Mapping on ResNet-34's Overall Performance for COVID-19 Detection

The COVID-19 pandemic has had a “devastating” impact on public health and well-being around the world. Early diagnosis is a crucial step to begin treatment and prevent more infections. In this sense, early screening approaches have demonstrated that in chest radiology images, patients present abnormalities that distinguish COVID-19 cases. Recent studies based on Convolutional Neural Networks (CNNs), using radiology imaging techniques, have been proposed to assist in the accurate detection of COVID-19. Radiology images are characterized by the opacity produced by “ground glass” which might hide powerful information for feature analysis. Therefore, this work presents a methodology to assess the overall performance of Resnet-34, a deep CNN architecture, for COVID-19 detection when pre-processing histogram equalization and color mapping are applied to chest X-ray images. Besides, to enrich the available images related to COVID-19 studies, data augmentation techniques were also carried out. Experimental results reach the highest precision and sensitivity when applying global histogram equalization and pink color mapping. This study provides a point-of-view based on accuracy metrics to choose pre-processing techniques that can improve CNNs performance for radiology image classification purposes.
Parema: an unpacking framework for demystifying VM-based Android packers

Android packers have been widely adopted by developers to protect apps from being plagiarized. Meanwhile, various unpacking tools unpack the apps through direct memory dumping. To defend against these off-the-shelf unpacking tools, packers start to adopt virtual machine (VM) based protection techniques, which replace the original Dalvik bytecode (DCode) with customized bytecode (PCode) in memory. This defeats the unpackers using memory dumping mechanisms. However, little is known about whether such packers can provide enough protection to Android apps. In this paper, we aim to shed light on these questions and take the first step towards demystifying the protections provided to the apps by the VM-based packers. We proposed novel program analysis techniques to investigate existing commercial VM-based packers including a learning phase and a deobfuscation phase.We aim at deobfuscating the VM-protection DCode in three scenarios, recovering original DCode or its semantics with training apps, and restoring the semantics without training apps. We also develop a prototype named Parema to automate much work of the deobfuscation procedure. By applying it to the online VM-based Android packers, we reveal that all evaluated packers do not provide adequate protection and could be compromised.
Evolving reservoir weights in the frequency domain

Reservoir Computing models are a class of recurrent neural networks that have enjoyed recent attention, in particular, their main family, Echo State Networks (ESNs). These models have a large number of hidden-hidden weights (in the so-called reservoir) forming a recurrent topology. The reservoir is randomly connected with fixed weights during learning: only readout parameters (from reservoir to output neurons) are trained; the reservoir weights are frozen after initialized. Since the reservoir structure is fixed during learning, only its initialization process has an impact on the model's performance. In this work, we introduce an evolutionary method for adjusting the reservoir non-null weights. Moreover, the evolutionary process runs on the frequency space corresponding to a Fourier transformation of the weights. We combine an evolutionary search in the Fourier space with supervised learning for the readout weights. The resulting algorithm, called EvoESN (Evolutionary ESN), obtains promising results modeling two well-known problems of the chaotic time-series area.
Reconstructing z3 proofs in KeY: there and back again

One of the main factors of the increasing power of deductive verification tools are modern SMT solvers. Unfortunately, SMT solvers usually do not produce proof artifacts that could be inspected or checked. KeY is a formal platform for the deductive verification of Java programs which produces explicit, browsable proofs. SMT solvers can be driven from within KeY, but this unfortunately breaks these proof transparency intentions. In this paper, we describe how we complemented the existing translation of proof obligations to Z3 (There .) with the replay of proof transcripts reported by Z3 inside KeY (. and Back Again). We describe the conceptual and engineering challenges we encountered for this round trip and present solutions for them. The replay technique has been implemented in KeY, and in the evaluation, we demonstrate the feasibility of the approach for first-order proof instances and investigate the effects of proof replay on the proof run time and size.
Project portfolio selection with defense capability options

This paper proposes a novel formulation of the project portfolio selection and scheduling problem inspired by the Future Defense Force Design process in the context of the Australian Defence Force capability development. The core objective of the problem is to maximize the total capability portfolio value attained by the selection and scheduling of a set of capability projects, grouped in various subsets referred to as capability options, while adhering to budgetary, scheduling, and operational constraints. To provide initial solutions to the proposed model, a custom heuristic is developed and used to seed an initial population for a Genetic Algorithm.
Games as Information Systems

In this work, we formally build the construct of games as information systems. Starting from models and definitions that describe them, we present models that show game elements: components, players, environment, and their relations; as information systems elements. This paper also presents the analysis of five games and their elements based on that premise. As a ratification of our rationale, we present tools and visions shared by both fields. The presented premise and rationale could be easily linked to video games but, moreover, they are valid to all forms of games like tabletop games or sports.
Grid‐Based Source Location Privacy Protection Schemes in IoT Wireless Sensor Networks

The popularity of Wireless sensor network (WSN) has emerged in the 21st century. It is one of the emerging technologies, which comprises of a collection of sensor nodes that are linked to each other through a network. This paper digs deep into the security of location privacy. Further, it highlights how the protocols allow electronic devices to communicate with one another safely through protection of data and location. There are different types of adversaries such as undirected random paths, directed random paths and network loop methods. These adversaries can threaten the security, thus understanding them priory is essential in the development of a better protocol, as each one has its strengths and weaknesses. Three protocols are compared and analyzed: Phantom Routing, Rumor Routing and Greedy Random Walk protocol. The best features from each protocol are selected and placed in an enhanced protocol. This work leads to a proposal for an energy-aware privacy preservation protocol, named “Phantom++”.
Self-organizing migrating algorithm with clustering-aided migration and adaptive perturbation vector control

The paper proposes the Self-organizing Migrating Algorithm with CLustering-aided migration and adaptive Perturbation vector control (SOMA-CLP). The SOMA-CLP is the next iteration of the SOMA-CL algorithm, further enhanced by the linear adaptation of the prt control parameter used to generate a perturbation vector. The latest CEC 2021 benchmark set on a single objective bound-constrained optimization was used for the performance measurement of the improved variant. The proposed algorithm SOMA-CLP results were compared and tested for statistical significance against four other SOMA variants.
DeepCrime: mutation testing of deep learning systems based on real faults

Deep Learning (DL) solutions are increasingly adopted, but how to test them remains a major open research problem. Existing and new testing techniques have been proposed for and adapted to DL systems, including mutation testing. However, no approach has investigated the possibility to simulate the effects of real DL faults by means of mutation operators. We have defined 35 DL mutation operators relying on 3 empirical studies about real faults in DL systems. We followed a systematic process to extract the mutation operators from the existing fault taxonomies, with a formal phase of conflict resolution in case of disagreement. We have implemented 24 of these DL mutation operators into DeepCrime, the first source-level pre-training mutation tool based on real DL faults. We have assessed our mutation operators to understand their characteristics: whether they produce interesting, i.e., killable but not trivial, mutations. Then, we have compared the sensitivity of our tool to the changes in the quality of test data with that of DeepMutation++, an existing post-training DL mutation tool.
GUIDER: GUI structure and vision co-guided test script repair for Android apps

GUI testing is an essential part of regression testing for Android apps. For regression GUI testing to remain effective, it is important that obsolete GUI test scripts get repaired after the app has evolved. In this paper, we propose a novel approach named GUIDER to automated repair of GUI test scripts for Android apps. The key novelty of the approach lies in the utilization of both structural and visual information of widgets on app GUIs to better understand what widgets of the base version app become in the updated version. A supporting tool has been implemented for the approach. Experiments conducted on the popular messaging and social media app WeChat show that GUIDER is both effective and efficient. Repairs produced by GUIDER enabled 88.8% and 54.9% more test actions to run correctly than those produced by existing approaches to GUI test repair that rely solely on visual or structural information of app GUIs.
Design of large-scale metaheuristic component studies

Metaheuristics employ a variety of different components using a wide array of operators to execute their search. This determines their intensification, diversification and all other behavioural features and is thus critical for success on different optimisation problems. Choosing the right components with the right operators remains a difficult task. In this paper we propose a design of experiments that should be used for extensive component studies. We demonstrate the applicability of this design by exploring the differences in operator specific performance in two closely related metaheuristic frameworks---the well-known (μ + λ)-Evolution Strategy and the strongly metaphor-focussed Invasive Weed Optimisation---where operators show varying degrees of similarity in different components. This experiment shows that similarity of operators does not comprehensively account for similarity in performance. Presumably small changes of an operator can influence the algorithmic behaviour more than the utilisation of a completely different operator in another component. Even when employed in different combinations, these influences remain strong. This emphasises the need for a more detailed analysis of the specific effects of components and their respective operators on the search process.
Ensuring correct cryptographic algorithm and provider usage at compile time

Using cryptographic APIs to encrypt and decrypt data, calculate digital signatures, or compute hashes is error prone. Weak or unsupported cryptographic algorithms can cause information leakage and runtime exceptions, such as a NoSuchAlgorithmException in Java. Using the wrong cryptographic service provider can also lead to unsupported cryptographic algorithms. Moreover, for Android developers who want to store their key material in the Android Keystore, misused cryptographic algorithms and providers make the key material unsafe. We present the Crypto Checker, a pluggable type system that detects the use of forbidden algorithms and providers at compile time. For typechecked code, the Crypto Checker guarantees that only trusted algorithms and providers are used, and thereby ensures that the cryptographic APIs never cause runtime exceptions or use weak algorithms or providers. The Crypto Checker is easy-to-use: it allows developers to determine which algorithms and providers are permitted by writing specifications using type qualifiers. We implemented the Crypto Checker for Java and evaluated it with 32 open-source Java applications (over 2 million LOC). We found 2 issues that cause runtime exceptions and 62 violations of security recommendations and best practices. We also used the Crypto Checker to analyze 65 examples from a public benchmark of hard security issues and discuss the differences between our approach and a different static analysis in detail.
Refactoring traces to identify concurrency improvements

It is often difficult to analyse why a program executes more slowly than intended. This is particularly true for concurrent programs. We describe and evaluate a system, Rehype, which takes Java programs, performs low-overhead tracing of method calls, analyses the resulting trace-logs to detect inefficient uses of concurrency constructs, and suggests source-code-oriented improvements. Rehype deals with task-based concurrency, specifically a future-based model of tasks. Implementing the suggested improvements on an industrial API server more than doubled request-processing throughput.
Deep Learning-based Anomaly Detection in Cyber-physical Systems

Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS). However, due to the increasing complexity of CPSs and more sophisticated attacks, conventional anomaly detection methods, which face the growing volume of data and need domain-specific knowledge, cannot be directly applied to address these challenges. To this end, deep learning-based anomaly detection (DLAD) methods have been proposed. In this article, we review state-of-the-art DLAD methods in CPSs. We propose a taxonomy in terms of the type of anomalies, strategies, implementation, and evaluation metrics to understand the essential properties of current methods. Further, we utilize this taxonomy to identify and highlight new characteristics and designs in each CPS domain. Also, we discuss the limitations and open problems of these methods. Moreover, to give users insights into choosing proper DLAD methods in practice, we experimentally explore the characteristics of typical neural models, the workflow of DLAD methods, and the running performance of DL models. Finally, we discuss the deficiencies of DL approaches, our findings, and possible directions to improve DLAD methods and motivate future research.
An evolutionary and neighborhood-based algorithm for optimization under low budget requirements

Noisy optimization problems pose several challenges to optimization algorithms under limited computational resources. The algorithm must balance the need to explore the search space and to exploit promising regions of this space. In this work we describe an algorithm that combines previous ideas to tackle the GECCO 2021 industrial challenge using an exploration and an exploitation step. The first step consists in an evolutionary algorithm combined with an experimental design, while the second phase is a neighborhood search embedded within a multi-armed ∈-greedy approach. The resulting algorithm is not only applicable to the challenge but also to more general problems with varying conditions.
"Re-ID BUFF": an enhanced similarity measurement based on genetic programming for person re-identification

Person re-identification (re-ID) is a fundamental link to ensure successful cross-camera tracking. Its overall process is divided into two stages, feature extraction and matching. In the matching stage, the simple Euclid measurement has limitations in score due to the lack of consideration of the vector directions. The majority of recent contributions focus on the use of neighborhood information of the query image, which makes the calculation process more complex. In addition, these similarity measurements are predefined and therefore evaluated. In this paper, we propose a re-ID similarity measurement based on Genetic Programming (GP). The similarity measurement formula is automatically evolved and constructed through the optimization using specific function set, terminal set and fitness function targeting re-ID. For the training process of GP, we propose the feature triplet-dataset like the triplet-loss based on the existing re-ID datasets. We conduct sufficient experiments on three benchmark datasets, comparing with features of different quality and various measurements. The proposed method has a better effect than the Euclid measurement and achieves a general improvement on the mAP and rank-1 of combination with other metrics. Therefore, our method can be used as a gain "buff" to enhance the score of other methods on the original basis.
Many-objective population visualisation with geons

This paper explores the use of geometrical ions (called geons) to represent solutions in the approximated Pareto front generated by multi- and many-objective optimisers. The construction of geons based objects (GBOs)for solutions to a 3- and 5-objective problem is outlined, and the visualisation is embedded in a tool that has been tested with expert users. The findings suggest that our approach is promising, with all users successfully engaging with the given tasks and 4 out of 6 managing to complete some of the tasks they were assigned. Results indicate that the use of geometry, rather than colour as is often used to convey properties of Pareto front approximations, is a useful way of embedding multi-objective data.
Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future research in HRL. Furthermore, we outline a few suitable task domains for evaluating the HRL approaches and a few interesting examples of the practical applications of HRL in the Supplementary Material.
An indicator of inefficient visualizations: the challenge of transparency during the COVID-19 pandemic in Brazil: An indicator of inefficient visualizations: the challenge of transparency during the COVID-19 pandemic in Brazil

The COVID-19 epidemic requires clear and reliable information to guide the population. Visualization is a powerful tool to contribute to the understanding of this data. However, just divulging this resource is not enough to guarantee this understanding. It is important to support users in analyzing this data, making this process easier and more transparent, especially for users with little (or no) literacy. In this work, we define an inefficient graphics indicator, that is, with the potential to be misinterpreted or difficult to understand, according to the basic guidelines of the data visualization area. These guidelines were selected through a literature review, forming a repository of practices that guide good visualization design and provide the indicator assessment items. This proposal can be applied inis necessary because we do not perceive the existence of standards, norms or quality indicators for data visualization that assist in the creation of this artifact efficiently or evaluate the existing ones for improvement, both manually and automatically. This approach can be applied in the most diverse scenarios, being initially in Brazil during the dissemination of COVID-19, analyzing the official visualizations and highlighting the failures of several epidemiological perspectives on the pandemic. Through a study with users where volunteers analyzed the official data and our recommendations, we demonstrated that the indicator is effective in detecting and helping to understand the visualization. We highlight the alert for the need for greater care in the creation of graphics by the government so as not to compromise the understanding of the citizens who use them.
Explaining SOMA: the relation of stochastic perturbation to population diversity and parameter space coverage

The Self-Organizing Migrating Algorithm (SOMA) is enjoying a renewed interest of the research community, following recent achievements in various application areas and renowned performance competitions. In this paper, we focus on the importance and effect of the perturbation operator in SOMA as the perturbation is one of the fundamental inner principles of SOMA. In this in-depth study, we present data, visualizations, and analysis of the effect of the perturbation on the population, its diversity and average movement patterns. We provide evidence that there is a direct relation between the perturbation intensity (set by control parameter prt) and the rate of diversity loss. The perturbation setting further affects the exploratory ability of the algorithm, as is demonstrated here by analysing the parameter space coverage of the population. We aim to provide insight and explanation of the impact of perturbation in SOMA for future researchers and practitioners.
Genetic improvement of routing in delay tolerant networks

Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of e.g. (possibly, unmanned) vehicles and humans where, despite a lack of continuous connectivity, data must be transmitted while the network conditions change due to the nodes' mobility. In these contexts, routing is NP-hard and is usually solved by heuristic "store and forward" replication-based approaches, which however produce relatively low delivery probabilities. Here, we genetically improve two routing protocols widely adopted in DTNs, namely Epidemic and PRoPHET, in the attempt to optimize their delivery probability. First, we dissect them into their fundamental components, i.e., functionalities such as checking if a node can transfer data, or sending messages to all connections. Then, we apply Genetic Improvement (GI) to manipulate these components as terminal nodes of evolving trees. We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes, and find that GI produces consistent gains in delivery probability in four cases.