Reviewerly/exHarmony huggingface dataset:
Adversarial example generation with adaptive gradient search for single and ensemble deep neural network

Deep Neural Networks (DNNs) have achieved remarkable success in specific domains, such as computer vision, audio processing, and natural language processing. However, researches indicate that deep neural networks are facing many security issues (e.g., adversarial attack, information forgery). In the field of image classification, adversarial samples generated by specific adversarial attack strategies can easily fool deep neural classification models into making unreliable predictions. We find that such adversarial attack algorithms induce large-scale pixel modifications in crafted images to maintain the effectiveness of the adversarial attack. Massive pixel modifications change the inherent characteristics of generated examples and cause large image distortion. To address the mentioned issues, we introduce an adaptive gradient-based adversarial attack method named Adaptive Iteration Fast Gradient Method (AI-FGM), which focuses on seeking the input's preceding gradient and adjusts the accumulation of perturbed entity adaptively for performing adversarial attacks. By maximizing the specific loss for generating adaptive gradient-based entities, AI-FGM calls for several gradient-based operators on the clean input to map crafted sample with the corresponding prediction directly. AI-FGM helps to reduce unnecessary gradient-based entity accumulation when processing adversary by adaptive gradient-based seeking strategy. Experimental results show that AI-FGM outperforms other gradient-based adversarial attackers in attacking deep neural classification models with fewer pixel modifications (AMP is 0.0017 with L2 norm in fooling Inception-v3) and higher success rate of invasion on deep neural classification networks in white-box and black-box attack strategy on public image datasets with different resolution.
Quality Indicators in Search-based Software Engineering

Search-Based Software Engineering (SBSE) researchers who apply multi-objective search algorithms (MOSAs) often assess the quality of solutions produced by MOSAs with one or more quality indicators (QIs). However, SBSE lacks evidence providing insights on commonly used QIs, especially about agreements among them and their relations with SBSE problems and applied MOSAs. Such evidence about QIs agreements is essential to understand relationships among QIs, identify redundant QIs, and consequently devise guidelines for SBSE researchers to select appropriate QIs for their specific contexts. To this end, we conducted an extensive empirical evaluation to provide insights on commonly used QIs in the context of SBSE, by studying agreements among QIs with and without considering differences of SBSE problems and MOSAs. In addition, by defining a systematic process based on three common ways of comparing MOSAs in SBSE, we present additional observations that were automatically produced based on the results of our empirical evaluation. These observations can be used by SBSE researchers to gain a better understanding of the commonly used QIs in SBSE, in particular, regarding their agreements. Finally, based on the results, we also provide a set of guidelines for SBSE researchers to select appropriate QIs for their particular context.
Estimation of Software Development Effort: A Differential Evolution Approach

Several software effort estimation techniques based on mathematical formulations have been proposed, however there is little consensus on the best technique for effort estimation, which can predict the estimated effort optimally for any given project. Much research has been carried out in finding out the most suitable parameter values for accurate effort estimation. This research investigates the efficacy of Differential Evolution algorithms in improving the parameter values for algorithmic models like CoCoMo and CoCoMo II. Parameter values were obtained by using the three successful mutation strategies in Differential Evolution. The proposed methodology was tested on two datasets from the Promise Repository. Test results were compared with the original CoCoMo and CoCoMo II models based on MMRE. The proposed differential evolution approach predicted the estimated effort more accurately than the original models for the two datasets under consideration.
Design and Development of Diagnostic Chabot for supporting Primary Health Care Systems

Technology is increasingly becoming a massive part of today’s healthcare scenario. Technology has changed the way how patients communicate with doctors and not only that, but also how healthcare is administered. Artificial intelligence and Chabots are two groundbreaking technologies that have changed how patients and doctors perceive healthcare. To make healthcare system more interactive a diagnostic Chabot is designed and developed using latest algorithms in machine learning, decision tree algorithm to help the user to form a diagnosis of their condition based on their symptoms. The system will be fed with information pertaining to various diseases and using NLP, it will be able to understand the user query and give a suitable response. The system can be used for effective information retrieval in a similar manner like siri, alexa etc but the scope will be limited to disease diagnosis.
Adaptive CNN Ensemble for Complex Multispectral Image Analysis

Multispectral image classification has long been the domain of static learning with nonstationary input data assumption. The prevalence of Industrial Revolution 4.0 has led to the emergence to perform real-time analysis (classification) in an online learning scenario. Due to the complexities (spatial, spectral, dynamic data sources, and temporal inconsistencies) in online and time-series multispectral image analysis, there is a high occurrence probability in variations of spectral bands from an input stream, which deteriorates the classification performance (in terms of accuracy) or makes them ineffective. To highlight this critical issue, firstly, this study formulates the problem of new spectral band arrival as virtual concept drift. Secondly, an adaptive convolutional neural network (CNN) ensemble framework is proposed and evaluated for a new spectral band adaptation. The adaptive CNN ensemble framework consists of five (05) modules, including dynamic ensemble classifier (DEC) module. DEC uses the weighted voting ensemble approach using multiple optimized CNN instances. DEC module can increase dynamically after new spectral band arrival. The proposed ensemble approach in the DEC module (individual spectral band handling by the individual classifier of the ensemble) contributes the diversity to the ensemble system in the simple yet effective manner. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new spectral band during online image classification. Moreover, the extensive training dataset, proper regularization, optimized hyperparameters (model and training), and more appropriate CNN architecture significantly contributed to retaining the performance accuracy.
Using convolutional neural networks for recognition of objects varied in appearance in computer vision for intellectual robots

The paper describes an effort to train a convolutional neural network capable of reliably recognizing complex objects that are highly varied in their shapes and appearances in images. Neural networks show very good results on objects that have constant appearances but may have trouble recognizing abstract objects that appear in different shapes, art-styles and lack solid structure, for example, national flags. In an image, a flag may appear waving on a pole, as an element of clothes, in a form of stickers, etc. Due to these differences in appearance computer vision systems may show unsatisfactory results on these types of objects. However, detecting such objects is a necessary task in computer vision, especially for intelligent robots in order to understand the environment. The aim of the research is to apply convolutional neural networks for the detection of flags. In this research, we prepared training and testing sets of objects, trained a neural network for detection task, conducted testing experiments and measured the neural net’s performance. These results can be applied in cognitive and robotics technologies as well as general computer vision tasks.
High-Speed Ship Detection in SAR Images by Improved Yolov3

Ship detection in synthetic aperture radar (SAR) images plays an important role in marine transportation, fishery management, and maritime disaster rescue. Nowadays, the current researches almost are focusing on improving detection accuracy while detection speed is neglected. However, it is also extraordinarily important to increase the ship detection speed, because it can provide real-time ocean observation and timely ship rescue. Therefore, in order to solve this problem, this paper proposes a high-speed SAR ship detection approach by improved you only look once version 3 (YOLOv3). We experimented on a public SAR ship detection dataset (SSDD) which has been used by many other scholars. Finally, the experimental results indicated that the detection speed of our proposed improved YOLOv3 is faster than current other methods, such as faster-regions convolutional neural network (Faster R-CNN), single shot multi-box detector (SSD), and original YOLOv3 under a same hardware environment. Meanwhile, the detection accuracy remains basically unchanged.
A Context-Aware Chatbot for Tourist Destinations

The cultural heritage is one of the most important resources of the territory. It represents one of the scenarios where new technologies can provide more interesting contributions. In particular, adaptive systems and related services may increase the promotion of cultural heritage. In fact, tourists can use several services able to filter the huge amount of data present on the network in order to only provide relevant information. The aim of this paper is to introduce a chatbot based on a Context-Aware System. This chatbot recommends contents and services according to tourist profiles and context. For testing the proposed architecture, a prototype was developed in order to support tourists during a visit to some cultural sites in Campania: Paestum, Pompeii and Herculaneum. The first experimental results are encouraging and show the potential of the proposed approach.
EDS pooling layer

Convolutional neural networks (CNNs) have been the source of recent breakthroughs in many vision tasks. Feature pooling layers are being widely used in CNNs to reduce the spatial dimensions of the feature maps of the hidden layers. This gives CNNs the property of spatial invariance and also results in speed-up and reduces over-fitting. However, this also causes significant information loss. All existing feature pooling layers follow a one-step procedure for spatial pooling, which affects the overall performance due to significant information loss. Not much work has been done to do efficient feature pooling operation in CNNs. To reduce the loss of information at this critical operation of the CNNs, we propose a new EDS layer (Expansion Downsampling learnable-Scaling) to replace the existing pooling mechanism. We propose a two-step procedure to minimize the information loss by increasing the number of channels in pooling operation. We also use feature scaling in the proposed EDS layer to highlight the most relevant channels/feature-maps. Our results show a significant improvement over the generally used pooling methods such as MaxPool, AvgPool, and StridePool (strided convolutions with stride > 1). We have done the experiments on image classification and object detection task. ResNet-50 with our proposed EDS layer has performed comparably to ResNet-152 with stride pooling on the ImageNet dataset.
FHCS: Hybridised optimisation for virtual machine migration and task scheduling in cloud data center

Cloud computing and virtualisation are recent approaches to develop minimum energy usage in virtualised cloud data centre (DC) for resource management. One of the major problems faced by cloud DCs is energy consumption which increases the cost of cloud user and environmental influence. Therefore, virtual machine (VM) consolidation is properly proposed in many approaches which reallocate the VMs by VM migration with the objective of minimum energy consumption. Here, VM consolidation based on the Fruit fly Hybridised Cuckoo Search (FHCS) algorithm is proposed to obtain the optimal solution with the help of two objective functions in cloud DC. This FHCS approach efficiently minimises the energy usage and resource depletion in cloud DC. The proposed work comparison is done with Ant Colony System (ACS), Particle Swarm Optimisation (PSO) algorithm and Genetic Algorithm (GA). The simulation conclusion reveals the advantage of the FHCS and VM migration method over existing procedures such as GA, PSO and ACS in terms of energy consumption and resource utilisation. The proposed method achieves 68 Kwh less energy and 72% less resources than existing methods. Simulation results have shown that energy consumption of the proposed method is reduced with less number of active PMs than other conventional approaches.
Missing Data Imputation With Bayesian Maximum Entropy for Internet of Things Applications

Internet of Things (IoT) enables the seamless integration of sensors, actuators, and communication devices for real-time applications. IoT systems require good quality sensor data in order to make real-time decisions. However, values are often missing from the sensor data collected owing to faulty sensors, a loss of data during communication, interference, and measurement errors. Considering the spatiotemporal nature of IoT data and the uncertainty of the data collected by sensors, we propose a new framework with which to impute missing values utilizing Bayesian maximum entropy (BME) as a convenient means to estimate the missing data from IoT applications. Missing sensor measurements adversely affect the quality of data, and consequently the performance and outcomes of IoT systems. Our proposed framework incorporates BME in order to impute missing values in diverse IoT scenarios by making use of the combination of low- and high-precision sensors. Our approach can incorporate the measurement errors of low-precision sensors as interval quantities along with the high-precision sensor measurements, making it highly suitable for real-time IoT systems. Our framework is robust to variations in data, requires less execution time, and requires only a single input parameter, thus outperforming existing IoT data imputation methods. The experimental results obtained for three IoT data sets demonstrate the superiority of the BME framework as regards accuracy, running time, and robustness. The framework can additionally be extended to distributed IoT nodes for the online imputation of missing values.
A conversational recommender system for diagnosis using fuzzy rules

Graded implications in the framework of Fuzzy Formal Concept Analysis are used as the knowledge guiding the recommendations. An automated engine based on fuzzy Simplification Logic is proposed to make the suggestions to the users. Conversational recommender systems have proven to be a good approach in telemedicine, building a dialogue between the user and the recommender based on user preferences provided at each step of the conversation. Here, we propose a conversational recommender system for medical diagnosis using fuzzy logic. Specifically, fuzzy implications in the framework of Formal Concept Analysis are used to store the knowledge about symptoms and diseases and Fuzzy Simplification Logic is selected as an appropriate engine to guide the conversation to a final diagnosis. The recommender system has been used to provide differential diagnosis between schizophrenia and schizoaffective and bipolar disorders. In addition, we have enriched the conversational strategy with two strategies (namely critiquing and elicitation mechanism) for a better understanding of the knowledge-driven conversation, allowing user’s feedback in each step of the conversation and improving the performance of the method.
Teaching R in the undergraduate ecology classroom: approaches, lessons learned, and recommendations

Abstract Learning ecology requires training in data management and analysis. Because of its transparent and flexible nature, R is increasingly used for data management and analysis in the field of ecology. Consequently, job postings targeting candidates with a bachelor's degree and a required knowledge of R have increased over the past ten years. In this paper, we begin by presenting data from the last ten years demonstrating the increase in the use of R, an open‐source programming environment, in ecology and its prevalence as a required skill in job descriptions. We then discuss our experiences teaching undergraduates R in two advanced ecology classes using different approaches. One approach, in a course with a field laboratory, focused on collecting, cleaning, and preparing data for analysis. The other approach, in a course without a field laboratory, focused on analyzing existing datasets and applying the results to content discussed in the lecture portion of the course. Our experiences determined that each approach had strengths and weaknesses. We recommend that above all, instructors of ecology and related subjects should be encouraged to include R in their coursework. Furthermore, instructors should be aware of the following: Learning R is a separate skill from learning statistics; writing R assignments is a significant time commitment for course preparation; and there is a tradeoff between teaching R and teaching content. Determining how one's course fits into the curriculum and identifying resources outside of the classroom for students’ continued practice will ensure that R training is successful and will extend beyond a one‐semester course.
Real Time Object Tracking for Intelligent Vehicle

The density of current road traffic frequently producing circumstances that endanger the protection of individuals. For the safety of individual, one has to keep the safe distance from an upcoming vehicle in a busy road. In an intelligent vehicle system, the selection of sensors, orientation and velocity of persons and other moving objects are significant for logical decisions in a given scenario. These acquired information can be deployed to offer a Driver Assistance System (DAS) for future intelligent vehicle. In the proposed work, vision-based sensors are castoff to follow the targeted object moving in road traffic. A real time data set is prepared for the proposed work to track the object positions for an intelligent vehicle in road traffic.
Nonadiabatic geometric quantum computation with optimal control on superconducting circuits

Quantum gates, which are the essential building blocks of quantum computers, are very fragile. Thus, to realize robust quantum gates with high fidelity is the ultimate goal of quantum manipulation. Here, we propose a nonadiabatic geometric quantum computation scheme on superconducting circuits to engineer arbitrary quantum gates, which share both the robust merit of geometric phases and the capacity to combine with optimal control technique to further enhance the gate robustness. Specifically, in our proposal, arbitrary geometric single-qubit gates can be realized on a transmon qubit, by a resonant microwave field driving, with both the amplitude and phase of the driving being time-dependent. Meanwhile, nontrivial two-qubit geometric gates can be implemented by two capacitively coupled transmon qubits, with one of the transmon qubits' frequency being modulated to obtain effective resonant coupling between them. Therefore, our scheme provides a promising step towards fault-tolerant solid-state quantum computation.
How explosive are cryptocurrency prices?

This paper revisits the issue of price explosiveness in cryptocurrency markets by analysing the prices of Bitcoin, XRP, Ether, and Litecoin not only expressed in US Dollars, but also the price of the latter three expressed in Bitcoin. There is strong evidence of explosive periods in cryptocurrency prices expressed in US Dollars, but also when XRP and Ether are expressed in Bitcoin. Whether or not these explosive periods can be interpreted as cryptocurrency bubbles requires a sufficient understanding of the fundamental value of cryptocurrencies. As this is currently insufficient, the term bubble should be used with more caution.
BEGAN v3: Avoiding Mode Collapse in GANs Using Variational Inference

In the field of deep learning, the generative model did not attract much attention until GANs (generative adversarial networks) appeared. In 2014, Google’s Ian Goodfellow proposed a generative model called GANs. GANs use different structures and objective functions from the existing generative model. For example, GANs use two neural networks: a generator that creates a realistic image, and a discriminator that distinguishes whether the input is real or synthetic. If there are no problems in the training process, GANs can generate images that are difficult even for experts to distinguish in terms of authenticity. Currently, GANs are the most researched subject in the field of computer vision, which deals with the technology of image style translation, synthesis, and generation, and various models have been unveiled. The issues raised are also improving one by one. In image synthesis, BEGAN (Boundary Equilibrium Generative Adversarial Network), which outperforms the previously announced GANs, learns the latent space of the image, while balancing the generator and discriminator. Nonetheless, BEGAN also has a mode collapse wherein the generator generates only a few images or a single one. Although BEGAN-CS (Boundary Equilibrium Generative Adversarial Network with Constrained Space), which was improved in terms of loss function, was introduced, it did not solve the mode collapse. The discriminator structure of BEGAN-CS is AE (AutoEncoder), which cannot create a particularly useful or structured latent space. Compression performance is not good either. In this paper, this characteristic of AE is considered to be related to the occurrence of mode collapse. Thus, we used VAE (Variational AutoEncoder), which added statistical techniques to AE. As a result of the experiment, the proposed model did not cause mode collapse but converged to a better state than BEGAN-CS.
Novel trajectory privacy-preserving method based on prefix tree using differential privacy

Location-based services, such as DiDi and bike sharing, are becoming increasingly popular. However, the use of these services raises privacy concerns. In the past few years, differential privacy technology has been applied to location information protection. However, most existing models largely fail to resist complex background-knowledge attacks. This paper proposes a novel privacy preservation method for trajectory data. It is based on a prefix tree and uses differential privacy. It should be noted that existing methods only consider either a certain location point or the entire trajectory. In the proposed prefix tree structure, the nodes of the tree store the trajectory segments. The parameter minimum description length method is combined with the Dijkstra method to select feature trajectory points that represent the entire trajectory, thus further reducing the (computational) complexity of data processing. To protect privacy, Laplacian noise is added to the location data of trajectory segments by using differential privacy. In addition, a background and contextual information attack model is proposed, and the corresponding protection method is provided. Finally, a Markov chain is used to limit the size of the noise added to the data. The proposed algorithm is compared with related state-of-the-art algorithms on a public dataset. The results demonstrate that our algorithm can ensure not only privacy but also data availability.