Reviewerly/exHarmony huggingface dataset:
Combining multi-label classifiers based on projections of the output space using Evolutionary algorithms

The multi-label classification task has gained a lot of attention in the last decade thanks to its good application to many real-world problems where each object could be attached to several labels simultaneously. Several approaches based on ensembles for multi-label classification have been proposed in the literature; however, the vast majority are based on randomly selecting the different aspects that make the ensemble diverse and they do not consider the characteristics of the data to build it. In this paper we propose an evolutionary method called Evolutionary AlGorithm for multi-Label Ensemble opTimization, EAGLET, for the selection of simple, accurate and diverse multi-label classifiers to build an ensemble considering the characteristics of the data, such as the relationship among labels and the imbalance degree of the labels. In order to model the relationships among labels, each classifier of the ensemble is focused on a small subset of the label space, resulting in models with a relative low computational complexity and lower imbalance in the output space. The resulting ensemble is generated incrementally given the population of multi-label classifiers, so the member that best fits to the ensemble generated so far, considering both predictive performance and diversity, is selected. The experimental study comparing EAGLET with state-of-the-art methods in multi-label classification over a wide set of sixteen datasets and five evaluation measures, demonstrated that EAGLET significantly outperformed standard MLC methods and obtained better and more consistent results than state-of-the-art multi-label ensembles.
Accuracy and minor embedding in subqubo decomposition with fully connected large problems: a case study about the number partitioning problem

Abstract In this work, we investigate the capabilities of a hybrid quantum-classical procedure to explore the solution space using the D-Wave 2000 Q T M quantum annealer device. Here, we study the ability of the quantum hardware to solve the number partitioning problem, a well-known NP-hard optimization model that poses some challenges typical of those encountered in real-world applications. This represents one of the most complex scenario in terms of qubits connectivity and, by increasing the input problem size, we analyze the scaling properties of the quantum-classical workflow. We find remarkable results in most instances of the model; for the most complex ones, we investigate further the D-Wave Hybrid suite. Specifically, we were able to find the optimal solutions even in the worst cases by fine-tuning the parameters that schedule the annealing time and allowing a pause in the annealing cycle.
Vulnerability Assessment and Penetration Testing to Enhance the Security of Web Application

In the present scenario, the usage of internet is enormous and is escalating day by day. Internet facilities are employed in almost every field of work and people are becoming depending on it, with the increasing dependency on the internet, concern regarding information security has been increased. Because most of the work, e-commerce, chatting, payment of the bill, etc. are work through over the internet. That is why security is most important for any web site. Basically, such security concern is high in the field of organizations, institutions, and the financial sector. This problem is greater in the field of the finance sector, this problem is greater in this field not only because the financial capital associated but also organizations and client sensitive and private data. If this data hack by the attacker then attacker or unauthorized user can use this data in the wrong way. To test the security in web applications, the company performs penetration testing which identifies web applications vulnerabilities and attackers actions. This paper is focused on web application security. In this proposed research work, a framework has been built to test the vulnerabilities. This framework has the same working module as that of a financial institutions website. After penetration testing, based on the vulnerability further, a framework will be designed which will provide more security to such web sites. The developed framework can be used in several institutions, company, Organization to test the vulnerability.
Path Planning of the Mobile Robot Using Fuzzified Advanced Ant Colony Optimization

Ant colony optimization (ACO) is a probabilistic optimization method. In this analysis, its application has been explored in mobile robotics for path planning. It provides multi-feedback information and robustness to the mobile robot during navigation. Due to the robustness of the advanced fuzzified ant colony optimization (FACO), the path planning task has been executed in the unstructured environment, and collision-free navigation has been achieved smoothly. For fuzzified advanced ant colony optimization (FAACO), path pheromone update scheme is divided into two categories like favorable and unfavorable path. Using these, path pheromone as the problems of conventional ACO like slow convergence has been sorted out. The advanced FACO improves the evaporation rate of pheromone to accelerate the convergence speed. Finally, the simulation results show the proposed method conquered the previous drawback.
Poisoning Attacks in Federated Learning

Federated Learning has recently gained attraction as a means to analyze data without having to centralize it from initially distributed data sources. Generally, this is achieved by only exchanging and aggregating the parameters of the locally learned models. This enables better handling of sensitive data, e.g. of individuals, or business related content. Applications can further benefit from the distributed nature of the learning by using multiple computer resources, and eliminating network communication overhead. Adversarial Machine Learning in general deals with attacks on the learning process, and backdoor attacks are one specific attack that tries to break the integrity of a model by manipulating the behavior on certain inputs. Recent work has shown that despite the benefits of Federated Learning, the distributed setting also opens up new attack vectors for adversaries. In this paper, we thus specifically study this manipulation of the training process to embed a backdoor on the example of a dataset for traffic sign classification. Extending earlier work, we specifically include the setting of sequential learning, in additional to parallel averaging, and perform a broad analysis on a number of different settings.
Ring Signature-Based Voting on Blockchain

Amid ongoing digitalization, the use case of electronic voting has naturally arisen, with attempts beginning as early as 2000. Blockchain technology has the potential to bring trust and built-in auditing tools to such systems. This paper presents a prototype of the system for collective voting. It is based on blockchain and linkable ring signatures to ensure the transfer of closed (secret) data without the loss of reliability and with respect for the privacy of group members.
Modelling Machine Learning For Analysing Crime News

Increasing crime rate and recurring crimes in India factors towards analyzing crime. Crime analysis is a way of observing the patterns of crime that happened recently and in past to get a better estimate of future crimes. Crime analysis is very important for the law enforcement and also helps crime detectives to find the criminals. Apart from this, common public also should be aware of the criminal activities so that they can take safety measures to protect their family and friends. Traditional methods of analyzing crime news involve reading the complete crime news and then analyzing it, which is very time consuming and vulnerable to human errors. Also many machine learning classifiers are used for classification of the old datasets of crime which will not be assistive enough to provide any report that analyzes crime news in real-time. The proposed system is a web-system which analyzes real-time crime data in the form of online news articles and gives a report of the news related to crime. The contents of the websites of newspapers are the input to the system. The site is crawled using a crawling program written in python language, and the data is stored in a temporary database. Using three different classifiers, the data is classified into crime-related data and non crime-related data. The final analysis wiil be displayed to the user in a tabular format with pie charts denoting crime and non-crime percentage.
Evaluating memory-hard proof-of-work algorithms on three processors

Most public blockchain systems, exemplified by cryptocurrencies such as Ethereum and Monero, use memory-hard proof-of-work (PoW) algorithms in consensus protocols to maintain fair participation without a trusted third party. The memory hardness, or the amount of memory access, of these PoW algorithms is to prevent the dominance of custom-made hardware of massive computation units, in particular, application-specific integrated circuit (ASIC) and field-programmable gate array (FPGA) machines, in the system. However, it is unclear how effective these algorithms are on general-purpose processors. In this paper, we study the performance of representative memory-hard PoW algorithms on the CPU, the Graphics Processing Unit (GPU), and the Intel Knights Landing (KNL) processors. We first optimize each algorithm for individual processors, and then measure their performance with number of threads and memory size varied. Our experimental results show that (1) the GPU dominates the CPU and the KNL processors on each algorithm, (2) all algorithms scale well with number of threads on the CPU and KNL, and (3) the size of accessed memory area affects each algorithm differently. Based on these results, we recommend CryptoNight with scratchpads of different sizes as the most egalitarian PoW algorithm.
SMOTE Approach for Predicting the Success of Bank Telemarketing

At present classification of imbalanced data is a major concern. Machine learning algorithm fails to achieve expected result when data is imbalance. Major challenge of imbalance dataset is that sometimes minority classes are useful but machine learning algorithms are tended to be biased towards the majority classes and ignore the minority classes. To achieve the better result from our machine we need to train our machine using relevant data in such a way that makes those machines more talented and can give the accurate decision by itself for an unknown result. Here we propose SMOTE approach for predicting the success of bank telemarketing. Data collected form direct marketing campaigns of a banking institution. We analyzed 150 features related with bank client and product. Feature selection process was explored in the modeling phase and performed with dataset. Our dataset includes 45211 instances and 17 attributes. This large set of banking transaction data is imbalanced. In this paper we will construct a model to make these dataset balance using Synthetic Minority Oversampling Technique (SMOTE) technique and analyse the performance using Naive Bayes algorithm. Our model will help to find best strategies for the improvement of next marketing campaign.
Blind GB-PANDAS: A Blind Throughput-Optimal Load Balancing Algorithm for Affinity Scheduling

Dynamic affinity load balancing of multi-type tasks on multi-skilled servers, when the service rate of each task type on each of the servers is known and can possibly be different from each other, is an open problem for over three decades. The goal is to do task assignment on servers in a real time manner so that the system becomes stable, which means that the queue lengths do not diverge to infinity in steady state (throughput optimality), and the mean task completion time is minimized (delay optimality). The fluid model planning, Max-Weight, and c-μ -rule algorithms have theoretical guarantees on optimality in some aspects for the affinity problem, but they consider a complicated queueing structure and either require the task arrival rates, the service rates of tasks on servers, or both. In many cases that are discussed in the introduction section, both task arrival rates and service rates of different task types on different servers are unknown. In this work, the Blind GB-PANDAS algorithm is proposed which is completely blind to task arrival rates and service rates. Blind GB-PANDAS uses an exploration-exploitation approach for load balancing. We prove that Blind GB-PANDAS is throughput optimal under arbitrary and unknown distributions for service times of different task types on different servers and unknown task arrival rates. Blind GB-PANDAS desires to route an incoming task to the server with the minimum weighted-workload, but since the service rates are unknown, such routing of incoming tasks is not guaranteed which makes the throughput optimality analysis more complicated than the case where service rates are known. Our extensive experimental results reveal that Blind GB-PANDAS significantly outperforms existing methods in terms of mean task completion time at high loads.
Coreference Resolution Method Using a Convolutional Neural Network

Coreference resolution is one of background tasks in the area of natural language processing. Corresponding state-of-the-art methods are based on machine learning algorithms and consist in detection of regularities between semantic and grammatical features of text entities. In this paper, the method of coreferent pairs detection based on the set of filtering sieves and a convolutional neural network has been suggested. Different current methods to detect coreferent pairs within a text have been analyzed. The set of filtering sieves to find candidates for the formation of coreferent pairs has been implemented according to the features of the Ukrainian language. A multichannel convolutional neural network has been designed. The training of the convolutional neural network has been performed. The effectiveness of the method on the Ukrainian-language corpus has been examined using typical metrics to the coreference resolution task. The results obtained may indicate that the method suggested can be used to solve the coreference resolution task for Ukrainian texts. The method can be adapted according to features of other languages and be applied to corresponding text corpora.
Evolutionary Artificial Neural Networks: Comparative Study on State-of-the-Art Optimizers

Artificial neural networks (ANN) have a great impact on research in the field of artificial intelligence. It has great capability besides the easy implementation, and due to that, it has been widely used in a wide area of real-life and industrial applications. Today, we can see a variety of ANNs such as feed-forward ANN, Kohonen self-organizing ANN, radial basis function (RBF) ANN, spiking ANN, etc. This chapter focuses on evolutionary ANN wherein the learning process is by nature-inspired optimization techniques instead of the classic routine. The focus of this chapter is the neuro-evolution-based ANN techniques by different state-of-the-art nature-inspired meta-heuristic optimization techniques and comparison of them over a monitoring system to detect the oil filter condition in agricultural machines (Ag machines). In this comparative study, the fourteen state-of-art meta-heuristic optimizers are compared in the same regard.
Control and Monitoring of IoT Devices Using Mixed Reality Developed by Unity Engine

Interconnected devices in the Internet of Things (IoT) networks are now a prominent group of diverse systems, such as manufacturing systems, industrial systems, smart homes, services, etc. A modern alternative for control and monitor IoT devices is the use of modern information and communication technologies, including virtual and mixed reality. One of the affordable technologies for promoting mixed reality is for example Apple ARKit or Android ARCore. The development of a system that ensures control of IoT devices using virtual or mixed reality requires a comprehensive multidisciplinary approach.
Participatory design in refugee camps: comparison of different methods and visualization tools

Shelters for the displaced can suffer from socio-cultural incompatibility and significant levels of occupant dissatisfaction. Participatory design (PD) is known to help reduce such issues. This is the first study to investigate the effectiveness of different PD methods at engaging and capturing users’ needs for shelter design in refugee camps. It also aimed to identify which visualization tools are best at: engaging participants; communicating designs (e.g. concept, size and materials); and facilitating proposing modifications. This is a particularly large study with 16 workshops and 161 participants. Two PD methods were deployed: (i) design-your-own (where refugees proposed their ideal shelter); (ii) adapt-a-design (where refugees evaluated and modified pre-existing shelter designs). The shelters in (ii) were presented using three visualization tools: computer models, physical prototypes and virtual reality. Design-your-own proved less engaging and led participants to produce designs similar to their existing shelters. Adapt-a-design stimulated more dialogue and was more informative. Physical prototypes facilitated engagement in shelter modifications, computer models proved least able to communicate concepts, while virtual reality was best at communicating scale and size.
Higher-order potentials for video object segmentation in bilateral space

We propose an effective approach to make segmentation for objects in videos with an initial input of the object masks in a few frames of the source video. In this method, we cast the segmentation task as a Markov Random Field (MRF) labeling problem. Different from the conventional MRF models, our model uses an additional term of higher-order potential to better propagate the global consistency among frames. The higher-order potential presented in this paper is significant for the proposed method because of its capability to keep the long-range consistency during segmentation. In order to make the MRF energy minimized, we also introduce a smart skill that makes the intractable higher-order potential "invisible" during the optimization so that the problem can be solved simply by applying a standard graph cut algorithm. Besides, the entire process is operated in a bilateral space, where the labeling can be inferred efficiently on the vertices that are sampled regularly from the bilateral grid. The results of a comparison of our method with a number of recently developed methods show that it performs favorably against state-of-the-art algorithms on multiple benchmark data sets in view of accuracy and achieves a much faster runtime performance.
Intelligent gaze tracking approach for trail making test

Trail making test is a cognitive impairment test used for understanding the visual attention during the visual search task. The classical paper pencil method measures the completion time of the participant and there was no mechanism for comparison across the participant with similar feature. The ps ychologist has to observe the reactions of the participants during the trial process and there is no mechanism to capture it. This study made an attempt to resolve the above problem and tried to infer additional parameters which can support psychologist to understand the participant performance in trail making test. The insight provided by the approach is to extract various features which helps a psychologist by providing individual profiling and group profiling of a person and can understand the group of people who show similar cognitive impairment while performing trail Making Test. The proposed Intelligent Gaze Tracking approach could classify the participant into three different groups like low, high and medium cognitive impairment based on the extracted gaze features. The proposed approach has been compared across existing literature survey to significantly show the advantage of the system in terms of identifying the people with similar characteristics in terms of cognitive impairment.
Novel fuzzy clustering‐based bias field correction technique for brain magnetic resonance images

Bias field correction is an essential pre-processing requirement for brain tissue segmentation task. Authentic brain tissue regions are highly useful for classification and detection of abnormalities. A poor resolution magnetic resonance (MR) image is produced with irregularities in structure, abnormalities in the intensity distribution and noise during the acquisition procedure. The existing bias field correction methods do not consider the spatial information. Further, the problem of equidistant pixels while clustering is not addressed. These problems lead to poor segmentation accuracy. To solve these problems, the authors suggest a novel biased fuzzy clustering technique for the problem on hand. The basic idea is to incorporate the spatial information by altering the membership matrix of standard fuzzy C-means clustering to lower the effect of noise and intensity inhomogeneity. It also helps in improving the segmentation accuracies of the tissue regions by assigning the equidistant pixels to a single cluster. The suggested technique is validated with different modalities of brain MR images. Various evaluation indices are computed followed by the statistical analysis to justify the superiority of the suggested technique in comparison to the state-of-the-art methods.
Accelerating deep reinforcement learning model for game strategy

In recent years, deep reinforcement learning has achieved impressing accuracies in games compared with traditional methods. Prior schemes utilized Convolutional Neural Networks (CNNs) or Long Short-Term Memory networks (LSTMs) to improve the performances of the agents. In this paper, we consider the issue from a different perspective when the training and inference of deep reinforcement learning are required to be performed with limited computing resources. Mainly, we propose two efficient neural network architectures of deep reinforcement learning: Light-Q-Network (LQN) and Binary-Q-Network (BQN). In LQN, The depth-wise separable CNNs are utilized in memory and computation saving. While, in BQN, the weights of convolutional layers are binary that help in shortening the training time and reduce memory consumption. We evaluate our approach on Atari 2600 domain and StarCraft II mini-games. The results demonstratethe efficiency of the proposed architectures. Though performances of agents in most games are still super-human, the proposed methods advance the agent from sub to super-human performance in particular games. Also, we empirically find that non-standard convolution and non-full-precision networks do not affect agent learning game strategy.