Reviewerly/exHarmony huggingface dataset:
Multi-UAV Cooperative Multi-Target Allocation Method based on Differential Evolutionary Algorithm

Aiming at the problem of multi-UAV cooperative target assignment, this paper first classifies the corresponding relationship between multi-UAV and multi-target. Then, considering the real battlefield environment, this paper analyzes the influencing factors of multi-UAV cooperative target assignment. Finally, the effective distance and the income from attacking targets are taken as objective functions, and a realistic target allocation model is established. According to the model, a multi-UAV target assignment method based on differential evolution algorithm is proposed. In this method, we designed the corresponding gene coding method, which solved the two special situations of UAV not executing mission and target not being attacked. This method can ensure that multiple UAVs can attack targets according to the optimal allocation. Finally, in the simulation results, the effectiveness of the proposed algorithm is verified by comparing the convergence speed and results with the traditional genetic algorithm.
Design and Evaluation of Fluctuating Power Logic to Mitigate Power Analysis at the Cell Level

In this article, we design a novel cell-level power-analysis countermeasure, named fluctuating power logic (FPL), which diffuses the correlation between the real power consumption and the fixed data transitions by employing a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cascade voltage logic</i> . The countermeasure further acts as a cell-level <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V_{DD}$ </tex-math></inline-formula> randomizer, making it a strong candidate for implementing algorithmic countermeasure and exploiting its noise generation capabilities. This proposed scheme is illustrated by a standard flip-flop (FF). HSPICE-based simulation results show that the modified FF is resistant against power analysis (PA) at the cost of doubled power dissipation. Two illustrative case studies of PRESENT and AES substitutions have been explored. Furthermore, our proposal can be combined with other cell-level countermeasures against PA, such as wave dynamic differential logic. The resistance is evaluated by the correlation PA and the test vector leakage assessment. The new logic outperforms other counterparts in consideration of both security and cost, which renders it as a practical solution for resource-constrained systems. The proposed cell-level countermeasure can naturally mitigate other side-channel analysis such as electromagnetic analysis.
Analysis of Malicious Email Detection using Cialdini’s Principles

This research proposes a technique for identifying the persuasion methods of attackers that are likely to appear in targeted emails. A “persuasion method” is a technique (such as impersonating a person of authority, or appealing to scarcity) which makes the recipient more psychologically willing to obey the will of the attacker. Estimating the presence of persuasion methods in email bodies using machine learning is expected to find application as an effective feature for detecting malicious email, and in tools for warning users that they are being persuaded.
Improved U-NET Semantic Segmentation Network

In order to enable neural networks to better process images, this paper proposed a network with residual network and attention mechanism (AM). The residual network solves the shortcomings of insufficient depth of U-NET network to a certain extent. The attention mechanism can adaptively fuse processed context information and place different attentions for different objects and shapes, instead of simply aggregating all processed information. The method proposed in this paper is compared with the network using the residual network alone and the network using the attention mechanism alone. The experimental results show that the network proposed in this paper performs better.
A Two-Level Noise-Tolerant Model for Relation Extraction with Reinforcement Learning

Distant supervision has been widely used to automatically label data for relation extraction, but inevitably suffers from wrong labeling problems. Existing methods solve the noisy problem by merely focusing on one aspect, either at the sentence-level or the bag-level. However, none consider the two levels as a whole. In this paper, we propose a deep reinforcement learning model to solve the noisy problem at both the bag level and the sentence level. For a bag, i.e., a set of sentences containing the same pair of entities, the sentence-level extractor serves as an agent which predicts the label for each sentence, and then determines the label for the bag. The bag-level extractor provides a delayed reward to the agent, and iteratively promotes its performance. The experimental results show that our two-level denoising model effectively improves the performance of distant supervision relation extraction compared to previous methods.
Cloud Calculator: A cloud assessment tool for the Public Administration

As cloud computing is becoming more popular as a way to host services and to improve businesses, its adoption still remains to be a clearly defined process. Specifically, when the adoption is to be made within the public administration (where additional constraints apply when compared to the private sector). Legislation needs to be created, standards need to be developed, and public organizations need to be in synch with their cloud goals and approaches. This paper proposes a tool that supports the architecture assessment of cloud migration or adoption initiatives. A systematic literature review is conducted to gain a better understanding of the characteristics of a system that are important to consider when analyzing the cloud viability of a system. Considering the technical aspects of cloud adoption, this paper proposes several criteria linked to software qualities. With the criteria set established, weights are then defined according to their respective importance in the system, supporting the readiness level classification that a system has regarding cloud computing, based on multi-criteria decision analysis. The developed solution is then applied in a case study, assessing the usefulness and effectiveness of the proposed tool for the cloud adoption process.
ImageDC: Image Data Cleaning Framework Based on Deep Learning

Although user-generated image data increases more and more quickly on the current Internet, many image methods have attracted widespread attention from industry and academia. Recently, some image classification approaches using deep learning have demonstrated that they can potentially enhance the accuracy of the classification based on the high quality datasets. However, the existing methods only consider the accuracy of the classification and ignore the quality of the datasets. To address these issues, we propose a new image data cleaning framework using deep neural networks, named ImageDC, to improve the quality of the datasets. ImageDC not only uses cleaning with the minority class to remove the images of the rarely classes, but also adopts cleaning with the low recognition rate to remove the noisy data to enhance the recognition rate of the datasets. Experimental results conducted on a variety of datasets demonstrate that our model significantly outperforms the whole approaches.
Feature Selection for Dimension Reduction of Financial Data for Detection of Financial Statement Frauds in Context to Indian Companies

The financial fraud detection problem involves analysis of the large financial datasets. Financial statement fraud detection process is concentrated on two major aspects: first, identification of the financial variables and ratios, also termed as features. Second, applying the data mining methods to classify the organizations into two broad categories: fraudulent and non-fraudulent organizations. If the input dataset contains large number of irrelevant and correlated features, the computational load of the machine learning technique increases and the effectiveness of the classification outcomes decreases. The feature selection process selects a subset of most significant attributes or variables that can be the representative of original data. This selected subset can help in learning the pattern in data at much less time and with accuracy, in order to produce useful information for decision-making. This article briefly states the methods applied in the prior studies for selecting the features for financial statement fraud detection. This article also presents an approach to feature selection using correlation-based filter selection methods in which feature selection is performed based on ensemble model, and tests the outcome of the approach by applying the mean ratio analysis on financial data of Indian companies.
A Variational Bayesian Approach for Estimating System Parameters and Process Noise

Bayesian estimators are commonly used to estimate the state of a system over time, whether the Kalman Filter (or its derivatives), a particle filter, a sliding window approach or batch-based approach. A common assumption with all these estimators is that the parameters of the dynamics and measurement noise sources are known a-priori. To overcome this weakness, previous approaches have simultaneously estimated the system state and the covariance of the inputs. All of these approaches, however, have made the assumption that the input noise is still uncorrelated over time. In this paper, we remove this assumption by inferring both system parameters (such as the parameters for a first-order Gauss-Markov process (FOGM)) and process noise covariance simultaneously. This estimation is performed using variational inference in conjunction with a Rauch-Tung-Striebel (RTS) smoother. We demonstrate significantly improved performance over a similar RTS-based approach that estimates Q but has the time constant of the FOGM added to the system state.
Cloud Model-Based Intelligent Evaluation Method in Marine Engine Room Simulator

With the implementation of the new international conventions, higher evaluation requirements for a marine engine room simulator have been put forward. Based on the cloud theory, an improved fuzzy comprehensive evaluation method was studied: First, the Delphi method was adopted to get the original cloud drops of the judgments, and the original judgment weight clouds were generated by the backward cloud generator. Second, the judgment cloud matrix was built using the comparison results of the original judgment weight clouds, and the cloud weights of the evaluation factors were further calculated. Third, the appraisal grade clouds were generated, and the cloud appraisal vector taking into account the importance of each factor for each appraisal grade was calculated. Finally, the evaluation cloud result E was aggregated, and the similarity vector between E and the grade clouds was calculated. The calculation process reflects an effective uncertainty conversion between a qualitative concept and quantitative characteristics. The effectiveness was verified using three interrelated examples. The results show the following: the expectation of E is mainly determined by the operating process and, second, by the expectation of the cloud weights; the uncertainty index and the randomness index of E are determined by the parameter values of the cloud weights and the impact of the closest grade cloud; and the similarity vector is directly affected by E and the distribution of the grade clouds. The introduction of cloud model theory into fuzzy comprehensive evaluation is an effective evaluation method.
FD-VAE: A Feature Driven VAE Architecture for Flexible Synthetic Data Generation

Variational autoencoders (VAEs) are artificial neural networks used to learn effective data encodings in an unsupervised manner. Each input x provided to a VAE is indeed mapped to an internal representation, say z, in a low-dimensional space, called the latent space, from which an approximate version \(\tilde{x}\) of x can be eventually reconstructed via a decoding phase. VAEs are very popular generative models because, by randomly sampling points from the latent space, we can generate novel and unseen data that still reflect the characteristics of the dataset used for the training. In many application domains, however, generating random instances is not enough. Rather, we would like mechanisms that can generate instances enjoying some high-level features that are desired by the users. To accomplish this goal, a novel VAE architecture—named Feature Driven VAE—is presented. Internally, it uses Gaussian Mixture Models to structure the latent space into meaningful partitions, and it allows us to generate data with any desired combination of features, even when that specific combination has been never seen in the training examples. The architecture is orthogonal to the underlying application domain. However, to show its practical effectiveness, a specialization to the case of image generation has been presented and implemented. Results of experimental activity conducted on top of it are eventually discussed.
Random input based prediction and transfer of heat in soil temperature using artificial neural network

The soil temperature affects the climate in a great manner and the soil temperature data can be used to make predictions about how the ecosystem is affected by heat or cold in global temperature. Various measurement techniques have been employed in analyzing the soil temperature. Among them, Artificial Neural Network (ANN) is used to perform mathematical formulation to learn patterns and relationships in the data. The present study provides a tool to predict the soil temperature for the year 2004 using random inputs taken between 1993 and 2003 under different cases at two different soil depths 10 cm and 20 cm using ANN. The analysis was carried out for both the annual and the seasonal waves. Thereby, heat gets depleted during the monsoon seasons and gets accumulated in the annual and the other season which help to study the atmospheric changes. The statistical validation of the predictions possesses an insignificant error when compared with that of the observed soil temperature.
Neural Entity Synonym Set Generation using Association Information and Entity Constraint

Automatically generating entity synonym sets (i.e., sets of terms that represent the same entity) is an important work for many entity-based tasks. Existing studies on entity synonym set generation either use a ranking plus pruning approach or take the problem as a two-phase task (i.e., extracting synonymy pairs, subsequently organizing these pairs into synonym sets). However, these approaches ignore the association semantics of entities and suffer from the error propagation issue. In this paper, we propose a neural-network-based entity synonym set generation approach that exploits association information and entity constraint to generate synonym sets from a given term (i.e., entity) vocabulary. Firstly, to learn whether a new term should be added into the synonym set, an association-aware set-term neural network classifier is proposed. In the classifier, not only the entity representations but also the entity association information is exploited for extracting synonymous features. Secondly, an entity-constraint-based synonym set generation algorithm is employed to apply the trained set-term neural network classifier to generate the entity synonym sets from the term vocabulary. Finally, we conduct the proposed approach on three real-world datasets. The experimental results demonstrate that the entity synonym set generation performance of the proposed approach is better than that of the compared approaches.
Design and Implementation of Intelligent Scoring System for Handwritten Short Answer Based on Deep Learning

Automatic scoring of subjective questions is the key to the intelligent marking system. Aiming at the problem of low accuracy in the existing subjective question automatic grading system and combining with the characteristics of teachers' artificial grading, this paper studies and implements an intelligent grading system of handwritten short answer based on deep learning.
Deep shape constrained network for robust face alignment

Recently, face alignment plays a significant role in lots of computer vision tasks. Due to the various head poses, diverse expressions and partial occlusions, face alignment has still been a great challenge. In this paper, we propose a new Deep Shape Constrained Network (DSCN) for robust face alignment. Specifically, our DSCN consists of a Shape Initialization Network (SIN) and a series of Shape Refinement Networks (SRN) in a cascaded way. SIN takes the holistic facial image as the input and generates promising preliminary facial landmarks for SRN. Each SRN consists of two task-specific streams: shape stream aiming to learn the constraint of global face shape, and the texture stream used to extract robust face feature. Finally, the network automatically learns to combine these two streams in an early fusion approach. Experimental results show that the proposed method outperforms state-of-the-art approaches on 300-W dataset, which consists of LFPW, HELEN and AFW.
Spatiotemporal Constraints for Sets of Trajectories with Applications to PMBM Densities

In this paper we introduce spatiotemporal constraints for trajectories, i.e., restrictions that the trajectory must be in some part of the state space (spatial constraint) at some point in time (temporal constraint). Spatiotemporal contraints on trajectories can be used to answer a range of important questions, including, e.g., “where did the person that were in area A at time t, go afterwards?”. We discuss how multiple constraints can be combined into sets of constraints, and we then apply sets of constraints to set of trajectories densities, specifically Poisson Multi-Bernoulli Mixture (PMBM) densities. For Poisson target birth, the exact posterior density is PMBM for both point targets and extended targets. In the paper we show that if the unconstrained set of trajectories density is PMBM, then the constrained density is also PMBM. Examples of constrained trajectory densities motivate and illustrate the key results.
A low-cost prototyping framework for human-robot desk interaction

Many current human-robot interactive systems tend to use accurate and fast - but also costly - actuators and tracking systems to establish working prototypes that are safe to use and deploy for user studies. This paper presents an embedded framework to build a desktop space for human-robot interaction, using an open-source robot arm, as well as two RGB cameras connected to a Raspberry Pi-based controller that allow a fast yet low-cost object tracking and manipulation in 3D. We show in our evaluations that this facilitates prototyping a number of systems in which user and robot arm can commonly interact with physical objects.
Editors' Introduction to the Special Section on Cloud Computing and Accounting Information Systems (JISC 2018)

ABSTRACT This introduction provides an overview to this special section and reports on the third Journal of Information Systems Conference (JISC 2018) held March 15 and 16, 2018 in Durham, NC. The conference was jointly sponsored by the AAA Accounting Information Systems (AIS) section and the Association of International Certified Professional Accountants (AICPA). This JIS research will help advance cloud computing research related to the domain of accounting information systems. The conference included seven research papers with related industry discussant feedback, three cloud industry keynote speakers, and three panels of practitioners and researchers to discuss the impact of the emergence and rapid maturing of cloud computing services. This editors' introduction outlines the major research paper topics represented in this special section, briefly introduces each paper, and highlights their main contributions. A comprehensive report on the full JISC 2018 conference is provided in the cloud commentary paper included in this special section.
Dual Graph Embedding for Object-Tag Link Prediction on the Knowledge Graph

Knowledge graphs (KGs) composed of users, objects, and tags are widely used in web applications ranging from E-commerce, social media sites to news portals. This paper concentrates on an attractive application which aims to predict the object-tag links in the KG for better tag recommendation and object explanation. When predicting the object-tag links, both the first-order and high-order proximities between entities in the KG propagate essential similarity information for better prediction. Most existing methods focus on preserving the first-order proximity between entities in the KG. However, they cannot capture the high-order proximities in an explicit way, and the adopted margin-based criterion cannot measure the first-order proximity on the global structure accurately. In this paper, we propose a novel approach named Dual Graph Embedding (DGE) that models both the first-order and high-order proximities in the KG via an auto-encoding architecture to facilitate better object-tag relation inference. Here the dual graphs contain an object graph and a tag graph that explicitly depict the high-order object-object and tag-tag proximities in the KG. The dual graph encoder in DGE then encodes these high-order proximities in the dual graphs into entity embeddings. The decoder formulates a skip-gram objective that maximizes the first-order proximity between observed object-tag pairs over the global proximity structure. With the supervision of the decoder, the embeddings derived by the encoder will be refined to capture both the first-order and high-order proximities in the KG for better link prediction. Extensive experiments on three real-world datasets demonstrate that DGE outperforms the state-of-the-art methods.
Discovering Drug-Drug and Drug-Disease Interactions Inducing Acute Kidney Injury Using Deep Rule Forests

Patients with Acute Kidney Injury (AKI) increase mortality, morbidity, and long-term adverse events. Therefore, early identification of AKI may improve renal function recovery, decrease comorbidities, and further improve patients' survival. To control certain risk factors and develop targeted prevention strategies are important to reduce the risk of AKI. Drug-drug interactions and drug-disease interactions are critical issues for AKI. Typical statistical approaches cannot handle the complexity of drug-drug and drug-disease interactions. In this paper, we propose a novel learning algorithm, Deep Rule Forests (DRF), which discovers rules from multilayer tree models as the combinations of drug usages and disease indications to help identify such interactions. We found that several disease and drug usages are considered having significant impact on the occurrence of AKI. Our experimental results also show that the DRF model performs comparatively better than typical tree-based and other state-of-the-art algorithms in terms of prediction accuracy and model interpretability.
$\alpha$ -Stable Lévy State-space Models for Manoeuvring Object Tracking

In this paper we present multidimensional α-stable state-space models for object tracking, expressed in continuous time as Lévy processes. In contrast with the conventional Gaussian models, these heavy-tailed α-stable models are more likely to exhibit extreme noise values, thus showing the capability for modeling of erratic manoeuvring behaviour. Despite the potential benefits, such models are usually highly intractable for inference and therefore have not yet been widely adopted in the tracking field. Here the models are represented in a conditionally Gaussian series form, so that the marginal (Rao-Blackwellised) particle filter can be employed to perform tracking and smoothing very efficiently. As the result, the simulation tracks present some sharp manoeuvres, owing to the heavy-tailed property, and experiments demonstrate improved performance on an intent inference problem from automotive UI with highly perturbed pointing data.