Reviewerly/exHarmony huggingface dataset:
Multi-objective Evolutionary Approach for the Performance Improvement of Learners using Ensembling Feature Selection and Discretization Technique on Medical Data

Background: Biomedical data is filled with continuous real values; these values in the feature set tend to create problems like underfitting, the curse of dimensionality and increase in misclassification rate because of higher variance. In response, pre-processing techniques on dataset minimizes the side effects and have shown success in maintaining the adequate accuracy. Aims: Feature selection and discretization are the two necessary preprocessing steps that were effectively employed to handle the data redundancies in the biomedical data. However, in the previous works, the absence of unified effort by integrating feature selection and discretization together in solving the data redundancy problem leads to the disjoint and fragmented field. This paper proposes a novel multi-objective based dimensionality reduction framework, which incorporates both discretization and feature reduction as an ensemble model for performing feature selection and discretization. Selection of optimal features and the categorization of discretized and non-discretized features from the feature subset is governed by the multi-objective genetic algorithm (NSGA-II). The two objectives, minimizing the error rate during the feature selection and maximizing the information gain, while discretization is considered as fitness criteria. Methods: The proposed model used wrapper-based feature selection algorithm to select the optimal features and categorized these selected features into two blocks namely discretized and nondiscretized blocks. The feature belongs to the discretized block will participate in the binary discretization while the second block features will not be discretized and used in its original form. Results: For the establishment and acceptability of the proposed ensemble model, the experiment is conducted on the fifteen medical datasets, and the metric such as accuracy, mean and standard deviation are computed for the performance evaluation of the classifiers. Conclusion: After an extensive experiment conducted on the dataset, it can be said that the proposed model improves the classification rate and outperform the base learner.
Computer cryptography through performing chaotic modulation on intrinsic mode functions with non‐dyadic number of encrypted signals

This study proposes a computer cryptographic system through performing the chaotic modulation on the intrinsic mode functions with a non-dyadic number of the encrypted signals. First, the empirical mode decomposition is applied to an input signal to generate a set of intrinsic mode functions. Then, these intrinsic mode functions are categorised into two groups of signals. Next, a type 1 polyphase is employed to represent each group of signals. These polyphase components are combined to generate a non-dyadic number of polyphase components. Second, the chaotic modulation is applied to these combined polyphase components for performing the encryption in the time frequency domain. To reconstruct the original signal, first, the chaotic demodulation is applied to the encrypt components to reconstruct the combined polyphase components. Then, the original groups of intrinsic mode functions are reconstructed through the type 2 polyphase representation and the original signal is reconstructed. Compared with the chaotic filter bank system, the proposed approach enjoys the nonlinear and adaptive property of the empirical mode decomposition. Therefore, a better security performance can be achieved particularly for the non-stationary signals. Compared with the conventional chaotic modulation approach, the proposed system allows performing the cryptography in the time frequency domain.
The Impact of Awareness of Being Monitored on Computer Usage Policy Compliance: An Agency View

ABSTRACT Computer abuse by employees has increased the potential for security vulnerabilities for organizations. Organizations have established various security countermeasures to prevent computer abuse and protect organizational information. However, these policies are only effective if followed. Thus, it is important for organizations to understand the factors that motivate employees to follow computer usage polices. We investigate the impact of different countermeasures, such as perceived sanctions, and awareness of being monitored on compliance with computer usage policies by drawing upon agency theory and general deterrence theory. After testing the hypothesized relationships using survey data, the results indicate that perceived sanction severity and certainty significantly influence intention to comply with computer usage policies. Furthermore, awareness of being monitored is found to significantly impact penalties. Study results further indicate that penalties may be effective only to the extent that organizations can detect employees' deviant behavior through managerial controls, such as computer monitoring.
The new speakers of Lombard

Abstract Lombard is one of the 12–15 non-recognized regional languages spoken in Italy. This article focuses on the new speakers of Lombard, i. e. people who have learned or are learning Lombard as a second language. Through an open questionnaire the author has probed into various aspects concerning these new speakers’ competence and learning trajectories, situations and opportunities for using the language, motivations, aims, commitment, metalinguistic awareness and the difficulties encountered in acquiring the language. Issues of authenticity, legitimacy and especially of identity and political allegiances have also been looked at. After a brief outline of the Lombard language and the research on new speakers that has been carried out so far, the article presents the methodology employed, followed by an analysis of the answers provided, which show that new speakers are very different from native speakers in sociolinguistic terms. A discussion of the results and some conclusions close the article.
A Comparative Study of Top-K High Utility Itemset Mining Methods

High Utility Itemset (HUI) mining problem is one of the important problems in the data mining literature. The problem offers greater flexibility to a decision maker to incorporate her/his notion of utility into the pattern mining process. The problem, however, requires the decision maker to choose a minimum utility threshold value for discovering interesting patterns. This is quite challenging due to the disparate itemset characteristics and their utility distributions. In order to address this issue, Top-K High Utility Itemset (THUI) mining problem was introduced in the literature. THUI mining problem is primarily a variant of the HUI mining problem that allows a decision maker to specify the desired number of HUIs rather than the minimum utility threshold value. Several algorithms have been introduced in the literature to efficiently mine top-k HUIs. This paper systematically analyses the top-k HUI mining methods in the literature, describes the methods, and performs a comparative analysis. The data structures, threshold raising strategies, and pruning strategies adopted for efficient top-k HUI mining are also presented and analysed. Furthermore, the paper reviews several extensions of the top-k HUI mining problem such as data stream mining, sequential pattern mining and on-shelf utility mining. The paper is likely to be useful for researchers to examine the key methods in top-k HUI mining, evaluate the gaps in literature, explore new research opportunities and enhance the state-of-the-art in high utility pattern mining.
A data reduction scheme for active authentication of legitimate smartphone owner using informative apps ranking

Smartphones are vulnerable to fraudulent use despite having strong authentication mechanisms. Active authentication based on behavioral biometrics is a solution to protect the privacy of data in smart devices. Machine-learning-based frameworks are effective for active authentication. However, the success of any machine-learning-based techniques depends highly on the relevancy of the data in hand for training. In addition, the training time should be very efficient. Keeping in view both issues, we’ve explored a novel fraudulent user detection method based solely on the app usage patterns of legitimate users. We hypothesized that every user has a unique pattern hidden in his/her usage of apps. Motivated by this observation, we’ve designed a way to obtain training data, which can be used by any machine learning model for effective authentication. To achieve better accuracy with reduced training time, we removed data instances related to any specific user from the training samples which did not contain any apps from the user-specific priority list. An information theoretic app ranking scheme was used to prepare a user-targeted apps priority list. Predictability of each instance related to a candidate app was calculated by using a knockout approach. Finally, a weighted rank was calculated for each app specific to every user. Instances with low ranked apps were removed to derive the reduced training set. Two datasets as well as seven classifiers for experimentation revealed that our reduced training data significantly lowered the prediction error rates in the context of classifying the legitimate user of a smartphone.
A wall-thickness compensation strategy for wax pattern of hollow turbine blade

As an important index affecting the aerodynamic performance and the structural strength of hollow turbine blades, the wall-thickness precision of the blade is mainly inherited from the positional relationship between the corresponding wax pattern and the internal ceramic core. However, due to locating errors, the actual position of ceramic core is always deviated from the ideal position, which makes it difficult to guarantee the wall-thickness precision of the wax pattern. To solve this problem, a wall-thickness compensation strategy is proposed in this paper. Firstly, based on the industrial computed tomography (ICT) technique and curve matching algorithms, a model reconstruction method is developed, with which the 3D model of a trial wax pattern can be easily constructed. After that, focusing on eliminating the wall-thickness errors of the trial wax pattern, an optimization method for the pose of the ceramic core in the wax pattern is proposed. Then, by mapping the optimal pose of the ceramic core to length adjustments of the locating rods, the wall-thickness errors of the wax pattern can be greatly reduced. A case study is also given to illustrate the effectiveness of the proposed compensation strategy.
Maximum margin object tracking with weighted circulant feature maps

Support vector machine (SVM) based tracking algorithms training with dense circulant samples have shown favourable performance due to its strong discriminative power and high efficiency. However, the challenges caused by the circulant sampling remain unaddressed. In this study, the authors give each training sample a weight based on their accuracy to reduce the influence of inaccurate samples. Moreover, they reform the SVM model with weighted circulant training samples. Secondly, they advocate an efficient solution by using the property of circulant matrices to solve the learning problem. Thirdly, a model update strategy is introduced to prevent the tracking models polluted by wrong samples. Experimental results on large benchmark datasets with 50 and 100 video sequences demonstrate that the authors’ tracking algorithms achieve state‐of‐art performance in terms of precision and accuracy. In addition, their tracker runs in real time.
Dictionary‐induced least squares framework for multi‐view dimensionality reduction with multi‐manifold embeddings

This study proposes a novel dimensionality reduction (DR) method for multi‐view datasets. The principal component analysis (PCA) idea of minimising least squares reconstruction errors is extended to consider both data distribution and penalty weights called dictionary to recover outliers free global structures from missing and noisy data points. In this way, PCA is viewed as a special instance of the authors’ proposed dictionary induced least squares framework (DLS). Furthermore, to appropriately handle multi‐view DR, we combine the DLS with multiple manifold embeddings (DLSME). Therefore it can obtain lower projections while maintaining a balance between preserving global structures with DLS and local structures with multi‐manifold embeddings. Extensive experiments on object and face recognition datasets verify that the DLS achieves better classification results with lower dimensional projections than PCA. Also, on many multi‐view datasets of visual recognition and web image annotation, the DLSME method demonstrates more effectiveness than Graph‐Laplacian PCA (gLPCA), robust PCA‐optimal mean, canonical correlation analysis (CCA), bilinear models (BLM), neighbourhood preserving embedding, locality preserving projections, and locality sensitive discriminant analysis.
An approach for distributed discussion and collaborative knowledge sharing: Theoretical and empirical analysis

We present a strategy for collaborative reasoning and automatic knowledge sharing in which multiple agents discuss about some piece of information until they reach an agreement. A structured argumentation system (DeLP) is used as the underlying knowledge representation and reasoning formalism, allowing agents to contribute to the discussion by posing or rejecting arguments, or objecting to atomic information. In this proposal, in order to be able to represent more general scenarios than other approaches in the literature, agents are allowed to believe in strict information and are allowed to freely join and leave the discussion at any moment. The proposed strategy automatically deals with the conflicts that may arise when agents pose contradictory strict information, which have to be solved to maintain the soundness of the discussion. The operational semantics of the strategy is defined using a transition system to formally characterise the agents’ behaviour and to prove its properties. An empirical comparison between the performance of different criteria that the agents can follow during the discussion is presented.
An Efficient Computing of Correlated Equilibrium for Cooperative $Q$-Learning-Based Multi-Robot Planning

Traditional multi-agent Q-learning (MAQL) induced planning needs to evaluate computationally expensive Nash/correlated equilibrium (CE) at a given joint state during both learning and planning phases. This paper introduces a novel approach to adapt composite rewards of all the agents in one Q-table in joint state-action space during learning, and uses these rewards to compute CE in an efficient way during the planning phases. Two schemes of MAQL have been proposed. If mutual cooperation among the agents leads to success of at least one agent and is enough to make the team successful, then scheme-I is employed. However, if an agent's success is contingent upon other agents' mutual cooperation as well as simultaneous success of the agents is mandatory then scheme-II is employed. New algorithms for multi-agent learning/planning have been proposed, centering on the said schemes. It is shown that the equilibrium obtained by the proposed algorithms and the traditional correlated Q-learning are identical. In order to restrict the exploration within the feasible joint states, constraint versions of the said algorithms are also proposed. An analysis is included to demonstrate the significant saving of computational time and space by the proposed algorithms. In addition, convergence analysis of the proposed algorithms is done. Experiments have been undertaken to validate the performance of the proposed algorithms in multi-robot planning on both simulated and real platforms.
Non-scanning measurement of position and attitude using two linear cameras

Most photogrammetry-based object position and attitude measurement systems use area cameras for two-dimensional (2D) image acquisition. However, the large volume of 2D images provided by area cameras limits the image capture speed, thus preventing the application of such methods to some high-speed measurements. Linear camera-based systems cannot be used to obtain real-time position and attitude parameters of non-specially designed objects because of scanning imaging and optimisation requirements. In this work, a novel position and attitude measurement method based on linear cameras was proposed. The position and attitude of objects can be obtained by analysing simultaneously recorded images of a specially designed five-line plane target using two calibrated linear cameras. This method can be used to carry out high-speed measurement of position and attitude parameters because no scanning imaging is required. In the present paper, we introduce the principle of this method, present verifications based on virtual and actual experiments, and analyse the main factors that affect measurement error.
Marginal tracking algorithm for hypersonic reentry gliding vehicle

Here, the trajectory of hypersonic reentry gliding vehicle is tracked in the East–North–Up (ENU) coordinate system (CS) using a novel marginal tracking algorithm. Firstly, the trajectories are compared and analysed through numerical simulation. The authors try to establish the manoeuvring models based on flight path angle and heading angle. However, they are all invisible in the ENU CS. Fortunately, inclination angle and azimuth angle can be utilised to replace them. Secondly, the trajectory is projected onto the longitudinal and horizontal observation planes and is tracked synchronously in them. The three-dimensional tracking is achieved through measurement data preprocessing, marginal filtering, and state merging. Finally, the parameters of manoeuvring models in two observation planes are set. Simulation shows that the algorithm has a good robustness and high tracking precision.
The contour fitting property of differential mutation

State-of-the-art global optimization techniques adapt the search range and direction to the objective function. Differential Evolution algorithms perform this adaptation implicitly, leading to a contour fitting property which has been empirically observed, but lacks theoretical grounding. In this paper, we formalize the contour fitting notion and derive an analytical model that links the differential mutation operator with the adaptation of the range and direction of search. Our analysis uses the Differential Mutation Evolutionary Algorithm (DMEA), which optimizes the multidimensional Gaussian objective function. Through our analysis, we are able to make several observations. Firstly, a normally distributed population remains normal in consecutive iterations. Moreover, parameters of the population distribution can be updated with explicit algebraic formulas. Furthermore, for a scaling factor below a critical value, the population reaches a stable state. Finally, the covariance matrix of a population in a stable state is proportional to the covariance matrix of the Gaussian objective function. The analytical results explaining the contour fitting property are confirmed with a simulation study. Although this work focuses on theoretical analyses, the proposed DE/prop/1 mutation and DMEA algorithm maintain population diversity and therefore, can lead to optimization by means of saddle-crossing and can become a basis for adaptive Markov Chain Monte Carlo sampling schemes or an element of strategy adaptive differential evolution variants. The latter approach was tested on the CEC 2017 benchmark and found to significantly improve the performance of the SaDE algorithm.
DevOps Round-Trip Engineering: Traceability from Dev to Ops and Back Again

DevOps engineers follow an iterative and incremental process to develop Deployment and Configuration (D&C) specifications. Such a process likely involves manual bug discovery, inspection, and modifications to the running environment. Failing to update the specifications appropriately leads to technical debt, including configuration drift, snowflake configurations, and erosion across environments. Despite the efforts that DevOps teams put into automating operations work, there is a lack of tools to support the development and maintenance of D&C specifications. In this paper, we propose Tornado, a two-way Continuous Integration (CI) framework (i.e., Dev Ops and Dev Ops) that automatically updates D&C specifications when the corresponding system changes, enabling bi-directional traceability of the modifications. Tornado extends the concept of CI, integrating operations work into development by committing code corresponding to manual modifications. We evaluated Tornado by implementing a proof of concept using Terraform templates, OpenStack and CircleCI, demonstrating its feasibility and soundness.
How Clever Is the FiLM Model, and How Clever Can it Be?

The FiLM model achieves close-to-perfect performance on the diagnostic CLEVR dataset and is distinguished from other such models by having a comparatively simple and easily transferable architecture. In this paper, we investigate in more detail the ability of FiLM to learn various linguistic constructions. Our results indicate that (a) FiLM is not able to learn relational statements straight away except for very simple instances, (b) training on a broader set of instances as well as pretraining on simpler instance types can help alleviate these learning difficulties, (c) mixing is less robust than pretraining and very sensitive to the compositional structure of the dataset. Overall, our results suggest that the approach of big all-encompassing datasets and the paradigm of “the effectiveness of data” may have fundamental limitations.
TS-BatPro: Improving Energy Efficiency in Data Centers by Leveraging Temporal–Spatial Batching

With the rapid scaling of data centers, understanding their power characteristics and optimizing data center energy consumption is a critical task. Typically, data centers are provisioned for peak load even when they are mostly operating at low utilization levels. This results in wasteful energy consumption requiring smart energy saving strategies. At the same time, latency critical application workloads have stringent quality of service (QoS) constraints that need to be satisfied. Optimizing data center energy with QoS constraints is challenging since different workloads can have variabilities in job sizes and distinct system utilization levels. Also, server configuration (e.g., the number of cores per server) can be different across data centers. Therefore, a single configuration for energy management that works well across these various scenarios, is not practical. In this paper, we propose TS-BatPro, a novel framework that judiciously integrates spatial and temporal job batching to save energy for multicore data center servers while meeting the QoS constraints for application workloads. TS-BatPro performs effective global job batching and scheduling by modeling job performance and power characteristics of multicore servers without any hardware modifications. TS-BatPro works on commodity server platforms and comprises two components: 1) a temporal batching engine that batches incoming jobs such that the servers can continue to reside in low-power states and 2) a spatial batching engine that prioritizes scheduling job batches to a small subset of servers. We develop a prototype of TS-BatPro on physical testbed with a cluster of servers and evaluate TS-BatPro on a variety of workloads. Our results show that TS-BatPro is able to achieve significant amount of energy savings under various job response time constraints and traffic patterns.