Reviewerly/exHarmony huggingface dataset:
Inferring Future Landscapes: Sampling the Local Optima Level

Connection patterns among Local Optima Networks (LONs) can inform heuristic design for optimisation. LON research has predominantly required complete enumeration of a fitness landscape, thereby restricting analysis to problems diminutive in size compared to real-life situations. LON sampling algorithms are therefore important. In this article, we study LON construction algorithms for the Quadratic Assignment Problem (QAP). Using machine learning, we use estimated LON features to predict search performance for competitive heuristics used in the QAP domain. The results show that by using random forest regression, LON construction algorithms produce fitness landscape features which can explain almost all search variance. We find that LON samples better relate to search than enumerated LONs do. The importance of fitness levels of sampled LONs in search predictions is crystallised. Features from LONs produced by different algorithms are combined in predictions for the first time, with promising results for this “super-sampling”: a model to predict tabu search success explained 99% of variance. Arguments are made for the use-case of each LON algorithm and for combining the exploitative process of one with the exploratory optimisation of the other.
Instance Segmentation Network With Self-Distillation for Scene Text Detection

Segmentation based methods have become the mainstream for detecting scene text with arbitrary orientations and shapes. In order to address challenging problems such as separating the text instances that are very close to each other, however, these methods often require time-consuming post-processing. In this paper, we propose an instance segmentation network (ISNet), which simultaneously generates prototype masks and per-instance mask coefficients. After linearly combining the two components, ISNet can implement fast text location. Furthermore, we apply self-distillation to train the ISNet and refine its detection accuracy. We have evaluated the proposed method on four popular benchmarks, i.e., ICDAR2015, ICDAR2017 MLT, CTW1500 and Total-Text, and the experimental results show that it can achieve better tradeoff between accuracy and efficiency for scene text detection.
Risk Entropy Modeling of Surveillance Camera for Public Security Application

Surveillance cameras are widely installed at public places around the world, and the video surveillance system plays an un-substitutable role in police work, especially in case investigation.The problem regarding the effectiveness and rationality of the video surveillance system comes into being in terms of its high demand for investment and rising public concern of over-construction potentially.To answer the question, it ought to establish mode and metrics for measuring effectiveness in theory.This article argued that the police video surveillance system is preferably a sensor network than a Physical Protect System (PPS) because its main feature is to provide the police officers with the visual information they need.Once the police cannot receive sufficient information from the system, decisions of public security are given based on limited or misleading information, and there may be some potential risks remained.Such risks of public security are not directly relevant to the integrity and value of the assets but the uncertainty of decisionmaking, which is different from the one of traditional PPS.In this paper, we proposed an entropy model for measuring the uncertainty based on attributions of video surveillance for law enforcement.Public security risk was divided into three types within the model according to the source of the risk, such as fixed targets (or restricted areas), moving objects, and video information quality.We verified the validity of the model by the simulation experiment of camera field optimization and discussed further work.
An evolutionary decomposition-based multi-objective feature selection for multi-label classification

Data classification is a fundamental task in data mining. Within this field, the classification of multi-labeled data has been seriously considered in recent years. In such problems, each data entity can simultaneously belong to several categories. Multi-label classification is important because of many recent real-world applications in which each entity has more than one label. To improve the performance of multi-label classification, feature selection plays an important role. It involves identifying and removing irrelevant and redundant features that unnecessarily increase the dimensions of the search space for the classification problems. However, classification may fail with an extreme decrease in the number of relevant features. Thus, minimizing the number of features and maximizing the classification accuracy are two desirable but conflicting objectives in multi-label feature selection. In this article, we introduce a multi-objective optimization algorithm customized for selecting the features of multi-label data. The proposed algorithm is an enhanced variant of a decomposition-based multi-objective optimization approach, in which the multi-label feature selection problem is divided into single-objective subproblems that can be simultaneously solved using an evolutionary algorithm. This approach leads to accelerating the optimization process and finding more diverse feature subsets. The proposed method benefits from a local search operator to find better solutions for each subproblem. We also define a pool of genetic operators to generate new feature subsets based on old generation. To evaluate the performance of the proposed algorithm, we compare it with two other multi-objective feature selection approaches on eight real-world benchmark datasets that are commonly used for multi-label classification. The reported results of multi-objective method evaluation measures, such as hypervolume indicator and set coverage, illustrate an improvement in the results obtained by the proposed method. Moreover, the proposed method achieved better results in terms of classification accuracy with fewer features compared with state-of-the-art methods.
Developing a Regression Model of Cooperative Learning Methodology in Pre-Service Teacher Education: A Sustainable Path for Transition to Teaching Profession

There is a growing interest in the study of active methodologies, especially cooperative learning. It will allow for skills related to the positive interdependence between the components of a group, individual responsibility, the encouragement of interaction, and the development of social skills to be put into practice by pre-service teachers, as well as for the sharing and assertiveness of the ideas of teamwork. The purpose of this study is to predict those variables that significantly positively affect pre-service teachers’ perceptions about the use of cooperative learning methodology in their initial training at university once it has been put into practice in the classroom for 3 months. For this, a quasi-experimental design was implemented to enable its practice, and subsequently the perceptions of the pre-service teachers were collected through the use of a questionnaire. The sample was composed of 140 pre-service teachers from the Faculty of Education of the University of Almeria (Spain). The overall results of the study determined that the interest of pre-service teachers in their subject, age, attendance of practical classes, and the type of education level they have had access to positively affect the perceptions of pre-service teachers, while positive academic performance causes a decline. These findings highlight the need to continue research, mainly on the question of why working in cooperative groups causes a decrease in academic performance.
Methods of Handling Unbalanced Datasets in Credit Card Fraud Detection

Nowadays fraudulent transactions of every type represent a major concern in the financial industry due to the total amount of money that are lost every year. Manually analyzing fraudulent transactions is unfeasible if we think at the huge amount of data and the complexity of bank fraud in the digitization era. In this context, the problem to detect the fraud can be achieved by machine-learning algorithms due to their ability of detecting small anomalies in very large datasets. The problem that arise here is that the datasets are highly unbalanced meaning that the non-fraudulent cases heavily dominates the fraudulent ones. In this paper, we are going to present three ways of handling unbalanced datasets by: resampling methods (undersampling and oversampling), cost-sensitive training and tree algorithms (decision tree, random forest and Naïve Bayes), emphasizing the idea of why the Receiver Operating Characteristics curve (ROC) should not be used on this type of datasets when measuring the performance of the algorithm. The experimental test was applied on a number of 890,977 banking transactions in order to observe the performance metrics of all the three methods mentioned above.
A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories

The breast ultrasound is not only one of major devices for breast tissue imaging, but also one of important methods in breast tumor screening. It is non-radiative, non-invasive, harmless, simple, and low cost screening. The American College of Radiology (ACR) proposed the Breast Imaging Reporting and Data System (BI-RADS) to evaluate far more breast lesion severities compared to traditional diagnoses according to five-criterion categories of masses composition described as follows: shape, orientation, margin, echo pattern, and posterior features. However, there exist some problems, such as intensity differences and different resolutions in image acquisition among different types of ultrasound imaging modalities so that clinicians cannot always identify accurately the BI-RADS categories or disease severities. To this end, this article adopted three different brands of ultrasound scanners to fetch breast images for our experimental samples. The breast lesion was detected on the original image using preprocessing, image segmentation, etc. The breast tumor’s severity was evaluated on the features of the breast lesion via our proposed classifiers according to the BI-RADS standard rather than traditional assessment on the severity; i.e., merely using benign or malignant. In this work, we mainly focused on the BI-RADS categories 2–5 after the stage of segmentation as a result of the clinical practice. Moreover, several features related to lesion severities based on the selected BI-RADS categories were introduced into three machine learning classifiers, including a Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN) combined with feature selection to develop a multi-class assessment of breast tumor severity based on BI-RADS. Experimental results show that the proposed CAD system based on BI-RADS can obtain the identification accuracies with SVM, RF, and CNN reaching 80.00%, 77.78%, and 85.42%, respectively. We also validated the performance and adaptability of the classification using different ultrasound scanners. Results also indicate that the evaluations of F-score based on CNN can obtain measures higher than 75% (i.e., prominent adaptability) when samples were tested on various BI-RADS categories.
Nanometric metrology by FIB-SEM-DIC measurements of strain field and fracture separation on composite metallic material

Currently, kinematic field measurements for studying the mechanical behavior of materials and structures use common optical methods, such as mark tracking techniques, grid methods and correlation techniques. These techniques are employed over a region of interest ranging from micro to millimeter scale. However, when studies need to be conducted on even smaller scales such as sub-micrometric scale, the use of more complex means of observation is required. In this case, the work can be achieved using the scanning electron microscope (SEM), and the Focused-Ion-Beam (FIB) as marking technique. An adaptation of Digital Image Correlation (DIC) method Heaviside-based Digital Image Correlation (H-DIC) is chosen to investigate the mechanical behavior and taking into account local fractures. As the studied problem is to extract strains from a fractured material using a displacement field measurement method, derivative computation for strain determination is not well adapted. The proposed approach consists in extracting the residual strains from local first gradients of H-DIC, which are less perturbed by the fractures. Various tests were performed to evaluate the validity of this new proposed approach. An application to study the mechanical behavior of a metallic composite (Al/ω-Al-Cu-Fe) is proposed. A particular exploitation of all field lies in the good separation of the strain fields and the cracked part. A discussion that focus on the comparison between a conventional DIC analysis and its extension was presented on the zones without or with fractures.
Security through block vault in a blockchain enabled federated cloud framework

Abstract The survivability of any organization in the event of disaster or attack could greatly depend on its offsite data recovery. But which provider could offer secure and resilient cyber security protection to offsite data is a major problem for individuals, businesses and organizations. In recent times, cyber-attacks are strategic and tactical. Adversaries are advanced with capability to access sensitive business digital assets. Their nefarious actions have exposed devastating flaws in many access controls at different levels, and are now security risk to systems, data storages and resources. In this paper, we present a Blockchain enabled federated cloud computing framework, to secure storage for offsite digital assets. Our framework is designed with a great degree of efficiency, privacy, scalability and restricted access control. It addresses authentication flaws and improve early detection of data breaches, by continuously evaluating subject’s access control and interaction with resources, using operation cost for monitoring and accountability. Evaluation showed proof of concept that our design and approach outperforms the traditional approaches.
Non-Cooperative Low-Complexity Detection Approach for FHSS-GFSK Drone Control Signals

The commercial drone market has substantially grown over the past few years. While providing numerous advantages in various fields and applications, drones also provide ample opportunities for misuse by irresponsible hobbyists or malevolent actors. The increasing number of safety/security incidents in which drones are involved has motivated researchers to find new and ingenious ways to detect, locate and counter this type of vehicles. In this paper, we propose a new method to detect frequency hopping spread spectrum - Gaussian frequency-shift keying (FHSS-GFSK) drone communication signals, in a non-cooperative scenario, where no prior information about the signals of interest is available. The system is designed to detect and retrieve data bit sequences through a compressive sampling approach, which includes the extraction of the reduced spectral information and a soft detection algorithm. The performance of the proposed approach is assessed in terms of bit error rate and compared with that of a Viterbi detector and a neural network-based detector. The effectiveness of the method described in the paper highlights the fact that current UAV communications are not infallible and present real security issues.
Hessian Complexity Measure for Genetic Programming-Based Imputation Predictor Selection in Symbolic Regression with Incomplete Data

Missing values bring several challenges when learning from real-world data sets. Imputation is a widely adopted approach to estimating missing values. However, it has not been adequately investigated in symbolic regression. When imputing the missing values in an incomplete feature, the other features that are used in the prediction process are called imputation predictors. In this work, a method for imputation predictor selection using regularized genetic programming (GP) models is presented for symbolic regression tasks on incomplete data. A complexity measure based on the Hessian matrix of the phenotype of the evolving models is proposed. It is employed as a regularizer in the fitness function of GP for model selection and the imputation predictors are selected from the selected models. In addition to the baseline which uses all the available predictors, the proposed selection method is compared with two GP-based feature selection variations: the standard GP feature selector and GP with feature selection pressure. The trends in the results reveal that in most cases, using the predictors selected by regularized GP models could achieve a considerable reduction in the imputation error and improve the symbolic regression performance as well.
Stair-climbing gait for a four-wheeled vehicle

Abstract This paper proposes a stair-climbing gait for a four-wheeled vehicle. The idea and methodology of climbing stairs are described, and the results of experiments are shown. Crawler-type vehicles and vehicles with special, complicated mechanisms are commonly used for climbing continuous steps (stairs). In contrast, in this paper, a four-wheeled vehicle with additional degrees of freedom, whose mechanism is not as complicated, is discussed. A gait algorithm based on a pace gait is proposed, and its performance is evaluated.
Affective Content-aware Adaptation Scheme on QoE Optimization of Adaptive Streaming over HTTP

The article presents a novel affective content-aware adaptation scheme (ACAA) to optimize Quality of Experience (QoE) for dynamic adaptive video streaming over HTTP (DASH). Most of the existing DASH adaptation schemes conduct video bit-rate adaptation based on an estimation of available network resources, which ignore user preference on affective content (AC) embedded in video data streaming over the network. Since the personal demands to AC is very different among all viewers, to satisfy individual affective demand is critical to improve the QoE in commercial video services. However, the results of video affective analysis cannot be applied into a current adaptive streaming scheme directly. Correlating the AC distributions in user's viewing history to each being streamed segment, the affective relevancy can be inferred as an affective metric for the AC related segment. Further, we have proposed an ACAA scheme to optimize QoE for user desired affective content while taking into account both network status and affective relevancy. We have implemented the ACAA scheme over a realistic trace-based evaluation and compared its performance in terms of network performance, QoE with that of Probe and Adaptation (PANDA), buffer-based adaptation (BBA), and Model Predictive Control (MPC). Experimental results show that ACAA can preserve available buffer time for future being delivered affective content preferred by viewer's individual preference to achieve better QoE in affective contents than those normal contents while remain the overall QoE to be satisfactory.
SecDedoop: Secure Deduplication with Access Control of Big Data in the HDFS/Hadoop Environment

With the rapid growth of storage providers, data deduplication is an essential storage optimization technique that greatly minimizes data storage costs by storing a unique copy of duplicate data. Nowadays, deduplication introduces various new challenges such as security and insufficient space issue. Hence, in this article, we propose a secure data deduplication with access control of big data over HDFS (Hadoop Distributed File System)/Hadoop environment, called SecDedoop. First, the system achieves security for data confidentiality by third party vendor using elliptic curve cryptography. There are two types of keys (public key and private key) generated for data retrieval. Second, we consider data deduplication. The user's original file is divided into a number of equal chunks. Then, each chunk (e.g., 1. txt) is tokenized into words and the weight of words is computed by using TF-IDF frequency. The SHA-3 hash computation is performed to the user's original file. If the hash value is not duplicate, then we store data in HDFS. The PSO (particle swarm optimization)-based MapReduce model is the proposed best data node selection. Initially, MapReduce process is finished for the user's original file and it results in the best set of data nodes; then, we apply PSO to compute the fitness value for best data node selection. Further, we consider MongoDB for fast indexing of the user's original files and also apply FCM (fuzzy-C-means clustering) for clustering the user's files. In this article, we consider the modified version of PSO and FCM to eliminate the open issues in conventional PSO and FCM. The performance of our proposed SecDedoop has been evaluated by using various performance metrics and also proved it outperforms better than previous approaches.
WS-LSMR: Malicious WebShell Detection Algorithm Based on Ensemble Learning

To solve the problem that the features produced by hidden means, such as code obfuscation and compression, in encrypted malicious WebShell files are not the same as those produced by nonencrypted files, a WebShell attack detection algorithm based on ensemble learning is proposed.First, this algorithm extracted the feature vocabulary of the unigrams and 4-grams based on opcode; subsequently, the 4-gram feature word weights were obtained according to the calculated Gini coefficient of the unigram feature words and used to select the features, which will be selected again based on the Gini coefficient of the 4-gram feature words.Consequently, a feature vocabulary that can detect encrypted and unencrypted WebShell files was constructed.Second, in order to improve the adaptability and accuracy of the detection method, an ensemble detection model called WS-LSMR, consisting of a Logistic Regression, Support Vector Machine, Multi-layer Perceptron and Random Forest, was constructed.The model uses a weighted voting method to determine the WebShell classification.This experiment demonstrated that compared with the traditional single WebShell detection algorithm, the recall rate and accuracy rate improved to 99.14% and 94.28%, respectively, which proves that this method has better detection performance.
LockerSwarm: An IoT-based Smart Locker System with Access Sharing

At any college-level institutions, students typically carry heavy belongings to the university every day. While generic lockers are available on campus, these lockers can only be used by one person at a time. In addition, information regarding locker locations and their availability are not available. This project focuses on the use of Internet of Things (IoT) and full stack software development to offer an alternative smart locker solution, called LockerSwarm. Instead of a physical key, each locker can be unlocked by scanning a Quick Response code (QR code), and its access can be shared among selected individuals. Users can search for the closest lockers, see the locations of all lockers, and check locker availability. Overall, this project sheds the new light on integrating IoT technologies to transform traditional lockers to smart lockers, which is a part of a smart campus initiative. With the prototype development and testing experience at Chulalongkorn University, Thailand, the entire LockerSwarm system is deemed to be easily replicable at any other university campuses.