Reviewerly/exHarmony huggingface dataset:
Analysis on the Efficiency of Multifactorial Evolutionary Algorithms

Many experimental studies have demonstrated the superiority of multifactorial evolutionary algorithms (MFEAs) over traditional methods of solving each task independently. In this paper, we investigate this topic from theoretical analysis aspect. We present a runtime analysis of a (4+2) MFEA on several benchmark pseudo-Boolean functions, which include problems with similar tasks and problems with dissimilar tasks. Our analysis results show that, by properly setting the parameter rmp (i.e., the random mating probability), for the group of problems with similar tasks, the upper bound of expected runtime of the (4+2) MFEA on the harder task can be improved to be the same as on the easier one. As for the group of problems with dissimilar tasks, the expected upper bound of (4+2) MFEA on each task are the same as that of solving them independently. This study theoretically explains why some existing MFEAs perform better than traditional methods in experimental studies and provides insights into the parameter setting of MFEAs.
A SHADE-Based Algorithm for Large Scale Global Optimization

During the last decade, large-scale global optimization has been a very active research area not only because of its many challenges but also because of its high applicability. It is indeed crucial to develop more effective search strategies to explore large search spaces considering limited computational resources. In this paper, we propose a new hybrid algorithm called Global and Local search using Success-History Based Parameter Adaptation for Differential Evolution (GL-SHADE) which was specifically designed for large-scale global optimization. Our proposed approach uses two populations that evolve differently allowing them to complement each other during the search process. One is in charge of exploring the search space while the other is in charge of exploiting it. Our proposed method is evaluated using the CEC’2013 large-scale global optimization (LSGO) test suite with 1000 decision variables. Our experimental results show that the new proposal outperforms one of the best hybrid algorithms available in the state of the art (SHADEILS) in the majority of the test problems adopted while being competitive with respect to several other state-of-the-art algorithms when using the LSGO competition criteria adopted at CEC’2019.
Hybrid Approach Using Machine Learning Techniques in Credit Card Fraud Detection

Crime financial services have a serious problem by credit card fraud. Every year billions of dollars are lost by credit card fraud. Lack of research and studies on analyzing real-world credit card data is there. In this paper, we apply machine learning algorithms to detect the credit card fraud using credit card datasets and compare their performance. We use hybrid algorithm model involving voting classifier to detect the fraud. The result positively indicates that the voting classifier method gives good accuracy, precision, recall, and F1 score in detecting fraud in credit card transactions. More than that, the work identifies the important variables in the dataset that may lead to higher accuracy in credit card fraud detection, and on the whole, we discuss about various types of classifiers and its performance in detecting fraudulent transactions.
Puzzle-AE: Novelty Detection in Images through Solving Puzzles.

Autoencoder, as an essential part of many anomaly detection methods, is lacking flexibility on normal data in complex datasets. U-Net is proved to be effective for this purpose but overfits on the training data if trained by just using reconstruction error similar to other AE-based frameworks. Puzzle-solving, as a pretext task of self-supervised learning (SSL) methods, has earlier proved its ability in learning semantically meaningful features. We show that training U-Nets based on this task is an effective remedy that prevents overfitting and facilitates learning beyond pixel-level features. Shortcut solutions, however, are a big challenge in SSL tasks, including jigsaw puzzles. We propose adversarial robust training as an effective automatic shortcut removal. We achieve competitive or superior results compared to the State of the Art (SOTA) anomaly detection methods on various toy and real-world datasets. Unlike many competitors, the proposed framework is stable, fast, data-efficient, and does not require unprincipled early stopping.
Novel Feature Selection for Artificial Intelligence Using Item Response Theory for Mortality Prediction

Feature selection is a critical component in supervised machine learning classification analyses. Extraneous features introduce noise and inefficiencies into the system leading to a need for feature reduction techniques. Many feature reduction models use the end-classification results in the feature reduction process, committing a circular error. Item Response Theory (IRT) examines the characteristics of features independent of the end-classification results, and provides high levels of information regarding feature utility. A two-parameter dichotomous IRT model was used to analyze 18 features from an intensive care unit data set with 2520 cases. The classification results showed that the features selected via IRT were comparable to that using more traditional machine learning approaches. Strengths and limitations of the IRT selection protocol are discussed.
Practical Performance of Space Efficient Data Structures for Longest Common Extensions.

For a text T[1,n], a Longest Common Extension (LCE) query lce_T(i,j) asks for the length of the longest common prefix of the suffixes T[i,n] and T[j,n] identified by their starting positions 1 ≤ i,j ≤ n. A classic problem in stringology asks to preprocess a static text T[1,n] over an alphabet of size σ so that LCE queries can be efficiently answered on-line. Since its introduction in the 1980’s, this problem has found numerous applications: in suffix sorting, edit distance computation, approximate pattern matching, regularities finding, string mining, and many more. Text-book solutions offer O(n) preprocessing time and O(1) query time, but they employ memory-heavy data structures, such as suffix arrays, in practice several times bigger than the text itself.Very recently, more space efficient solutions using O(nlogσ) bits of total space or even only O(log n) bits of extra space have been proposed: string synchronizing sets [Kempa and Kociumaka, STOC'19, and Birenzwige et al., SODA'20] and in-place fingerprinting [Prezza, SODA'18]. The goal of this article is to present well-engineered implementations of these new solutions and study their practicality on a commonly agreed text corpus. We show that both perform extremely well in practice, with space consumption of only around 10% of the input size for string synchronizing sets (around 20% for highly repetitive texts), and essentially no extra space for fingerprinting. Interestingly, our experiments also show that both solutions become much faster than naive scanning even for finding common prefixes of moderate length, contradicting a common belief that sophisticated data structures for LCE queries are not competitive with naive approaches [Ilie and Tinta, SPIRE'09].
Context-Aware Based Evolutionary Collaborative Filtering Algorithm

Recommender systems are tools that provide personalized suggestions of items for users. They must be able to adapt constantly to user preferences and behavior in order to generate relevant recommendations. However, initial works in this field do not consider the context in which the users are. In recent years, a new recommendation technique, called Context-Aware Recommender System (CARS), has emerged. This approach integrates contextual information about users and/or items in the recommendation process to satisfy even more users’ needs. Therefore, accurate prediction depends upon the degree to which a recommendation method has incorporated the relevant contextual data. To address this issue, we propose to combine user based collaborative filtering with the Genetic Algorithm based meta-heuristic in order to provide better predictions for users. The proposed model uses a weighting function which incorporates the contextual factors that influence the users’ decisions. It is based on the Genetic Algorithm based meta-heuristic to estimate, for each contextual parameter, a degree of importance that would reduce the mean absolute error and increase the F-measure. Experimental results from Movielens dataset validate that our proposed algorithm improves recommendations accuracy.
Research on Path Planning of UAV Based on Ant Colony Algorithm with Angle Factor

Abstract In this paper, aiming at the slow convergence problem of ant colony algorithm in UAV three-dimensional path planning, the heuristic function and local search strategy of improved ant colony algorithm with angle factor are proposed. The simulation results show that the ability of the improved ant colony algorithm to find the optimal solution in the three-dimensional environment is improved. Compared with the traditional algorithm, the overall route planning is shortened by 22 km, and the convergence speed is 51.3% of the original algorithm. The algorithm not only reduces the complexity of UAV track search, but also effectively improves the convergence speed and search efficiency of ant colony algorithm. It shows that the improved ant colony algorithm is feasible and effective in UAV three-dimensional path planning, and provides a new strategy for three-dimensional path planning.
Inferring Streaming Video Quality from Encrypted Traffic

Inferring the quality of streaming video applications is important for Internet service providers, but the fact that most video streams are encrypted makes it difficult to do so.We develop models that infer quality metrics (i.e., startup delay and resolution) for encrypted streaming video services. Our paper builds on previous work, but extends it in several ways. First, the models work in deployment settings where the video sessions and segments must be identified from a mix of traffic and the time precision of the collected traffic statistics is more coarse (e.g., due to aggregation). Second, we develop a single composite model that works for a range of different services (i.e., Netflix, YouTube, Amazon, and Twitch), as opposed to just a single service. Third, unlike many previous models, our models perform predictions at finer granularity (e.g., the precise startup delay instead of just detecting short versus long delays) allowing to draw better conclusions on the ongoing streaming quality. Fourth, we demonstrate the models are practical through a 16-month deployment in 66 homes and provide new insights about the relationships between Internet "speed" and the quality of the corresponding video streams, for a variety of services; we find that higher speeds provide only minimal improvements to startup delay and resolution.
DeepTable: a permutation invariant neural network for table orientation classification

Abstract Tables are a common way to present information in an intuitive and concise manner. They are used extensively in media such as scientific articles or web pages. Automatically analyzing the content of tables bears special challenges. One of the most basic tasks is determination of the orientation of a table: In column tables, columns represent one entity with the different attribute values present in the different rows; row tables are vice versa, and matrix tables give information on pairs of entities. In this paper, we address the problem of classifying a given table into one of the three layouts horizontal (for row tables), vertical (for column tables), and matrix. We describe DeepTable, a novel method based on deep neural networks designed for learning from sets. Contrary to previous state-of-the-art methods, this basis makes DeepTable invariant to the permutation of rows or columns, which is a highly desirable property as in most tables the order of rows and columns does not carry specific information. We evaluate our method using a silver standard corpus of 5500 tables extracted from biomedical articles where the layout was determined heuristically. DeepTable outperforms previous methods in both precision and recall on our corpus. In a second evaluation, we manually labeled a corpus of 300 tables and were able to confirm DeepTable to reach superior performance in the table layout classification task. The codes and resources introduced here are available at https://github.com/Marhabibi/DeepTable .
Native gold from the Kamenka-Barabanovsky and Kharuzovka alluvial placers (Northwest Salair Ridge, Western Siberia, Russia): Typomorphic features and possible bedrock sources

Northwest Salair Ridge, is one of the oldest alluvial gold mining regions; it is located in the western part of the Altai-Sayan folded area. The placers localized along the Kharuzovka and Kamenka-Barabanovsky creeks (the right-branch of the Suenga River) were chosen for investigation. The only bedrock deposit in the study area is the Novolushnikovsky gold deposit located in the Suenginsky placer field of the Egoryevsky district. The source of gold supply for the Kharuzovka and Kamenka-Barabanovsky placers spatially related to the hypothetical continuation of the type II’ ore zones of the Novolushnikovsky deposit. The composition of gold grains derived from placers was determined based on a representative collection. A distinctive feature of native gold from type II’ ores is unusually high content of Hg, up to 20 wt%. The composition of gold grains from studied placers shows that the average Hg content is 1.5–5 wt%. However, there is a group of Hg-rich placers (<1% of the collection), where the mercury content is as high as 16 wt%. We suggest that possible alteration of the gold composition manifested both in the rims and in the cores is represented by significant loss of Hg, while the other analyzed components underwent relative enrichment. The first time reported demercurization processes occurring under the supergenic conditions determine this alteration. The erosion of Novolushnikovsky deposit type II’ ore zones are the source of supply of Hg-rich gold in the Kharuzovka and Kamenka-Barabanovsky placers. These placers were analyzed for the existence of mineral inclusions in gold grains. The presence of placer gold grains with mineral inclusions, that partially or fully correspond to mineral association of ores of the Novolushnikovsky deposit, as well as an unusual Hg-content of the native gold suggest several sources of gold with a similar mineralization style. The undiscovered sources of gold are might be also of intrusion related type.
Line Integral Approach to Extended Dissipative Filtering for Interval Type-2 Fuzzy Systems

This article is concerned with the problems of extended dissipativity analysis and filter design for interval type-2 (IT2) fuzzy systems. Based on the line integral Lyapunov function, a sufficient condition of asymptotic stability and extended dissipativity of the systems under consideration is established. A LMI-based equivalent condition to the obtained one in a nonlinear form is provided by combining congruence transformation with change of variables. This LMI condition obtained is more general than the one which is based on the common quadratic Lyapunov function. Meanwhile, in terms of parameterization, the extended dissipative filter is developed which guarantees the asymptotic stability and extended dissipativity for the filtering error system. Furthermore, our filter obtained by the parameterization method includes the one obtained by the equivalent transformation method as a special case. Two simulation examples are provided to show the merits and effectiveness of the proposed approach.
Online Sequential Model for Multivariate Time Series Prediction With Adaptive Forgetting Factor

In the process of online prediction of multivariable non-stationary time series by kernel extreme learning machine (KELM), the dynamic characteristics of the system which are difficult to determine have always posed a big problem. We propose an online sequential prediction model with an adaptive forgetting factor (AFF) for multivariable time series to solve this problem. The multivariable time series instead of variable itself is reconstructed firstly. AFF is introduced into the objective function and can be adjusted iteratively and adaptively with the system changes. As a result, higher weight can be allocated for the fresh and more important samples while the old failure samples can be quickly forgotten. The model sparsification uses a fast leave-one-out cross-validation (FLOO-CV) method to set a prediction error threshold so that samples can be selected conditionally to form a dictionary. Besides, the dictionary parameters, including AFF and kernel parameters, are recursively updated simultaneously without increasing calculation complexity. The experimental results show that, compared with four fashionable KLEM methods, the proposed AFF-OSKELM has a better dynamic tracking ability and adaptability. Moreover, compared with single variable prediction, the spatial reconstructed multivariable has higher prediction accuracy and stability.
Block-chain Implementation of Letter of Credit based Trading system in Supply Chain Domain

On a global scale, Supply-Chain Management(SCM) is a network of amenities that runs through different processes involving procurement of raw materials, storage and movement of these materials and converting them into products to the distribution of final products to consumers. All these seems simple in theory, practically efficient management of such a system is challenging. In order to resolve and overcome these inefficiencies and save Organization's money, various technologies are being used. Amongst these, blockchain is reconnoitering new ways to change the overall game. This work proposes a BlockChain based revolutionary Supply-chain system addressing the deficiencies that the traditional system could not address in its entirety. The proposed Blockchain system achieves transparent record keeping and provenance tracking with reduced cost using a distributed, immutable and highly reliable ledger concept that records the history of products right from its origination to its buyer without the need of a third trusted party. This work is implemented using Hyperledger fabric and proves the efficiency of the proposed system.
OSACT4 Shared Task on Offensive Language Detection: Intensive Preprocessing-Based Approach.

The preprocessing phase is one of the key phases within the text classification pipeline. This study aims at investigating the impact of the preprocessing phase on text classification, specifically on offensive language and hate speech classification for Arabic text. The Arabic language used in social media is informal and written using Arabic dialects, which makes the text classification task very complex. Preprocessing helps in dimensionality reduction and removing useless content. We apply intensive preprocessing techniques to the dataset before processing it further and feeding it into the classification model. An intensive preprocessing-based approach demonstrates its significant impact on offensive language detection and hate speech detection shared tasks of the fourth workshop on Open-Source Arabic Corpora and Corpora Processing Tools (OSACT). Our team wins the third place (3rd) in the Sub-Task A Offensive Language Detection division and wins the first place (1st) in the Sub-Task B Hate Speech Detection division, with an F1 score of 89% and 95%, respectively, by providing the state-of-the-art performance in terms of F1, accuracy, recall, and precision for Arabic hate speech detection.
Quantized Neural Networks: Characterization and Holistic Optimization

Quantized deep neural networks (QDNNs) are necessary for low-power, high throughput, and embedded applications. Previous studies mostly focused on developing optimization methods for the quantization of given models. However, quantization sensitivity depends on the model architecture. Also, the characteristics of weight and activation quantization are quite different. This study proposes a holistic approach for the optimization of QDNNs, which contains QDNN training methods as well as quantization-friendly architecture design. Synthesized data is used to visualize the effects of weight and activation quantization. The results indicate that deeper models are more prone to activation quantization, while wider models improve the resiliency to both weight and activation quantization.
Identity-Invariant Facial Landmark Frontalization For Facial Expression Analysis

We propose a frontalization technique for 2D facial landmarks, designed to aid in the analysis of facial expressions. It employs a new normalization strategy aiming to minimize identity variations, by displacing groups of facial landmarks to standardized locations. The technique operates directly on 2D landmark coordinates, does not require additional feature extraction and as such is computationally light. It achieves considerable improvement over a reference approach, justifying its use as an efficient preprocessing step for facial expression analysis based on geometric features.
Improving Learning in Virtual Learning Environments Using Affective Pedagogical Agent

Emotions are part of human life, and they are present on several occasions, like decision making and in social interactions. Computational identification of emotions in texts can be useful in many applications, especially in distance learning courses. This research introduces an animated pedagogic agent, integrated to a Moodle virtual learning environment, with the objective of assisting the tutor in accompanying students, helping the students to acquire knowledge, identifying their emotions, and motivating the student to participate in activities and discussions. As a way of assessing students' emotional state, an experiment was conducted using real data from a completed course, involving students. The results obtained are promising, evidencing the importance of knowing the emotional state of the students, contributing to the learning process.
Information Hiding In Image Enhancement

This paper proposes an information hiding method to embed data while executing image enhancement steps. The 2D Median Filter is adapted and re-engineered to demonstrate the feasibility of this concept. In particular, the filteringembedding steps are performed for each pixel in a sliding window manner. Pixels enclosed within the predefined window (neighborhood) are gathered, linearized and sorted. Then, the linearized pixels are divided into partitions, in which each partition is assigned to represent a certain sequence of bits. The performance of the proposed method is evaluated by using the BSD300 dataset for various settings. The embedding capacity, image quality, data extraction error rate are reported and analyzed. Besides, the robustness of the proposed method against brute force attack is also discussed. In the best case scenario, when the window size is 7 x 7, ~ 0.97 bpp is achieved with acceptable image quality while having ~ 3.5% data extraction error rate.