Reviewerly/exHarmony huggingface dataset:
Anomaly Detection Using Modified Differential Evolution: An Application to Banking and Insurance

We propose two Modified Differential Evolution driven subspace based optimization models for anomaly detection in customer credit card churn detection, automobile insurance fraud detection and customer credit card default detection. Sparsity coefficient is chosen as the objective function for discovering anomalies. Also, we employed an external performance measure as selection constraint, namely, precision multiplied by recall at every iteration after a pre-specified iteration count. The proposed technique outperformed a bunch of baseline algorithms for anomaly detection, for example, Local Outlier Factor, Angle based Outlier Detection, K-means, Partition Around Medoids and also the proposed model without invoking the external performance measure in terms of precision and Area Under ROC Curve (AUC) indicating that the proposed method a viable alternative for anomaly detection.
Transformations towards clean functional code

The programming style has an impact on the readability and comprehensibility of the source code, and it may also affect run-time performance. This statement also holds for functional languages when the functional style is mixed with imperative design. In this paper, we present a couple of methods that can refactor imperatively styled Erlang source-code into a more functionally styled one. This can be done by transforming unnecessary calls to length, hd and tl into pattern matching or by lifting particular nested expressions. The results of our investigations indicate that these refactorings can not only shorten the length of the source code but also affect the complexity/readability. In this paper, we present some refactorings; moreover, real-life examples and data for its validation.
Lexical diversity in bilingual speakers of Croatian and Italian

The aim of this study was to determine whether measures of lexical diversity (LD) can be used to assess general lexical knowledge. This study focused on 30 sequential Croatian/Italian bilinguals aged between 5 and 7 years, who were compared with 30 Croatian and 30 Italian monolinguals matched in age. LD was assessed from narrative language samples using standard measures and compared with Peabody Picture Vocabulary Test (PPVT) results. Two LD measures, the type-token ratio (TTR) and Maas Index based on TTR transformation, did not differentiate between bilinguals and monolinguals, whereas several other did: total number of words (NTW), number of different words (NDW), measure D, moving average type-token ratio (MATTR) and hypergeometric diversity of D (HD-D). NTW, NDW, D, MATTR and HD-D were predictive of PPVT results for monolinguals, whereas NDW, TNW and D (with replacements, Dr) were predictive of PPVT results for bilinguals. Our results suggest that LD measures can be reliable measures of bilinguals’ vocabulary knowledge and can adequately reflect PPVT results in languages other than English.
Analyzing the Effect of Reading Patterns using Eye Tracking Measures

Scientific literature is crucial for researchers to inspire novel research ideas and find state-of-the-art solutions to various scientific problems. This paper presents a pilot study of a reading task for novice researchers using eye-tracking measures. The study focused on the scan path, fixations, and pupillary activity of the participants.
A non‐cooperative game model for reliability‐based task scheduling in cloud computing

Summary Cloud computing is a newly emerging distributed system. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes, so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum, or the overall system performance is optimal. Unlike task scheduling based on time or cost before, aiming at the special reliability requirements in cloud computing, we propose a non‐cooperative game model for reliability‐based task scheduling approach. This model takes the steady‐state availability that computing nodes provide as the target, takes the task slicing strategy of the schedulers as the game strategy, then finds the Nash equilibrium solution. We also design a task scheduling algorithm based on this model. It can be seen from the experiments that our task scheduling algorithm is better than the so‐called balanced scheduling algorithm.
High-dimensional multi-level image thresholding using self-organizing migrating algorithm

Multi-level image thresholding is a common approach to image segmentation for which population-based metaheuristic algorithms present an interesting alternative to conventional methods that are based on a exhaustive search. In this paper, we propose a novel multi-level image thresholding algorithm based on the Self-Organizing Migrating Algorithm (SOMA), in particular SOMA Team To Team Adaptive (SOMA T3A), a recent variant of SOMA, and an entropy-based fitness function. We evaluate our algorithm on a set of benchmark images on high-dimensional search spaces and with regards to fitness function value and peak signal-to-noise ratio (PSNR). Experimental results demonstrate excellent thresholding performance and our algorithm to outperform nine other state-of-the-art metaheuristics.
Population-based evolutionary distributed SGD

Neural model training is a time-consuming task where exploiting parallelism is of utmost importance. Employing data-parallelism in stochastic gradient descent (SGD) by partitioning the training dataset is a popular approach; however, algorithmic inefficiencies when operating at large minibatch sizes limit the degree of parallelism, a problem termed the scalability limit. In the face of this scalability challenge, we propose using Evolutionary Algorithms (EA) as a meta-algorithm together with SGD for training neural models. Our training scheme, <u>P</u>opulation-based <u>E</u>volutionary <u>SGD</u> (PESGD) combines local SGD training on each training node with a periodic evolutionary step which selects the best performing models to generate a new population of models for the next iteration. We believe that the complementarity of SGD and EA for neural model training can be exploited well in PESGD.
The "Vertigo Effect" on Your Smartphone: Dolly Zoom via Single Shot View Synthesis

Dolly zoom is a technique where the camera is moved either forwards or backwards from the subject under focus while simultaneously adjusting the field of view in order to maintain the size of the subject in the frame. This results in perspective effect so that the subject in focus appears stationary while the background field of view changes. The effect is frequently used in films and requires skill, practice and equipment. This paper presents a novel technique to model the effect given a single shot capture from a single camera. The proposed synthesis pipeline based on camera geometry simulates the effect by producing a sequence of synthesized views. The technique is also extended to allow simultaneous captures from multiple cameras as inputs and can be easily extended to video sequence captures. Our pipeline consists of efficient image warping along with depth-aware image inpainting making it suitable for smart-phone applications. The proposed method opens up new avenues for view synthesis applications in modern smart-phones.
Information Extraction in Digital Libraries: First Steps towards Portability of LBD Workflow

Literature-Based Discovery (LBD) refers to the process of detecting implicit, novel knowledge linkages hidden in scientific digital libraries and its contribution in accelerating research innovations is widely recognised. Despite significant advances, almost all the prior research efforts suffer from a major research deficiency which is lack of portability. That is, the existing LBD models are highly dependent on specialised and domain-dependent knowledge resources that restrict their applicability to limited problem areas or domains. However, LBD, the process of discovering new knowledge from unstructured text that potentially leads to novel research innovations is crucial despite the domain. Thus, this study proposes an interdisciplinary LBD framework by circumventing the existing impediments in the LBD workflow towards promoting portable scientific problem solving. To this end, we considered the revolutionary opportunities offered through Semantic Web by employing DBpedia for the first time in the LBD workflow. The suitability of our proposals to overcome the prevailing limitations are evaluated by comparing them with commonly used domain specific resources.
Regularized Length Changeable Extreme Learning Machine with Incremental Learning Enhancements for Remaining Useful Life Prediction of Aircraft Engines

The main objective of this works is to study and improve the performances of the Single hidden Layer Feedforward Neural network (SLFN) for the application of Remaining Useful Life (RUL) prediction of aircraft engines. The most common problems in SLFNs based old training algorithms such as backpropagation are time consuming, over-fitting and the appropriate network architecture identification. In this paper a new incremental constructive learning algorithm based on Extreme Learning Machine algorithm is proposed for founding the appropriate architecture of a neural network under less computational costs. The aim of the proposed training approach is to study its maximum capabilities during RUL prediction by reducing over-fitting and human intervention. The performances of the proposed approach which are evaluated on C-MAPPS dataset and compared with its original variant from the literature. Experimental results proved that the new algorithm outperforms the old one in many metrics evaluations.
Evaluation of classification techniques for identifying fake reviews about products and services on the internet

Abstract: With the e-commerce growth, more people are buying products over the internet. To increase customer satisfaction, merchants provide spaces for product and service reviews. Products with positive reviews attract customers, while products with negative reviews lose customers. Following this idea, some individuals and corporations write fake reviews to promote their products and services or defame their competitors. The difficulty for finding these reviews was in the large amount of information available. One solution is to use data mining techniques and tools, such as the classification function. Exploring this situation, the present work evaluates classification techniques to identify fake reviews about products and services on the Internet. The research also presents a literature systematic review on fake reviews. The research used 8 classification algorithms. The algorithms were trained and tested with a hotels database. The CONCENSO algorithm presented the best result, with 88% in the precision indicator. After the first test, the algorithms classified reviews on another hotels database. To compare the results of this new classification, the Review Skeptic algorithm was used. The SVM and GLMNET algorithms presented the highest convergence with the Review Skeptic algorithm, classifying 83% of reviews with the same result. The research contributes by demonstrating the algorithms ability to understand consumers’ real reviews to products and services on the Internet. Another contribution is to be the pioneer in the investigation of fake reviews in Brazil and in production engineering.
The Quest for Usable Usability Heuristics for Game Developers

Human Computer Interaction (HCI) research has provided many processes and methods for ensuring good usability during software development. The importance of usability in games has been acknowledged. However, there is a lack of research examining usability activities during actual game development and the suitability of different usability methods for different phases of game development. Game development industry, although growing fast and already including certain large and successful companies, consists of a huge number of small-to-medium-sized enterprises and start-ups. For these companies, practical, developer-oriented tools for ensuring game usability are needed, as these companies do not have resources for hiring usability specialists for taking care of usability. This paper reviews the concept of game usability and existing research on usability methods for game development. In addition, the paper proposes a set of usable usability heuristics as a practical, developer-oriented tool to be used during the game development process as well as reports on a small-scale survey study on game usability, heuristic evaluation and usability of usability heuristics in game development.
Tree-like constructions in topology and modal logic

Within ZFC, we develop a general technique to topologize trees that provides a uniform approach to topological completeness results in modal logic with respect to zero-dimensional Hausdorff spaces. Embeddings of these spaces into well-known extremally disconnected spaces then gives new completeness results for logics extending S4.2.
Secure design and verification of Erlang systems

Security is a critical part of software development, companies have the utmost responsibility to protect their customers data against any threat. Secure design is a key enabler, since it cultivates security awareness in software projects from day zero. In this paper it is shown how to apply the principles of secure design to Erlang software projects. An Erlang specific method to identify trust zones is presented. The high risk vulnerabilities of the Erlang ecosystem are reviewed and grouped together using the CIA triad model. A dataflow based static analysis together with a prototype to verify security posture of a trust zone are introduced and evaluated using Riak Core as a case study.
Retrieval of Frequent Itemset Using Improved Mining Algorithm in Hadoop

Today in parallel mining, extraction of frequent patterns from a huge dataset in a short time is a very difficult task. Frequent pattern mining not only plays a vital role in framing of association rule but plays an important role in effective classification and clustering also. Apriori, FP-Growth, and Eclat algorithms are very basic algorithms in frequent patterns mining but they are lagging in balancing of workload, fault-tolerance, and synchronization. To overcome this, recently proposed algorithm focuses on parallelization of a large number of machines in a distributed computing environment using MapReduce framework. For contributing in this case, we propose improved frequent itemset mining algorithm. This algorithm helps in finding frequent itemset from a huge dataset. It uses the concept of clustering for effective utilization of space and easy retrieval, in which large pattern sets are divided into discrete and uniform clusters, and each cluster is characterized by its center point. For pattern matching, we use FP-Growth algorithm. We are considering parameters like time and accuracy for comparing the existing system with the proposed system. Finally, we show that the proposed system is more accurate and requires less time to find frequent itemset. We have used online Retail Dataset, as we have a large amount of data for mining, we have extracted items that were bought by each customer. The existing system takes 99 s for discovering frequently occurred items. While our new approach for finding frequent itemsets takes very less time. It takes only 10 s, so it saves time more efficiently. Our new technique is implemented with the help of Locality Sensitive hashing technique, moving k-means, and FP-Growth algorithm.
Quality Assurance in Computer-Assisted Translation in Business Environments

Increased use of computer-assisted translation (CAT) technology in business settings with augmented amounts of tasks, collaborative work, and short deadlines give rise to errors and the need for quality assurance (QA). The research has three operational aims: 1) methodological framework for QA analysis, 2) comparative evaluation of four QA tools, 3) to justify introduction of QA into CAT process. The research includes building of translation memory, terminology extraction, and creation of terminology base. Error categorization is conducted by multidimensional quality (MQM) framework. The level of mistake is calculated considering detected, false, and not detected errors. Weights are assigned to errors (minor, major, or critical), penalties are calculated, and quality estimation for translation memory is given. Results show that process is prone to errors due to differences in error detection, harmonization, and error counting. Data analysis of detected errors leads to further data-driven decisions related to the quality of output results and improved efficacy of translation business process.
QoS-constrained multi-objective distributed data-intensive web service composition - NSGA-II with repair method

Distributed Data-intensive Web service composition (DWSC) aims to find service compositions with multiple conflicting objectives, i.e. minimising response time and cost. Additionally, it often needs to satisfy user-defined QoS constraints, i.e. budget constraints or response time constraints. To solve QoS-constrained multi-objective distributed DWSC problems we propose a knowledge-based repair method for NSGA-II to effectively and efficiently search for Pareto-optimal service composition solutions that satisfy QoS constraints. Experiments show that our method outperforms an existing state-of-the-art repair method on several commonly used on standard benchmark datasets.
Label-Attentive Hierarchical Attention Network for Text Classification

The performance of text classification largely hinges on features extraction from text through representation learning. Recently, many deep learning models have been proved to be the state-of-the-art method for text classification. However, those approaches always ignore exploiting global clues, such as label information, which result in partial semantic loss. This paper proposes a text classification framework called Label-Attentive Hierarchical Attention Network (LAHAN). Firstly, to exploit global clues better and avoid semantic loss between text and label information, LAHAN generates label-attentive embedding by introducing the joint features of text words and labels information. Then a hierarchical architecture is adopted to utilize the label information on both word level and sentence level, which brings improvement to extract a better hierarchical text representation. Finally, the classification layer of our model predicts the category based on the full-text label-attentive representations. Experimental results on the several benchmark datasets demonstrate that our proposed method achieves improvement on accuracy compared to other state-of-the-art baselines.
Analysis of Cybersecurity Mechanisms with respect to Dependability and Security Attributes

Embedded electronic systems need to be equipped with different types of security mechanisms to protect themselves and to mitigate the effects of cybersecurity attacks. These mechanisms should be evaluated with respect to their impacts on dependability and security attributes such as availability, reliability, safety, etc. The evaluation is of great importance as, e.g., a security mechanism should never violate the system safety. Therefore, in this paper, we evaluate a comprehensive set of security mechanisms consisting of 17 different types of mechanisms with respect to their impact on dependability and security attributes. The results show that, in general, the use of these mechanisms have positive effect on system dependability and security. However, there are at least three mechanisms that could have negative impacts on system dependability by violating safety and availability requirements. The results support our claim that the analyses such as the ones conducted in this paper are necessary when selecting and implementing an optimal set of safety and security mechanisms.
Fast and Accurate Training of Ensemble Models with FPGA-based Switch

Random projection is gaining more attention in large scale machine learning. It has been proved to reduce the dimensionality of a set of data whilst approximately preserving the pairwise distance between points by multiplying the original dataset with a chosen matrix. However, projecting data to a lower dimension subspace typically reduces the training accuracy. In this paper, we propose a novel architecture that combines an FPGA-based switch with the ensemble learning method. This architecture enables reducing training time while maintaining high accuracy. Our initial result shows a speedup of 2.12-6.77 times using four different high dimensionality datasets.
Score Level Fusion for Improving Writer Retrieval in Handwritten Document Databases

Writer retrieval in handwritten document databases is used in various aims such as authenticating old manuscripts, and predicting the writer of anonymous texts. Such systems try to retrieve all documents of a given writer without having a prior knowledge about its identity. This paper introduces the use of score level fusion for improving the retrieval results. Several rules that are based on Sum operator, triangular Norms and Co-norms are employed to combine outputs of several writer retrieval systems. The fusion step is carried out by considering three individual writer retrieval systems, that are based on Edge Hinge Distribution features, Run-length Local Binary Pattern and Histogram Of Templates. Experiments are conducted on ICDAR2011 dataset. The results obtained reveal that fusion rules can give an improvement around 2% in MAP over the scores derived from individual systems.
Improved Multi-Scale Local Difference Features for Off-Line Handwritten Signature Verification

In the present work, we propose the multi-scale Local Difference Features (LDF) for improving handwritten signature verification. This descriptor is similar to local binary patterns, since it highlights intensity differences between a pixel and its neighbors. Presently, we propose a multi-scale implementation of LDF in which, neighboring pixels are taken on several radiuses. This helps to adapt LDF to variations of handwritten strokes within signatures. The verification stage is carried out by using Support Vector Machines classifier. The performance assessment is elaborated on two benchmark datasets. Obtained error rates highlight the effectiveness of the proposed features, since they outperform several state of the art results.
Finding a better basis on binary representation through DNN-based epistasis estimation

Epistasis indicating problem difficulty can be used to evaluate the basis of the problem space. However, calculating epistasis is difficult as it requires searching all solutions. In this study, a method that estimates epistasis using deep neural networks is proposed for basis evaluation. As a result of applying the proposed method to the Variant-OneMax problem, the method was able to successfully make estimations epistasis and significantly reduce the computing time.
Query expansion via learning change sequences

Proksch has proved the changed terms of source code negatively affect code search quality. However, current query expansion (QE) methods always ignore it. In this paper we propose a novel QE method based on the semantics of change sequences (QESC). It not only captures which changes occurred by ext racting change sequences from Github commits, but also understands why changes occurred by learning sequence semantics with Deep Belief Network (DBN). Thus it could extract relevant terms to expand or irrelevant terms to exclude from the changes semantically similar to a query. Our experimental results show QESC outperforms the existing QE methods by 15–23% in terms of precision on inspecting the first query result.