Reviewerly/exHarmony huggingface dataset:
COMPUTATIONAL INTELLIGENCE FOR SHOEPRINT RECOGNITION

Shoeprint marks present valuable information for forensic investigators to resolve a crime. These marks can be helpful to find the brand of the shoe and can make the investigation easier. In this paper, we present an associative model-based algorithm to match noisy shoeprint patterns with a brand of shoe. The shoeprints are corrupted with additive, subtractive and mixed noises. A particular case of subtractive noise are partial shoeprints such as toe, heel, left-half and right-half prints. The Morphological Associative Memories (MAMs) were applied. Both memories, max and min, recognize noisy shoeprints corrupted with 98% additive and subtractive noise, respectively, with an effectiveness of 100%. The images corrupted with mixed noise were recognized when the additive or subtractive noise applied was greater than the mixed noise; in this case, the recalling was around 70%, otherwise, both memories failed to recognize the shoeprints.
Introducing a Method for Combining Supervised and Semi-Supervised Methods in Fraud Detection

As electronic transactions are growing, fraud cases are also growing drastically. Detection of frauds is a complicated task and limiting fraud detection systems to certain kinds of detection methods like supervised or unsupervised methods does not seem efficient. In this paper, a combination framework for fraud detection systems, consisting of both supervised and semi-supervised methods in three main components namely rule-based component, trend-analysis-based component and, a scenario-based component is proposed. The rule-based component is the supervised part of the framework and decision tree which is a cost-insensitive classification algorithm is used for this component. In the trend-analysis-based component, which is the semi-supervised part of our proposed framework, the normal behavior of users are modeled and the extent of dissimilarities of newly-arrived transactions are calculated. Finally, in the scenario-based component which is another semi-supervised part of the proposed framework, the extent of similarities of the sequence of transactions to known fraud scenarios are calculated. The final result is gained through combining the results of all these three components using in a parallel mode. By combining the outputs of all these components together using the SUM function, the detection rate has increased remarkably (about 7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ).
Proof theory for quantified monotone modal logics

Abstract This paper provides a proof-theoretic study of quantified non-normal modal logics (NNML). It introduces labelled sequent calculi based on neighbourhood semantics for the first-order extension, with both varying and constant domains, of monotone NNML, and studies the role of the Barcan formulas in these calculi. It will be shown that the calculi introduced have good structural properties: invertibility of the rules, height-preserving admissibility of weakening and contraction and syntactic cut elimination. It will also be shown that each of the calculi introduced is sound and complete with respect to the appropriate class of neighbourhood frames. In particular, the completeness proof constructs a formal derivation for derivable sequents and a countermodel for non-derivable ones, and gives a semantic proof of the admissibility of cut.
Expanded Blaum–Roth Codes With Efficient Encoding and Decoding Algorithms

An expansion of Blaum-Roth codes by incorporating a vertical parity on p×p arrays, where p is a prime number, is described. The vertical parity allows for the local recovery of a symbol within a column without invoking the remaining columns. Efficient encoding and erasure decoding procedures are presented, in particular, for the case of two column parities.
Information Security Policy Perceived Compliance Among Staff in Palestine universities: An Empirical Pilot study

In today's interconnected world, universities recognize the importance of protecting their information assets from internal and external threats. Being the possible insider threats to Information Security, employees are often coined as the weakest link. Both employees and organizations should be aware of this raising challenge. Understanding staff perception of compliance behaviour is critical for universities wanting to leverage their staff capabilities to mitigate Information Security risks. Therefore, this research seeks to get insights into staff perception based on factors adopted from several theories by using proposed constructs i.e. "perceived" practices/policies and "perceived" intention to comply. Drawing from the General Deterrence Theory, Protection Motivation Theory, Theory of Planned Behaviour and Information Reinforcement, within the context of Palestine universities, this paper integrates staff awareness of Information Security Policies (ISP) countermeasures as antecedents to “perceived” influencing factors (perceived sanctions, perceived rewards, perceived coping appraisal, and perceived information reinforcement). The empirical study is designed to follow a quantitative research approaches, use survey as a data collection method and questionnaires as the research instruments. Partial least squares structural equation modelling is used to inspect the reliability and validity of the measurement model and hypotheses testing for the structural model. The research covers ISP awareness among staff and seeks to assert that information security is the responsibility of all academic and administrative staff from all departments. Overall, our pilot study findings seem promising, and we found strong support for our theoretical model.
A Comparison of Web Data Extraction Techniques

Extracting a structured text data from a published webpages has drawn attention in the last decade, the process of web data extraction has many challenges, due to variety of web data and the unstructured from of HTML files. The aim of this survey is to provide a comprehensive overview of current web data extraction techniques, in term of extracted data quality, where the redundant and the noise data should be eliminated. Merits and demerits for each web information extraction technique will be stated, and finally a classification framework for the discussed techniques will be provided.
A Variable Search Space Strategy Based on Sequential Trust Region Determination Technique

The complexity of an optimization problem is determined by its decision and objective spaces. Over the past few decades, a large number of works have focused on the performance improvement of metaheuristic algorithms via the objective space, whereas studies related to the decision space have attracted little attentions. Moreover, metaheuristic algorithms may not obtain satisfactory results within an entire feasible region, even if sufficient computational resources are available. Therefore, reducing the search space (i.e., finding a trust region) may be an effective method to ensure that the convergence is sufficiently close to the global optimal region. However, inappropriate subspace size may also weaken the performance of algorithms except for ones with a sufficiently small search space. To alleviate aforementioned problems, a variable search space (VSS) strategy based on a sequential trust region determination approach is proposed in this paper. In the VSS, the entire optimization process is divided into two stages: the first stage is to use an optimization approach for sequentially finding the trust domain of each variable and then determine the best-matched subspace; the second stage is to employ the optimization method for searching an optimal/near-optimal solution within the found trust region. The effectiveness of the VSS is evaluated using two widely used test suites, that is, IEEE CEC2014 and BBOB2012. Experimental results indicate that improving the algorithm performance is an important method for tackling problems, but locating a trust region is also beneficial for metaheuristic algorithms to improve the solution precision, especially for complex optimization problems.
Multi-label Learning from Noisy Labels with Non-linear Feature Transformation

Multi-label classification is an essential problem in image classification, because there are usually multiple related tags associated with each image. However, building a large scale multi-label dataset with clean labels can be very expensive and difficult. Therefore, utilizing a small set of data with verified labels and massive data with noise labels to build a multi-label classification model becomes valuable for practical applications. In this paper, we propose a teacher-student network with non-linear feature transformation, leveraging massive dataset with noisy labels and a small dataset with verified data to learn a multi-label classifier. We use a non-linear feature transformation to map the feature space and the label space. We first pre-train both the teacher and student networks with noisy labels and then train both networks jointly. We build a multi-label dataset based on MS COCO2014 for performance evaluation, in which both noisy label and verified label are given for each image. Experimental results on our dataset and the public-domain multi-label dataset (OpenImage) show that the proposed approach is effective in leveraging massive noisy labels to build multi-label classifiers.
A new methodology applied to dynamic object detection and tracking systems for visually impaired people

The presence of dynamic objects (DO) affect the mobility of the visually impaired people (VIP) and so it is important to indicate the safest areas to move in. This article presents a new methodology for detecting and tracking DO to reproduce their paths. This methodology consists of: a requirements analysis specific to the context of the VIP; the development of four architectures adapted to the VIP; the generation of a 3D map composed of paths made by DO; and a comparative analysis between the architectures in order to obtain the best solution from the union of the best characteristics detected. These architectures, the main adaptations being related to the factors of illumination and movement undergone by the acquisition sensors that can interfere with the process of DO segmentation. Experiments were performed in different environments with the absence and presence of movement of the acquisition sensors.
Toward the Realization of Encoder and Decoder Using Deep Neural Networks

Deep learning continues to offer groundbreaking technologies in communications systems just as it has in a variety of other fields. Despite the notable advancements of technologies based on DNN-based technologies in recent years, their high computational complexity has been a major obstacle to the application of DNN in practical communications systems where real-time operation is required. In this sense, challenges regarding its practical implementation must be addressed before the proliferation of DNN-based intelligent communications becomes a reality. To the best of the authors' knowledge, the present article is the first to present an efficient learning architecture and related design strategies including link-level verification through the implementation of digital circuits using hardware description language (HDL) to mitigate this challenge and to deduce the feasibility and potential of DNNs in communications systems. In particular, a DNN is applied to an encoder and a decoder to enable flexible adaptation with respect to their system environments, without the need for any domain-specific information. Extensive investigations and interdisciplinary design considerations, including a DNNbased autoencoder structure, learning framework, and implementation of a low-complexity digital circuit for real-time operation, are taken into account, all of which support the use of DNNbased communications in practice.
Reasoning with preferences in service robots

Service Robots should be able to reason about preferences when assisting people in common daily tasks. This functionality is useful, for instance, to respond to action directives that conflict with the user’s interest or wellbeing or when commands are underspecified. Preferences are defeasible know ledge as they can change with time or context, and should be stored in a non-monotonic knowledge-base system, capable of expressing incomplete knowledge, updating defaults and exceptions dynamically, and handling multiple extensions. In this paper a knowledge-base system with such an expressive power is presented. Non-monotonicity is handled using a generalization of the Principle of Specificity, which states that in case of knowledge conflict the most specific proposition should be preferred. Reasoning about preferences is used on demand through conversational protocols that are generic and domain independent. We describe the general principles underlying such protocols and their implementation through the SitLog programming language. We also show a demonstration scenario in which the robot Golem-III assists human users using such protocols and preferences stored in its non-monotonic knowledege-base service.
Building a Knowledge Graph for the Air Traffic Management Community

Historically, most of the focus in the knowledge graph community has been on the support for web, social network, or product search applications. This paper describes some of our experience in developing a large-scale applied knowledge graph for a more technical audience with more specialized information access and analysis needs – the air traffic management community. We describe ATMGRAPH, a knowledge graph created by integrating various sources of structured aviation data, provided in large part by US federal agencies. We review some of the practical challenges we faced in creating this knowledge graph.
Web attacks: defeating monetisation attempts

According to Internet Live Stat, the total number of online websites has already soared over 1.9 billion and is steadily increasing. 1 The most popular content management systems (CMS) installed on such websites are PHP-based frameworks such as Wordpress and Joomla, which account for (as of December 2018) 59% and 6% of market share. 2,3 A large number of those websites belong to small companies and individuals that may have few motivations to pay thousands of dollars for security maintenance services. Also, due to development costs, the owners – even the developers they hire – may prefer an off-the-shelf CMS such as Joomla to accelerate deployment. A large proportion of the nearly two billion websites now on the Internet are run by individuals and organisations without the skills or resources to make them secure. These sites are targets for cyber criminals looking to exploit weaknesses to ‘monetise’ them. Van-Linh Nguyen, Po-Ching Lin and Ren-Hung Hwang of the National Chung Cheng University, Taiwan, examine the tactics employed by criminals and present a framework for preventing such attacks, while acknowledging that this is a battle that is likely to continue.
A Deep-Learning Enabled Traffic Analysis Engine for Video Source Identification

This paper presents a deep-learning based traffic analysis system for identifying streaming video sources within encrypted tunnels. The overall goal is to extract information about applications and traffic sources for system administrators and internet service providers to perform better resource allocation and other traffic management operations. The proposed architecture uses a deep neural network (DNN) for traffic analysis-based video source classification. The DNN model is trained and validated using an experimental setup that simulates real-world complex situations such as traffic combinations within encrypted VPN tunnels. The implemented DNN classifiers are shown to provide acceptable classification accuracies for single and combined multi-stream scenarios when trained with our proposed features.
Tablet use in primary education

The existence of studies that support the usefulness of tablets in primary education is encouraging. For this reason, this research analyses this device’s didactic use. The present project is part of a qualitative narrative research. The data of experiences supported by the tablet were collected through nine discussion groups and a set of observation techniques (with 76 primary education students aged between 6 and 11). It is concluded that tablets are not used systematically in primary education. Spanish language and literature are the subjects where the tablet is used the most, although it is also used in mathematics to a lesser extent. On the subject of type of activities carried out in the classroom, worth mentioning are those related to classifications and the ones related to identification and representation of concepts.
Applied Mobile-Assisted Seamless Learning Techniques in MOOCs

As Massive Open Online Courses (MOOCs) are nowadays used in an increasingly ubiquitous manner, the learning process gets disrupted every time learners change context. Mobile-Assisted Seamless Learning (MSL) techniques have been identified to reduce unwanted overhead for learners and streamline their learning process. However, technical implementations vary across the industry. This paper examines existing MSL research and applied techniques in the context of MOOCs. Therefore, we discussed related MSL research topics. Afterward, eleven characteristic MSL features were selected and compared their implementations across five major MOOC platforms. While web applications provide a bigger feature set, mobile clients offer advanced offline capabilities. Based on the findings, a concept outlines how MSL features can enhance the learning experience on MOOC platforms while considering the technical feasibility.
A Multi-purpose Convolutional Neural Network for Simultaneous Super-Resolution and High Dynamic Range Image Reconstruction

High dynamic range (HDR) UHD-TVs are being rapidly deployed in consumer markets, offering a highly realistic experience to customers. However, these HDR UHD-TVs still need to handle the legacy low resolution (LR) video of standard dynamic range (SDR). In this paper, we propose a convolutional neural network based structure for the joint learning of super-resolution and inverse tone-mapping, which can be used for converting LR-SDR legacy video to high resolution (HR) HDR video. Our proposed structure is designed to perform three tasks: (i) SDR-to-HDR conversion of LR images, (ii) super-resolution of LR-SDR images to HR-SDR images and (iii) joint conversion from LR-SDR to HR-HDR images. We show the effectiveness of our proposed joint learning CNN architecture with extensive experiments.
Combining Background Subtraction and Convolutional Neural Network for Anomaly Detection in Pumping-Unit Surveillance

Background subtraction plays a fundamental role for anomaly detection in video surveillance, which is able to tell where moving objects are in the video scene. Regrettably, the regular rotating pumping unit is treated as an abnormal object by the background-subtraction method in pumping-unit surveillance. As an excellent classifier, a deep convolutional neural network is able to tell what those objects are. Therefore, we combined background subtraction and a convolutional neural network to perform anomaly detection for pumping-unit surveillance. In the proposed method, background subtraction was applied to first extract moving objects. Then, a clustering method was adopted for extracting different object types that had more movement-foreground objects but fewer typical targets. Finally, nonpumping unit objects were identified as abnormal objects by the trained classification network. The experimental results demonstrate that the proposed method can detect abnormal objects in a pumping-unit scene with high accuracy.
Hybrid Binary Bat Algorithm with Cross-Entropy Method for Feature Selection

Feature selection aims to find an optimal subset from a given set of features. As this task can be seen as a challenging combinatorial optimization problem while the classical optimization techniques have some limitations in solving it. In this paper we propose a novel hybrid metaheuristic, improved Binary Bat Algorithm with Cross-Entropy method (BBACE), for feature selection. In the proposed BBACE algorithm, the Cross-Entropy method is embedded in Bat Algorithm to make good balance between exploitation and exploration based on co-evolution. The performance of the proposed method is evaluated on 10 standard benchmark datasets from UCI repository and compared with some well-known wrapper feature selection techniques such as GA, PSO, and ALO. The experimental results demonstrate the efficiency of the proposed approach in selecting the most informative attributes for classification and improving the classification accuracy.
Reliability verification of a vision-based dynamic displacement measurement for system identification

In this paper, dynamic experiments of various amplitudes with a broad band of frequencies were carried out using a shaking table in order to validate the wide-range reliability of a vision-based displacement measurement system. In addition, the dynamic characteristics of a multiple-degree-of-freedom model structures were estimated using the vision-based measurement method. As a result of error analysis through comparison with a laser displacement sensor (LDS), it was determined that the accuracy of dynamic displacement measurement by the camcorder in the vision-based system is affected by the frequency of the measurement object. From these results, it is judged that the CMOS sensor type has the rolling shutter effect when the velocity between the model structure and the camcorder is high. The accuracy of dynamic characteristics estimation was determined by comparing the dynamic characteristics estimated using the LDSs and the accelerometers. The vision-based measurement system using the camcorder in both time domain and frequency domains showed very similar results to the existing LDS and accelerometer measurements.
Decision Support for an Adversarial Game Environment Using Automatic Hint Generation

The Hint Factory is a method of automatic hint generation that has been used to augment hints in a number of educational systems. Although the previous implementations were done in domains with largely deterministic environments, the methods are inherently useful in stochastic environments with uncertainty. In this work, we explore the game Connect Four as a simple domain to give decision support under uncertainty. We speculate how the implementation created could be extended to other domains including simulated learning environments and advanced navigational tasks.
Creating “informating” systems using Agile development practices: an action research study

We use action research to understand the concepts of automating and informating, and study whether agile development practices could be an enabler of informating functionalities. Two questions based on these contrasts and connections motivate this research. Our first research question is: What are the characteristics of an informating functionality and how does an informating functionality differ from an automating functionality? Our second research question is: What role could agile practices play in the development of informating functionalities? In this paper, we discuss the actions, reflections and lessons learned from three action research iterations while developing an admissions application for a business college.