Reviewerly/exHarmony huggingface dataset:
A Collaborative Cybersecurity Education Program

This chapter presents an implementation of a cybersecurity education program. The program aims to address some issues identified in current cybersecurity teaching in higher education on a European level, like the fragmentation of cybersecurity expertise or resource shortage, resulting in few higher education institutions to offer full degree programs. As a result of the Erasmus+ strategic partnership project SecTech, the program tries to overcome those issues by introducing collaborative development to cybersecurity education. SecTech lays the foundations for a collaborative education program, like the definition of a clear content, module and delivery structure, and the appropriate tool support to facilitate collaboration and content reuse. Additional effort is required to achieve long-term success, including the creation of a community that drives the content creation and maintenance, as well as an independent governance structure to steer the project in the long-term. While the project focuses on European collaboration, a global community is envisioned.
Registration between histopathological images with different stains and an MRI Image of pancreatic cancer tumor

In this paper, we report on the construction of a pancreatic tumor model that represents the relationship between the tumor growth and the micro anatomical structures. The former, the tumor growth, is described by referring to the temporal series of MRI images of the whole body and the latter, the micro structures of the tumor, is described by a spatial series of microscopic images of thin-sections sliced from the extracted pancreatic tumor. For the model construction, we developed new non-rigid registration methods for (1) accurate description of tumor growth, (2) reconstruction of 3D microscopic images, and (3) registration between an MRI image and corresponding microscopic images. In addition, we constructed a neural network that can generate a set of fake microscopic image patches of a pancreatic tumor that corresponds to each voxel inside the tumor region in an MRI image. The outlines of the methods are introduced and some examples of experimental results are demonstrated.
Key point location method for pedestrians in depth images based on deep learning

One difficulty that remains in image processing is the accurate location of key points in depth images. This paper presents an intelligent location method for identifying key points in depth images based on deep convolutional neural networks. This study used Kinect to process images, calculating th e differences in depth as well as the directional gradient in subject depth images. The entirety of each depth image was traversed through a sliding window to identify the feature vector. Principal component analysis was used to reduce image dimensions. The random forest technique was used to select characteristics of strong classification as well as to actualize training and testing. A depth convolutional neural network was used to detect key points in images of pedestrians. During the study, an experimental test was conducted in a general environment under various conditions, including occlusion and low light. Even under these suboptimal conditions, the detection rate of the proposed method was 87.72%. Furthermore, this method was compared with the GEBCF and FCF algorithms, and proved to increase the detection rate by 0.92% and 0.68%, respectively. Using the depth convolutional neural network in the pedestrian key point positioning experiment, the average error obtained when comparing the predicted point coordinates to the sample mark coordinates was 2.102 pixels. These experimental results show that this method has good accuracy and robustness for the key point location problem of pedestrians in depth images.
LinesLab: A Flexible Low‐Cost Approach for the Generation of Physical Monochrome Art

Abstract The desire for the physical generation of computer art has seen a significant body of research that has resulted in sophisticated robots and painting machines, together with specialized algorithms mimicking particular artistic techniques. The resulting setups are often expensive and complex, making them unavailable for recreational and hobbyist use. In recent years, however, a new class of affordable low‐cost plotters and cutting machines has reached the market. In this paper, we present a novel system for the physical generation of line and cut‐out art based on digital images, targeted at such off‐the‐shelf devices. Our approach uses a meta‐optimization process to generate results that represent the tonal content of a digital image while conforming to the physical and mechanical constraints of home‐use devices. By flexibly combining basic sets of positional and shape encodings, we are able to recreate a wide range of artistic styles. Furthermore, our system optimizes the output in terms of visual perception based on the desired viewing distance, while remaining scalable with respect to the medium size.
Word sense induction in bengali using parallel corpora and distributional semantics

One of the most challenging research problems in natural language processing (NLP) is that of word sense induction (WSI). It involves discovering senses of a word given its contexts of usage without the use of a sense inventory which differentiates it from traditional word sense disambiguation (WSD ). This paper reports a work on sense induction in Bengali, a less-resourced language, based on distributional semantics and translation based context vectors learned from parallel corpora to improve the task performance. The performance of the proposed method of sense induction was compared with the k-means algorithm, which was considered as the baseline in our work. A dataset for sense induction was created for 15 Bengali words, encompassing a total of 111 contexts. The proposed model, in both mono and cross-lingual settings, outperformed k-means in precision (P), recall (R) and F-scores. K-means based sense induction produced average P, R and F-scores of 0.71, 0.73 and 0.66 respectively. The average P, R and F-scores produced by the mono-and cross-lingual settings of the proposed algorithm are 0.77, 0.73, 0.68 and 0.81, 0.77 and 0.72 respectively.
Behavioral model identification and classification of multi-component systems

Learning model from the execution traces has been considered in several domains such as traffic classification, malware detection, and software engineering since it distinguishes which processes actually executed through their traces. Behavioral model learning and classification (in terms of learned models) are taken into accounts to eliminate the shortages of models derived based on non-behavioral features and improve the resulting classifications. So far, no general method has been proposed to automatically derive behavioral models. To this aim, we assume that the models of applications can be abstractly defined in terms of how they execute their depending components, well-known in the domain. To automatically derive such models, we extend the passive automata learning by considering the behavior of the depending components in addition to the observed behaviors. The state merging algorithm of the learning process is equipped with a new equivalence relation which aggregate states modulo counter abstraction of symmetric reduction. To improve the generated model to cover unobserved behaviors, we leverage the technique of complex event processing to complete the model with the unseen interleaving of actions due to the concurrent execution of components. The derived models, specified by parametrized transition systems, can distinguish different executions of instances of each component by assigning a unique symbolic identifier to each instantiation and parameterizing actions with such identifiers. The learned models are used to distinguish the executions of applications in an interleaved execution trace of different systems. The detection procedure is more complicated for parametric models because of the need for relating the information of the trace to symbolic identifiers as the parameters. We utilize runtime verification techniques in a three-step novel approach so as to enhance the performance of the matching process for a trace. To illustrate the applicability of our approach, we have employed it for traffic classification in the network domain and then applied it on some real applications. To demonstrate the effectiveness of our approach in this domain, we compare it to related approaches in terms of their true positive rate, false positive rate, and test time. Our results indicate that our technique prevents including invalid traces so that unobserved behaviors are covered with an acceptable precision. • We extend the passive automata learning for multi-component applications. • The learning approach aggregates those states preserving the behavior of components. • We provide an automata-based classifier based on the runtime verification technique. • We compare our classifier with statistical ones for network applications.
The design of geographic information system determination of slum area to improve villages in supporting green and clean in Surabaya City using Fuzzy-AHP method

One of the problems of an urban area is the existence of many pockets of slum area, which certainly raises many problems for the local government and the general public. Slum housing greatly affects the environment and community sanitation, as well as urban planning. To detect slum housing problems, it is necessary to have an integrated computer information system, especially one that can be used for mature planning and able to combine spatial information system and information called the Geographic Information System (GIS). This information system is equipped with Decision Support System to recommend the determination of slum areas using the Fuzzy AHP method. Fuzzy AHP is a method that is an amalgamation of Fuzzy method and AHP method. The fuzzy type used was Fuzzy Triangle Number. The fuzzy value was defined for each alternative on each criterion using the linear membership function down, ascending the triangle as an approximation to obtain the degree of membership. The output of this system is a recommendation ranking of slum locations based on predetermined criteria.
Social Media Committees

Social media management in academic libraries poses various challenges. Developing and sustaining a social media committee (SMC) is a positive step toward addressing these challenges. Copley Library at the University of San Diego and Saskatchewan Polytechnic Library have both invested time and energy to develop, improve, and sustain SMCs. Each library’s strategy involves creating a connection between the library and academic community, and harnessing and sharing the library’s voice. While the SMCs in this chapter share some similarities, there are also differences that reflect their distinct library environments. The authors offer tips and best practices that can be adapted by other academic libraries and librarians.
A GPU-Based Metaheuristic for Workflow Scheduling on Clouds

Scientific workflows are being used today in a number of areas. As they grow in complexity and importance, cloud computing emerges as an important execution environment. In this scenario, scheduling the workflow tasks and data on the cloud ensuring proper use of the computational resources is one of the key issues in the management of workflow execution. Although many workflow schedulers have been proposed in the literature, few of them deal with heterogeneous computing resources and data file assignment. The Hybrid Evolutionary Algorithm to Task Scheduling and Data File Assignment Problem (HEA-TaSDAP) addresses these two problems simultaneously, but the scheduling is time consuming, especially if we consider large scale workflows. In this work, we propose optimizations on HEA-TaSDAP by taking advantage of the massive parallelism provided by GPUs, leveraging the scheduling of larger instances in a reasonable amount of time. Our parallel solution provided about 98.83% of reductions in the scheduling time, keeping the quality of the solutions.
Pilot study to generate image features by deep autoencoder for computer-aided detection systems

We propose an automatic feature generation by deep convolutional autoencoder (deep CAE) without lesion data. The main idea of the proposed method is based on anomaly detection. Deep CAE is trained by only normal volume patches. Trained deep CAE calculates low-dimensional features and reproduction error from 2.5 dimensional (2.5D) volume patch. The proposed method was evaluated experimentally with 150 chest CT cases. By using both previous features and the deep CAE based features, an improved classification performance was obtained; AUC=0.989 and ANODE=0.339.
Web science in Europe

research-article Share on Web science in Europe: beyond boundaries Authors: Steffen Staab University of Southampton, U.K University of Southampton, U.KView Profile , Susan Halford University of Bristol, U.K University of Bristol, U.KView Profile , Wendy Hall University of Southampton, U.K University of Southampton, U.KView Profile Authors Info & Claims Communications of the ACMVolume 62Issue 4April 2019 pp 74https://doi.org/10.1145/3312569Published:20 March 2019Publication History 1citation3,007DownloadsMetricsTotal Citations1Total Downloads3,007Last 12 Months103Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
ETL Processes Security Modeling

The development of information systems (IS) in a secure environment or condition is a complex task that involves many additional basic security protocols, policies as well as industry standards on passwords, anti-virus programs, firewalls and data encryption. However, in traditional IS development lifecycles, security is either ignored or added as an afterthought, which does not assure the system complete security. So, it is necessary to give more importance to this issue and consider it as part of IS development process. In this context, the authors should guarantee the security of ETL (Extract, Transform, Load) processes, which are among the most critical and complex tasks during DW development project. In this study, security management is carried out for ETL processes by proposing a meta-model integrating the security concepts from the security requirements to the necessary preventive and / or corrective treatments. The proposed meta-model is validated with instantiation.
C4.5 Crossover Algorithm Based on MapReduce in University Big Data Analysis

With the continuous development of digitization and informationization, students' data generated by various kinds of application systems in colleges and universities is also accelerating. A big data environment of colleges and universities has been formed. The data contains a wealth of information that requires new storage and analysis tools for processing. Therefore, in this paper, big data processing platform Hadoop is used to analyze college big data, and C4.5 cross-improvement algorithm is proposed based on the low efficiency and over-fitting of decision tree C4.5 algorithm, and then verified in student grants data Improve the feasibility of the algorithm. Finally, the validity of the improved algorithm is verified from different evaluation criteria. The experimental results show the effectiveness and feasibility of the improved algorithm proposed in this paper, which provides a new idea and method for big data analysis in colleges and universities.
Person Re-identification Algorithm Based on Logistic Regression Loss Function

The feature description of pedestrians is the key to the task of person re-identification. The deep feature extracted from convolutional neural networks proved to have good robustness. Deep feature learning based on convolutional neural network also achieved good performance in person re-identification tasks. However, the feature discriminant is not strong under the constraint of traditional classification loss function, which seriously restricts the person re-identification performance. In order to enhance the discrimination of features, this paper uses the idea of logistic regression to associate the distance between samples and the similarity probability and proposes a logistic regression loss function to constrain the training of the network. The logistic regression loss function proposed in this paper can constrain the network to make the distance between similar samples small, and the distance between different classes of samples large. Secondly, by introducing the positive sample distance threshold, this paper avoids that the network constrains the same sample with some very different samples from being too small, while the distance constraint to other samples fails. Finally, the proposed logic regression loss function is embedded into the Siamese convolutional neural network for end-to-end person re-identification. This paper validates the effectiveness of the algorithm on the mainstream databases of Market1501 and CUHK03.
Face Recognition Based on Collaborative Representation Discriminant Projections

In this paper, a supervised feature extraction algorithm named collaborative representation discriminant projections (CRDP) is proposed for face recognition. CRDP explicitly takes into account the within-class neighboring information and between-class neighboring information. CRDP constructs graph and corresponding edge weights simultaneously through collaborative representation (CR). Further, by integrating graph embedding(GE) with Fisher criterion, the discriminating power of CRDP is further boosted. The experimental results on the PIE face databases show that CRDP can achieve better recognition performance.
A Service-Oriented Approach for the Cognitive Factory — A Position Paper

Contrary to former fears, factories will not be deserted in the foreseeable future. The challenge will be the development of digital approaches that assist human beings in manufacturing environments by processing the enormous amount of data available. The proposed approach allows the implementation of manufacturing functionalities based on artificial intelligence. The objective is to design a Cognitive Factory that is able to both reactively and proactively assist humans in complex manufacturing environments. To this purpose, a combination of the Digital Shadow concept and a Service-based Intelligence is presented.
Aural Textiles. Hybrid practices for data-driven design.

(2019). Aural Textiles. Hybrid practices for data-driven design. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 1163-1175.
Rack-Scale Capabilities: Fine-Grained Protection for Large-Scale Memories

Rack-scale systems with large, shared, disaggregated, and persistent memory need solid protection and authorization techniques. Our solution uses a memoryside capability enforcement processor that gates memory accesses through extended capabilities, enables finegrained access control beyond a single address space, and minimally disrupts the programming model.
Estimating average fidelity error based on local random quantum circuits

This paper proposes a general method to estimate the practical average fidelity error using an ϵ -approximate unitary 2t -design. We then build a specific model of local random circuits to construct the ϵ -approximate unitary t -design under a tighter bound on the diamond norm of the corresponding error. For a twirled t -tensor product noisy channel constructed by local random circuits, we extend the estimation of average fidelity using approximate unitary 2t -designs to an -fold noise scenario. Compared to previous randomized benchmarking, the average fidelity estimated by the method of local random circuits requires less experiments but longer circuit lengths for large n quantum system.
Priority Adversarial Example in Evasion Attack on Multiple Deep Neural Networks

Deep neural networks (DNNs) provide superior per-formance on machine learning tasks such as image recognition, speech recognition, pattern recognition, and intrusion detection. However, an adversarial example created by adding a little noise to the original data can lead to misclassification by the DNN, and the human eye cannot detect the difference from the original data. For example, if an attacker generates a modified left-turn road sign to be incorrectly categorized by a DNN, an autonomous vehicle with the DNN will incorrect classify the modified left-turn road sign as a right-turn sign, whereas a human will correctly classify the modified sign as a left-turn sign. Such an adversarial example is a serious threat to a DNN. Recently, a multi-target adversarial example was introduced that causes misclassification by several models within each target class using a single modified image. However, it has the vulnerability that as the number of target models increases, the overall attack success rate is reduced. Therefore, if there are several models that the attacker wishes to target, the attacker needs to control the attack success rate for each model by considering the attack priority for each model. In this paper, we propose a priority adversarial example that considers the attack priority for each model in cases targeting several models. The proposed method controls the attack success rate for each model by adjusting the weight of the attack function in the generation process, while maintaining minimum distortion. We used Tensorflow, a widely used machine learning library, and MNIST as the dataset. Experimental results show that the proposed method can control the attack success rate for each model by considering the attack priority of each model while maintaining minimum distortion (on average 3.95 and 2.45 in targeted and untargeted attacks, respectively).
‘The Duty to Remember v the Right to be Forgotten: Holocaust Archiving and Research, and European Data Protection Law’: Notes from Arye Schreiber’s seminar hosted by NINSO, the Northumbria Internet &amp;amp; Society Research Interest Group

Justice is elusive, particularly if it is the mirror of society and law: the seminar given by Arye Schreiber is a powerful statement underlining the boundaries the law sets to justice, giving a new perspective with regard to the horrific happenings during the Holocaust. “Justice” in this context, represents the interaction between a painful sentiment of “never will I forget but always will I need to remember”, guided by the moral imperative to provide justice for victims, through Holocaust research. The seminar brings together two bodies of literature on Holocaust research and privacy laws, arguing that privacy illustrated a major barrier to justice for Holocaust victims and continues to be an impediment in light of the new data protection regulation, the General Data Protection Regulation (GDPR). The present contribution is a report of Arye Schreiber’s seminar organised by the Northumbria Internet & Society Research Interest Group (NINSO). The NINSO group is a multidisciplinary research group that shares research interests on technology and its significance in law, computer-science, social sciences, and engineering. Being interested in a holistic outlook of technological developments, it frequently organises seminars that aim to strengthen both, collaborations as well as the quality of academic research in this area.