Reviewerly/exHarmony huggingface dataset:
Awareness and Utilization of Anti-Plagiarism Detection Software among Academic Staff of Yusuf Maitama Sule University

Abstract The study investigates the academic staffs’ awareness and utilization of anti-plagiarism detection software in academic writing. Quantitative research methodology was used, using survey research design to collect data from the respondents. One hundred and eighty-three (183) copies of questionnaire were administered to the academic staff of Yusuf Maitama Sule University, Kano out of which one hundred and fifty-three were duly returned and found usable which represents 83.6%. Data collected were analyzed using descriptive analysis through the use of frequency and percentages. Findings revealed that, respondents were aware of anti-plagiarism detection software and they came to know about the software through their thesis/ dissertation particularly Quetext and Turnitin. Less than half of the respondents were highly aware while others are moderately aware and only few were completely not aware of anti-plagiarism detection software. It also found that, less than half of the respondents used anti-plagiarism detection software which means majority were not actively using to checkmate academic writings as the institution under study was not using or adopt any anti-plagiarism detection software for academic activities. The study finally revealed that, majority of the respondent unanimously agreed that using anti-plagiarism tools will deter learners from plagiarizing in the first place, improve students’ ability to avoid plagiarizing, it will also assist in building a community of academic integrity. However, the study recommends that here is still need for academic staff to been lighten about the existence and functions of the software, there is need for the Yusuf Maitama Sule University, Kano to adopt and implement the use of anti-plagiarism detection software to checkmate and or reduce the level of academic dishonesty and the university management need to understand that using antiplagiarism tools will deter learners from trying to plagiarized and assist in building a good community of research. Keywords: Awareness, Utilization, Plagiarism, Anti-Plagiarism & Detection Software.
SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis

To represent the biological variability of clinical neuroimaging populations, it is vital to be able to combine data across scanners and studies. However, different MRI scanners produce images with different characteristics, resulting in a domain shift known as the ‘harmonisation problem’. Additionally, neuroimaging data is inherently personal in nature, leading to data privacy concerns when sharing the data. To overcome these barriers, we propose an Unsupervised Source-Free Domain Adaptation (SFDA) method, SFHarmony. Through modelling the imaging features as a Gaussian Mixture Model and minimising an adapted Bhattacharyya distance between the source and target features, we can create a model that performs well for the target data whilst having a shared feature representation across the data domains, without needing access to the source data for adaptation or target labels. We demonstrate the performance of our method on simulated and real domain shifts, showing that the approach is applicable to classification, segmentation and regression tasks, requiring no changes to the algorithm. Our method outperforms existing SFDA approaches across a range of realistic data scenarios, demonstrating the potential utility of our approach for MRI harmonisation and general SFDA problems. Our code is available at https://github.com/nkdinsdale/SFHarmony.
Fingerprinting Violating Machines with In-Memory Protocol Artifacts

Cyber crime has increased as a side effect of the dramatic growth in internet deployment. Identifying machines that are responsible about crimes is a vital step in an attack investigation. Tracking the IP address of the attacker to its origin is indispensable. However, apart from finding the attacker's (possible) machine, it is inevitable to provide supportive proofs to bind the attack to the attacker's machine, rather than depending solely on the IP address of the attacker, which can be dynamic. This paper proposes to implant such supportive proofs by utilising the internals of three well-known internet protocols: IP, TCP, and ICMP. Our results show that there can be potential proofs in the structures of these protocols. In addition, because a violator is unaware of (and has no control over) the involved protocols, the investigation process is empowered with stealth. To the best of our knowledge, we are the first to utilise protocol remnants in fingerprinting violating machines.
Students Expectations from Mobile Devices for Mobile Learning

Mobile devices are becoming ever more widely available, with growing functionality, giving students access to educational material anytime and anywhere. Thus, mobile devices have become more attractive for education. However, owing to the nature of mobile devices, such as small screen size, it poses number of challenges to create a usable learning environment. Therefore, it becomes noteworthy to explore usability of these devices and the students' expectations. The present empirical study regarding the use of personal digital assistants (PDAs) aimed to explore expectations of students from mobile devices in order to support mobile learning. Data was collected from 30 university students through pre and post interviews while students were conducting a set of e-learning actions, such as uploading files, communicating and acquiring course contents, on a mobile device. With the analyses of interviews using constant comparative approach, expectations of the students were classified into 11 categories which are related to interaction and usability.
SLA-aware task allocation with resource optimisation on cloud environment

Cloud computing is a materialised environment for several users due to its unpredictable nature. However cloud is a challengeable environment for many researches because of its hardness on predicting the accurate status of virtual machines (VM) in order to allocate resources for cloud consumers. Many researchers focus on optimisation and scheduling algorithms for predicting accurate status about VMs and their researches fail in providing the predominant results. In order to furnish efficacious results, we propose a novel cloud framework with resource optimisation. Two special components are chosen on it: one is resource manager (RM) and the other one is virtual machine re-allocator (VMRA). This framework has several methodologies like task clustering, task allocation, VM clustering and re-allocation of VMs. Specifically we introduce SLA-priority clustering algorithm for several tasks available on cloud, and these are allocated using ETC matrix with PSO algorithm. Our proposed cloud framework can be implemented using a CloudSim simulation tool which furnishes the efficient results. Thus our process effectively allocates resources on cloud environment and we also solve the problem of re-allocation of VM, power efficient VM re-allocation algorithm is proposed to solving the random failure issues.
The First Paper on Experimental Observation of Interference Fringes with Feeble Light (Sir Geoffrey Ingram Taylor)

On the editor’s selection, this chapter reproduces a brief biography of Sir Geoffrey Ingram Taylor (1886–1975) in Sect. 11.1, and a text of Taylor’s paper of the year 1909 “Interference fringes with feeble lightInterference fringes with feeble light Taylor ” (Sect. 11.2), the first experimental observation of interference fringes with feeble light.
RDFBones – making research explicit: an extensible digital standard for research data

A fundamental impediment to the adoption of digital standards in physical anthropology is the vast diversity of this area of research. Even within osteology, many investigations require some modification of existing standards to suit their specific study designs. This might be a reason for researchers not to use database software based exclusively on one particular standard. It also makes it difficult to keep track of research data compatibility and to process data from different investigations in one database system. Up to now, comprehensive and monolithic data standards have failed to address these issues. We propose a different approach, concentrating on the exact definition of individual data items. These are the building blocks researchers can use to describe the various aspects of their research, like skeletal inventories, research methods and work flows, resulting data and their processing employing mathematical transformations or textual conclusions. Because the building blocks of these descriptions are defined beforehand, the degree of compatibility between different investigations becomes evident. Our data standard, RDFBones, is an RDF (Resource Description Framework) ontology, containing a number of classes and properties for describing anthropological research and materials. Individual researchers can use these elements to define their methodology. That way, RDFBones helps to build standards, instead of prescribing them. Once a standard is formulated, however, it can be published and shared otherwise, supporting uniform methodology. RDFBones also creates a perfect means for sustained long-term data storage.
Combining Parts of Speech, Term Proximity, and Query Expansion for Document Retrieval

Document retrieval systems recover documents from a database and order them according to their perceived relevance to a user's search query. This is a difficult task for machines to accomplish because there exists a semantic gap between the meaning of the terms in a user's literal query and a user's true intentions. The main goal of this study is to modify the Okapi BM25 document retrieval system to improve search results for textual queries and unstructured, textual corpora. This research hypothesizes that Okapi BM25 is not taking full advantage of the structure of text inside documents. This structure holds valuable semantic information that can be used to increase the model's accuracy. Modifications that account for a term's part of speech, the proximity between a pair of related terms, the proximity of a term with respect to its location in a document, and query expansion are used to augment Okapi BM25. The study resulted in 87 modifications which were all validated using open source corpora. The top scoring modification from the validation set was then tested under the Lisa corpus and the model performed 10.25% better than Okapi BM25 when evaluated under mean average precision.
Cost-Constrained Classifier Cascade using Sparse Probit Models : Invited Presentation

Feature selection is a common problem in pattern recognition. Though often motivated by the curse of dimensionality, feature selection also has the added benefit of reducing the cost of extracting features from test data. In this work, sparse probit models are modified to incorporate feature costs. A single-classifier approach, Cost-Constrained Feature optimization (CCFO), is compared to a new ensemble method referred to as the Cost-Constrained Classifier Cascade (C4). The C4 method utilizes a boosting framework that accommodates per-sample feature selection. Experimental results compare C4, CCFO, and baseline sparse kernel classification on two data sets with asymmetric feature costs, illustrating that C4 can yield similar or better accuracy and more economical use of expensive features.
Human-Centered Design in the Energy Turnaround Project Enera – It Pays to Go Off the Beaten Track!

Abstract In this article, we give insights into the development of an ‘interface of energy’, which is developed as part of the energy turnaround project ‘enera’. This interface shall allow communication between the human and the future energy network which will be dominated by renewable energy sources and on-site power generation. We show how we applied Human-Centered Design methods to address the challenge of designing a user interface for an infrastructure that is still in development. Further, we show how this approach was successfully combined with public relations, such as feedback sessions on prototypes as part of an open Barcamp. We give insights into interviews, profiles, personas, public operations, user needs, prototyping and testing. As a special feature we conducted prototyping and prototype testing workshops in a residential house within the project region. These turned out to be very successful for many reasons, e. g., in that the natural environment served as a creative stimulator. Besides, the workshops had a lasting effect on the participants, who were members of the project as well as volunteers living in the project region.
Pemanfaatan CFSRv2 untuk Statistical Downscaling menggunakan Principal Component Regression dan Partial Least Square

This research used CFSRv2 data as output data general circulation model. CFSRv2 involves some variables data with high correlation, so in this research is using principal component regression (PCR) and partial least square (PLS) to solve the multicollinearity occurring in CFSRv2 data. This research aims to determine the best model between PCR and PLS to estimate rainfall at Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station by comparing RMSEP value and correlation value. Size used was 3×3, 4×4, 5×5, 6×6, 7×7, 8×8, 9×9, and 11×11 that was located between (-40) N - (-90) S and 1050 E -1100 E with a grid size of 0.5×0.5 The PLS model was the best model used in stastistical downscaling in this research than PCR model because of the PLS model obtained the lower RMSEP value and the higher correlation value. The best domain and RMSEP value for Bandung geophysical station, Bogor climatology station, Citeko meteorological station, and Jatiwangi meteorological station is 9 × 9 with 100.06, 6 × 6 with 194.3, 8 × 8 with 117.6, and 6 × 6 with 108.2, respectively.
Perceptual Attention-based Predictive Control

In this paper, we present a novel information processing architecture for safe deep learning-based visual navigation of autonomous systems. The proposed information processing architecture is used to support a perceptual attention-based predictive control algorithm that leverages model predictive control (MPC), convolutional neural networks (CNNs), and uncertainty quantification methods. The novelty of our approach lies in using MPC to learn how to place attention on relevant areas of the visual input, which ultimately allows the system to more rapidly detect unsafe conditions. We accomplish this by using MPC to learn to select regions of interest in the input image, which are used to output control actions as well as estimates of epistemic and aleatoric uncertainty in the attention-aware visual input. We use these uncertainty estimates to quantify the safety of our network controller under the current navigation condition. The proposed architecture and algorithm is tested on a 1:5 scale terrestrial vehicle. Experimental results show that the proposed algorithm outperforms previous approaches on early detection of unsafe conditions, such as when novel obstacles are present in the navigation environment. The proposed architecture is the first step towards using deep learning-based perceptual control policies in safety-critical domains.
Gesture Recognition in RGB Videos UsingHuman Body Keypoints and Dynamic Time Warping

Gesture recognition opens up new ways for humans to intuitively interact with machines. Especially for service robots, gestures can be a valuable addition to the means of communication to, for example, draw the robot's attention to someone or something. Extracting a gesture from video data and classifying it is a challenging task and a variety of approaches have been proposed throughout the years. This paper presents a method for gesture recognition in RGB videos using OpenPose to extract the pose of a person and Dynamic Time Warping (DTW) in conjunction with One-Nearest-Neighbor (1NN) for time-series classification. The main features of this approach are the independence of any specific hardware and high flexibility, because new gestures can be added to the classifier by adding only a few examples of it. We utilize the robustness of the Deep Learning-based OpenPose framework while avoiding the data-intensive task of training a neural network ourselves. We demonstrate the classification performance of our method using a public dataset.
A Real-Time Auto Calibration Technique for Stereo Camera

Calibration of the internal and external parameters of a stereo vision camera is a well-known research problem in the computer vision. Usually, to get accurate 3D results the camera should be manually calibrated accurately as well. This paper proposes a robust approach to auto calibration stereo camera without intervention of the user. There are several methods and techniques of calibration that have been proven, in this work, we exploiting the geometric constraint, namely, the epipolar geometry. We specifically focus to use seven techniques for features extraction (SURF, BRISK, FAST, FREAK, MinEigen, MSERF, and SIFT), however, tries to establish the correspondences between points extracted in stereo images with various matching techniques (SSD, SAD, Hamming). Then we exploit the fundamental matrix to estimate the epipolar line by choosing the perfect eight-point algorithms (Norm8Point, LMedS, RANSAC, MSAC, and LTS). A large number of experiments have been carried out, and very good results have been obtained by comparison and choice of the perfect technique in every stage.
Multidimensional signal interpolation based on parametric space dimension reduction

we reduce the dimension of a parametric space in the adaptive interpolation of a multidimensional signal. A hybrid adaptive interpolator underlies the dimensionality reduction. The multidimensional hybrid interpolator uses structurally different algorithms to interpolate multidirectional sections of the signal. The approximation of some sections of the signal by other sections underlies the interrelations between signal sections. The adaptive parametric interpolation of intra-sectional samples accounts for intra-sectional interrelations between signal samples. Computational experiments in real multidimensional signals prove the efficiency of the hybrid adaptive interpolator.
The Origin of Clusters in Recurrent Neural Network State Space

Cluster analysis has been successfully applied to the problem understanding hidden unit representations in both feed-forward and recurrent neural networks. While the topological properties of feed-forward networks may support the use of cluster analysis, the results described within this paper suggest that applications to recurrent networks are not justified. This paper illustrates how clustering fails to provide useful insights into the underlying task-dependent information processing mechanism of recurrent networks. In this paper, I first demonstrate that randomly generated networks display a surprising amount of clustering before training. Then I explain that the clustering structure emerges, not in response to the task training, but because of the volume-reducing iterated mappings that comprise the commonly used recurrent neural networks models.
Overview of Task Understanding of Unmanned Combat Systems

Unmanned combat systems, as new weapons to overturn the rules of future war, have been the focus of research and development of military powers. To realize the efficient combat coordination between human and unmanned systems, the task understanding of unmanned combat systems is critical. Concerning the task understanding problems of unmanned combat systems, the basic concepts and significance are first discussed Then the existing methods for the task understanding of unmanned systems are summarized and analyzed A general process of the task understanding of unmanned combat systems and a task understanding framework based on deep learning are proposed The process defines the inputs, outputs, and triggering conditions. The deep learning based framework is capable of dealing with complex combat situations, which has certain reference significance for the task understanding of unmanned combat systems.
ONLINE INTERACTION – A NEW SCALE OF THE COMMON EUROPEAN FRAMEWORK OF REFERENCE

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 1794-1798ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.0514Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Privacy-preserving classification of personal text messages with secure multi-party computation : an application to hate-speech detection

Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations. We propose the first privacy-preserving solution for text classification that is provably secure. Our method, which is based on Secure Multiparty Computation (SMC), encompasses both feature extraction from texts, and subsequent classification with logistic regression and tree ensembles. We prove that when using our secure text classification method, the application does not learn anything about the text, and the author of the text does not learn anything about the text classification model used by the application beyond what is given by the classification result itself. We perform end-to-end experiments with an application for detecting hate speech against women and immigrants, demonstrating excellent runtime results without loss of accuracy.
Control System Shell of Mobile Robot with Voice Recognition Module

The paper presents the final result of the project, in which the main objective was to develop and implement a semi-autonomous control system of the mobile robot equipped with a voice recognition software module. In the first part of the paper, the description of a mobile robot is given. Next, the main development process of the control system is described. It is shown that the proposed system is designed with the use of UML diagrams taking into account both software and hardware parts. In order to guarantee a partial autonomy of the robot, it is decided to apply a Bayesian-based behavioral controller. In such a way, the control system shell is obtained. In the implementation phase of the project voice recognition, Bayesian-based behavioral controller and command management functionalities were embedded into the robot as well as operator applications, allowing robot control with voice commands. Verification studies were carried out to show merits and limitations of the proposed solution.
Efficient revocable CP-ABE for big data access control in cloud computing

Due to huge volume of big data, cloud is a better choice to store big data. Since the cloud is not trustworthy, privacy and access control is a big concern. Ciphertext policy attribute-based encryption (CP-ABE) is a promising technique to enable both privacy and access control in the cloud. However, directly applying CP-ABE scheme for big data in the cloud is a challenging task because of revocation. Existing CP-ABE with revocation schemes are lacking in efficiency. In this paper, we propose an efficient revocable CP-ABE (R-CP-ABE) scheme for big data access control in cloud using proxy-based updates in which the proxy server performs the ciphertext and secret key updates instead of data owner and data user respectively during revocation. This outsourced updates during revocation reduces the communication and computation overhead of data owner and data users. In security analysis, we prove that our R-CP-ABE scheme is secure against chosen plain-text and user collusion attacks. In addition, we also show that our scheme achieves forward and backward secrecy. The performance analysis demonstrates that our method is efficient when comparing with existing schemes.