Reviewerly/exHarmony huggingface dataset:
The mediating role of planned behaviour on deterrence initiatives aimed at managing local government workplace compliance

Orientation: Compliance culture within the South African local government context is perceived to be critical to sustainable municipality service delivery. Improving compliance culture is therefore important.Research purpose: This article therefore developed a compliance framework based on deterrence theory (DT) and the theory of planned behaviour (TPB) to identify human behavioural factors to be considered in the development and use of work procedures as a way to improve workers’ compliance with work procedures.Motivation for the study: Reports (e.g. Auditor General reports) on municipality service delivery fall short of providing a clear assessment and study of work and human behavioural dynamics in relation to compliance with work procedures.Research approach/design and method: This article tested a conceptual model focused on the effect of deterrence initiatives on workplace compliance as mediated by the theory-planned behaviour. Data were collected from 119 workers in two municipalities of the Eastern Cape Province in South Africa through a structured questionnaire and analysed using structural equation model.Main findings: Findings show that a positive attitude towards compliance to work procedures cannot be achieved through intimidation; reinforcement has a positive effect on attitude, subjective norms and perceived behavioural control (PBC). However, only the PBC aspect of the TPB explains significant mediation between deterrence and compliance.Practical/managerial implications: The study demonstrates how compliance culture can be engendered by non-intimidating deterrent measures and PBC.Contributions/value-add: The negative effect of intimidation on compliance culture and the positive effect of PBC on compliance attitude of workers are critical contributions.
Optimization of Artificial Neural Networks with Genetic Algorithms for Biometric Pattern Recognition

Abstract The process of pattern recognition in the biometrics is particularly important. The patterns can differ from each other a lot, and even the samples can be significantly different from the templates. The Artificial Neural Networks can be applied as a universal approximator to recognize the patterns with more flexibility. However the topology of the networks determines the processing time and complexity of the hardware of the physical environments. The Genetic Algorithms can be used with success in optimization problems like in this situation, the topology of the neural network is more optimal if we apply the Genetic Algorithms. This study introduce an algorithm in which a tailor made algorithm correcting the topology to enhance the effectiveness of the process.
Continuous Hyper-parameter Configuration for Particle Swarm Optimization via Auto-tuning

Hyper-Parameter configuration is a relatively novel field of paramount importance in machine learning and optimization. Hyper-parameters refers to the parameters that control the behavior of algorithms and are not tuned directly by such algorithms. For hyper-parameters of an optimization algorithm such as Particle Swarm Optimization, hyper-parameter configuration is a nested optimization problem. Usually, practitioners needs to use a second optimization algorithm such as grid search or random search to find proper hyper-parameters. However, this approach forces practitioners to know about two different algorithms. Moreover, hyper-parameter configuration algorithms also have hyper-parameters that need to be considered. In this work we use Particle Swarm Optimization to configure its own hyper-parameters. Results show that hyper-parameters configured by PSO are competitive with hyper-parameters found by other hyper-parameter configuration algorithms.
Application Research of Convolutional Neural Network in Remote Sensing Image Registration

In order to solve the problems of large workload of feature extraction and inaccurate feature matching when traditional image registration algorithms are used in remote sensing image processing, we combine affine transformation network, bilinear interpolation and Convolutional Neural Network to to meet the needs of remote sensing image registration accuracy in practical engineering. Firstly, the reference image is enlarged by designed affine transformation network; then, the convolution neural network extracts different features of reference image and training image, learns mapping relationship between images by labels and predict the transformation relationship between reference image and sensed image directly. The experimental results show that compared with the widely accepted SIFT and SURF, our proposal can significantly improve the accuracy of remote sensing image registration.
AR/VR Strategy Considerations for Academic Computing Services

As higher education institutions take up augmented and virtual reality (AR/VR) tools and applications, important choices and challenges arise in deciding how to approach the planning and design of infrastructure, spaces, and the services to be offered. The observations in this paper are based upon early experiences of growing AR/VR spaces and support in one university setting. The emerging services, space designs, and choice of technology and training offered are informed both by consideration of the varied AR/VR environments being explored in the curriculum as well as a general interest in supporting AR/VR use by individuals and groups in non-curricular settings. The goals of this paper include: 1) briefly surveying commonalities and differences of AR and VR as they're being applied to teaching and learning; 2) considering service design approaches and decision points around AR/VR technologies; 3) and noting challenges for developing sustainable approaches to AR/VR support.
Bibliometric Analysis of Publications in the Scopus Database: A Study at Diponegoro University during 2014-2018

This paper presents the strategy of Diponegoro University to increase its research productivity towards the world-class university. Bibliometric indicators are employed to characterize the research activities of Diponegoro University, Indonesia with production in the period of 2014-2018 in publications that are indexed in the Scopus database. The number of documents and their comparison with other universities, and productive authors are of particular interest. The results reflect that the university documents improve significantly during the research time frame. In comparison with other top ten universities in Indonesia, for the last five years, Diponegoro University has achieved the fastest rate of publication (up to 471%). However, compared to other universities in neighboring countries, Diponegoro University is still left behind. In addition, it is shown that prolific authors contributing to documents in Scopus are uneven in terms of origin of faculty. Based on these results, a recommendation with respect to the research productivity is discussed in this paper.
Usability evaluation to approve an information system design (case study: Immunization monitoring interface design)

Prototyping is a useful technique to support the development process of building a software application. It is beneficial particularly when the developer focuses on the user. However, in the requirements phase, the developer has problems to give visualization about the task that should be done when the user interacts with the information system. So, this paper aims to demonstrate that with the usability evaluation in the requirements phase, it can help the IS developer to achieve approval from the potential user of the IS. This paper employs a case study in the process of developing Immunization Monitoring Information System (IMIS) at the Srondol Community Health Centre ( Pusat kesehatan Masyarakat / PUSKESMAS), Semarang, Indonesia. For the experiment, this paper only uses two potential IMIS users. The IMIS interface will be tested for its usefulness with variable effectiveness, efficiency, and easy use. The output generated from the experiment was an effective, efficient and easy-to-use interface design for IMIS from the perceptions of child guardians and the perspective of Community Health Centre staff. This paper provides recommendations, that in the requirement process, usability tests can be used to get approval and recommendations from potential user of information systems.
BATSense: Anomalous Security Event Detection using TBATS Machine Learning

The BATSense anomaly detection methodology has been running in a large university (~50k students) successfully detecting anomalies across 300k devices for a year. We use machine learning, specifically the TBATS forecasting algorithm, to predict future trends for the number of events per second for a variety of device types. The forecasted values are compared against actual observations to alert security personnel of significant deviations. Anomalies are detected based on logs relevant to security events; they may be a system change, a system failure or a football game. Forecasts are never perfect, but when measured over 51 days of use we have shown that false positives can be manageable (1 per week) for true positives of 1 per day. The result is a methodology that is useful to security personnel because the detected anomalies are based on logs of interest to them.
Upsampling Inertial Sensor Data from Wearable Smart Devices using Neural Networks

Inertial sensor data collected from wearable smart devices such as smartwatches are expected to be used in various smart applications such as video game controllers, hand drawing, hand writing, gestural input devices, human activity recognition, and remote communication using sign language. However, since the maximum sampling rate of inertial sensors in commercial smartwatches is restricted, capturing fine-grained body movements using the low-sampled signals is difficult for these sensors. Therefore, this study proposes a new method for generating high sampling rate signals from the low-sampled signals by upsampling the low-sampled signals using interpolation with an artificial neural network. Because it is impossible to obtain "non-existent" data from low-sampled signals according to the information theory, we estimate these data from experience, i.e., using high-sampled signals prepared in advance for training. This is possible because trajectories of a sensor are restricted by the skeletal structure of the body part to which the sensor is attached.
Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach

Automatic Classification of Wireless Signals (ACWS), which is an intermediate step between signal detection and demodulation, is investigated in this paper. ACWS plays a crucial role in several military and non-military applications, by identifying interference sources and adversary attacks, to achieve efficient radio spectrum management. The performance of traditional feature-based (FB) classification approaches is limited due to their specific input feature set, which in turn results in poor generalization under unknown conditions. Therefore, in this paper, a novel feature-based classifier Neural-Induced Support Vector Machine (NSVM) is proposed, in which the features are learned automatically from raw input signals using Convolutional Neural Networks (CNN). The output of NSVM is given by a Gaussian Support Vector Machine (SVM), which takes the features learned by CNN as its input. The proposed scheme NSVM is trained as a single architecture, and in this way, it learns to minimize a margin-based loss instead of cross-entropy loss. The proposed scheme NSVM outperforms the traditional softmax-based CNN modulation classifier by managing faster convergence of accuracy and loss curves during training. Furthermore, the robustness of the NSVM classifier is verified by extensive simulation experiments under the presence of several non-ideal real-world channel impairments over a range of signal-to-noise ratio (SNR) values. The performance of NSVM is remarkable in classifying wireless signals, such as at low signal-to-noise ratio (SNR), the overall averaged classification accuracy is &gt; 97% at SNR = −2 dB and at higher SNR it achieves overall classification accuracy at &gt; 99%, when SNR = 10 dB. In addition to that, the analytical comparison with other studies shows the performance of NSVM is superior over a range of settings.
DeMetrA - Decentralized Metering with user Anonymity and layered privacy on Blockchain

Wear and tear are essential in establishing the market value of an asset. From shutter counters on DSLRs to odometers inside cars, specific counters, that encode the degree of wear, exist on most products. But malicious modification of the information that they report was always a concern. Our work explores a solution to this problem by using the blockchain technology, a layered encoding of product attributes and identity-based cryptography. Merging such technologies is essential since blockchains facilitate the construction of a distributed database that is resilient to adversarial modifications, while identity-based signatures set room for a more convenient way to check the correctness of the reported values based on the name of the product and pseudonym of the owner alone. Nonetheless, we reinforce security by using ownership cards deployed around NFC tokens. Since odometer fraud is still a major practical concern, we discuss a practical scenario centered on vehicles, but the framework can be easily extended to many other assets.
The applicability of on-line contextual calibration to a neural network based monocular collision avoidance system on a UAV

Contextual calibration for object detection is a technique where a pretrained network collects attractive false positives during a calibration phase and use this calibration data for further training. This paper investigates the applicability of this method to a vision based onboard sense and avoid system, which requires intruder aircraft detection in camera images. Various landscape and sky backgrounds were generated by Unreal4 3D engine for calibration tests. Contextual calibration is a promising candidate for handling extreme situations which are not covered well in the training data.
Use of Natural Information Redundancy in On-Line Testing of Computer Systems and their Components

This paper is devoted to the problem of on-line testing the digital circuits in the use of natural information redundancy for results calculated during the performing arithmetic operations on approximate data. Those data are represented usually in floating-point formats. It's shown the dominant development of computer systems in critical applications that support approximate calculations performed on the measurement results obtained from sensors. Information redundancy is considered as the basis for solving analysis issues, including the on-line testing of digital circuits. The existence of natural information redundancy in the result codes of all arithmetic operations on approximate data is proved. These codes contain forbidden values that are calculated under the action of faults. Another type of natural information redundancy inherent in data formats in unused positions is considered as well. The role of natural information redundancy in a version form for increasing the checkability of the circuits and trustworthiness of result in the FPGA components of safety-related systems is noted.
Hybrid Logical Clocks for Database Forensics: Filling the Gap between Chain of Custody and Database Auditing

Database audit records are important for investigating suspicious actions against transactional databases. Their admissibility as digital evidence depends on satisfying Chain of Custody (CoC) properties during their generation, collection and preservation in order to prevent their modification, guarantee action accountability, and allow third-party verification. However, their production has relied on auditing capabilities provided by commercial database systems which may not be effective if malicious users (or insiders) misuse their privileges to disable audit controls, and compromise their admissibility. Hence, in this paper, we propose a forensically-aware distributed database architecture that implements CoC properties as functional requirements to produce admissible audit records. The novelty of our proposal is the use of hybrid logical clocks, which compared with a previous centralised vector-clock architecture, has evident advantages as it (i) allows for more accurate provenance and causality tracking of insider actions, (ii) is more scalable in terms of system size, and (iii) although latency is higher (as expected in distributed environments), 70 per cent of user transactions are executed within acceptable latency intervals.
New protocols of cognitive data management and sharing in cloud computing

Summary In this paper, we shall discuss new classes of cognitive systems dedicated the tasks of dividing and managing shared information not only in simple horizontal structures but also primarily in vertical structures. An example of such a structure is a computer cloud where, from various levels of access to information, one can execute the management process, both with respect to the entire information resources, as well as to individual pieces of data, including shared information. A special type of information is a service, which, similar to various types of data, is subject to the concealment process. In this work, we shall present universal algorithms for shared data management, such that can be used in concealment protocols both for data and services. Due to the above, this paper will treat the term “service” as a special type of data, constituting the basis of their sharing and management processes. Moreover, we shall determine the impact of the proposed algorithms on the enhancement of data/service management processes in Cloud Computing as well as the impact of the service management and data sharing processes in Could Computing on decision‐taking processes in systems supporting the management processes.
Gaussian Process Based Model-free Control with Q-Learning

The aim of this paper is to demonstrate a new algorithm for Machine Learning (ML) based on Gaussian Process Regression (GPR) and how it can be used as a practical control design technique. An optimized control law for a nonlinear process is found directly by training the algorithm on noisy data collected from the process when controlled by a sub-optimal controller. A simplified nonlinear Fan Coil Unit (FCU) model is used as an example for which the fan speed control is designed using the off-policy Q-learning algorithm. Additionally, the algorithm properties are discussed, i.e. learning process robustness, Gaussian Process (GP) kernel functions choice. The simulation results are compared to a simple PI design based on a linearized model.
Improved Medical Image Segmentation Algorithm Using Two-Step CNN Architecture

Segmentation of glandular cells (glands) is the basic step in an automated cancer grading system. Existing methods for gland segmentation based on convolution neural network (CNN) apply combined datasets of both benign and malignant glands for deriving a single learning model. However, benign and malignant glands have very different structural appearances; so we thought the process will demonstrate better performance if we apply different CNN models for small-scale image sets. Therefore, in this paper, we propose a two-step CNN learning model that segments the glands by applying different data models according to the data type. Our proposed segmentation system comprises the following two steps: a CNN model for classification and a CNN model for segmentation. The first step is to classify whether the input image is benign or malignant glands using the slightly modified pre-trained VGG16 model. In the second step, the gland is segmented by applying one of two modified U-Net networks according to the classified results. Experimental results demonstrated that our two-step model has better segmentation performance compared to a single learning model for both benign and malignant data.
Exploring Lightweight Efficiency of ForkAES

Recently the ForkAES construction was proposed by Andreeva et al. for efficiently performing authenticated encryption of very short messages on next generation IoT devices. The ForkAES tweakable block cipher uses around one and a half AES encryption calls to produce a pair of ciphertexts for any given plaintext. However the only downside of the construction is that it needs to store an extra state of 128 bits in addition with the storage elements required to perform AES encryption. Thus a hardware implementation of ForkAES would require additional circuit area to accommodate the extra state. In this paper, we first show that it is possible to implement ForkAES without any additional storage elements other than those required to implement AES, if the AES circuit can additionally perform decryption. Such an implementation naturally requires more clock cycles to perform ForkAES operations. We extend the recently proposed Atomic AES v2.0 architecture to realize ForkAES and compare the area-latency trade-offs incurred with and without an additional storage. The area of the most compact ForkAES design takes about 1.2 times that of AES. In the second part of the paper we look at another important parameter of lightweight efficiency, i.e. energy. It is well known that round based constructions for AES are the most energy efficient ones. We extend the so-called “ $$S_3K_2$$ ” construction of Banik et al. (IEEE HOST 17) to realize ForkAES in an energy-preserving manner, and compare the effects of some design choices. The energy consumption of our best ForkAES design takes about 2 times that of AES. From lightweight design perspective, our results hence demonstrate that although ForkAES lives up to its promise (of being roughly 1.5 times that of AES) in terms of its area, the same does not hold for its energy consumption.
Adaptable Anomaly Detection in Traffic Flow Time Series

Analysis of traffic data is an essential component of many intelligent transportation system applications where the quality of data plays an important role. Traffic data collected through sensors such as loop detectors often contain anomalies, e.g. due to malfunctioning detectors or anomalous traffic conditions. Regardless of the rooting cause, such data can heavily affect the results of the subsequent analysis (e.g. traffic prediction). There are several challenges regarding anomaly detection, including absence of universal definition of anomaly, change of traffic pattern over time, as well as unavailability of labeled data, use-case driven analysis. In this paper, a new anomaly detection method for traffic univariate time-series is proposed which does not assume labeled historical data yet uses expert feedback to deal with the fluid definition of anomaly. The method is exemplified and evaluated by applying it on real traffic time series data collected through loop detectors installed in an urban road network in Europe. Employing the proposed method as a pre-process of traffic state estimation can increase the accuracy measure as well as ease the learning of different traffic patterns.