Reviewerly/exHarmony huggingface dataset:
Collaborative Justice and Harm Reduction in Cyberspace: Policing Indecent Child Images

The exponential increase on the internet of indecent images of children (IIOC) has been followed by a transformation within criminal justice. The scale, nature and rapid technological evolution of such crimes—often of distant initial geographical origin—requires collaborative justice and harm reduction arrangements with internet companies and NGOs. The diminished reach (declining criminal justice interventions) and power (even in identifying crimes for intervention) of state authority with the current collaborative model, however, has resulted in inadequate social regulation and policing in response to IIOC crimes on the surface web. There is a considerable risk that the Online Harms White Paper proposals to establish overarching government authority to generally reduce harmful conduct will not fully resolve problems that go much wider than the technological, commercial and consumer protection on the surface web issues emphasised in that document. Only political choices about funding and fundamental rights compliant legislation can (a) prevent the hollowing out of criminal justice capacity and capabilities to deal with IIOC offenders and (b) ensure an essential compatibility and consistency in police operational ability—including the access sought to anonymised communication data via an encryption key—and legal principles when dealing with IIOC crimes across all levels of the internet, including ‘the dark web’. These issues are examined as a case study in civic epistemology about the influence of neoliberalism in technologically focused policy making.
CNN Based Perception System for Collision Avoidance in Mobile Robots using Stereo Vision

In this paper, a CNN based perception system for collision avoidance in mobile robots is presented. In the considered scenario, a mobile robot is ordered to reach a target on its workspace, where several types of objects influence a collision risk for a suitable movement to the desired goal. To ensure collision-free planning through the environment, a set of convolutional neural networks in parallel are employed to detect a set of static or dynamic objects of interest in the environment, as well as objects on the floor that could imply a collision risk during a movement execution. Afterward, stereo vision and filtering algorithms are employed to recover and track the spatial position of detections, in order to generate enough information to plan a collision-free trajectory. All the above steps are evaluated in real-time and real environments, proving to be enough robust and fast for a wide range of mobile robot applications.
The impact of transformational leadership on innovative capability: Mediating role of employee optimism

This study aims to examine the mediating effect of employee optimism on the relationship between transformational leadership and innovative capability in the context of batik small and medium enterprises (SMEs) in Indonesia.This study applied the convenience sampling method to survey 186 employees at batik SMEs in Central Java Indonesia through a self-administrated questionnaire.Data were analyzed using smart PLS v.3 to test the hypotheses.The result show that optimism partially mediated the idealized influence of transformational leader behavior on innovative capability.In addition, optimism fully mediated the individualized consideration of transformational leader behavior on innovative capability.This present study provides both empirical and theoretical contribution focusing on leader behavior, employee optimism and innovative capability.
Principal component analysis-assisted selection of optimal denoising method for oil well transient data

Abstract Oil and gas production wells are often equipped with modern, permanent or temporary in-well monitoring systems, either electronic or fiber-optic, typically for measurement of downhole pressure and temperature. Consequently, novel methods of pressure and temperature transient analysis (PTTA) have emerged in the past two decades, able to interpret subtle thermodynamic effects. Such analysis demands high-quality data. High-level reduction in data noise is often needed in order to ensure sufficient reliability of the PTTA. This paper considers the case of a state-of-the-art intelligent well equipped with fiber-optic, high-precision, permanent downhole gauges. This is followed by screening, development, verification and application of data denoising methods that can overcome the limitation of the existing noise reduction methods. Firstly, the specific types of noise contained in the original data are analyzed by wavelet transform, and the corresponding denoising methods are selected on the basis of the wavelet analysis. Then, the wavelet threshold denoising method is used for the data with white noise and white Gaussian noise, while a data smoothing method is used for the data with impulse noise. The paper further proposes a comprehensive evaluation index as a useful denoising success metrics for optimal selection of the optimal combination of the noise reduction methods. This metrics comprises a weighted combination of the signal-to-noise ratio and smoothness value where the principal component analysis was used to determine the weights. Thus the workflow proposed here can be comprehensively defined solely by the data via its processing and analysis. Finally, the effectiveness of the optimal selection methods is confirmed by the robustness of the PTTA results derived from the de-noised measurements from the above-mentioned oil wells.
Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration

In multisensor systems, the signal processing delay, measurement acquisition delay, and other factors will lead to imprecisely time-stamped measurements, namely, the problem of time-offset. To deal with the measurement time offsets in distributed multisensor systems, a distributed multisensor multitarget tracking algorithm with time-offset registration is proposed. The local processors track multiple targets in the presence of false alarms and missed detections based on the joint probabilistic data association (JPDA) algorithm and the extended Kalman filter (EKF), providing the time-biased local tracks. In the global processor, in allusion to the global track accuracy degradation introduced by the time offsets of local tracks, the equivalent measurements are firstly constructed based on local tracks by using the inverse Kalman filter. The pseudo-measurement equation of time offset for constant velocity targets is derived and the pseudo-measurement calculation method is presented. Then, the pseudo-measurement based relative time-offset estimation algorithm is presented, by using the recursive least squares estimation (RLSE) and the Kalman filter (KF) to jointly estimate the state in space and time domains, respectively. Finally, a framework of distributed multisensor multitarget tracking with time-offset registration is presented, where the time-varying relative time-offset estimation and compensation, 'equivalent measurement to global track' association, and global track update are included. Simulations for multisensor multitarget tracking in the presence of false alarms and missed detections are conducted, demonstrating that the present algorithm effectively improves the accuracy of fused global tracks.
Convergence-Aware Neural Network Training

Training a deep neural network(DNN) is expensive, requiring a large amount of computation time. While the training overhead is high, not all computation in DNN training is equal. Some parameters converge faster and thus their gradient computation may contribute little to the parameter update; in nearstationary points a subset of parameters may change very little. In this paper we exploit the parameter convergence to optimize gradient computation in DNN training. We design a light-weight monitoring technique to track the parameter convergence; we prune the gradient computation stochastically for a group of semantically related parameters, exploiting their convergence correlations. These techniques are efficiently implemented in existing GPU kernels. In our evaluation the optimization techniques substantially and robustly improve the training throughput for four DNN models on three public datasets.
Novice university educators: professional, psychological and motivational spheres of adaptation

The article is focused on the issue of adaptation of novice university teachers. The purpose of this study is: 1. to assess the professional readiness of the novice university educators; 2. to find out the problems they face in professional, psychological and motivational spheres; 3. to compare the frequency of the detected problems within the groups of novice teachers with 1-3 year and 3-5 year experience. The article presents the results of a study conducted among novice educators (N 40) at Peter the Great St. Petersburg Polytechnic University. Results made it possible to figure out the main problems teachers face in professional, psychological and motivational spheres of induction process. Comparison of novice teachers (1-3 year experience) and young teachers (3-5 year experience) resulted in the findings that although there is a positive tendency in all the spheres of adaptation, the difference between the values of novice and young teachers is in most cases statistically insignificant. It makes it possible to conclude that university educator with experience of about 5 years still need support that can be provided by mentoring and individual assistance, performed in the form of on-the-job training, instructional interactions with mentors and experienced colleagues.
BPNet: Branch-pruned Conditional Neural Network for Systematic Time-accuracy Tradeoff

Recently, there have been attempts to execute the neural network conditionally with auxiliary classifiers allowing early termination depending on the difficulty of the input, which can reduce the execution time or energy consumption without any or with negligible accuracy decrease. However, previous studies do not consider how many or where the auxiliary classifiers, or branches, should be added in a systematic fashion. In this paper, we propose Branch-pruned Conditional Neural Network (BPNet) and its methodology in which the time-accuracy tradeoff for the conditional neural network can be found systematically. We applied BPNet to SqueezeNet, ResNet-20, and VGG-16 with CIFAR-10 and 100. BPNet achieves up to 3.15× of speedup without any accuracy drop compared to the base networks.
Side-Channel Sensing: Exploiting Side-Channels to Extract Information for Medical Diagnostics and Monitoring

Information within systems can be extracted through side-channels; unintended communication channels that leak information. The concept of side-channel sensing is explored, in which sensor data is analysed in non-trivial ways to recover subtle, hidden or unexpected information. Practical examples of side-channel sensing are well known in domains such as cybersecurity (CYB), but are not formally recognised within the domain of medical diagnostics and monitoring (MDM). This article reviews side-channel usage within CYB and MDM, identifying techniques and methodologies applicable to both domains. We establish a systematic structure for the use of side-channel sensing in MDM that is comparable to existing structures in CYB, and promote cross-domain transferability of knowledge, mindsets, and techniques.
PE-HEALTH: Enabling Fully Encrypted CNN for Health Monitor with Optimized Communication

Cloud-based Convolutional neural network (CNN) is a powerful tool for the healthcare center to provide health condition monitor service. Although the new service has future prospects in the medical, patient's privacy concerns arise because of the sensitivity of medical data. Prior works to address the concern have the following unresolved problems: 1) focus on data privacy but neglect to protect the privacy of the machine learning model itself; 2) introduce considerable communication costs for the CNN inference, which lowers the service quality of the cloud server. To push forward this area, we propose PE-HEALTH, a privacy-preserving health monitor framework that supports fully-encrypted CNN (both input data and model). In PE-HEALTH, the medical Internet of Things (IoT) sensor serves as the health condition data collector. For protecting patient privacy, the IoT sensor additively shares the collected data and uploads the shared data to the cloud server, which is efficient and suited to the energy-limited IoT sensor. To keep model privacy, PE-HEALTH allows the healthcare center to previously deploy, and then, use an encrypted CNN on the cloud server. During the CNN inference process, PE-HEALTH does not need the cloud servers to exchange any extra messages for operating the convolutional operation, which can greatly reduce the communication cost.
Masked Autoencoder for Distribution Estimation on Small Structured Data Sets

Autoregressive models are among the most successful neural network methods for estimating a distribution from a set of samples. However, these models, such as other neural methods, need large data sets to provide good estimations. We believe that knowing structural information about the data can improve their performance on small data sets. Masked autoencoder for distribution estimation (MADE) is a well-structured density estimator, which alters a simple autoencoder by setting a set of masks on its connections to satisfy the autoregressive condition. Nevertheless, this model does not benefit from extra information that we might know about the structure of the data. This information can especially be advantageous in case of training on small data sets. In this article, we propose two autoencoders for estimating the density of a small set of observations, where the data have a known Markov random field (MRF) structure. These methods modify the masking process of MADE, according to conditional dependencies inferred from the MRF structure, to reduce either the model complexity or the problem complexity. We compare the proposed methods with some related binary, discrete, and continuous density estimators on MNIST, binarized MNIST, OCR-letters, and two synthetic data sets.
Design of Internet of Things Online Oral English Teaching Platform Based on Long-term and Short-term Memory Network

In order to overcome the problems of low fluency and operability in the current online oral English teaching platform, an online oral English teaching platform based on internet of things is proposed. This platform uses the internet of things to design the architecture of online oral English teaching platform. The virtual teaching environment was constructed and the oral English teaching system was used to correct the pronunciation and mouth movements of the users. A part-of-speech labelling model based on the long and short time memory network is constructed, and an attention mechanism is introduced into the long and short time memory network. Experimental results show that the network delay of the platform designed in this paper varies between 0.26 s and 0.37 s, the required cost and development time are reduced by about 50%, and the user-friendly performance of the man-machine interface is improved by 13.20%.
ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks

Closing the gap between the hardware requirements of state-of-the-art convolutional neural networks and the limited resources constraining embedded applications is the next big challenge in deep learning research. The computational complexity and memory footprint of such neural networks are typically daunting for deployment in resource constrained environments. Model compression techniques, such as pruning, are emphasized among other optimization methods for solving this problem. Most existing techniques require domain expertise or result in irregular sparse representations, which increase the burden of deploying deep learning applications on embedded hardware accelerators. In this paper, we propose the autoencoder-based low-rank filter-sharing technique (ALF). When applied to various networks, ALF is compared to state-of-the-art pruning methods, demonstrating its efficient compression capabilities on theoretical metrics as well as on an accurate, deterministic hardware-model. In our experiments, ALF showed a reduction of 70% in network parameters, 61% in operations and 41% in execution time, with minimal loss in accuracy.
POSTER: Data Leakage Detection for Health Information System based on Memory Introspection

The abundance of highly sensitive personal information in the Health Information System (HIS) has made it a prime target of data breach attacks. However, securing the system with existing Data Leakage Prevention (DLP) solutions is difficult due to a lack of security perimeter and diverse composition of software components. We propose the use of hypervisor-based memory introspection for implementing data leakage detection in such an environment. The approach looks for the presence of sensitive raw data in the memory of both the client machines and the server machines, transcending the dependence of pre-existing security perimeters. It is inherently compatible with different types of application software and robust against transport or at-rest data encryption. A prototype has been built on the Bareflank hypervisor and the OpenEMR platform. The evaluation results confirmed the effectiveness of the approach.
The Effect of Using Crossword Puzzle Towards Students’ Vocabulary Mastery in the Eleventh Grade Students of SMA Muhammadiyah 2 Bandar Lampung in Academic Year 2017-2018

This research describes the use of Crossword Puzzle to improve students’ vocabulary mastery. Moreover, it was to find out whether Crossword Puzzle is effective to improve students’ vocabulary mastery at the second grade students of SMA Muhammadiyah 2 Bandar Lampung. This research used experimental design in which the samples of the research were assessed both before and after an experimental treatment. The subject of the research consisted of 68 students, they were divided into experimental and control group. The instruments used for collecting the data were test which was consisted of pretest and posttest. The data analysis of this research was attempted by using independent sample T-test which was calculated through computing with statistical package for Social Science (SPSS) version 20.The result indicated that the students’ mean score of experimental and control group in post-test were significantly different. It showed that t-count was higher than t-table (4.454 &gt; 1.9966) and the sig. 2-tailed value was lower than sig. Level (0.000 &gt; 0.05). Therefore the null hypothesis (Ho) was rejected and the alternative hypothesis (Ha) was accepted. It meant that there was effective impact of using crossword puzzle towards students’ vocabulary mastery. Decision based on facts, using crossword puzzle as a technique was effective to improve students’ vocabulary. Therefore, it was suggested the use of crossword puzzle as a technique in teaching English generally and teaching Vocabulary specifically
Designing large quantum key distribution networks via medoid-based algorithms

The current development of quantum mechanics and its applications suppose a threat to modern cryptography as it was conceived. The abilities of quantum computers for solving complex mathematical problems, as a strong computational novelty, is the root of that risk. However, quantum technologies can also prevent this threat by leveraging quantum methods to distribute keys. This field, called Quantum Key Distribution (QKD) is growing, although it still needs more physical basics to become a reality as popular as the Internet. This work proposes a novel methodology that leverages medoid-based clustering techniques to design quantum key distribution networks on commercial fiber optics systems. Our methodology focuses on the current limitations of these communication systems, their error loss and how trusted repeaters can lead to achieve a proper communication with the current technology. We adapt our model to the current data on a wide territory covering an area of almost 100,000 km2, and prove that considering physical limitations of around 45 km with 3.1 error loss, our design can provide service to the whole area. This technique is the first to extend the state of the art network's design, that is focused on up to 10 nodes, to networks dealing with more than 200 nodes.
Semantic Image Analogy with a Conditional Single-Image GAN

Recent image-specific Generative Adversarial Networks (GANs) provide a way to learn generative models from a single image instead of a large dataset. However, the semantic meaning of patches inside a single image is less explored. In this work, we first define the task of Semantic Image Analogy: given a source image and its segmentation map, along with another target segmentation map, synthesizing a new image that matches the appearance of the source image as well as the semantic layout of the target segmentation. To accomplish this task, we propose a novel method to model the patch-level correspondence between semantic layout and appearance of a single image by training a single-image GAN that takes semantic labels as conditional input. Once trained, a controllable redistribution of patches from the training image can be obtained by providing the expected semantic layout as spatial guidance. The proposed method contains three essential parts: 1) a self-supervised training framework, with a progressive data augmentation strategy and an alternating optimization procedure; 2) a semantic feature translation module that predicts transformation parameters in the image domain from the segmentation domain; and 3) a semantics-aware patch-wise loss that explicitly measures the similarity of two images in terms of patch distribution. Compared with existing solutions, our method generates much more realistic results given arbitrary semantic labels as conditional input.
UMUCorpusClassifier: Compilation and evaluation of linguistic corpus for Natural Language Processing tasks

This demonstration has been supported by the Spanish National Research Agency (AEI) and the European Regional Development Fund (FEDER/ERDF) through projects KBS4FIA (TIN2016-76323-R) and LaTe4PSP (PID2019-107652RB-I00). In addition, Jose Antonio Garcia-Diaz has been supported by Banco Santander and University of Murcia through the Doctorado industrial programme.
Virtual Reality Technology In The Preparation Of Geography Teachers

The article considers the possibility of using virtual and mixed reality in the preparation of geography teachers. The modern regulatory framework and requirements for teachers in Russia were analyzed. It is shown that new pilot projects in education require teachers with skills in working with virtual reality. The experience of preparing students of a pedagogical university in geography without the use of this technology is generalized; problems and their possible solutions using virtual technologies are indicated. Attention is paid to the application of technologies of mixed and virtual reality in the study of various areas in the training of geography teachers. VR headsets were used in practical classes with students. During study of complex spatial objects, virtual reality technology allowed students to learn more the object of study, understand the essence of the studied object, and understand the three-dimensional model, while panoramic pictures increased the degree of immersion. The application of this technology is appropriate for tasks that do not require the making of complex content for a virtual reality environment, due to the need to create a large amount of content, its dynamics, as well as ease of use.