Reviewerly/exHarmony huggingface dataset:
Content Recommendation Algorithm for Intelligent Navigator in Fog Computing Based IoT Environment

With the development of the Internet and mobile technologies, the Internet of Things (IoT) era has arrived.Vehicle networking technology can not only facilitate people's travel but also effectively alleviate traffic congestion.The development of fog computing technology provides unlimited possibilities for the Internet of Vehicles (IoV).Intelligent navigator is a very important part of human-computer interaction in IoV.It carries a large number of tasks of recommending content for users.In order to get more accurate recommendation content, we propose a weighted interest degree recommendation algorithm using association rules for intelligence in the IoV.First, the user data are analyzed to establish the association rule mining algorithm.Second, the user interest score is predicted by analyzing the relevance between user interests to recommend personalized service for the user.From the simulation results, we can see that the proposed algorithm can achieve higher recommendation accuracy.
N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network

Recommender systems are becoming more and more important in our daily lives. However, traditional recommendation methods are challenged by data sparsity and efficiency, as the numbers of users, items, and interactions between the two in many real-world applications increase fast. In this paper, we propose a novel clustering recommender system based on node2vec technology and rich information network, namely, N2VSCDNNR, to solve these challenges. In particular, we use a bipartite network to construct the user-item network and represent the interactions among users (or items) by the corresponding one-mode projection network. In order to alleviate the data sparsity problem, we enrich the network structure according to user and item categories, and construct the one-mode projection category network. Then, considering the data sparsity problem in the network, we employ node2vec to capture the complex latent relationships among users (or items) from the corresponding one-mode projection category network. Moreover, considering the dependence on parameter settings and information loss problem in clustering methods, we use a novel spectral clustering method, which is based on dynamic nearest-neighbors (DNNs) and a novel automatically determining cluster number (ADCN) method that determines the cluster centers based on the normal distribution method, to cluster the users and items separately. After clustering, we propose the two-phase personalized recommendation to realize the personalized recommendation of items for each user. A series of experiments validate the outstanding performance of our N2VSCDNNR over several advanced embedding and side information based recommendation algorithms. Meanwhile, N2VSCDNNR seems to have lower time complexity than the baseline methods in online recommendations, indicating its potential to be widely applied in large-scale systems.
Toward an Efficient Deep Pipelined Template-Based Architecture for Accelerating the Entire 2-D and 3-D CNNs on FPGA

3-D convolutional neural networks (3-D CNNs) are used efficiently in many computer vision applications. Most previous work in this area has concentrated only on design and optimization of accelerators for 2-D CNNs, with few attempts having been made to accelerate 3-D CNNs on FPGA. We find the acceleration of 3-D CNNs on FPGA to be challenging due to their high computational complexity and storage demands. More importantly, although the computational patterns of 2-D and 3-D CNNs are analogous, the conventional approaches that have been adopted for acceleration of 2-D CNNs may be unfit for 3-D CNN acceleration. In this paper, in order to accelerate 2-D and 3-D CNNs using a uniform framework, we first propose a uniform template-based architecture that uses templates based on the Winograd algorithm to ensure the rapid development of 2-D and 3-D CNN accelerators. Then, with the aim of efficiently mapping all layers of 2-D/3-D CNNs onto a pipelined accelerator, techniques are developed to improve the throughput and computational efficiency of the accelerator, including layer fusion, layer clustering, and workload-balancing scheme. Finally, we demonstrate the effectiveness of the deep pipelined architecture by accelerating real-life 2-D and 3-D CNNs on the state-of-the-art FPGA platform. On VCU118, we achieve 3.7 TOPS for VGG-16, which outperforms state-of-the-art FPGA-based CNN accelerators. Comparisons with CPU and GPU solutions demonstrate that our implementation of 3-D CNN achieves gains of up to 17.8× and 64.2× in performance and energy relative to a CPU solution, and a 5.0× energy efficiency gain over a GPU solution.
mRNA: Enabling Efficient Mapping Space Exploration for a Reconfiguration Neural Accelerator

Deep learning accelerators have emerged to enable energy-efficient and high-throughput inference from edge devices such as self-driving cars and smartphones, to data centers for batch inference such as recommendation systems. However, the actual energy efficiency and throughput of a deep learning accelerator depends on the deep neural network (DNN) loop nest mapping on the processing element array of an accelerator. Moreover, the efficiency of a mapping dramatically changes by the target DNN layer dimensions and available hardware resources. Therefore, the optimal mapping search problem is a non-trivial high-dimensional optimization problem. Although several tools and frameworks exist for compiling to CPUs and GPUs, we lack similar tools for deep learning accelerators. To deal with the optimized mapping search problem in deep learning accelerators, we propose mRNA (mapper for reconfigurable neural accelerators), which automatically searches optimal mappings using heuristics based on domain knowledge about deep learning and an energy/runtime cost evaluation framework. mRNA targets MAERI, a recently proposed open-source deep learning accelerator that provides flexibility via reconfigurable interconnects, to run the unique mappings for each layer generated by mRNA. In realistic machine learning workloads from MLPerf, the optimal mappings identified by mRNA framework provides 15% to 26% lower runtime and 55% to 64% lower energy for convolutional layers and 24% to 67% lower runtime and maximum 67% lower energy for fully connected layers compared to simple reference mappings manually picked for each layer.
Cybersecurity Curriculum Design: A Survey

The threat of cyber attacks continue to grow with the increasing number of sophisticated and successful targeted cyber attacks throughout the globe. To address this issue, there is a dire need for cybersecurity professionals with adequate motivation and skills to prevent, detect, respond, or even mitigate the effect of such threats. To this end, several cybersecurity educational programs and concentrations have been established over the past years both at the graduate and undergraduate levels. Moreover, a number of initiatives taken by the government standard bodies in the cybersecurity domain have emerged to help in framing cybersecurity education. Due to the interdisciplinary (and sometimes multidisciplinary) nature of cybersecurity, educational institutions face many issues when designing a cybersecurity curriculum. In this context, we believe that there is a desideratum to provide a big picture of the overall efforts done so far in the direction of cybersecurity curriculum design. In this paper, we present an overview and comparison of existing curriculum design approaches for cybersecurity education. This survey will help the researchers and educators to have an overview of the existing approaches for the purpose of developing a suitable and more effective cybersecurity curriculum in their future endeavour.
Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

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Nurturing Constructive Disagreement - Agonistic Design with Neurodiverse Children

Participatory design (PD) with heterogeneous groups poses particular challenges, requiring spaces in which different agendas or visions can be negotiated. In this paper we report on our PD work with two groups of neurodiverse children to design technologies that support co-located, social play. The heterogeneity in the groups in terms of abilities, conceptions of play, motivations to be involved and individual preferences has challenged us to think of the design process and its outcomes as spaces for continuous negotiation. Drawing on the notion of agonistic PD, we sought not to necessarily reconcile all views, but foster constructive disagreement as a resource for and possible outcome of design. Using our project work as a case study, we report on controversies, big and small, and how they manifested themselves in the processes and outcomes. Reflecting on our experiences, we discuss possible implications on the notion of democratising technology innovation.
Workflow scheduling using Jaya algorithm in cloud

Summary Cloud computing is an on‐demand service that can be accessed by a user according to his requirements through the Internet. Multiple users can request any amount of services, so scheduling of those services is a crucial task in cloud computing. Scheduling is a way of assigning the work to a computer resource. We have multiple tasks at a time that are waiting to be allotted to multiple computer resources. Various optimization algorithms have been used to do task scheduling so that total execution cost is minimized. In this paper, we have implemented Jaya optimization algorithm for workflow scheduling and have compared it with four nature‐inspired algorithms, namely, particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), honey bee, and cat swarm optimization (CSO), keeping the fitness function same for all of them using CloudSim. Previously, work has been done on PSO, GA, ACO, honey bee, and CSO using different criteria. The results are compared on the basis of execution cost and makespan of the algorithm on both an independent set of tasks and a set of tasks that follow a workflow schedule. Benchmark functions such as Montage, CyberShake, Inspiral, and Sipht are used for workflow scheduling. It has been observed that Jaya outperforms the other algorithms as it produces similar results in the least amount of time as it converges very quickly.
The Role of Physical Props in VR Climbing Environments

Dealing with fear of falling is a challenge in sport climbing. Virtual reality (VR) research suggests that using physical and reality-based interaction increases the presence in VR. In this paper, we present a study that investigates the influence of physical props on presence, stress and anxiety in a VR climbing environment involving whole body movement. To help climbers overcoming fear of falling, we compared three different conditions: Climbing in reality at 10 m height, physical climbing in VR (with props attached to the climbing wall) and virtual climbing in VR using game controllers. From subjective reports and biosignals, our results show that climbing with props in VR increases the anxiety and sense of realism in VR for sport climbing. This suggests that VR in combination with physical props are an effective simulation setup to induce the sense of height.
The Effect of Rotational Jitter on 3D Pointing Tasks

Even when in a static position, data acquired from 6 Degrees of Freedom (DoF) trackers is affected by noise, which is typically called jitter. In this study, we analyzed the effects of 3D rotational jitter on Virtual Reality (VR) controllers in a 3D Fitts' law experiment, which explored how such jitter affects user performance. Eight subjects performed a Fitts' law experiment with or without additional jitter on the cursor. Results show that while error rate significantly increased above ±0.5° jitter and subjects' effective throughput started to decrease significantly above ±1° jitter, there was no significant effect on users' movement time. Further, the Fitts's law movement time model was affected when ±2° jitter was applied to the tracker. According to these results, ±0.5° jitter on the controller does not significantly affect user performance for the tasks explored here. The results of our study can guide the design of 3D controller and tracking systems for 3D user interfaces.
Padding Methods in Convolutional Sequence Model

Today, there is a wide range of research cases about end-to-end trained and sequence-to-sequence models applied in the task of handwritten character recognition. Most of which mark the combination between convolutional neural network (CNN) as a feature extraction module and recurrent neural network (RNN) as a sequence-to-sequence module. Notably, the CNN layer can be fed with dynamic sizes of input images while the RNN layer can tolerate dynamic lengths of input data, which subsequently makes up the dynamic feature of the recognition models. However, when the number one priority is to minimize the training timespan, the models are to receive training data in the form of mini-batch, which requires resizing or padding images into an equal size instead of using original multiple-size pictures due to the fact that most of the deep learning frameworks (such as keras, tensorflow, caffe, etc.) only accept the same-size input and output in one mini-batch. Actually, this practice may lower the model dynamicity in the training process. So, the question is whether it might be a trade-off between the effectiveness (level of accuracy) and the time optimization of the model. In this paper, we will examine different impact of various padding and non-padding methods on the same model architecture for Japanese handwriting recognition before finally concluding on which method has the most reasonable training time but can produce an accuracy rate of up to 95%.
Parallel real-time quantum random number generator

The quantum random number generation exploits inherent randomness of quantum mechanical processes and measurements. The real-time generation rate of quantum random numbers is usually limited by electronic bandwidth and data processing rates. Here we use a multiplexing scheme to create a fast real-time quantum random number generator based on continuous variable vacuum fluctuations. Multiple sideband frequency modes of a quantum vacuum state within a homodyne detection bandwidth are concurrently extracted as the randomness source. Parallel postprocessing of raw data from three subentropy sources is realized in one field-programmable gate array (FPGA) based on Toeplitz-hashing extractors. A cumulative generation rate of 8.25 Gbps in real time is achieved. The system relies on optoelectronic components and circuits that could be integrated in a compact, economical package.
Secure Cover Selection for Steganography

Existing cover selection methods for steganography cannot resist pooled steganalysis. This paper proposes a secure cover selection method which is able to resist pooled steganalysis and single object steganalysis meanwhile. To resist pooled steganalysis, the maximum mean discrepancy (MMD) distance between the stego set and a clear arbitrary image set is kept not larger than a normal threshold during cover selection, where the threshold is the MMD distance between two clear arbitrary image sets. Under this constraint, a searching strategy is designed to select the minimal steganographic distortion images within the affordable computational complexity to resist single object steganalysis. With the selected covers, the security of steganography is guaranteed against both single object steganalysis and pooled steganalysis. The experimental results demonstrated the effectiveness of the proposed method.
Emittance Spectroscopy and Broadband Thermal Remote Sensing Applied to Phosphorite and Its Utility in Geoexploration: A Study in the Parts of Rajasthan, India

The contrast in the emissivity spectra of phosphorite and associated carbonate rock can be used as a guide to delineate phosphorite within dolomite. The thermal emissivity spectrum of phosphorite is characterized by a strong doublet emissivity feature with their absorption minima at 9 µm and 9.5 µm; whereas, host rock dolomite has relatively subdued emissivity minima at ~9 µm. Using the contrast in the emissivity spectra of phosphorite and dolomite, data obtained by the thermal bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor were processed to delineate phosphorite within dolomite. A decorrelation stretched ASTER radiance composite could not enhance phosphorite rich zones within the dolomite host rock. However, a decorrelation stretched image composite of selected emissivity bands derived using the emissivity normalization method was suitable to enhance large surface exposures of phosphorite. We have found that the depth of the emissivity minima of phosphorite gradually has increased from dolomite to high-grade phosphorite, while low-grade phosphate has an intermediate emissivity value and the emissivity feature can be studied using three thermal bands of ASTER. In this context, we also propose a relative band depth (RBD) image using selected emissivity bands (bands 11, 12, and 13) to delineate phosphorite from the host rock. We also propose that the RBD image can be used as a proxy to estimate the relative grades of phosphorites, provided the surface exposures of phosphorite are large enough to subdue the role of intrapixel spectral mixing, which can also influence the depth of the diagnostic feature along with the grade. We have validated the phosphorite pixels of the RBD image in the field by carrying out colorimetric analysis to confirm the presence of phosphorite. The result of the study indicates the utility of the proposed relative band depth image derived using ASTER TIR bands for delineating Proterozoic carbonate-hosted phosphorite.
Contrast Optimization using Elitist Metaheuristic Optimization and Gradient Approximation for Biomedical Image Enhancement

Biomedical image analysis is one of the most challenging and inevitable part of the computer aided diagnostic systems. Automated analysis of the image can detect various diseases automatically without human intervention. Computer vision and artificial intelligence can sometimes defeat human diagnostic power and can reveal some hidden information from the biomedical images. In the field of health care, accurate results are highly required within stipulated amount of time. But to increase accuracy, proper preprocessing with sophisticated algorithms is required. Low quality image can affect processing algorithm which can leads to the poor result. Therefore, sophisticated preprocessing methods are required to get reliable results. Contrast is one of the most important parameter for any image. Poor contrast may cause several problems for computer vision algorithms. Conventional algorithms for contrast adjustment may not be suitable for many purposes. Sometimes, these methods can generate some images that may lose some critical information. In this work, a contrast optimization method based on well-known metaheuristic technique called genetic algorithm with elitism is used that can enhance the biomedical images for better analysis. A new kernel has been proposed to detect the edges. Obtained results illustrate the efficiency of the proposed algorithm.
Artificial Intelligence-Aided Automated Detection of Railroad Trespassing

Trespassing is the leading cause of rail-related deaths and has been on the rise for the past 10 years. Detection of unsafe trespassing of railroad tracks is critical for understanding and preventing fatalities. Witnessing these events has become possible with the widespread deployment of large volumes of surveillance video data in the railroad industry. This potential source of information requires immense labor to monitor in real time. To address this challenge this paper describes an artificial intelligence (AI) framework for the automatic detection of trespassing events in real time. This framework was implemented on three railroad video live streams, a grade crossing and two right-of-ways, in the United States. The AI algorithm automatically detects trespassing events, differentiates between the type of violator (car, motorcycle, truck, pedestrian, etc.) and sends an alert text message to a designated destination with important information including a video clip of the trespassing event. In this study, the AI has analyzed hours of live footage with no false positives or missed detections yet. This paper and its subsequent studies aim to provide the railroad industry with state-of-the-art AI tools to harness the untapped potential of an existing closed-circuit television infrastructure through the real-time analysis of their data feeds. The data generated from these studies will potentially help researchers understand human factors in railroad safety research and give them a real-time edge on tackling the critical challenges of trespassing in the railroad industry.
The Physical and Emotional Toll of Identity Theft Victimization: A Situational and Demographic Analysis of the National Crime Victimization Survey

The present study investigates the physical and emotional consequences of suffering an identity-related criminal victimization. In addition to reporting the extent and nature of these consequences, the present research also examines the potential determinants of the physical harms victims suffer following identity theft. Data from an analytic sample of 2,299 victims of identity theft from the U.S. National Crime Victimization Survey are analyzed, and results suggest that identity theft victimization produces consequences beyond the financial. Physical ailments and new medical conditions are often reported as a result of the crime. Further, several features of the incident were significantly related to whether victims experienced these harms, as were personal characteristics of the victim. These findings suggest that responses to identity-related victimizations – beyond financial remedies – should be considered by victims, practitioners, and policymakers.