Reviewerly/exHarmony huggingface dataset:
Smart Mobile Learning Environment for Programming Education in Nigeria: Adaptivity and Context-Aware Features

The proliferation and diffusion of mobile devices and the growing number of its users presents the opportunity for developers of educational technology to provide tools that will support both the learners and the instructors. Adaptive and context-aware features of smart learning systems are built to render support to learners in such a way that learning is possible anywhere, anytime and at the learner’s convenience. Nowadays, mobile devices including smartphones, personal digital assistant, and wearable devices possess advanced technological features that can support the development of the smart learning environment (SLE). This study investigated the recent research contributions on adaptivity and context-aware features of SLE for programming education. The study also surveyed the perception of students and teachers in Nigeria higher education institutions regarding adaptive and context-aware features of SLE. To provide answers to the research questions raised in the study, we applied a mixed method approach consisting the literature review and quantitative methods. The study presents some existing solutions for programming education with adaptive or context-aware features of SLE. Finally, we proposed a model for designing an SLE with adaptivity and context-awareness features that take into cognizance the learner’s preferences and needs.
Classification of Anti-phishing Solutions

Phishing is an online fraud through which phisher gains unauthorized access to the user system to lure the personal credentials (such as username, password, credit/debit card number, validity, CVV number, and pin) for financial gain. Phishing can be carried out in many ways: through emails, phone calls, instant messages, advertisements, and popups on the website and poisoning the DNS. To protect the users from phishing, many anti-phishing toolbars/extensions had been developed. These anti-phishing tools prevent the Internet users not to fall a victim of phishing scams. No anti-phishing approach can give 100 % security. In this paper, we present a complete classification of an anti-phishing solution in algorithmic perspective. The taxonomy helps in understanding various anti-phishing approaches and algorithms developed for phishing detection. Popular anti-phishing toolbars are taken to show the media they address, mode of operation, and their pros and cons. It also provides further research gap that has to be addressed.
Classification of Spectrally Efficient Constant Envelope Modulations Based on Radial Basis Function Network and Deep Learning

Despite its significance, modulation classification of constant envelope modulations (CEM) has not gained worthy attention in AMC literature so far. Two neural network-based architectures, i.e., radial basis function network (RBFN) and sparse-autoencoder-based deep neural network (DNN) are proposed and analyzed for the classification of spectrally efficient CEM modulations. A blind classification method which does not require any a-priori information about the channel or CEM specifics is based on the effectiveness of proposed hybrid feature space (HFS), used to train the trending neural network classifiers. Classification performance of both networks is analyzed for the typical additive white Gaussian noise (AWGN) channel and less explored, unfriendly, frequency-selective fading environment under the impact of Doppler shift.
A Multimodal Interface for Virtual Information Environments

Continuing advances in multimodal technology, machine learning, and virtual reality are providing the means to explore and develop multimodal interfaces that are faster, more accurate, and more meaningful in the interactions they support. This paper describes an ongoing effort to develop an interface using input from voice, hand gestures, and eye gaze to interact with information in a virtual environment. A definition for a virtual environment tailored for the presentation and manipulation of information is introduced along with a new metaphor for multimodal interactions within a virtual environment.
Practical security of the continuous-variable quantum key distribution with real local oscillators under phase attack

Continuous-variable quantum key distribution (CVQKD) with a real local oscillator (LO) is confronted with new security problems due to the reference pulses transmitted together with quantum signals over the insecure quantum channel. In this paper, we propose a method of phase attack on reference pulses of the CVQKD with real LOs. Under the phase attack, the phase drifts of reference pulses are manipulated by eavesdroppers, and then the phase compensation error is increased. Consequently, the secret key rate is reduced due to the imperfect phase compensation for quantum signals. Based on the noise model of imperfect phase compensation, the practical security of the CVQKD under phase attack is analyzed. Besides, we propose an effective method to detect the intensity of phase attack, in which the deviation of phase compensation error on quantum signals and that on reference signals are monitored in real time. The simulation results show that the security analysis is accurate and the method of phase attack detection is feasible.
Rough Hypercuboid Based Generalized and Robust IT2 Fuzzy C-Means Algorithm

One of the important issues in pattern recognition and machine learning is how to find natural groups present in a dataset. In this regard, this paper presents a novel clustering algorithm, called rough hypercuboid-based interval type-2 fuzzy c-means (RIT2FCM). It judiciously integrates the merits of the rough hypercuboid approach, c-means algorithm, and interval type-2 fuzzy set, to address the uncertainty associated with real-life datasets. Using the concept of hypercuboid equivalence partition matrix (HEM) of rough hypercuboid approach, the lower approximation and boundary region of each cluster are implicitly defined, without using any prespecified threshold parameter. The interval-valued fuzzifier is applied to address the uncertainty coupled with different parameters of rough-fuzzy clustering algorithms, where the determination of the appropriate value of fuzzifier is a difficult task. An analytical formulation on the convergence analysis of the proposed RIT2FCM algorithm, along with a theoretical bound of its fuzzifier, is also introduced. The efficacy of the proposed RIT2FCM method is extensively compared with that of several existing clustering algorithms, using some cluster validity and classification rate indices on various real-life datasets. The proposed algorithm performs better than the state-of-the-art c-means algorithms in 92.59% cases, with respect to different cluster validity indices, in lesser computation time.
Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation

Despite the recent works on knowledge distillation (KD) have achieved a further improvement through elaborately modeling the decision boundary as the posterior knowledge, their performance is still dependent on the hypothesis that the target network has a powerful capacity (representation ability). In this paper, we propose a knowledge representing (KR) framework mainly focusing on modeling the parameters distribution as prior knowledge. Firstly, we suggest a knowledge aggregation scheme in order to answer how to represent the prior knowledge from teacher network. Through aggregating the parameters distribution from teacher network into more abstract level, the scheme is able to alleviate the phenomenon of residual accumulation in the deeper layers. Secondly, as the critical issue of what the most important prior knowledge is for better distilling, we design a sparse recoding penalty for constraining the student network to learn with the penalized gradients. With the proposed penalty, the student network can effectively avoid the over-regularization during knowledge distilling and converge faster. The quantitative experiments exhibit that the proposed framework achieves the state-ofthe-arts performance, even though the target network does not have the expected capacity. Moreover, the framework is flexible enough for combining with other KD methods based on the posterior knowledge.
Efficiency Measurement of Cloud Service Providers Using Network Data Envelopment Analysis

An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements <xref ref-type="bibr" rid="ref13" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[13]</xref> . To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, the non-oriented network slacks-based measure (SBM) model and conventional SBM model with the assumptions of constant returns to scale (CRS) and variable returns to scale (VRS) are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models.
CrowdStart: Warming up cold-start items using crowdsourcing

The cold-start problem is one of the critical challenges in personalized recommender systems. A lot of existing work has been studied to exploit a user-item rating matrix as well as additional information for users/items, e.g., user profiles, item contents, and social relationships among users. However, because existing work is primarily biased to the auxiliary information for users/items, it is difficult to identify various and reliable item neighbors that are relevant to cold-start items. To alleviate this limitation, we propose a new crowd-enabled framework, called CrowdStart, which is an integrated human-machine approach for new item recommendation. The main contributions of the CrowdStart framework are two-fold: (1) To find various and reliable item neighbors for new items, we design two-step crowdsourcing tasks that harness not only machine-only algorithms but also the knowledge of crowd workers (including a few experts and a large number of non-expert workers in a crowdsourcing platform). (2) We develop a novel hybrid model to exploit the user-item rating matrix, the content information about items, and the crowd-based item neighbors from human knowledge into new item recommendation. To evaluate the effectiveness of the CrowdStart framework, we conduct extensive experiments including both a user study and simulation tests. Through the empirical study, we found that the CrowdStart framework provides relevant, diverse, reliable, and explainable crowd-based neighbors for new items and the crowd-based neighbors are meaningful for improving the accuracy of new item recommendation. The datasets and detailed experimental results are available at https://goo.gl/1iXTUE.
Consensus Based Strong Tracking Adaptive Cubature Kalman Filtering for Nonlinear System Distributed Estimation

The distributed cubature Kalman filter is widely used in the field of target tracking, however, the presence of model uncertainties will undermine its tracking stability and effectiveness for tracking maneuvering target. In order to eliminate this effect on maneuvering target tracking, this paper develops a distributed consensus based strong tracking cubature Kalman filter scheme. First, each node obtains its local estimation with the usage of local observations via strong tracking cubature Kalman filter, where the suboptimal fading factor and adaptive factor are introduced for adaptively modifying the filter gain. Then, the designed filter gain is used for updating the local state estimation. Second, after all, nodes have achieved its local estimation, each node exchanges its local estimation to its neighbors and updates its local estimation according to the consensus communication protocol. It can be further proved that the distributed interaction between neighbors will contribute to enhancing the tracking stability. The detailed proof for stochastic boundedness of the estimation error is analyzed by introducing a stochastic process. Simulation results demonstrate that the proposed algorithm can achieve higher tracking accuracy than the existing methods for tracking a maneuvering target.
Pathfinding in hierarchical representation of large realistic virtual terrains for simulation systems

A prominent pathfinding challenge is the fast computation of path plans in large virtual terrain environments used in simulation systems. Based on a hierarchical approach for pathfinding and terrain representation, this work details the design, the implementation and the test of a quadtree-based structure in the irregular grid navigation map representation of large realistic virtual terrain environments. To achieve this goal, it is described how a hierarchical global A* pathfinding algorithm searches for a path in a coarse initial irregular grid structure then proceeding with the search in refined regions of interest where obstacles are found. Thus, the proposed solution presents as major contribution the enhanced quadtree-based map representation for pathfinding in large virtual terrains. The pathfinding response time of this solution is statistically compared with different hierarchical/non-hierarchical and regular/irregular terrain representation structures instantiated in the modeling of "small" realistic terrain scenarios. After these experiments, similar ones are developed in a massive (large) virtual terrain inserted into a real-life simulation system for the development of military tactical training exercises. The results show that the response time required to generate pathfinding results can be reduced when the proposed global pathfinding technique over the quadtree-based hierarchical and irregular navigation map representation of the large virtual terrain is explored in the development of simulation systems.
Emergency Response Using HoloLens for Building Evacuation

Emergency response in indoor building evacuation is needed for rescue and safety management. One of the challenges is to provide user-specific personalized evacuation routes in real time. Early hands-on experiences with the Microsoft HoloLens augmented/mixed reality device have given promising results for building evacuation applications. A range of use cases are tested, including data visualization and immersive data spaces, in-situ visualization of 3D models and full-scale architectural form visualization. We present how the mixed reality technology can provide spatial contextualized 3D visualization that promotes knowledge acquisition and support cognitive mapping. Our HoloLens application gives a visual representation of a building in 3D space, allowing people to see where exits are in the building. It also gives a path to the various exits; a shortest path to the exits as well as directions to a safe zone from their current position. This paper describes the system architecture and implementation of the augmented reality application to leverage the Microsoft HoloLens for emergency response during a building evacuation. The experimental results show the effectiveness of our Microsoft HoloLens application in an emergency evacuation by offering 3D visualizations of multilevel spaces while adding the spatial context that allows the individual to better understand their position and evacuation path when evacuation is necessary. We believe that AR technologies like HoloLens could be adopted by people for building evacuating during emergencies as it offers enriched experience in navigating large-scale environments.
Generalized Information-Theoretic Criterion for Multi-Label Feature Selection

Multi-label feature selection that identifies important features from the original feature set of multi-labeled datasets has been attracting considerable attention owing to its generality compared to conventional single-label feature selection.The unimportant features are filtered by scoring the dependency of features to labels.In conventional multi-label feature filter studies, the score function is obtained by approximating a dependency measure such as joint entropy because direct calculation is often impractical due to the presence of multiple labels with limited training patterns.Although the efficacy of approximation can differ depending on the characteristics of the multi-label dataset, conventional methods presume a certain approximation method, leading to a degenerated feature subset if the presumed approximation is inappropriate for the given dataset.In this study, we propose a strategy for selecting an approximation among a series of approximations depending on the number of involved variables and consequently instantiate a score function based on the chosen approximation.The experimental results demonstrate that the proposed strategy and score function outperform conventional multi-label feature selection methods.
Context-Aware Privacy Preservation in a Hierarchical Fog Computing System

Fog computing faces various security and privacy threats. Internet of Things (IoTs) devices have limited computing, storage, and other resources. They are vulnerable to attack by adversaries. Although the existing privacy-preserving solutions in fog computing can be migrated to address some privacy issues, specific privacy challenges still exist because of the unique features of fog computing, such as the decentralized and hierarchical infrastructure, mobility, location and content-aware applications. Unfortunately, privacy-preserving issues and resources in fog computing have not been systematically identified, especially the privacy preservation in multiple fog node communication with end users. In this paper, we propose a dynamic MDP-based privacy-preserving model in zero-sum game to identify the efficiency of the privacy loss and payoff changes to preserve sensitive content in a fog computing environment. First, we develop a new dynamic model with MDP-based comprehensive algorithms. Then, extensive experimental results identify the significance of the proposed model compared with others in more effectively and feasibly solving the discussed issues.
Optimized-Eight-State CV-QKD Protocol Outperforming Gaussian Modulation Based Protocols

In this paper, an optimized-eight-state CV-QKD protocol is proposed significantly outperforming previously introduced discrete modulation (DM) protocols as well as the corresponding Gaussian modulation (GM)-based CV-QKD protocols for practical reconciliation efficiencies values in terms of both secret-key rate (SKR) and achievable distance.The proposed CV-QKD protocol also outperforms, in terms of SKR, the corresponding high-cost single-photon DV-QKD scheme, employing an array of multiplexed single-photon detectors, for several orders of magnitude.We also describe a generalized RF-assisted CV-QKD scheme with heterodyne detection applicable to arbitrary DM scheme, insensitive to the laser phase noise and frequency offset fluctuations.
HookTracer: A System for Automated and Accessible API Hooks Analysis

The use of memory forensics is becoming commonplace in digital investigation and incident response, as it provides critically important capabilities for detecting sophisticated malware attacks, including memory-only malware components. In this paper, we concentrate on improving analysis of API hooks, a technique commonly employed by malware to hijack the execution flow of legitimate functions. These hooks allow the malware to gain control at critical times and to exercise complete control over function arguments and return values. Existing techniques for detecting hooks, such the Volatility plugin apihooks, do a credible job, but generate numerous false positives related to non-malicious use of API hooking. Furthermore, deeper analysis to determine the nature of hooks detected by apihooks typically requires substantial skill in reverse engineering and an extensive knowledge of operating systems internals. In this paper, we present a new, highly configurable tool called hooktracer, which eliminates false positives, provides valuable insight into the operation of detected hooks, and generates portable signatures called hook traces, which can be used to rapidly investigate large numbers of machines for signs of malware infection.
Sensitivity analysis beyond linearity

A wide array of graphical models can be parametrized to have atomic probabilities represented by monomial functions. Such a monomial structure has proven very useful when studying robustness under the assumption of a multilinear model where all monomials have either zero or one exponents. Robustness in probabilistic graphical models is usually investigated by varying some of the input probabilities and observing the effects of these on output probabilities of interest. Here the assumption of multilinearity is relaxed and a general approach for one-way sensitivity analysis in non-multilinear models is presented. It is shown that in non-multilinear models sensitivity functions have a polynomial form, conversely to multilinear models where these are simply linear. The form of various divergences and distances under different covariation schemes is also formally derived. Proportional covariation is proven to be optimal in non-multilinear models under some specific choices of varied parameters. The methodology is illustrated throughout by an educational application.
Super accurate low latency object detection on a surveillance UAV

Drones have proven to be useful in many industry segments such as security and surveillance, where e.g. on-board real-time object tracking is a necessity for autonomous flying guards. Tracking and following suspicious objects is therefore required in real-time on limited hardware. With an object detector in the loop, low latency becomes extremely important. In this paper, we propose a solution to make object detection for UAVs both fast and super accurate. We propose a multi-dataset learning strategy yielding top eye-sky object detection accuracy. Our model generalizes well on unseen data and can cope with different flying heights, optically zoomed-in shots and different viewing angles. We apply optimization steps such that we achieve minimal latency on embedded on-board hardware by fusing layers, quantizing calculations to 16-bit floats and 8-bit integers, with negligible loss in accuracy. We validate on NVIDIA's Jetson TX2 and Jetson Xavier platforms where we achieve a speed-wise performance boost of more than 10×.