Reviewerly/exHarmony huggingface dataset:
Levels of Immersion for Language Learning from 2D to Highly Immersive Interactive VR

This discourse provides an overview of the levels of immersion that immersive media educational tools provide in terms of interactivity and immersion, ranging from 2D displays on a flat screen to Highly Immersive Interactive (HII) experiences rendered in a Virtual Reality Head Mounted Display (HMD). These immersive media provide a range of affordances for learning, such as the feeling of physical presence in remote locations, social learning, experiential learning through replicable simulations, and social interactions. The article maps the levels of immersion to learning objectives associated with language learning.
Multi-Tier Attention Network using Term-weighted Question Features for Visual Question Answering

Visual Question Answering (VQA) is a multi-modal challenging task that accepts an image and a natural language question about that image as inputs and desires to find the correct answer. This AI-complete task necessitates the fine-grained joint understanding of the two input modalities. Inspired by the success of attention mechanism in the task of efficient comprehension of visual-language features for VQA, this paper proposes a Multi-Tier Attention Network (MTAN) with the major component being term-weighted question-guided visual attention. Additionally, we introduce a novel Supervised Term Weighting (STW) scheme named ‘qf.obj.cos’ to semantically weight words utilizing the notion of visual object detection. This can be generalized to other vision-language comprehension tasks like image captioning, text-to-image-retrieval, multi-modal summarization etc. In effect, the proposed system allows the generation of more discriminative visual features from the progressive steps of question guided visual attention where question embedding is indeed guided by semantic term weighting. MTAN is quantitatively and qualitatively evaluated on the benchmark DAQUAR dataset and an extensive set of ablations are studied to demonstrate the individual significance of each of the components of the system. Experimental results certify that MTAN performs better than the previous works using the same dataset.
Denoising Medical Images Using Deep Learning in IoT Environment

Medical Resonance Imaging (MRI) is a noninvasive, nonradioactive, and meticulous diagnostic modality capability in the field of medical imaging. However, the efficiency of MR image reconstruction is affected by its bulky image sets and slow process implementation. Therefore, to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network (SANR_CNN) for eliminating noise and improving the MR image reconstruction quality. The proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality, and SARN algorithm is used for building a dictionary learning technique for denoising large image datasets. The proposed SANR_CNN model also preserves the details and edges in the image during reconstruction. An experiment was conducted to analyze the performance of SANR_CNN in a few existing models in regard with peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). The proposed SANR_CNN model achieved higher PSNR, SSIM, and MSE efficiency than the other noise removal techniques. The proposed architecture also provides transmission of these denoised medical images through secured IoT architecture.
Question Tagging via Graph-guided Ranking

With the increasing prevalence of portable devices and the popularity of community Question Answering (cQA) sites, users can seamlessly post and answer many questions. To effectively organize the information for precise recommendation and easy searching, these platforms require users to select topics for their raised questions. However, due to the limited experience, certain users fail to select appropriate topics for their questions. Thereby, automatic question tagging becomes an urgent and vital problem for the cQA sites, yet it is non-trivial due to the following challenges. On the one hand, vast and meaningful topics are available yet not utilized in the cQA sites; how to model and tag them to relevant questions is a highly challenging problem. On the other hand, related topics in the cQA sites may be organized into a directed acyclic graph. In light of this, how to exploit relations among topics to enhance their representations is critical. To settle these challenges, we devise a graph-guided topic ranking model to tag questions in the cQA sites appropriately. In particular, we first design a topic information fusion module to learn the topic representation by jointly considering the name and description of the topic. Afterwards, regarding the special structure of topics, we propose an information propagation module to enhance the topic representation. As the comprehension of questions plays a vital role in question tagging, we design a multi-level context-modeling-based question encoder to obtain the enhanced question representation. Moreover, we introduce an interaction module to extract topic-aware question information and capture the interactive information between questions and topics. Finally, we utilize the interactive information to estimate the ranking scores for topics. Extensive experiments on three Chinese cQA datasets have demonstrated that our proposed model outperforms several state-of-the-art competitors.
Katyusha Acceleration for Convex Finite-Sum Compositional Optimization

Structured optimization problems arise in many applications. To efficiently solve these problems, it is important to leverage the structure information in the algorithmic design. This paper focuses on convex problems with a finite-sum compositional structure. Finite-sum problems appear as the sample average approximation of a stochastic optimization problem and also arise in machine learning with a huge amount of training data. One popularly used numerical approach for finite-sum problems is the stochastic gradient method (SGM). However, the additional compositional structure prohibits easy access to unbiased stochastic approximation of the gradient, so directly applying the SGM to a finite-sum compositional optimization problem (COP) is often inefficient. We design new algorithms for solving strongly convex and also convex two-level finite-sum COPs. Our design incorporates the Katyusha acceleration technique and adopts the mini-batch sampling from both outer-level and inner-level finite-sum. We first analyze the algorithm for strongly convex finite-sum COPs. Similar to a few existing works, we obtain linear convergence rate in terms of the expected objective error; from the convergence rate result, we then establish complexity results of the algorithm to produce an ε-solution. Our complexity results have the same dependence on the number of component functions as existing works. However, because of the use of Katyusha acceleration, our results have better dependence on the condition number κ and improve to [Formula: see text] from the best-known [Formula: see text]. Finally, we analyze the algorithm for convex finite-sum COPs, which uses as a subroutine the algorithm for strongly convex finite-sum COPs. Again, we obtain better complexity results than existing works in terms of the dependence on ε, improving to [Formula: see text] from the best-known [Formula: see text].
S2-aware network for visual recognition

Capturing the comprehensive information of various sizes and shapes of images in the same convolution layer is typically a challenging task in computer vision. There are two main kinds of methods for capturing those features. The first uses the inception structure and its variants. The second utilizes larger convolution kernels on specific layers or stacks with more convolution blocks. However, these methods can result in computationally intensive or vanishing gradients. In this paper, to accommodate feature distributions with different sizes, shapes and reduce computational cost, we propose a width- and depth-aware module named the WD-module to match feature distributions. Moreover, the proposed WD-module consumes less computational cost and parameters compared with traditional residual convolution layers. To verify the effectiveness of our proposed method, a size- and shape-aware backbone network named S2A-Net was built, which was obtained by stacking the WD-modules. By visualizing heat maps and features, the proposed S2A-Net can adapt to objects with different sizes and shapes in visual recognition tasks and learn more comprehensive characteristics. Experimental results show that the proposed method has higher accuracy in image recognition and outperforms other state-of-the-art networks with the same numbers of layers.
Heterogeneous Transfer in Deep Learning for Spectrogram Classification in Cognitive Communications

Machine learning offers performance improvements and novel functionality, but its life cycle performance is understudied. In areas like cognitive communications, where systems are long-lived, life cycle trade-offs are key to system design. Herein, we consider the use of deep learning to classify spectrograms. We vary the label-space over which the network makes classifications, as may emerge with changes in use over a system’s life cycle, and compare heterogeneous transfer learning performance across label-spaces between model architectures. Our results offer an empirical example of life cycle challenges to using machine learning for cognitive communications. They evidence important trade-offs among performance, training time, and sensitivity to the order in which the label-space is changed. And they show that fine-tuning can be used in the heterogeneous transfer of spectrogram classifiers.
Addressing Hindsight Bias in Multigoal Reinforcement Learning

Multigoal reinforcement learning (RL) extends the typical RL with goal-conditional value functions and policies. One efficient multigoal RL algorithm is the hindsight experience replay (HER). By treating a hindsight goal from failed experiences as the original goal, HER enables the agent to receive rewards frequently. However, a key assumption of HER is that the hindsight goals do not change the likelihood of the sampled transitions and trajectories used in training, which is not the fact according to our analysis. More specifically, we show that using hindsight goals changes such a likelihood and results in a biased learning objective for multigoal RL. We analyze the hindsight bias due to this use of hindsight goals and propose the bias-corrected HER (BHER), an efficient algorithm that corrects the hindsight bias in training. We further show that BHER outperforms several state-of-the-art multigoal RL approaches in challenging robotics tasks.
PRIMER: Profiling Interrupts using Electromagnetic Side-Channel for Embedded Devices

Recent proliferation of CPS and IoT devices has led to an increasing demand for analyzing performance and timing of event-driven computational activity, especially interrupts and exceptions. However, these devices typically lack hardware resources, power, and system-software infrastructure for profiling/monitoring such events. Even when feasible, the profiling/monitoring activity itself can perturb the performance and timing of the timing-sensitive activity to be analyzed, therefore producing misleading results. Thus, we present PRIMER, a novel approach for profiling interrupts. PRIMER leverages existing unintentional (side-channel) electromagnetic emanations of the profiled/monitored device to identify its asynchronous execution (e.g., interrupt handlers). PRIMER leaves the monitored system (and its behavior) completely unchanged, requires no system resources or support, and introduces neither overheads nor perturbation in the monitored system. We validate PRIMER by analyzing signals that correspond to five different types of interrupts on an IoT device (ARM Cortex-M), achieving 99.5% accuracy (with no false positives), and on an MSP430 microcontroller-based device with even better accuracy. We also demonstrate the effectiveness of PRIMER in analyzing page faults and network interrupts when executing real-world applications on a more sophisticated embedded device (ARM Cortex-A8), and show that the results provided by PRIMER can provide useful insights about an application's interaction with the system's virtual memory and network-oriented services.
Route outlining of humanoid robot on flat surface using MFO aided artificial potential field approach

Humanoid robots, with their overall resemblance to a human body, is modeled for flawless interaction with human-made tools or the environment. In this study, navigation of humanoid robot using hybrid Artificial potential field (APF) and Moth flame optimization (MFO) approach have been performed. The hybrid approach provides the final turning angle (FTA), which is optimum to avoid collision with the hindrances. APF utilizes a negative potential field and a positive potential field to find the location of obstacles and target, respectively. The navigation starts towards the target; when the robot interacts with the obstacle, APF provides an intermediate angle (IA). The IA, along with the position of the obstacle, is fed into MFO as an input. This technique provides the FTA (optimum) to avoid collisions and guide a robot to the target. It is implemented in a single humanoid system and a multi-humanoid system. The presence of multiple humanoids can create the chance of inter-collision. It is dismissed by employing a dining philosopher controller to the proposed technique. Simulations and experiments are accomplished on simulated and real humanoid NAO. The coherency in the behavior of the results evaluated by the simulations and real-time experiments demonstrates the efficiency of the proposed AI technique. Comparisons are performed with a previously used method to validate the robustness of the technique.
Hand Gesture Recognition Using Convex Hull-Based Approach

Gesture recognition is a tool that can be used to control any common device by effortless hand gestures. The goal behind gesture recognition is to minimize the gap between the digital world and the physical world. This wireless interaction creates this algorithm much friendlier to the user. This paper tells the technological characteristics of gesture-controlled user interface (GCUI), and also recognizes its trend and application. It is observed that GCUI now offers practical opportunities for application-specific areas, especially for people who are not comfortable with input devices which are commonly used. This project implements an advanced image processing application to recognize the gestures and process them in real time for better and reliable results. To recognize the gesture the ratio of the percentage of area not covered by hand in the convex hull is found. The optimized code is easily integrated with a Raspberry-Pi processor or microcontroller for a fully functional robot.
Elemental variability of prehistoric ceramics from postglacial lowlands and its implications for emerging of pottery traditions – An example from the pre-Roman Iron Age

The article presents the first research on the elemental diversity of the prehistoric pottery obtained from the European Postglacial Plain, performed on a large scale (over 500 fragments of pottery were examined). A new analysis approach to the results of the elemental composition of the pottery was presented by using the unsupervised classification of chemical elements. This method is based on the advanced selection of features. The subset of elements that best describes the variability of the chemical composition of ceramics is searched, which allows to create the groups of ceramic artifacts corresponding to their origin or production technology. The correspondence between the existing locations and the clusters obtained using the Gaussian Mixture Model was controlled by using the entropy-based V-measure. Data analysis revealed that elemental composition of the pottery is more complex processes that simple provenance, possibly related to the emerging local pottery traditions.
Toward Obstacle Avoidance for Mobile Robots Using Deep Reinforcement Learning Algorithm

The state-of-the-art deep reinforcement learning algorithm, i.e., the deep deterministic policy gradient (DDPG), has achieved good performance in continuous control problems for the robotics. However, the conventional experience replay mechanism of the DDPG algorithm stores the experience explored by the mobile robot in the bufer pool, and trains the neural network through random sampling, without considering whether the transition is valuable, which can probably influence the network performance. To overcome the limitation, the DDPG framework with separating experience is developed for mobile robot collision-free navigation in this study, to replay the transitions of valuable and the failed experience discretely. Additionally, environment state vector is designed including mobile robot and obstacles, the reward function and action space are also designed. The simulation results show that the proposed model can possess the collision-free navigation capacity to deal with multiple obstacles.
Recurrent knowledge tracing machine based on the knowledge state of students

Abstract Knowledge tracing (KT) aims to closely trace the knowledge level of students during their learning. KT is often implemented by intelligent tutoring systems (ITS) to predict the student performance, and schedule the individual learning plan for each student. However, most existing KT models cannot predict the knowledge forgetting well due to the lack of temporal information and some KT models for knowledge forgetting prediction are not accurate enough. To resolve these issues, this paper proposes a recurrent knowledge tracing machine (RKTM), which temporally enriches the encoding of knowledge tracing machine (KTM) and difficulty, student ability, skill, and student skill practice history (DAS3H) with the knowledge state of students. RKTM consists of two major components, including the tracing component and the predicting component. The tracing component temporally traces the knowledge state of a student, while the predicting component captures the interaction between the current learning scenario and the current knowledge state of that student and provides accurate prediction of student performance. Experiments show that the proposed RKTM well outperforms KTM, DAS3H, and some other models.
Empowerment of rural young people in informal farm entrepreneurship: the role of corporate social responsibility in Nigeria’s oil producing communities

Purpose The purpose of this paper is to critically examine the corporate social responsibility (CSR) initiatives of multinational oil companies in Nigeria. Its main focus is to investigate the impact of the global memorandum of understanding (GMoU) on equipping rural young people with essential farming skills and knowledge for the adoption and application of modern agricultural inputs in the Niger Delta region. Design/methodology/approach This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 800 rural young people were sampled across the oil producing region. Findings The results from the use of combined propensity score matching and logit model indicate that the GMoU model has a significant impact on the development of informal farm entrepreneurship generally, but somewhat undermined rural young people in the targeted agricultural clusters. Practical implications This suggests that youth-specific CSR farm projects can be effective in providing young people with the extra push needed to tackle the knowledge gap and poor agronomic that erect the below-per yield and lack of competitiveness of small-holder farmers in the region. Social implications It implies that a coherent and integrated CSR response from the business would be necessary to unlock investment opportunities on young people in farms for agricultural competitiveness and food security in Africa. Originality/value This study adds to the literature on informal farm entrepreneurship and rural communities’ debate in sub-Saharan Africa. It concludes that business has obligation to help in solving problems of youth unemployment in developing countries.
Fresnel diffraction from sector apertures

The derivation of the analytic representation of the diffraction field from the sector aperture in the Fresnel zone is presented. The new solution is expressed in the form of a convergent series on incomplete cylindrical functions in the Poisson form that makes it possible to calculate and obtain three-dimensional patterns of the field amplitude in the whole image plane. The obtained patterns correspond to the Fresnel approximation and are true for arbitrary values of the opening angle and for the radius of sectoral aperture.
A Novel PoW Scheme Implemented by Probabilistic Signature for Blockchain

PoW (Proof of Work) plays a significant role in most blockchain systems to grant an accounting right over decentralized participants and ensure tamper resistance.Though hash functions are generally exploited for PoW due to their merits on summering, anti-collision, and irreversibility, they cannot certify that the bookkeeper is exactly the worker.Thereafter, such insistence may lead to abuse or even embezzlement of computing power for the benefit of malicious miners.To preserve the functionality of PoW but also bind the miners' signing keys with their works, we build a post-quantum PoW scheme by changing the approximate closest vector norm for probabilistic NTRUSign.Different from the schemes based on hash functions, our scheme takes signing as the proof of work where signature verification is just the evidence of block reward.We also presented a method to adjust the difficulty of signing by modifying the probability of generating a correct signature.The performance of our scheme is also analyzed theoretically and experimentally, which implies its practicability and advantages.
Comprehensive Review and Future Research Directions on Dynamic Faceted Search

In modern society, the increasing number of web search operations on various search engines has become ubiquitous due to the significant number of results presented to the users and the incompetent result-ranking mechanism in some domains, such as medical, law, and academia. As a result, the user is overwhelmed with a large number of misranked or uncategorized search results. One of the most promising technologies to reduce the number of results and provide desirable information to the users is dynamic faceted filters. Therefore, this paper extensively reviews related research articles published in IEEE Xplore, Web of Science, and the ACM digital library. As a result, a total of 170 related research papers were considered and organized into five categories. The main contribution of this paper is to provide a detailed analysis of the faceted search’s fundamental attributes, as well as to demonstrate the motivation from the usage, concerns, challenges, and recommendations to enhance the use of the faceted approach among web search service providers.