Reviewerly/exHarmony huggingface dataset:
The Paradox of Virtual Embodiment: The Body Schema in Virtual Reality Aesthetic Experience

"New technologies implied in art creation and exhibition are modifying the traditional landmarks on which aesthetics has always focused. In particular, Virtual Reality artworks call the body into question when it comes to living a bodily experience within exhibitions accessible through technological tools that expand the human body’s capabilities and motor potential. The body's status is challenged in its traditional unity, that of a subject of experience living in a world where the spatial configuration is relatively constant. Conversely, in Virtual Reality, the spatial aspect is novel to our body which needs to adapt to unpredicted and disorientating motor schemas. Therefore, the Virtual Reality aesthetic experience takes place into a novel configuration for the human body: hybrid and split into the virtual realm. Keywords: Aesthetics; Virtual Reality; Embodiment; Digital art; Bodily awareness "
Identity-Based Video Summarization

Kulkarni, Soummya Bhagit, Darshana Modak, MasoodaThe process of automatic video summarization involves identification of frames automatically that contains essential information, which would not have been possible if it was not for machine learning. There are lots of algorithms that can be used for summarization of videos based on face recognition and speaker verification, for instance: face recognition could be performed by caffe-based model containing facial landmarks, principal component analysis, discrete cosine transform, 3D acceptance methods, Gabor wavelets method, etc. This work has been centered on a caffe-based model containing facial landmarks for face recognition in an efficient manner. There are a lot of factors affecting face recognition like accuracy, time limitations, process speed, and availability. With all these in mind, caffe-based model was selected for being accurate and having fast computation time.
Self-adaptive resolution method for multiple industrial communication protocols

Industrial communication faults may appear at all layers of the communication network with the industrial field environment becoming more and more complex. Protocol analysis is an important way to evaluate the quality of industrial communication and locate industrial communication faults. Nowadays, the parsing of industrial communication protocols (ICP) usually relies on certain specific methods or software. When users need to modify existing protocols or add new protocols, it is difficult to extend based on the existing parsing software because of different protocol formats and methods. This paper proposes a more general protocol description method, which is suitable for describing a variety of ICP, and realizes the self-adaptation resolution of different ICP.
Neuromorphic Image Processing with a VCSEL Neuron

Photonic spiking VeSEL-neurons are used for high-speed neuromorphic image processing. We demonstrate that a VCSEL-Neuron system using sequential kernel operators successfully reveals all edge-features from 5000 complex images of the MNIST handwritten digit dataset.
Knowledge dialogue generation with multi-head attention mechanism

Abstract An increasing number of research efforts are focusing on knowledge dialogue generation. Less attention is focused on increasing knowledge diversity in generated responses. A model of knowledge selection guided by a multi-head attention mechanism is proposed. First, the current input discourse and knowledge content are input into the Bi-GRU module to obtain the coding vector, and then obtain multiple aspects of semantics from the user input discourse coding vector based on the multi-head attention mechanism, so as to select different knowledge. A punishment item method is proposed to force different attention to focus on different aspects, and finally, use the user input and selected knowledge for the decoding stage. Experiments with manual and automated evaluations have proven that the model is superior to the baseline model compared to previous work.
Mudahnya BM: A Context-Aware Mobile Cloud Learning Application Using Semantic-Based Approach

A great potential of various learning environment in mobile learning application can clearly be seen in current pandemic situation. The accessibility of the learning resources needs to be available from anywhere anytime despite having strong or poor internet connection. It has motivated researchers to imply context-aware capability in improving the accessibility of the learning resources. This paper presents a development process of Mudahnya BM mobile application that follows a fundamental concept of Mobile Cloud Learning (MCL). Mudahnya BM is an application to learn basic Malay language for learner 7–12 years old. The injection of extrinsic and intrinsic context-aware help the application to improve the reasoning process for finding available learning resources from service providers. Semantic-based approach is applied in the reasoning process. This study involved the end users for evaluation purposes. 33 randomized scenarios have been tested using One-Sample Wilcoxon Signed Rank test. The result shows a positive impact to the population.
Measurement data approximation by pseudo-random sequences

The article describes a method of background processing (reduction) of measurement data, which allows such data to be represented by a set of samples of a pseudo-random generator. The core of the method, the principal points, and some recommendations to implement it are given in the paper. The findings of the research of the suggested method on test signals are presented. They show its crucial operability, at least at an SNR value not lower than 3.0. The method proposed by the authors provides acceptable values of the backing up error up to 0.25%, but if a quite large amount of pseudo-random sequences is given. It is also shown that the data processed by this method has a size of approximately half than the input data. So moreover, the method makes redundancy compression of encoded data possible.
Shortest Path Planning Based on the Ant Algorithm Considering the Return Error

Many algorithms need to be optimized and adjusted for specific environments in order to realize practical applications in engineering. The object of interest in a project is to minimize the moving time of the honeycomb container under the requirement that each hole in the container needs to arrive at the designated location once. By optimizing the order of each hole arriving at the designed location and the detailed moving path, the optimal moving path of the honeycomb container can be found. There are many essentially similar problems in engineering, such as drilling tacks, spot welding tasks etc. Such problems can be modeled into the traveling salesman problem, that the salesman needs to traverse all set points and eventually returns to the starting point. Some intelligent algorithms are used to solve traveling salesman problems now, such as simulated annealing algorithm, genetic algorithm, ant algorithm etc. However, some errors must be taken into account in practical applications of these algorithms in engineering. In this paper, the ant colony algorithm considering the return error is proposed to deal with the path planning problem in conditions where the return error existing mechanical driving devices.
Adversarial Examples for Evaluating Math Word Problem Solvers

Standard accuracy metrics have shown that Math Word Problem (MWP) solvers have achieved high performance on benchmark datasets. However, the extent to which existing MWP solvers truly understand language and its relation with numbers is still unclear. In this paper, we generate adversarial attacks to evaluate the robustness of state-of-the-art MWP solvers. We propose two methods, Question Reordering and Sentence Paraphrasing to generate adversarial attacks. We conduct experiments across three neural MWP solvers over two benchmark datasets. On average, our attack method is able to reduce the accuracy of MWP solvers by over 40% on these datasets. Our results demonstrate that existing MWP solvers are sensitive to linguistic variations in the problem text. We verify the validity and quality of generated adversarial examples through human evaluation.
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting

Accurate and trustworthy epidemic forecasting is an important problem that has impact on public health planning and disease mitigation. Most existing epidemic forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations; e.g. it is difficult to specify meaningful priors in Bayesian NNs, while methods like deep ensembling are computationally expensive in practice. In this paper, we fill this important gap. We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP, which directly models the probability density of the forecast value. EPIFNP leverages a dynamic stochastic correlation graph to model the correlations between sequences in a non-parametric way, and designs different stochastic latent variables to capture functional uncertainty from different perspectives. Our extensive experiments in a real-time flu forecasting setting show that EPIFNP significantly outperforms previous state-of-the-art models in both accuracy and calibration metrics, up to 2.5x in accuracy and 2.4x in calibration. Additionally, due to properties of its generative process,EPIFNP learns the relations between the current season and similar patterns of historical seasons,enabling interpretable forecasts. Beyond epidemic forecasting, the EPIFNP can be of independent interest for advancing principled uncertainty quantification in deep sequential models for predictive analytics
Verified Framework for Distributed Processing Cost Reduction using Excess Cloud Resources

Distributed computing is one of the important technologies for processing big data. A distributed computing system is based on the use of many computing devices by linking them together to process data. A distributed computing system can be leveraged using cloud resources. Pricing for booking cloud resources varies and leasing redundant resources are less expensive with some drawbacks. In this paper, distributed processing cost reduction using excess cloud resources verified framework is presented. The framework is based on using redundant resources via cloud services. The framework is verified through simulation. As a result of implementing the framework, it was found that the use of excess cloud resources reduces the cost of implementing a distributed computing system by 67% compared to use on-demand cloud resources.
The attack strategy for a quantum key distribution protocol based on Bell’s theorem

Abstract A new eavesdropping strategy is proposed for the Quantum Key Distribution (QKD) protocol. This scheme represents a new kind of intercept/resend strategy based on Bell’s theorem. Quantum key distribution (QKD) provides the foremost reliable form of secure key exchange, using only the input-output statistics of the devices to realize information-theoretic security. In this paper, we present an improved QKD protocol that can simultaneously distribute the quantum secret key. We are already using the QKD protocol with simulated results matched completely with the theoretical concepts.
Local Differential Privacy for Regret Minimization in Reinforcement Learning

Reinforcement learning algorithms are widely used in domains where it is desirable to provide a personalized service. In these domains it is common that user data contains sensitive information that needs to be protected from third parties. Motivated by this, we study privacy in the context of finite-horizon Markov Decision Processes (MDPs) by requiring information to be obfuscated on the user side. We formulate this notion of privacy for RL by leveraging the local differential privacy (LDP) framework. We establish a lower bound for regret minimization in finite-horizon MDPs with LDP guarantees which shows that guaranteeing privacy has a multiplicative effect on the regret. This result shows that while LDP is an appealing notion of privacy, it makes the learning problem significantly more complex. Finally, we present an optimistic algorithm that simultaneously satisfies $\varepsilon$-LDP requirements, and achieves $\sqrt{K}/\varepsilon$ regret in any finite-horizon MDP after $K$ episodes, matching the lower bound dependency on the number of episodes $K$.
Interactive Reinforcement Learning for Autonomous Behavior Design

Reinforcement Learning (RL) is a machine learning approach based on how humans and animals learn new behaviors by actively exploring their environment that provides them positive and negative rewards. The interactive RL approach incorporates a human-in-the-loop that can guide a learning RL-based agent to personalize its behavior and/or speed up its learning process. To enable HCI researchers to make advances in this area, we introduce an interactive RL framework that outlines HCI challenges in the domain. By following this taxonomy, HCI researchers can (1) design new interaction techniques and (2) propose new applications. To help the role (1) researchers, we describe how different types of human feedback can adapt an RL model to perform as the users intend. We help researchers perform the role (2) by proposing generic design principles to create effective RL applications. Finally, we list current open challenges in interactive RL and what we consider the most promising research directions in this research area.
Enhanced and Verifiable Keyword Search over Encrypted Data

As cloud computing is an attractive energizing technology for the reason that diverse resources are provided with more flexibility and cost effective to the end user, based on their demand. Considering the privacy issue of the cloud users and untrustworthiness of cloud servers the data has to be encrypted before deployed on the cloud. A series of problem arises with the encrypted data storage such as How searching is done on the outsourced data? How to notice user-side verifiability of search results to withstand malicious cloud servers? By what method the user will decide which is the required data that contains the keyword? Pointing to these challenges, in this paper, we introduce an enhanced verifiable keyword search technique called Trustworthy Keyword search Scheme over Encrypted data [TKSE] without interference of third party. Depending on the digital signature’s authenticated information, the user is allowed to perform a search on the deployed encrypted data as well as the user can verify the outcome of the cloud data that accomplishes the previous search requirements. This approach specifically realizes server-side verifiability from being framed by malicious data owners in the data storage phase and protects honest cloud servers.
Usage Trends Aware VM Placement in Academic Research Computing Clouds

Academic Research Computing Clouds are widely deployed worldwide by research institutions to support researcher's computations. While the nature of the hosted use-cases is diverse, literature and institutions' published guides point to highly parallel HPC workloads and state-heavy workloads as two popular use-cases in research-computing clouds. Additionally, our prior investigation has uncovered unique patterns in the users' VM-provisioning behaviors in four of Canada's research-computing clouds. These patterns, i.e., usage-trends, were generated by examining nearly 1 million VMs created by researchers over four and half years. The usage trends mimicked behaviors of provisioning highly parallel HPC workloads and state-heavy portals. In this paper, we exploit the knowledge of these usage trends to guide VM placement decisions in research-computing IaaS clouds. We propose a delayed-provisioning algorithm that postpones resource allocations for incoming VM requests in anticipation of running VMs termination, minimizing the number of active PMs and PM underutilization. We examine the performance of the proposed algorithm compared to other placement algorithms using a real-life validation dataset of nearly 850 thousand VMs. The results show significant improvements ranging from 7% to 33% reduction in the number of active PMs and up to 61% reduction in the number of hourly underutilized PMs.
Deep Belief Network based Machine Learning for Daily Activities Classification

Human activity recognition has been a very active topic in pervasive computing for several years for its important applications in assisted living, healthcare, and security surveillance. Many researchers are finding and representing the details of human body gestures to determine human activity. While simple activities can be easily recognized only by acceleration data, our research has focused on the recognition and understanding the various activities in daily living. In this work, we address this problem by proposing approach theory of deep learning with the Deep belief network. Deep belief network comprises a series of Restricted Boltzmann Machines will be formed by superimposed multiple Restricted Boltzmann Machines and training the model parameters for data reconstruction, feature construction and classification. We tested our approach on PASCAL VOC datasets. The experimental results indicate that our proposed approach offers significant performance improvements with the maximum of 79.8%.
Triplet Siamese Network Model for Lithium-ion Battery Defects Classification Using Few-shot Learning Approach

Abstract In this paper, we propose a triplet siamese model for lithium-ion battery defects classification. It is a difficult task to detect the surface defects of lithium-ion batteries with stainless steel surface. The lack of three-dimensional information and the lack of marker datasets due to reflections prevent two-dimensional computer vision detection methods from meeting classification needs. In this work, the multiple exposure structured light method is utilized to obtain the three-dimensional shape of a lithium-ion battery with a stainless steel surface. The defect point cloud with three-dimensional information is obtained by this method, and then the 3D information of the defect point cloud is converted into grayscale information, and the grayscale image is used as the target domain data of the triplet siamese network. The public dataset MiniImageNet is utilized as the training data of the triplet siamese network model. The accuracies of the experimental results are 88.9%, 95.6%, and 97.8% for 1-shot, 5-shot, and 10-shot respectively. This result proves that our method can be used for lithium-ion battery defect detection.
Performance Analysis of Low Power Interference Cancellation Architecture for OFDM System

Orthogonal Frequency Division Multiplexing (OFDM) is a wireless communication technology that is used for highly reliable and high data rate communication. In a multi-user OFDM system, the interference has occurred in the receiver side between the consecutive OFDM symbols. This interference reduces the performance of the OFDM system. To achieve good quality in received symbols the interference level should be minimized. The conventional cancellation system requires higher interference reduction time and power. These limitations of the conventional interference cancellation architectures for OFDM systems are overcome by proposing efficient and low power interference cancellation architecture. Hence, this paper proposes a novel and efficient architecture based on logic gates for interference cancellation in multi-user OFDM systems. The proposed design consist of multiplexers, inverters and OR gate. The heuristic parameters for the proposed cancellation architecture are computed by performing an XOR operation. Compared to existing architecture, the proposed interference cancellation architecture consumes 7 mW of power consumption in the Virtex processor, 33.59 mW of power consumption in Spartan 3E processor and 0.029 mW of power consumption in the CPLD processor. This proposed interference cancellation architecture consumes fewer hardware resources and consumes low power. The proposed system is designed using Verilog High Definition Language (HDL) and synthesized in Xilinx Project Navigator 12.1i. Further, this paper also proposes gate diffusion input (GDI) based implementation of proposed interference cancellation architecture to analyse a delay and power consumption compared to other logic style implementation.
Information and data ecologies

This chapter addresses questions related to the complex relationships between information, data, and human beings, frequently treated as the foundation of information and data ecologies. We focus on issues that have varied interfaces with literacies, but are not literacies in the proper sense of the word. The first part of this chapter focuses on openness, reproducibility, credibility, and sharing of digital data. Attention is given to research data’s Findability, Accessibility, Interoperability, and Reuse. There is also a short discussion of the relationship between research data and copyright. In the second part, data journals and data papers are targeted, and attention is paid to the problems of measuring and evaluating research data. The third part touches on varied issues, such as possible coauthorships between librarians and researchers, research data management, reputation management, information and data overload, posttruth phenomena and the influence of posttruth, as well as the deluge of publications related to the COVID-19 pandemic.
Cooperative Path Planning for the Reconnaissance UAV with Multi-payload

In this paper, path planning for the UAV with electronic signal reconnaissance equipment, CCD and SAR for reconnaissance of ground targets in two-dimension is considered. Firstly, the requirement of the mission is analyzed and the view of the payload is converted into the distance between the target and the UAV. Secondly, the A* algorithm is proposed combined with the track point expansion method, whose evaluation function is developed to quickly find the optimal paths for the UAV with reconnaissance missions. Finally, the digital simulation results are carried out on the path planning for the UAV when tracking stationary, constant velocity and maneuvering targets. The numerical simulation results are carried out to demonstrate the validity and effectiveness of the proposed path planning strategy.
LightFPGA: Scalable and Automated FPGA Acceleration of LightGBM for Machine Learning Applications

FPGAs are used for high speed machine learning inference, and are proving to be much faster and efficient than CPU. LightGBM is a gradient boosting algorithm that uses decision tree-based learners. In this work, we have developed a library named LightFPGA which extracts details from a pre-trained LightGBM model and generates the corresponding Verilog RTL for FPGA implementation. Since the whole process of code generation is automated, the design is scalable to the LightGBM model trained for any given dataset. Further, the library performs testing and accuracy verification of the implementation by generating testbench. Our results show that using LightFPGA, around 100–400× improvement in latency as compared to CPU can be achieved without any reduction in inference accuracy. Further, it has been observed in the tests performed, that the FPGA implementation of LightGBM offers around 7–8 folds of power reduction, as compared to CPU.