Reviewerly/exHarmony huggingface dataset:
Museo de Aeronáutica a través de Realidad Virtual

“Museum of Aeronautics across Virtual Reality” is an educational approach project, aimed at the general public, whose main contribution is the research of aeronautics reflected on a 3D virtual museum. The virtual museum has a room of biographies of prominent figures of the history of aeronautics and a 3D aircraft room. The methodology used is that of Mora Virtual Reality, which is a variation of the waterfall method, consisting of: Analysis, 3D Modeling, Virtual Terrain Setting, Interaction and Navigability in the virtual world, as well as tests: modular, integration and usability tests, finally corrections. The full use of the methodology led us to obtain satisfactory results.
Asymptotics of certain conditionally identically distributed sequences

The probability distribution of a sequence X=(X1,X2,…) of random variables is determined by its predictive distributions P(X1∈⋅) and P(Xn+1∈⋅∣X1,…,Xn), n≥1. Motivated by applications in Bayesian predictive inference, in Berti et al. (2020), a class C of sequences is introduced by specifying such predictive distributions. Each X∈C is conditionally identically distributed. The asymptotics of X∈C is investigated in this paper. Both strong and weak limit theorems are provided. Conditions for X to converge a.s., and for X not to converge in probability, are given in terms of the predictive distributions. A stable CLT is provided as well. Such a CLT is used to obtain approximate credible intervals.
Fog-Based Video Surveillance System for Smart City Applications

Natesha, B. V. Guddeti, Ram Mohana ReddyWith the rapid growth in the use of IoT devices in monitoring and surveillance environment, the amount of data generated by these devices is increased exponentially. There is a need for efficient computing architecture to push the intelligence and data processing close to the data source nodes. Fog computing will help us to process and analyze the video at the edge of the network and thus reduces the service latency and network congestion. In this paper, we develop fog computing infrastructure which uses the deep learning models to process the video feed generated by the surveillance cameras. The preliminary experimental results show that using different deep learning models (DNN and SNN) at the different levels of fog infrastructure helps to process the video and classify the vehicle in real time and thus service the delay-sensitive applications.
Simpler FM-index for parameterized string matching

In parameterized string matching, a string comprises static and parameterized symbols. Two strings are said to be matched if there exists a one-to-one mapping of parameterized symbols onto itself such that it transforms one string into the other. Traditionally, to construct a full-text index for this problem, suffixes are transformed in a manner referred to as previous occurrence encoding. Although a space-efficient backward searching based data structure has been proposed recently, the data structure is specialized and complex owing to the nature of the encoding scheme. In this study, we demonstrate that a slight modification of the encoding scheme, by using ∞ instead of 0 to denote the first occurrences of each parameterized symbol, enables us to develop a much simpler FM-index for this problem, which only comprises two wavelet trees and one range maximum query index.
Chaotic Neural Network with Trigonometric Function Self-feedback

A new chaotic neural network is constructed by introducing trigonometric function into the self-feedback term of the transient chaotic neural network; By analyzing the inverse bifurcation graph and the maximum Lyapunov exponent of the single neuron, the chaotic dynamic characteristics of the network are analyzed; The energy function of the chaotic neural network is constructed. Its stability is analyzed and the condition of gradual stability of the network is given; The network parameters for solving the traveling salesman problem are set. The simulation results show that the new model can avoid local minima effectively and is effective for solving combinatorial optimization problems.
nZESPA: A Near-3D-Memory Zero Skipping Parallel Accelerator for CNNs

Convolutional neural networks (CNNs) are one of the most popular machine learning tools for computer vision. The ubiquitous use in several applications with its high computation-cost has made it lucrative for optimization through accelerated architecture. State-of-the-art has either exploited the parallelism of CNNs, or eliminated computations through sparsity or used near-memory processing (NMP) to accelerate the CNNs. We introduce NMP-fully sparse architecture, which acquires all three capabilities. The proposed architecture is parallel and hence processes the independent CNN tasks concurrently. To exploit the sparsity, the proposed system employs a dataflow, namely, Near-3D-Memory Zero Skipping Parallel dataflow or nZESPA dataflow. This dataflow maintains the compressed-sparse encoding of data that skips all ineffectual zero-valued computations of CNNs. We design a custom accelerator which employs the nZESPA dataflow. The grids of nZESPA modules are integrated into the logic layer of the hybrid memory cube. This integration saves a significant amount of off-chip communications while implementing the concept of NMP. We compare the proposed architecture with three other architectures which either do not exploit sparsity (NMP-dense) or do not employ NMP (traditional-fully sparse) or do not include both (traditional-dense). The proposed system outperforms the baselines in terms of performance and energy consumption while executing CNN inference.
MEXN: Multi-Stage Extraction Network for Patent Document Classification

The patent document has different content for each paragraph, and the length of the document is also very long. Moreover, patent documents are classified hierarchically as multi-labels. Many works have employed deep neural architectures to classify the patent documents. Traditional document classification methods have not well represented the characteristics of entire patent document contents because they usually require a fixed input length. To address this issue, we propose a neural network-based document classification for patent documents by designing a novel multi-stage feature extraction network (MEXN), which comprise of paragraphs encoder and summarizer for all paragraphs. MEXN features analysis of the whole documents hierarchically and providing multi-labels outputs. Furthermore, MEXN preserves computing performance marginally increase. We demonstrate that the proposed method outperforms current state-of-the-art models in patent document classification tasks with multi-label classification experiments for USPD datasets.
Correlated High Average-Utility Itemset Mining

Sethi, Krishan Kumar Ramesh, DharavathHigh average-utility itemset (HAUI) mining is an advancement over high utility itemset mining, where average-utility is used instead of utility measure to discover meaningful patterns. It has been discussed in several past studies that significance of utility-based patterns can be amplified if items in the patterns are correlated. In this paper, we propose a HAUI mining algorithm named correlated high average-utility itemset (CoHAI) miner which provides highly profitable and non-redundant correlated patterns. CoHAI miner is a one-phase algorithm which uses a vertical list structure to store utility and correlation information to apply pruning and candidate generation. Moreover, use of vertical list avoids repetitive dataset scans to calculate the utility of candidate itemsets. We performed rigorous experiments to compare the CoHAI miner with existing algorithm. The results show that the CoHAI miner performs efficiently and produces more meaningful patterns than existing algorithm.
Trillium: The code is the IR

GPUs are the platform of choice for many general purpose workloads such as machine learning. This is driving demand for better GPGPU support in virtualized environments like the cloud. Despite significant research attention, GPGPU virtualization remains largely an open problem due to the challenge of balancing performance against key virtualization properties: compatibility, isolation, and interposition. Consequently, two different approaches to GPGPU virtualization have been adopted by the industry: Cloud service providers, such as AWS, support GPU-capable VMs using PCIe-passthrough techniques that bypass virtualization entirely, sacrificing its benefits; Virtualization vendors, such as BitFusion and Dell XaaS, support GPGPU virtualization using user-space API-remoting, which retains some of the benefits of virtualization, but elides hypervisor interposition, thereby giving up key virtualization properties.We hypothesize that while API-remoting may be the only viable software virtualization technique (as it interposes the only practical interface), API-remoting should not be implemented purely in user-space. We revisit VMware's SVGA in the context of GPGPU computing and find that hypervisor-mediated API-remoting is efficient: Decoupling device virtualization from GPU ISA virtualization is key to preserving the raw speedup from GPGPU acceleration, while also preserving the benefits of hypervisor-mediation: migration, isolation, fairness, etc.
A dictionary for translation from natural to formal data model language

Abstract The paper describes our current research activities and results related to developing knowledge‐based systems to support the creation of entity‐relationship (ER) models. The authors based obtaining an ER model in textual form on translation from one language into another, that is, from an English controlled natural language into the formalized language of an ER data model. Our translation method consisted of creating translation rules of sentential form parts into ER model constructs based on the textual and character patterns detected in the business descriptions. To enable the computer analyses necessary for creating translation mechanisms, we created a linguistic corpus that contains lists of the business descriptions and the texts of other business materials. From the corpus, we then created a specific dictionary and linguistic rules to automate the business descriptions' translation into the ER data model language. Before that, however, the corpus was enriched by adding annotations to the words related to ER data model constructs. In this paper, we also present the main issues uncovered during the translation process and offer a possible solution with utility evaluation: applying information‐extraction performance measures to a set of sentences from the corpus.
Multiattribute approximate nearest neighbor search based on navigable small world graph

Summary Approximate nearest neighbor search (ANNS) is widely employed to find the most similar data efficiently from a large‐scale dataset. The key of ANNS is to construct an effective index to prune the search space and retrieve the approximate data rather than the exact one in a very short time. Most of the existing ANNS methods only adopt the distance metric to build an index, which cannot support multiattribute ANNS. In this article, we present a novel approach for multiattribute ANNS based on navigable small world (NSW) graph, called MA‐NSW. Given a dataset, MA‐NSW (i) builds a proximity subgraph overlay for each multiattribute combination, and integrates all overlays to a hierarchical index, (ii) adopts a navigation tree to access related subgraph overlay and prune unrelated overlays, and (iii) gets nearest neighbor results from the related overlays by greedy search. MA‐NSW guarantees efficiency and it is defined in terms of arbitrary metric spaces (eg, Euclidean distance and cosine similarity). Performance evaluation has demonstrated that the proposed approach shows superior performance in multiattribute ANNS.
Image Caption Generation via Unified Retrieval and Generation-Based Method

Image captioning is a multi-modal transduction task, translating the source image into the target language. Numerous dominant approaches primarily employed the generation-based or the retrieval-based method. These two kinds of frameworks have their advantages and disadvantages. In this work, we make the best of their respective advantages. We adopt the retrieval-based approach to search the visually similar image and their corresponding captions for each queried image in the MSCOCO data set. Based on the retrieved similar sequences and the visual features of the queried image, the proposed de-noising module yielded a set of attended textual features which brought additional textual information for the generation-based model. Finally, the decoder makes use of not only the visual features but also the textual features to generate the output descriptions. Additionally, the incorporated visual encoder and the de-noising module can be applied as a preprocessing component for the decoder-based attention mechanisms. We evaluate the proposed method on the MSCOCO benchmark data set. Extensive experiment yields state-of-the-art performance, and the incorporated module raises the baseline models in terms of almost all the evaluation metrics.
A Real-time Human Activity Recognition Approach with Generalization Performance

The current human activity recognition (HAR) methods need training data from users. The data collection causes discomfort to the users and most of the studies ignore the real-time performance of classification. This paper presents a real-time human activity recognition approach with strong generalization performance. It uses existing dataset to avoid long-term data collection of subjects, so that the machine can be quickly applied to each specific individual. Also, it takes advantage of both combined accuracy and limited feature selection proposed by this paper to implement feature-selection-based transfer learning which improves HAR in both real-time and generalization performance. In view of the recognition time and accuracy, the depth neural network is selected, changeable structure of which is more suitable for feature selection. This approach utilizes four inertial measurement units placed on the outside of human thighs and shanks. A total of seven activities are taken into account that includes level-walking, upstairs, downstairs, uphill, downhill, standing and sitting. The experiments are performed on six healthy male subjects in free-living settings to evaluate the efficacy of the algorithm. This approach achieved a notable activity recognition accuracy of 98.89%, and reported a fast average activity classification time of 28.6 ms.
Optimal Image Feature Ranking and Fusion for Visual Question Answering

Visual Question Answering (VQA) is a moderately new and challenging multi-modal task, which endeavors to discover an answer for a given pair of an image and a relating question. This AI-complete task gains attraction from numerous researchers from the areas computer vision (CV) and natural language processing (NLP) due to its various potential applications. The general flow of VQA algorithms consists of image feature extraction, question feature extraction and joint comprehension of these two to generate an appropriate answer. Existing VQA systems did not pay attention to input feature extraction, but only celebrated different ways of multi-modal embedding. This paper proposes to improve the task of VQA by feature-level fusion of visual information. The goal of feature fusion is to consolidate relevant information from two or more feature vectors into a solitary one with additional discriminative power. Unlike simple concatenation, this paper uses discriminative correlation analysis (DCA) for fusion, which is the only method that incorporates the class structure into the feature-level fusion. Since the VQA systems are generally modeled as classification systems by treating the correct answers as classes, class-specific DCA suits well here. The newly created fused feature vectors are close to the right answers and thus raise the role of image understanding in VQA. The experimental results show the effectiveness of the new approach on DAQUAR dataset with mutual information (MI) as an evaluation metric.
A Non-cooperative Game-Theoretic Approach for Conflict Resolution in Multi-agent Planning

This paper presents FENOCOP, a game-theoretic approach for solving non-cooperative planning problems that involve a set of self-interested agents. Each agent wants to execute its own plan in a shared environment but the plans may be rendered infeasible by the appearance of potential conflicts; agents are willing to coordinate their plans in order to avoid conflicts during a joint execution. In order to attain a conflict-free combination of plans, agents must postpone the execution of some of their actions, which negatively affects their individual utilities. FENOCOP is a two-level game approach: the General Game selects a Nash equilibrium among several combinations of plans, and the Scheduling Game generates, for a combination of plans, an executable outcome by introducing delays in the agents’ plans. For the Scheduling Game, we developed two algorithms that return a Pareto optimal and fair equilibrium from which no agent would be willing to deviate.
Collaborative Digital Research: Case Study of Text Mining a Corpus of Academic Journals

More faculty are making the foray into digital scholarship as its tools offer novel and efficient approaches to answering research questions. However, faculty seeking to incorporate digital methods into their research may not have the necessary technical skills. Increasingly, libraries are adding digital scholarship support to the research support offered to faculty. In this article, we document the collaboration between a team of librarians and researchers to leverage each group’s unique skills to analyse and better understand the breadth and specific usage of social class and socioeconomic status terminology used in American Counselling Association (ACA) journal articles over a 17-year span. The work process and outcomes of this project are explained to offer an example of how such collaborative digital scholarship projects can be implemented.
Towards real-time DNA biometrics using GPU-accelerated processing

Abstract Decoding the human genome in the past decades has brought into focus a computationally intensive operation through DNA profiling. The typical search space for these kinds of problems is extremely large and requires specialized hardware and algorithms to perform the necessary sequence analysis. In this paper, we propose an innovative and scalable approach to exact multi-pattern matching of nucleotide sequences by harnessing the massively parallel computing power found in commodity graphical processing units. Our approach places careful consideration on preprocessing of DNA datasets and runtime performance, while exploiting the full capabilities of the heterogeneous platform it runs on. Finally, we evaluate our models against real-world DNA sequences.
Representing Temporal Dependencies in Smart Home Activity Recognition for Health Monitoring

Long term health conditions, such as fall risk, are traditionally diagnosed through testing performed in hospital environments. Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident's behaviour can be produced from sensor data, and then compared over time. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs which consider the temporal dependencies present in the sensor data in order to produce richer representations and improved classification accuracy. The LSTM approaches are compared to the performance of a selection of baseline classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier.
Trained Model Reuse of Autonomous-Driving in Pygame with Deep Reinforcement Learning

Autonomous-Driving technology has begun to bring great convenience to daily trip, transportation, and surveying harsh environment. Considering that deep reinforcement learning has requirements for the convergence performance of the training results, and the actual training results sometimes cannot converge steadily or fail to reach the training goals, in this paper, the trained model reuse method was proposed, which can use the trained model generates Q(St, At) and can be used as a part of Deep Reinforcement Learning model, and this model was built based on the value function that could predict the Q value corresponding to the various actions performed in the environment state. In the Pygame platform, a simplified traffic simulation environment was set, it is observed that the Autonomous-Driving vehicle could run smoothly without collision in a fixed-length test simulation environment, and this trained model reuse method could help autonomous vehicle accelerate the learning process, obtain better simulation results during most of the training process, save simulation time and computing resources.
Metallographic image segmentation of GCr15 bearing steel based on CGAN

A novel deep learning segmentation method based on Conditional Generative Adversarial Nets (CGAN) is proposed, being U-GAN in this paper to overtake shortcomings of the metallographic images of GCr15 bearing steel, such as multi-noise, low contrast and difficult to segment. The results of experiment indicate that the proposed model is the most accurate comparing with the digital image processing methods and deep learning methods on carbide particle segmentation. The average Dice’s coefficient of similarity measure function is 0.9158, which is the state-of-the-art performance on dataset.
Classification of Fashion Images Using Transfer Learning

Computer vision, a specialization of machine learning aims to train machines to learn and interpret visual content based on its features. Many CNN based architectural models are developed to classify fashion images. The basic accuracy loss or misclassification occurs in similar fashion items. The reason for this is the minute features may not be extracted efficiently by existing models. Therefore, in the proposed study we utilize the feature concatenation property, knowledge consolidation and inductive feature transfer of DenseNet architecture for categorization of fashion MNIST dataset. The performance analysis is done by generating a confusion matrix. Further, we compare the performance of proposed architecture with the state of the art image classification architectures in literature. The analysis indicates that our proposed system outperforms the existing architectures.
A Tweakable Key Alternating Lightweight Cipher for Internet of Things

Internet of things (IoT) is a global network of uniquely addressable interconnected things, based on standard communication protocols. As the number of devices connected to the IoT escalates, they are becoming a likely target for hackers. Also, the limited resources of IoT devices makes the security on top of the actual functionality of the device. Therefore, the cryptographic algorithm for such devices has to be devised as small as possible. To tackle the resource constrained nature of IoT devices, this article presents a lightweight cryptography algorithm based on a single permutation and iterated Even-Mansour construction. The proposed algorithm is implemented in low cost microcontrollers, thus making it suitable for a wide range of IoT nodes.