Reviewerly/exHarmony huggingface dataset:
End-to-End Architectures for ASR-Free Spoken Language Understanding

Spoken Language Understanding (SLU) is the problem of extracting the meaning from speech utterances. It is typically addressed as a two-step problem, where an Automatic Speech Recognition (ASR) model is employed to convert speech into text, followed by a Natural Language Understanding (NLU) model to extract meaning from the decoded text. Recently, end-to-end approaches were emerged, aiming at unifying the ASR and NLU into a single SLU deep neural architecture, trained using combinations of ASR and NLU-level recognition units. In this paper, we explore a set of recurrent architectures for intent classification, tailored to the recently introduced Fluent Speech Commands (FSC) dataset, where intents are formed as combinations of three slots (action, object, and location). We show that by combining deep recurrent architectures with standard data augmentation, state-of-the-art results can be attained, without using ASR-level targets or pretrained ASR models. We also investigate its generalizability to new wordings, and we show that the model can perform reasonably well on wordings unseen during training.
Exploiting Local Degradation Characteristics and Global Statistical Properties for Blind Quality Assessment of Tone-Mapped HDR Images

Tone mapping operators (TMOs) are developed to convert a high dynamic range (HDR) image into a low dynamic range (LDR) one for display with the goal of preserving as much visual information as possible. However, image quality degradation is inevitable due to the dynamic range compression during the tone-mapping process. This accordingly raises an urgent demand for effective quality evaluation methods to select a high-quality tone-mapped image (TMI) from a set of candidates generated by distinct TMOs or the same TMO with different parameter settings. A key element to the success of TMI quality evaluation is to extract effective features that are highly consistent with human perception. Towards this end, this paper proposes a novel blind TMI quality metric by exploiting both local degradation characteristics and global statistical properties for feature extraction. Several image attributes including texture, structure, colorfulness and naturalness are considered either locally or globally. The extracted local and global features are aggregated into an overall quality via regression. Experimental results on two benchmark databases demonstrate the superiority of the proposed metric over both the state-of-the-art blind quality models designed for synthetically distorted images (SDIs) and the blind quality models specifically developed for TMIs.
Joint image compression and encryption using a novel column-wise scanning and optimization algorithm

In this research, a joint image compression and encryption scheme is presented which works for different image dimensions 256X256, 512X512 and 1024X1024 and different image sizes. The proposed scheme is efficient as compression and encryption is done simultaneously which makes computations faster and secure at the same time. The compression scheme deployed is like JPEG compression but uses column-wise scanning and optimization instead of zigzag scanning. In entropy phase coding, Huffman coding of JPEG compression is replaced with Arithmetic coding. Image encrypted using XOR encryption is unbreakable, as used earlier in one-time pad. Compressed data is XORed with randomly generated number. The proposed scheme is implemented for grayscale images using MATLAB software version 2016a. The experimental results showed that the scheme provides good compression ratio, secures against attacks and takes less computation time. This can be further extended for RGB images of different dimension 256X256, 512X512 and 1024X1024 and different image sizes.
Geochemical anomalies of critical elements (Be, Co, Hf, Sb, Sc, Ta, V, W, Y and REE) in soils of western Andalusia (Spain)

A collection of 694 soil samples was taken from 367 sites (at a density of 1 site per 120 km2) in the western of Andalusia (SW Spain) to establish regional geochemical background of emerging critical elements on the < 2 mm soil fraction, and provide threshold values for anomaly detection to support mineral exploration in the survey area. The spatial distribution pattern of the soil geochemical anomalies is primarily influenced by the occurrence of magmatic alignments and metallogenic belts in the southern zones of the Iberian Massif. Soils over granitic parent materials are marked by W, Be, Ta and REE anomalies, whereas those derived from mafic igneous rocks contain anomalous concentrations of Sc, Co and V. Most of the threshold exceedances for Hf and Sb appear to be linked to soils on pelitic metasediments. The distribution of Sb and Co outliers is also related to sulfide ore occurrences in the bedrock and could be affected by anthropogenic influences. The Santa Olalla igneous complex, the granodioritic pluton of the Los Pedroches batholith and the magmatic alignment of Villaviciosa de Córdoba-La Coronada may represent exploration opportunities for high-tech elements, mainly for REE, although the identification of promising areas requires further investigation.
BCV-Predictor: A bug count vector predictor of a successive version of the software system

Predicting the number of bugs in a software system intensifies the software quality and turns down the testing effort required in software development. It reduces the overall cost of software development. The evolution of hardware, platform, and user requirements leads to develop the next version of a software system. In this article, we formulate a problem and its novel solution, i.e., we are considering the prediction of the bug count vector of a successive version of a software system. After predicting the bug count vector in the next version of a software, the developer team leader can adequately allocate the developers in respective fault dense modules, in a more faulty dense module, more number of developers required. We have conducted our experiment over seven PROMISE repository datasets of different versions. We build metadata using a concatenation of different versions of the same software system for conducting experiments. We proposed a novel architecture using deep learning called BCV-Predictor. BCV-Predictor predicts the bug count vector of the next version software system; it is trained using metadata. To the best of our knowledge, no such work has been done in these aspects. We also address overfitting and class imbalance problem using random oversampling method and dropout regularization techniques. We conclude that BCV-Predictor is conducive to predicting the bug count vector of the next version of the software. We found five out of seven meta datasets reaches to more than 80% accuracy. In all seven meta datasets, Mean Squared Error (MSE) lies from 0.71 to 4.715, Mean Absolute Error (MAE) lies from 0.22 to 1.679, MSE and MAE over validation set lie between 0.84 to 4.865, and 0.22 to 1.709 respectively. We also compared the performance of BCV-Predictor with eleven baselines techniques and found the proposed approach outperform on most of the meta-datasets.
Face Recognition - A One-Shot Learning Perspective

Ability to learn from a single instance is something unique to the human species and One-shot learning algorithms try to mimic this special capability. On the other hand, despite the fantastic performance of Deep Learning-based methods on various image classification problems, performance often depends having on a huge number of annotated training samples per class. This fact is certainly a hindrance in deploying deep neural network-based systems in many real-life applications like face recognition. Furthermore, an addition of a new class to the system will require the need to re-train the whole system from scratch. Nevertheless, the prowess of deep learned features could also not be ignored. This research aims to combine the best of deep learned features with a traditional One-Shot learning framework. Results obtained on 2 publicly available datasets are very encouraging achieving over 90% accuracy on 5-way One-Shot tasks, and 84% on 50-way One-Shot problems.
Inpainted Image Quality Evaluation Based on Saliency Map Features

Digital image inpainting is one of the most important areas in image processing science. Digital image inpainting is a set of methods to inpaint or refill the damaged areas of the images. Given the increasing use of image inpainting and the lack of a good metric for evaluating image inpainting, there is a challenge in this field. In this study an objective evaluation method for image inpainting is developed. In the proposed method, first, 100 images were inpainted using exemplar-based algorithm, then, the saliency map and its complementary region in the original image are obtained and based on saliency map features, a new objective measure for evaluation of inpainted images is proposed. A term called compensation have been taken into account. To assess the performance of the proposed objective measure, inpainted images are also evaluated using a subjective test. The experiments demonstrate that the proposed objective measure correlates with qualitative opinion in a human observer study. Finally, the objective measure is compared against three other measures and the results show that our proposed objective measure is better than the others.
Reliability Evaluation of Compressed Deep Learning Models

Neural networks are becoming deeper and more complex, making it harder to store and process such applications on systems with limited resources. Model pruning and data quantization are two effective ways to simplify the necessary hardware by compressing the network with relevant-only nodes and reducing the required data precision. Such optimizations, however, might come at a cost of reliability since critical nodes are now more exposed to faults and the network is more sensitive to small changes. In this work, we present an extensive empirical investigation of transient faults on compressed deep convolutional neural networks (CNNs). We evaluate the impact of a single bit flip over three CNN models with different sparsity configurations and integer-only quantizations. We show that pruning can increase the resilience of the system by 9× when compared to the dense model. Quantization can outperform the 32-bit floating-point baseline by adding 27.4× more resilience to the overall network and up to 108.7× when combined with pruning. This makes model compression an effective way to provide resilience to deep learning workloads during inference, mitigating the need for explicit error correction hardware.
A Multi-View Human Action Recognition System in Limited Data Case using Multi-Stream CNN

In recent years, Convolutional Neural Networks (CNNs) have been extensively used for human action recognition. However, training a convolutional neural network by limited data is a challenging problem. In this paper, a multi-stream 3D-CNN structure is proposed for multi-view human action recognition. In this model, a four-stream 3D-CNN with handcrafted features, containing optical flow and gradients in horizontal and vertical directions, is proposed as a solution to improve the recognition performance in the case of limited data. This model combines multi-view four-stream 3D-CNNs from different views. The proposed multi-stream 3D-CNN is applied to IXMAS and NIXMAS multi-view datasets. The experiments illustrate superior results in comparison with state-of-the-art methods. The results show 3.58% improvement in comparison with single stream 3D-CNN architecture using raw video data in IXMAS dataset. However, with more limitations in number of training data in NIXMAS dataset, results show remarkable improvement in comparison with single stream 3D-CNN structure that is 12.6%.
Federated Reinforcement Learning for Controlling Multiple Rotary Inverted Pendulums in Edge Computing Environments

Reinforcement learning has recently been studied in various fields and also used to optimally control real devices (e.g., robotic arms). In this paper, we try to allow multiple reinforcement learning agents to learn optimal control policy on their own devices of the same type but with slightly different dynamics. For such multiple devices, there is no guarantee that an agent who interacts only with one device and learns the optimal control policy will also control another device well. Therefore, we may need to apply independent reinforcement learning to each device individually, which requires time-consuming effort. To solve this problem, we propose a new federated reinforcement learning architecture where each agent working on its independent device shares their learning experience with each other, and transfers a mature policy model parameters into other agents. We incorporate the Actor-Critic PPO algorithm into each agent in the proposed collaborative architecture, and propose an efficient procedure for the gradient sharing and the model transfer. We also use edge computing to solve network problems that occur when training multiple real devices at the same time. Using multiple rotary inverted pendulum devices, we demonstrate that the proposed federated reinforcement learning scheme can effectively facilitate the learning process for multiple devices, and that the learning speed can be faster if more agents are involved.
Violence Detection from Videos using HOG Features

Advancement of modern technologies over the last few decades drive action recognition to become an essential topic in surveillance systems. Violent action poses a significant threat to our freedom and social security. Therefore, it is crucial to classify violence in surveillance scenarios like in a prison, gymnasium, car parking, and so on. In this work, a novel method is proposed to identify violence in different circumstances. Initially, some frames are selected from each video clip using image subtraction and averaging techniques and Histogram of Oriented Gradient (HOG) is applied to extract lower level features. Finally, Support Vector Machine (SVM), Logistic Regression, Random Forest, Linear Discriminant Analysis (LDA), Naïve Bayes and K-Nearest Neighbors (KNN) are used for the classification purpose. The system is able to achieve the highest accuracy rate of 86% while using Random Forest classifier. The experimental result on the benchmark dataset shows significant accuracy and an improvement over the previously proposed methods.
Predicting Cryptocurrency Value using Sentiment Analysis

This paper cross validates thesis given by few authors on the impact of social media on cryptocurrency prices. Initially, the focus is on the Bitcoin, later on, a similar model can be used for other cryptocurrencies. Sentiment scores of tweets and news feeds are considered along with historical prices and its volume to predict prices. Experimental results show that there is not much impact of sentiment scores unless these scores are not biased to one particular class.
Innovation and Challenges of Blockchain in Banking: A Scientometric View

Blockchain has been gaining focus in research and development for diverse industries in recent years.Nevertheless, innovations that impact to the banking nurture a potential for disruptive impact globally for economic reasons; however it has received less scholarly attention.Hence the effect of blockchain technologies on banking industry is systematically reviewed.The relevant literature is extracted from Scopus, Web of Science and bibliometric techniques are applied.While a bulk of earlier papers focuses only on bit coins, a broader framework is envisaged that synthesizes interdisciplinary thematic areas for advancement; hence novelty in current work.A few practical and theoretical implications for stakeholders in view of technology, law and management are discussed.
The challenge and opportunities of quantum literacy for future education and transdisciplinary problem-solving

Resulting from cross-disciplinary dialogue between physicists, computer scientists, educationalists, and industrial end users, we propose the concept of quantum literacy as one means of addressing the transdisciplinary nature of the complex problems that we see at the heart of issues around global sustainability. In this way, quantum literacy can contribute to UN Sustainable Development Goal 4, Quality Education. We argue that quantum literacy, as defined here, addresses the challenges of learning and skills acquisition within a highly bounded discipline and of access to the kind of powerful knowledge that should be more accessible to a wide group of learners throughout the life course, both students and professionals. It is increasingly important that the knowledge of quantum technologies is accessible to those who work with real world applications in a more inclusive way. We therefore argue for the importance of addressing pedagogic issues when powerful knowledge consists of dense concepts, as well as complex and hierarchical relations between concepts, in addition to presenting a strong barrier to entry in the form of mathematics. We introduce a specific puzzle visualization learning tool through which to achieve these pedagogic ends with respect to quantum computation. Visualization through puzzles can enable non-specialists to develop an intuitive, but still rigorous, understanding of universal quantum computation and provide a facility for non-specialists to discover increasingly complex and new quantum algorithms. Using the Hong-Ou-Mandel optical effect from quantum mechanics, we demonstrate how visual methods such as those made possible through the puzzle visualization tool, can be very useful for understanding underlying complex processes in quantum physics and beyond and therefore support the aims of quantum literacy.
Satellite-based spectral mapping (ASTER and landsat data) of mineralogical signatures of beach sediments: a precursor insight

Detailed investigation on texture and mineralogy of beach sediments helps to understand their nature of deposition and potential targets of strategic mineral deposits. The beach sediments from the coast of Thiruvananthapuram, the southernmost district of Kerala, India, have been studied to understand the variation in grain size by using the spectral indices as derived from the visible-NIR-TIR bands of Landsat and ASTER remote sensing data. Further, an attempt has been made to map the distribution of strategic minerals present in beach sands using standardized hyperspectral analysis techniques. The grain size shows a remarkable variation from medium sand to fine sand. The THM (Total Heavy Minerals) content was estimated to about 80.04% and 52.33% along the coast of Kovalam and Varkala, respectively. The ilmenite mineral predominantly exists in these areas, followed by monazite, sillimanite, rutile, zircon, garnet, leucoxene, and Kyanite. The hyperspectral analysis extracts two endmembers of ilmenite and light minerals from the Landsat and ASTER imagery, which could be successfully, mapped using the SAM classification algorithm. The satellite-derived map showing texture and mineralogy has been validated with the results of laboratory analysis and shows strong correlation almost in all locations. This study illustrates the potential use of satellite remote sensing techniques for the mapping of natural resources, especially mineral resources.
Distributed gas concentration prediction with intelligent edge devices in coal mine

Gas disaster can be triggered by gas concentrations exceeding standard levels, and gas concentration prediction system can reduce the occurrence of gas disaster by predicting the trend of gas concentration and alerting engineers to take necessary measures whenever needed. With the increasing use of intelligent edge devices in coal mines and the limitations of some existing systems, developing a new gas concentration prediction system for large-scale intelligent edge devices has become an important issue. This work proposes to address the issue through a novel method for predicting gas concentrations by taking full advantage of multidimensional data in an intelligent edge system. Specifically, 1) it proposed a Single hidden layer Random Weights Neural Network (SRWNN) as the prediction model, which is based on interval prediction rather than point prediction; 2) It employs a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to train SRWNN; 3) To significantly reduce the time consumed during model training and facilitate real-time predictions, it proposes a distributed gas concentration prediction scheme based on an intelligent edge system; and 4) it conducts extensive experiments by using actual industrial data collected from a company to demonstrate the superior performance of the proposed method.
Learning Process of Agile Scrum Methodology With Lego Blocks in Interactive Academic Games: Viewpoint of Students

The rapid growth of Information Technology led to the development of a multitude of smartphone systems worldwide. As a result, the number of educational institutions offering courses in areas such as programming and software engineering increased. However, traditional processes for software development did not keep pace with changing technologies. In the last few years, software development became more dynamic and iterative, requiring stakeholders to work as a team and deliver higher quality projects in less time, by using methods such as Agile Development (e.g., Scrum and Extreme Programming (XP)). Although some institutions approach this content in graduation courses, many students and professor are indifferent towards it, resulting in low enthusiasm and practice. This article presents the real case of a classroom activity to teach Scrum concepts by using Lego blocks. At the end of the classes, students were asked to rate the effectiveness of the activity. The results showed that dynamic games and palpable activities are more effective than theoretical or video lessons.
Refinedbox: Refining for fewer and high-quality object proposals

Recently, object proposal generation has shown value for various vision tasks, such as object detection, semantic instance segmentation, multi-label image classification, and weakly supervised learning, by hypothesizing object locations. We are motivated by the fact that many traditional proposal methods generate dense proposals to cover as many objects as possible but that i) they usually fail to rank these proposals properly and ii) the number of proposals is very large. For example, the well-known object proposal generation methods, Edge Boxes and Selective Search, can achieve high detection recall with thousands of proposals per image. But the large number of generated proposals makes subsequent analyses difficult due to the large number of false alarms and heavy computation load. To significantly reduce the number of proposals, we design a computationally lightweight neural network to refine the initial object proposals. The refinement consists of two parallel processes, re-ranking and box regression. The proposed network can share convolutional features with other high-level tasks by joint training, so the proposal refinement can be very fast. We show a joint training example of object detection in this paper. Extensive experiments demonstrate that our method can achieve state-of-the-art performance with a few proposals compared with some well-known proposal generation methods.