Reviewerly/exHarmony huggingface dataset:
VDA: Deep Learning based Visual Data Analysis in Integrated Edge to Cloud Computing Environment

In recent years, video surveillance technology has become pervasive in every sphere. The manual generation of videos’ descriptions requires enormous time and labor, and sometimes essential aspects of videos are overlooked in human summaries. The present work is an attempt towards the automated description generation of Surveillance Video. The proposed method consists of the extraction of key-frames from a surveillance video, objects detection in the key-frames, natural language (English) description generation of the key-frames, and summarizing the descriptions. The key-frames are identified based on a structural similarity index measure. Object detection in a key-frame is performed using the architecture of Single Shot Detection. We used Long Short Term Memory (LSTM) to generate captions from frames. Translation Error Rate (TER) is used to identify and remove duplicate event descriptions. Term frequency-inverse document frequency (TF-IDF) is used to rank the event descriptions generated from a video, and the top-ranked the description is returned as the system generated a summary of the video. We evaluated the Microsoft Video Description Corpus (MSVD) data set to validate our proposed approach, and the system produces a Bilingual Evaluation Understudy (BLEU) score of 46.83.
LocaLibras

The Brazilian sign language (Libras) is, by law, the second official language of the country. However, a large part of the listeners do not know it. The lack of knowledge of Libras by listeners and the Portuguese language by deaf damages communication between them. The existing computational applications concentrate on the translation of the two languages and disregard regionalization. Besides, they do not promote the independence of the deaf. In this paper, a multi-platform application is presented that helps the deaf to relate places with their signs in Libras, also facilitating their journey with the use of routes. We carried out a study with people who understood Libras, including evaluation, observation, and interviews. The results obtained enabled the perception of problems that negatively affected the use of the application and served to signal improvements in interaction. We believed that the study would allow the appropriation of knowledge discovered in other contexts of use.
The journey towards dollarization: the role of the tourism industry

There has been an increasing wave of globalization since the turn of the millennium. This study focuses on two by-products of globalization: dollarization and tourism. Empirical studies have ignored the possible relationship between dollarization and tourism. However, we hypothesize that a booming tourism industry will fuel increase in the usage and circulation of foreign currencies, thus increasing the level of dollarization. The objective of this study is to examine the extent to which the tourism industry exacerbates the dollarization process of selected Sub-sahara African (SSA) countries. Using Tobit regression, we found that tourism positively affects dollarization. Our results are robust to: (i) alternative measures of tourism; (ii) accounting for endogeneity and outlier effects.
Exploring the Potential of using Power as a First Class Parameter for Resource Allocation in Apache Mesos Managed Clouds

We propose a resource allocation policy that uses (a) Power as a first class parameter as an indicator of the computational intensity of a task and its potential impact on peak power draw, and (b) Power Tolerance as an indicator of a task's sensitivity towards degradation of performance as a result of resource contention. Through experimentation and analysis, we present coarse-grained and fine-grained Power Tolerance assignment methods that can be employed to make smarter peak power performance trade-offs. Our experiments show that (a) cloud operators can benefit from a uniform workload-wide setting of Power Tolerance to achieve significant reduction in peak power consumption, (b) fine-grained Power Tolerance assignment methods show potential in making smarter peak power and performance trade-offs.
Bayesian Program Learning for Modeling and Classification of RF Emitters

In this work, we demonstrate an initial application of Bayesian program learning (BPL) to learn models for individual radio frequency (RF) transmitters based on a single training signal for each transmitter. Once learned, these models are used to classify individual RF transmitters based on one signal observation. BPL improves upon other machine learning techniques by learning and classifying effectively from small amounts of training data. BPL programs represent concepts as probabilistic generative models expressed as structured procedures in an abstract description language. These models explicitly account for both concept-specific and context-dependent mechanisms, allowing them to perform well under dynamic environmental conditions. In this ongoing research, we demonstrate our system using signals from a small population of software-defined radios (SDRs) with known signal encodings in a laboratory environment, and provide a path forward for expanding it to larger populations, more signal types, and challenging transmission environments.
Improving the energy efficiency of using solar panels

Abstract The article describes a method for increasing the efficiency of using solar panels when using a two-axis system for tracking the sun. A mathematical model of solar panel operation has been developed for the following applications: stationary installation of solar panels and the use of a two-axis solar tracker. The solar tracker has the ability to rotate the solar panel by the azimuth angle from 0 to 170 degrees, by the Zenith angle from 0 to 90 degrees. The paper presents a method for calculating power generation by solar panels using a solar tracker and a variant of stationary installation of solar panels. Plots of the PV panel as a function of time at stationary position of the solar panels and using a solar tracker. As a result of mathematical modeling, when using a solar tracker, compared with a stationary installation of a solar panel, electricity generation increases by 89.9% on the example of June 22, and by the example of December 22, the increase in electricity generation is 19.6 %. Using a solar tracker during the year increases the efficiency of electricity generation by solar panels by an average of 45-55%. Data on azimuth angles of sunrise and sunset, as well as the Zenith angle depending on the time of day for the 22nd day of each month are given in the table below. The results obtained are presented for coordinates: 45 degrees 3 minutes North latitude 41 degrees 59 minutes East longitude, which corresponds to the locality of the city of Stavropol in the Russian Federation.
Identification of Spurious Labels in Machine Learning Data Sets using N-Version Validation

Machine learning components are becoming popular for the automotive industry. More and more data sets become available for training machine learning components. All of them provide ground truth labels for images. The labeling process is expensive and potentially error-prone. At the same time, label correctness defines the business value of a data set. In this paper, we use N-Version approach to assess the label quality in a data set. The approach combines N state-of-the-art neural networks and aggregates their results in a single verdict using majority voting. We analyze this majority vote against the ground truth label and compute the percentage of disagreeing pixels along with other metrics, enabling the automated and detailed analysis of label quality on data sets. We evaluate our methodology by classifying the BDD100K drivable area data set. The evaluation shows that the approach identifies misclassified scenes or inconsistencies between label semantics for similar scenes.
Pitching Motion Matching Based on Pose Similarity Using Dynamic Time Warping

By comparing multiple videos, it is possible to find the differences between the movements by different players or between motions of the same player in separate situations. To compare multiple motions, it is necessary to match the timing of the motions. However, it is difficult to synchronize the timing since the motion speed and usage of the body varying by player and situation. Therefore, in this paper, we propose a matching method for two pitching motions based on pose similarity using Dynamic Time Warping (DTW). This method enables accurate matching of two pitching motions in many cases.
Rotat3D: A Knowledge Graph Embedding using Relational Rotation in 3D Vector Space

To extract the entities and understand the relations in knowledge graphs is probably one of the challenging research focuses recently in machine learning community. In this paper, we aim to study one of the narrow-down problem of learning the embedding of entities and relations in knowledge graphs. From using a 3D rotation transformation in high dimensional vector spaces, we present a new method for knowledge graph embedding named Rotat3D. More specifically, the 3D-valued embeddings will be used to represent for the entities in the graphs, in which the rotations are modeled as popular rotation formulation in 3D vector spaces. Experimental results, carried out on four common benchmark datasets for link prediction, have shown that our proposed Rotat3D method is able to infer the common relation patterns in a graph more easily, and also has a critical improvement compared with some other state-of-the-art methods.
Thermal Imaging on Smart Vehicles for Person and Road Detection: Can a Lazy Approach Work?

This paper proposes the addition of a thermal camera to an RGB system with the goal of improving person and road detection reliability in unfavorable weather and illumination conditions. Custom data is gathered on an experimental vehicle and used for development and testing. For person detection, we propose a novel multi-modal approach, where bounding boxes are initially obtained from RGB and thermal images using YOLOv3-tiny. We then identify high-intensity connected components in thermal images to compensate for missed detections. Detections from the two cameras and the two algorithms are finally weighed and combined into a confidence map. Using the proposed fusion method, recall and precision are improved compared to using RGB only, without the need to retrain the network. For thermal-based road segmentation, we achieve an average precision of 94.2% after re-training MultiNet's KittiSeg decoder on a small thermal dataset, while using pre-trained weights for MultiNet's VGG-based encoder. These results show that the addition of thermal cameras to perception systems of autonomous vehicles can bring substantial benefits with minimal labelling, implementation effort and training requirements.
On sharpness of error bounds for multivariate neural network approximation

Abstract Single hidden layer feedforward neural networks can represent multivariate functions that are sums of ridge functions. These ridge functions are defined via an activation function and customizable weights. The paper deals with best non-linear approximation by such sums of ridge functions. Error bounds are presented in terms of moduli of smoothness. The main focus, however, is to prove that the bounds are best possible. To this end, counterexamples are constructed with a non-linear, quantitative extension of the uniform boundedness principle. They show sharpness with respect to Lipschitz classes for the logistic activation function and for certain piecewise polynomial activation functions. The paper is based on univariate results in Goebbels (Res Math 75(3):1–35, 2020. https://rdcu.be/b5mKH )
A Deep Regression Model for Safety Control in Visual Servoing Applications

In Human-Robot Interaction scenarios, a human often needs to interact or closely working with objects and/or the robot. Hence the safety aspect needs to be taken care of in the Human-Robot Interaction scenarios. In this paper, we apply a deep learning approach to learning an optimal repulsive pose. The end effector of the robot will move the optimal repulsive pose if the human hand is too close to the end effector. We use a ResNet based deep regression model to learn the weights between the input i.e. the hand position + Tool Center Point position and output i.e. the repulsive pose. We evaluate the model with different readouts and loss functions. With the Fully Connected readout, the Mean absolute Error in the x, y and z directions are between 7.4 mm and 7.7 mm. The model inference time is also smaller than the computation time of calculating the optimal repulsive pose online.
MathFun: Examining the Effectiveness of Calculic Model in Designing App for Dyscalculia Children

Abstract Learning disability can vary from dyslexia (reading), dyscalculia (math) and dysgraphia (writing) where it focuses on certain learning disability that is being faced by the children. Dyscalculia is one of the common learning disabilities where in this category children are lack in ability of studying the math. However, with the enhancement of technologies, variety of application can be created in aiding the children learning process such as mobile learning by using mobile app. This paper focuses on examining the design model (Calculic Model) in designing a mobile app for Dyscalculia children. MathFun is a mobile app created by the mobile app developer while following the Calculic Model approach for Malaysia Dyscalculia children. The outcomes of this paper view on the effectiveness of the model towards building a mobile application for these children. Usability was performed in order to assess the usability and verifying the effectiveness of MathFun. This study involved 3 teachers and three children. Descriptive analysis was performed from the collected data. Based on the outcome, it’s shows that by using the suggested model there is an increased in the acceptance and usability of the application by the children.
Dominance-Partitioned Subgraph Matching on Large RDF Graph

Subgraph matching on a large graph has become a popular research topic in the field of graph analysis, which has a wide range of applications including question answering and community detection. However, traditional edge-cutting strategy destroys the structure of indivisible knowledge in a large RDF graph. On the premise of load-balancing on subgraph division, a dominance-partitioned strategy is proposed to divide a large RDF graph without compromising the knowledge structure. Firstly, a dominance-connected pattern graph is extracted from a pattern graph to construct a dominance-partitioned pattern hypergraph, which divides a pattern graph as multiple fish-shaped pattern subgraphs. Secondly, a dominance-driven spectrum clustering strategy is used to gather the pattern subgraphs into multiple clusters. Thirdly, the dominance-partitioned subgraph matching algorithm is designed to conduct all isomorphic subgraphs on a cluster-partitioned RDF graph. Finally, experimental evaluation verifies that our strategy has higher time-efficiency of complex queries, and it has a better scalability on multiple machines and different data scales.
Multimodal Interactive Dining with the Sensory Interactive Table: Two Use Cases

This paper presents two use cases for a new multimodal interactive instrument: the Sensory Interactive Table. The Sensory Interactive Table is an instrumented, interactive dining table, that measures eating behavior - through the use of embedded load cells - and interacts with diners - through the use of embedded LEDs. The table opens up new ways of exploring the social dynamics of eating. The two use cases describe explorations of the design space of the Sensory Interactive Table in the context of the social space of eating. The first use case details the process of co-designing and evaluating applications to stimulate children to eat more vegetables. The second use case presents the process of designing and evaluating applications to stimulate young adults to reduce their eating speed in a social setting. The results show the broad potential of the design space of the table across user groups, types of interactions, as well as the social space of eating.
Real-Time Ball Speed Stabilization Acquisition using Low Pass Filter on Robot Soccer ERSOW

This paper presents the acquisition and stabilization of the ball speed data on the Robot Soccer ERSOW. The skills such as receiving and passing ball needs a good quality of ball speed data, but the ball speed data on robot ERSOW is very unstable. However, we argued that this unstable ball speed data can be minimized using the Low Pass Filter (LPF). The LPF method can reduce the average ball speed changing rate on the speed-x data around 4.57 cm/s on LPF cutoff value 0.75, 4.9 cm/s on cutoff value 0.8, and 8.6 cm/s on cutoff value 0.99. Furthermore, the speed-y is changing from 43.02 cm/s into 12.05 cm/s on LPF cutoff value 0.75, 14.02 cm/s on LPF cutoff value 0.8, and 17.7 cm/s on LPF cutoff value 0.99. The result is quite good because the smaller value of the average speed changing rate indicates the data is more stable.
Detecting Urgency Status of Crisis Tweets: A Transfer Learning Approach for Low Resource Languages

We release an urgency dataset that consists of English tweets relating to natural crises.The set is annotated along with annotations of their corresponding urgency status.Additionally, we release evaluation datasets for two low-resource languages, i.e.Sinhala and Odia, and demonstrate an effective zero-shot transfer from English to these two languages by training cross-lingual classifiers.We adopt cross-lingual embeddings constructed using different methods to extract features of the tweets, including a few state-of-the-art contextual embeddings such as BERT, RoBERTa and XLM-R.We train a variety of classifier architectures, supervised and semi supervised, on the extracted features.We also further experiment with ensembling the various classifiers.With very limited amounts of labeled data in English and zero data in the low resource languages, we show a successful framework of training monolingual and cross-lingual classifiers using deep learning methods which are known to be data hungry.Specifically, we show that the recent deep contextual embeddings are also helpful when dealing with very small-scale datasets.Classifiers that incorporate RoBERTa yield the best performance for the English urgency detection task, with 25% F1 score absolute improvement over the baselines.For the zero-shot transfer to low resource languages, classifiers that use LASER features perform the best for Sinhala transfer while XLM-R features benefit the Odia transfer the most.
Abstractive Sentence Summarization with Encoder-Convolutional Neural Networks

Summarization is the task of condensing a piece of text to produce a short version while preserving important elements and the meaning of content There have two main methods to summarize the text such as extractive summarization and abstractive summarization. Abstractive Sentence Summarization generates a shorter version of a set of documents while attempting to preserve its meaning. In this work, we introduce an architecture called the pointer-gen E-Conv (PGEC) whose conditioning is the combination between pointer-generator and a novel convolutional network with a weight normalization. Our model gains a 32.28 ROUGE-1 score on the Gigaword test set and a 27.13 ROUGE-1 score on the DUC 2004 dataset These results have shown that PGEC outperforms the recently proposed methods on both datasets.
Commodity Ecology: A Virtual Community Platform for Promoting Responsible Consumption and Production to Achieve SDG #12

We live in a society built on three major disruptive technologies: (1) the Internet, upon which we are increasingly served by and even ruled by (2) online virtual community platforms, which are increasingly accessible using (3) handheld mobile devices. A fourth disruptive technology is coming: blockchain technology which is beginning to revolutionize the exchange of information in areas such as cryptocurrency, supply chain management, healthcare and smart contracts. Owing to these four technological developments, we now live among real/virtual ‘smart regions’ that on their own are capable of deliberating, finding, and buying/selling en masse toward their own better choices in sustainable material choices and better waste handling. These trends are utilized by the first online blockchain-based virtual community platform for ‘smart regions’ that facilitates this global, multi-regional drive toward more democratic, holistic, and sustainable ecological design in all of our consumptive choices. Commodity Ecology is a model, rubric, and checklist for sustainability of 130 material categories, on a virtual platform for deliberation in over 860 distinct ecoregions simultaneously. The United Nations Academic Impact Office called Commodity Ecology a top global initiative for actually achieving Sustainable Development Goal #12, Responsible Consumption and Production.
Neural Compound-Word (Sandhi) Generation and Splitting in Sanskrit Language

This paper describes neural network based approaches to the process of the formation and splitting of word-compounding, respectively known as the Sandhi and Vichchhed, in Sanskrit language. Sandhi is an important idea essential to morphological analysis of Sanskrit texts. Sandhi leads to word transformations at word boundaries. The rules of Sandhi formation are well defined but complex, sometimes optional and in some cases, require knowledge about the nature of the words being compounded. Sandhi split or Vichchhed is an even more difficult task given its non uniqueness and context dependence. In this work, we propose the route of formulating the problem as a sequence to sequence prediction task, using modern deep learning techniques. Being the first fully data driven technique, we demonstrate that our model has an accuracy better than the existing methods on multiple standard datasets, despite not using any additional lexical or morphological resources. The code is being made available at https://github.com/IITD-DataScience/Sandhi_Prakarana
HRAE: Hardware-assisted Randomization against Adversarial Example Attacks

With the rapid advancements of the artificial intelligence, machine learning, especially neural networks, have shown huge superiority over humans in image recognition, autonomous vehicles and medical diagnosis. However, its opacity and inexplicability provide many chances for malicious attackers. Recent researches have shown that neural networks are vulnerable to adversarial example (AE) attacks. In the testing stage, it fools the model by adding subtle perturbations to the original sample to misclassify the input, which poses a serious threat to safety-critical areas such as autonomous driving. In order to mitigate this threat, this paper proposes a hardware-assisted randomization method against AEs, where an approximate computing technique in hardware, voltage over-scaling (VOS), is used to randomize the training set of the model, then the processed data are used to generate multiple neural network models, finally multiple redundant models are used for the integrated classification and detection of the AEs. Various AE attacks on the proposed defense are evaluated to prove its effectiveness.