Reviewerly/exHarmony huggingface dataset:
Speeding up the detection of non-iconic and iconic gestures (SPUDNIG): A toolkit for the automatic detection of hand movements and gestures in video data

Abstract In human face-to-face communication, speech is frequently accompanied by visual signals, especially communicative hand gestures. Analyzing these visual signals requires detailed manual annotation of video data, which is often a labor-intensive and time-consuming process. To facilitate this process, we here present SPUDNIG (SPeeding Up the Detection of Non-iconic and Iconic Gestures), a tool to automatize the detection and annotation of hand movements in video data. We provide a detailed description of how SPUDNIG detects hand movement initiation and termination, as well as open-source code and a short tutorial on an easy-to-use graphical user interface (GUI) of our tool. We then provide a proof-of-principle and validation of our method by comparing SPUDNIG’s output to manual annotations of gestures by a human coder. While the tool does not entirely eliminate the need of a human coder (e.g., for false positives detection), our results demonstrate that SPUDNIG can detect both iconic and non-iconic gestures with very high accuracy, and could successfully detect all iconic gestures in our validation dataset. Importantly, SPUDNIG’s output can directly be imported into commonly used annotation tools such as ELAN and ANVIL. We therefore believe that SPUDNIG will be highly relevant for researchers studying multimodal communication due to its annotations significantly accelerating the analysis of large video corpora.
Fuzzy Galactic Swarm Optimization with Dynamic Adjustment of Parameters Based on Fuzzy Logic

In this work, a fuzzy method for dynamic adjustment of parameters in galactic swarm optimization is presented. Galactic swarm optimization is based on the movement of stars and galaxies in the universe, as well as their attractive influence allowing the use of multiple cycles of exploration and exploitation to solve complex optimization problems. It has been observed in the literature that the utilization of fuzzy systems for dynamic adjustment of parameters in metaheuristic algorithms produces good results when compared to using fixed parameters in the algorithms. In this work, the adjustment of the c3 and c4 parameters is made through the use of fuzzy systems because these parameters have a significant role in the operation of galactic swarm optimization. We tested the fuzzy approach with a set of benchmark mathematical functions and with the fuzzy controller of the water tank problem to measure the performance. Finally, a comparison of the results is presented among the proposed method and other metaheuristics.
Improving Spectral Clustering with Deep Embedding and Cluster Estimation

Spectral clustering is one of the most popular modern clustering algorithms. It is easy to implement, can be solved efficiently, and very often outperforms other traditional clustering algorithms such as k-means. However, pectral clustering would be insufficient when dealing with most datasets which have complex statistical properties and quires the user to specify the number of clusters (called k). To address these two problems, in this paper, we propose an approach to extending spectral clustering with deep embedding and estimation of the number of clusters. Specifically, we first generate the deep embedding via learning a deep autoencoder, which transforms the raw data into the lower dimensional representations that suitable for clustering. We then provide an effective method to estimate the number of clusters by learning a softmax autoencoder from the deep embedding. We finally extend spectral clustering with the learned embedding and the estimated number. An extensive experimental study on several image and text datasets illustrates the effectiveness and efficiency of our approach.
On-line Training and Monitoring of Robot Tasks through Virtual Reality

Currently, the implementation of virtual, augmented and mixed realities-based solutions is one of the megatrends in the Industrial Automation domain. In this context, Virtual Reality (VR) permits the development of virtual environments that can be used for different purposes, such as designing, monitoring and/or training industrial machinery. Moreover, the access to such environments can be remote, facilitating the interaction of humans with cyber models of real-world systems without the need of being at the system facilities. This article presents a virtual environment that has been developed within VR technologies not only for training and monitoring robot tasks but also to be done at robot operation runtime within an on-line mode. In this manner, the user of the presented environment is able to train and monitor de tasks at the same time that the robot is operating. The research work is validated within the on-line training and monitoring tasks of an ABB IRB 14000 industrial robot.
Artificial Neural Network Based University Chatbot System

Chatbots have effectively reduced human efforts by providing automated human-like solutions for various business and societal problems. This paper is an elaborate description of the design and implementation of a University Counselling Auto-Reply Bot, that is capable of providing answers to queries related to the field of Engineering at our University level. The appropriate NLP techniques are applied to our University Data, developed in the JSON format and the Feedforward neural model is used for training this dataset, the issue of overfitting was handled. The Chat Application is then deployed on Facebook Messenger, the response is visible to the User on the Facebook Messenger interface, to provide them with an effective interaction platform. The end-user testing was conducted in two phases; the probability scores of the correct responses were improved to 0.72 in the second phase from 0.46 in the first phase after devising additional training phrases and keywords to the dataset.
VoteChain: A Blockchain Based E-Voting System

In the past, electronic voting systems have not seen widespread adoption due to data privacy concerns. Previously proposed e-voting systems make use of a central database to store data, resulting in the servers used to store these databases being a single point of failure. These systems have also been found to be vulnerable to DoS attacks, leading to concerns over their reliability. Blockchains have been used to build secure and scalable distributed systems which have shown several benefits over centralized systems. They have seen uses in sectors ranging from finance and healthcare to food and energy. In this paper, we present VoteChain, a blockchain based voting system to help bring transparency and security to polls. We report on our implementation of VoteChain, as well as the results obtained in testing the system in a real-world poll which prove that such a system can be used in practice for large-scale elections.
Decryption Failure Is More Likely After Success

The user of an imperfectly correct lattice-based public-key encryption scheme leaks information about their secret key with each decryption query that they answer—even if they answer all queries successfully. Through a refinement of the D'Anvers–Guo–Johansson–Nilsson–Vercauteren–Verbauwhede failure boosting attack, we show that an adversary can use this information to improve his odds of finding a decryption failure. We also propose a new definition of $$\delta $$-correctness, and we re-assess the correctness of several submissions to NIST's post-quantum standardization effort.
Sparse spectral signal reconstruction for one proposed nine-band multispectral imaging system

Multispectral filter array (MSFA) imaging with one single sensor is a portable and inexpensive means of acquiring spectral image which is widely used for object detection, material analysis and mechanical system diagnosis. The most challenging task for MSFA imaging is the multispectral demosaicking with the aim of reconstructing the captured raw/mosaic image, especially for the systems with many bands which result in higher sparseness of the raw data. In this paper, we present a 9-band MSFA imaging system in a repetitive 4 × 4 filter array on a single sensor, and propose a demosaicking algorithm for reconstructing the raw spectral image. Within the 4 × 4 MSFA pattern, the fifth spectral band takes up half of the total spatial position while the remaining eight bands occupy 1/16 respectively. To reconstruct the sparse raw data, we first recover the fifth band by propagating the neighboring sampled pixels to the unsampled position using the image gradients, and then employ the reconstructed fifth band as a guided image to demosaick the other bands with the guided filter and residual interpolation. Finally, we estimate the spectral reflectance values from the multispectral image and the characterization matrix. In the experiment, we evaluate the performance of the 9-band imaging system with the binary tree-based edge-sensing (BTES) algorithm, compressed sensing (CS) algorithm, and our proposed demosaicking algorithm. The experiment results demonstrate that our demosaicking algorithm not only outperforms BTES and CS algorithms in terms of objective image quality, e.g., PSNR values and spectral errors, but also reduces the demosaicking artifacts in terms of subjective evaluations.
Augmenting Knowledge through Statistical, Goal-oriented Human-Robot Dialog

Some robots can interact with humans using natural language, and identify service requests through human-robot dialog. However, few robots are able to improve their language capabilities from this experience. In this paper, we develop a dialog agent for robots that is able to interpret user commands using a semantic parser, while asking clarification questions using a probabilistic dialog manager. This dialog agent is able to augment its knowledge base and improve its language capabilities by learning from dialog experiences, e.g., adding new entities and learning new ways of referring to existing entities. We have extensively evaluated our dialog system in simulation as well as with human participants through MTurk and real-robot platforms. We demonstrate that our dialog agent performs better in efficiency and accuracy in comparison to baseline learning agents. Demo video can be found at https://youtu.be/DFB3jbHBqYE.
Implementation of a ROS-Based Autonomous Vehicle on an FPGA Board

Due to the development of high-performance LSIs, autonomous vehicle will be realized in a few years. The FPGA Design Competition is one of the valuable opportunities to demonstrate an autonomous vehicle on miniature roads. In this paper, we develop a ROS-based autonomous vehicle which is implemented on an FPGA board as a mock car. ROS is a common framework designed to implement various types of robots. Utilizing the ROS-based platform, we develop a model car for the demonstration of an autonomous vehicle. The on-board programmable logic is used to off-load the processing of image recognition such as lane detection, traffic signal detection, and obstacle detection. The implementation results demonstrate that we can successfully implement essential components of the vehicle on an FPGA board with the ROS-based system.
Fast regularity-constrained plane fitting

Man-made environments typically comprise planar structures that exhibit numerous geometric relationships, such as parallelism, coplanarity, and orthogonality. Making full use of these relationships can considerably improve the robustness of algorithmic plane fitting of complex scenes. This research leverages a constraint model requiring minimal prior knowledge to implicitly establish relationships among planes. We introduce a method based on energy minimization to reconstruct the planes consistent with our constraint model. The proposed algorithm is efficient, easily to understand, and simple to implement. The experimental results show that our algorithm successfully fits planes under high percentages of noise and outliers. This is superior to other state-of-the-art regularity-constrained plane fitting methods in terms of speed and robustness.
Biometric Voting Machine Based on Fingerprint Scanner and Arduino

This paper describes design of Biometric Voting Machine Using Fingerprint Scanner and Arduino for voting in institutes and organizations. Indian constitution empowers its citizen to exercise right to vote. Election decides the future of country, so that the system used for voting should be trustworthy. The conventional system for voting is ballot paper and Electronic voting machine too, has many flaws and trust issues. To eradicate malpractice and defrauding of the above methods of voting, we have designed an advanced system by using arduino and Fingerprint module. In this system, a person has to register a fingerprint ID with the system which will be centrally stored in arduino. In organizations, educational institutes, a co-operative bank, maximum number of votes elect head of organization that holds the office of public interest. For confirmation of voter, the name of the candidate will be displayed on LCD for whom the voter has cast a vote. It has simple hardware design and it is easily accessible. In case user wants to remove any of stored ID then the user need to press DEL key, after pressing DEL key, LED will ask to select ID that is to be deleted. After pressing OK key, the selected ID will be deleted and LCD will display that which ID has been deleted successfully. This system is flexible to use.
Entropic Risk Measure in Policy Search

With the increasing pace of automation, modern robotic systems need to act in stochastic, non-stationary, partially observable environments. A range of algorithms for finding parameterized policies that optimize for long-term average performance have been proposed in the past. However, the majority of the proposed approaches does not explicitly take into account the variability of the performance metric, which may lead to finding policies that although performing well on average, can perform spectacularly bad in a particular run or over a period of time. To address this shortcoming, we study an approach to policy optimization that explicitly takes into account higher order statistics of the reward function. In this paper, we extend policy gradient methods to include the entropic risk measure in the objective function and evaluate their performance in simulation experiments and on a real-robot task of learning a hitting motion in robot badminton.
Quantifying the sustainability of Bitcoin and Blockchain

Purpose The authors develop new quantitative methods to estimate the level of speculation and long-term sustainability of Bitcoin and Blockchain. Design/methodology/approach The authors explore the practical application of speculative bubble models to cryptocurrencies. They then show how the approach can be extended to provide estimated brand values using data from Google Trends. Findings The authors confirm previous findings of speculative bubbles in cryptocurrency markets. Relatedly, Google searches for cryptocurrencies seem to be primarily driven by recent price rises. Overall results are sufficient to question the long-term sustainability of Bitcoin with the suggestion that Ethereum, Bitcoin Cash and Ripple may all enjoy technical advantages relative to Bitcoin. Our results also demonstrate that Blockchain has a distinct value and identity beyond cryptocurrencies – providing foundational support for the second generation of academic work on Blockchain. However, a relatively low estimated long-term growth rate suggests that the benefits of Blockchain may take a long time to be fully realised. Originality/value The authors contribute to an emerging academic literature on Blockchain and to a more established literature exploring the use of Google data within business analytics. Their original contribution is to quantify the business value of Blockchain and related technologies using Google Trends
Model Predictive Control for an Aerial Manipulator Opening a Hinged Door

Aerial manipulation has been widely studied to be employed in various tasks such as exploration and transportation. To incorporate aerial manipulation into more sophisticated tasks like pulling or pushing a heavy cargo, an active interaction with surrounding structures should be considered. Unlike physical contact with a static structure which was mainly studied in previous papers, interaction with a movable structure requires a consideration of dynamics of the structure which makes the scenario more complex. In this paper, an aerial manipulator opening a hinged door is presented. Coupled dynamics between an aerial manipulator and a hinged door is derived, and a model predictive control (MPC) algorithm using iterative Linear Quadratic Regulator (iLQR) method for the derived dynamic equation is proposed. Through our proposed control strategy, sub-optimal state and input trajectories robust to model uncertainties while satisfying input constraints are generated. Our dynamic model and control algorithm are validated through simulations.
Layout Analysis on Challenging Historical Arabic Manuscripts using Siamese Network

This paper presents layout analysis for historical Arabic documents using siamese network. Given pages from different documents, we divide them into patches of similar sizes. We train a siamese network model that takes as an input a pair of patches and gives as an output a distance that corresponds to the similarity between the two patches. We used the trained model to calculate a distance matrix which in turn is used to cluster the patches of a page as either main text, side text or a background patch. We evaluate our method on challenging historical Arabic manuscripts dataset and report the F-measure. We show the effectiveness of our method by comparing with other works that use deep learning approaches, and show that we have state of art results.
Water 4.0: An Integrated Business Model from an Industry 4.0 Approach

Water is one of the most valuable natural resource used for everyday living. The era of Fourth Industrial Revolution (4IR); also referred to as Industry 4.0 has brought about different “Smart thinking” such as smart city, smart mobility, smart manufacturing and smart factory. In the water industry, the Germany Water Partnership is championing the Water 4.0 initiative since 2016. The essence of Water 4.0 initiative is to harness the digital revolution or decentralization of the water industry. This paper takes a close look at the business side of Water 4.0 from an 4IR perspective and aimed to develop an “Integrated Business Model” for Water 4.0 revolution. This paper is expected to answer this research question “can water industry be regarded as an integrated business?”. This paper started with a comprehensive literature reviews around the title of the research and integrated business model in relation to water industry. The literature reviews were analyzed and examined, and relevant factors considered suitable for integrated business model which are carefully selected for Water 4.0 paradigm.
Learning Q-network for Active Information Acquisition

In this paper, we propose a novel Reinforcement Learning approach for solving the Active Information Acquisition problem, which requires an agent to choose a sequence of actions in order to acquire information about a process of interest using on-board sensors. The classic challenges in the information acquisition problem are the dependence of a planning algorithm on known models and the difficulty of computing information-theoretic cost functions over arbitrary distributions. In contrast, the proposed framework of reinforcement learning does not require any knowledge on models and alleviates the problems during an extended training stage. It results in policies that are efficient to execute online and applicable for real-time control of robotic systems. Furthermore, the state-of-the-art planning methods are typically restricted to short horizons, which may become problematic with local minima. Reinforcement learning naturally handles the issue of planning horizon in information problems as it maximizes a discounted sum of rewards over a long finite or infinite time horizon. We discuss the potential benefits of the proposed framework and compare the performance of the novel algorithm to an existing information acquisition method for multi-target tracking scenarios.
Overview of Neural Machine Translation for English-Hindi

Artificial intelligence is getting shrewd in real time with massive amounts of computational power and high in demand in last quarter more than billion dollars. The Intensification of Neural Networks in Machine learning playing major revolution in Machine Translation (MT). Looking same aspect, we have prepared a paper on Machine Translation for Hindi using Neural Machine Translation techniques. The Basis of this paper is to translate the source language into target language with the help of sentence structure for source language (English sentence) and corresponding reordering rules for target language (Hindi sentence) using a deep neural network (DNN).When the source input that is English sentences are exactly matched with the target language database then the suitable translation for related input is directly fetched from the database. The fuzzy matching between the input sentence and the sample sentence in the database is done with cosine similarity between the sentence structure of input sentence and the sample sentence and then the reordering rule of the most alike sentence to the input sentence is used for translation.Extracting the structure of the source language sentence and extracting the reordering rule for target sentence is the core part of the translator. Reordering of words in spite of that remains one of the complex problems.It is exciting to implement Deep Neural Network and fuzzy logic to build intelligent reordering rules machine learning system.
Online Motion Planning Over Multiple Homotopy Classes with Gaussian Process Inference

Efficient planning in dynamic and uncertain environments is a fundamental challenge in robotics. In the context of trajectory optimization, the feasibility of paths can change as the environment evolves. Therefore, it can be beneficial to reason about multiple possible paths simultaneously. We build on prior work that considers graph-based trajectories to find solutions in multiple homotopy classes concurrently. Specifically, we extend this previous work to an online setting where the unreachable (in time) part of the graph is pruned and the remaining graph is reoptimized at every time step. As the robot moves within the graph on the path that is most promising, the pruning and reoptimization allows us to retain candidate paths that may become more viable in the future (a) as the environment changes, essentially enabling the robot to dynamically switch between numerous homotopy classes. We compare our approach against prior work without the homotopy switching capability and show improved performance across several metrics in simulation with a 2D robot in multiple dynamic environments under noisy measurements and execution.
Optical Flow Estimation with Deep Learning, a Survey on Recent Advances

One of the many components used in biometrics is optical flow estimation. This could be due to the fact that motion is an inseparable attribute of our (visual) world and hence it is a valuable resource of data needed to tackle many real-world problems. Indeed, technologies that use object detection, motion detection, object tracking, gait recognition as well as video compression heavily rely on optical flow estimation. This chapter explores recent advances in optical flow estimation, while mainly focusing on estimation techniques based on deep learning (DL). In fact, recent advancements in deep learning are seemingly making a shift in the optical flow estimation research field. This chapter begins with reviewing traditional (handcrafted) approaches, then introduces the more recent approaches, and finally gets concluded with surveying deep learning approaches.