Reviewerly/exHarmony huggingface dataset:
Learning Multimodal Rewards from Rankings

Learning from human feedback has shown to be a useful approach in acquiring robot reward functions. However, expert feedback is often assumed to be drawn from an underlying unimodal reward function. This assumption does not always hold including in settings where multiple experts provide data or when a single expert provides data for different tasks -- we thus go beyond learning a unimodal reward and focus on learning a multimodal reward function. We formulate the multimodal reward learning as a mixture learning problem and develop a novel ranking-based learning approach, where the experts are only required to rank a given set of trajectories. Furthermore, as access to interaction data is often expensive in robotics, we develop an active querying approach to accelerate the learning process. We conduct experiments and user studies using a multi-task variant of OpenAI's LunarLander and a real Fetch robot, where we collect data from multiple users with different preferences. The results suggest that our approach can efficiently learn multimodal reward functions, and improve data-efficiency over benchmark methods that we adapt to our learning problem.
Visually Guided Motor Control: Adaptive Sensorimotor Mapping with On-line Visual-Error Correction

A neural network architecture combining an adaptive sensory-motor mapping and an online visual error correction (VEC) mechanism is proposed. The sensory-motor network (SMN) acquires a consistent intermodal mapping through primary circular reactions. During performance, SMN generates feed-forward control commands to the motor networks. This feed-forward command is augmented by VEC which is a feed-back control mechanism. The feedback allows the system to correct immediately during performance, errors that may arise from various sources including intermodal inconsistencies. This dual feed-forward feedback control enables the network to maintain the accuracy of its performance while inconsistencies are resolved through long-term adaptation. We present simulation results that illustrate the long-term learning as well as immediate correction capabilities of the network.
Introduction of AI Techniques and Approaches

From the past six decades, the technology that has evolved as a field is artificial intelligence (AI). The term AI signifies the intelligence shown by machines and works contrary to the natural intelligence that is illustrated by all living objects. AI is a multidisciplinary field that amalgamates diverse subfields such as engineering, biology, linguistics, mathematics, and computer science. On a major part, AI deals with problem-solving concerns in various scenarios, learning as well as reasoning. AI has a dynamic nature and deals with an unimaginable volume of data (facts and figures). The concept of AI is presented in this chapter in detail. Currently, the AI domain is categorized into three subgroups, namely, weak AI, strong AI, and the superintelligence aspect. In the research sector, AI is still in its initial stages and has a weak AI perspective. This chapter focuses on these subgroups and also tries to relate it to the normal lifestyle of a person. There are diverse ways in which AI is providing a convenient lifestyle to people that include digital media, self-driving cars, fraud prevention, recommendations, etc. One particular example of a self-driving car is autonomous land vehicle in a neural network. AI also provides home-security solutions for enhancing the lifestyle of people. This chapter focuses on these approaches of AI. Machine learning (ML), which is a subgroup of AI, is also growing at a tremendous pace in the field of research and development. This chapter also highlights the importance of ML in research and development and also focuses on its types and techniques. The types include supervised, unsupervised, and reinforcement learning. The techniques include regression, classification, clustering, and deep learning concepts. In the computer science and engineering domain, there are various techniques and approaches for AI also. The era of AI is divided into three subdomains, namely, neural network (the 1950s), ML (the 1980s), and the deep learning scenario (present day). In the year 1994, an AI program “Deep Blue” has beaten the chess grandmaster Garry Kasparov. The details of it are also highlighted in this chapter, along with the other examples of AI. The diverse and unique approaches of AI include support vector machines (SVMs), artificial neural networks (ANNs), natural language processing (NLP), etc. SVM analyzes data that is used for the process of regression and classification. It is a supervised learning algorithm. With the help of AI, the interaction between the human and computer in a natural language can be performed. This depicts another subdomain of AI, i.e., NLP. In human brain, there is a biological neural network that depicts the form of natural intelligence. When this neural network computation is performed using machines, it depicts an ANN that is a collection of interconnected nodes. All these techniques and approaches are well depicted in this chapter. Moreover, this chapter also depicts the diverse examples of AI that have revolutionized the world and had made the lives of the people easy and convenient.
Identifying Human Factors for Remote Guidance on Physical Tasks

Remote collaboration is becoming increasingly crucial, especially currently when travel is restricted because of the Covid-19 pandemic. People are looking for real-time and no-travel solutions to enable remote collaboration with colleagues and experts. A lot of research has been conducted on how to support remote guidance on physical tasks. However, these studies have mainly focused on development of technical components to support collaboration, while less attention has been paid into exploring and evaluating human factors that could influence remote collaboration. The aim of this paper is to identify human factors including culture, language, trust and social status for their possible effects on remote collaboration by reviewing their effects on computer-supported collaboration. This review adds more critical views of human perspectives into the current research mostly-focused on the technical side of remote guidance.
The blockchain impacts in accounting auditing / Os impactos da cadeia de bloqueios na auditoria contábil

This paper aims to identify the main impacts of Blockchain as accounting audit as well the advantages and disadvantages facing future challenges to auditing. The methodology used was basic qualitative research, and review topics with exploratory aims, such as technical procedures. The data collection was in CAPES (Coordination for the Improvement of Higher Education Personnel) and ICAEW (The Institute of Chartered Accountants in England and Wales) websites and the data analysis and interpretation consisted in classifying and deducting. It was identified that Blockchain is little related to accounting auditing. Results show that the number of financial institutions and organizations that invest in Blockchain is rising, considering that it is related to a distributed ledger that provides a major transparency in the financial transactions of the organizations, which suggests that in the very near future financial institutions and organizations will use Blockchain to interact with each other and in this context internal and external auditing might be continuous, having in mind that in Blockchain financial transactions are available in real time for the auditors and available to auditing. In addition, the distributed ledger enables auditors to access information in new formats, and consequently changes in work roles will occur. In this way, corporative reports will have more consistent and reliable information. However, many countries do not know how to regulate Blockchain or do not offer any legal environment to it.
FIERY: Future Instance Prediction in Bird's-Eye View From Surround Monocular Cameras

Driving requires interacting with road agents and predicting their future behaviour in order to navigate safely. We present FIERY: a probabilistic future prediction model in bird's-eye view from monocular cameras. Our model predicts future instance segmentation and motion of dynamic agents that can be transformed into non-parametric future trajectories. Our approach combines the perception, sensor fusion and prediction components of a traditional autonomous driving stack by estimating bird's-eye-view prediction directly from surround RGB monocular camera inputs. FIERY learns to model the inherent stochastic nature of the future solely from camera driving data in an end-to-end manner, without relying on HD maps, and predicts multimodal future trajectories. We show that our model outperforms previous prediction baselines on the NuScenes and Lyft datasets. The code and trained models are available at this https URL.
Semi-supervised Attention-Guided VNet for Breast Cancer Detection via Multi-task Learning

Due to the rapid increase incidence of breast cancer, automated breast volume scanner (ABVS) is developed to detect breast cancer rapidly and accurately, which can automatically scan the whole breast with less manual operation. However, it is challenging for clinicians to segment the tumor region and further identify the benign and malignant tumors from the ABVS images since it has the large image size and low data quality. For this reason, we propose an effective 3D deep convolutional neural network for multi-task learning from ABVS data. Specifically, a new VNet structure is designed using deep attentive module for performance boosting. In addition, a semi-supervised mechanism is introduced to address the issue of insufficient labeled training data. Due to the difference of the tumor size, we create a two-stage process and fit the small size tumor via volume refinement block for further performance improvement. The experimental results on our self-collected data demonstrate that our model has achieved Dice coefficient of 0.764 for 3D segmentation and F1-score of 81.0% for classification. Our network outperforms the related algorithm as well.
A formal model for ledger management systems based on contracts and temporal logic

A key component of blockchain technology is the ledger, viz., a database that, unlike standard databases, keeps in memory the complete history of past transactions as in a notarial archive for the benefit of any future test. In second-generation blockchains such as Ethereum, the ledger is coupled with smart contracts, which enable the automation of transactions associated with agreements between the parties of a financial or commercial nature. The coupling of smart contracts and ledgers provides the technological background for very innovative application areas, such as Decentralized Autonomous Organizations (DAOs), Initial Coin Offerings (ICOs), and Decentralized Finance (DeFi), which propelled blockchains beyond cryptocurrencies that were the only focus of first generation blockchains, such as Bitcoin. However, the currently used implementation of smart contracts as arbitrary programming constructs has made them susceptible to dangerous bugs that can be exploited maliciously and has moved their semantics away from that of legal contracts. We propose here to recompose the split and recover the reliability of databases by formalizing a notion of contract modeled as a finite-state automaton with well-defined computational characteristics derived from encoding in terms of allocations of resources to actors, as an alternative to the approach based on programming. To complete the work, we use temporal logic as the basis for an abstract query language that is effectively suited to the historical nature of the information kept in the ledger.
Performance Considerations for Quantum Radar

Quantum radar takes advantage of quantum correlations. Detection concepts have been devised to facilitate a gain which exceeds the gain of a classical radar. Quantum radar can use squeezed microwave states and benefit from the photon entanglement. Low power operation is required for this benefit. In the contribution we investigate aspects for modeling of quantum radar relevant to the quantum radar's performance.
Kinematics Analysis and Trajectory Planning Simulation of Manipulator Based on AUBO-I5

In this paper, AUBO-I5 manipulator is used for kinematics analysis and trajectory planning. Firstly, the linkage coordinate system of AUBO-I5 manipulator is established by M-D-H method, and the homogeneous transformation matrix between corresponding coordinate systems is calculated. The kinematic modeling and analysis of the manipulator are carried out and the mapping relationship between joint space and Cartesian space is obtained. According to the principle of forward and inverse kinematics, the five-order interpolation trajectory planning of the manipulator is completed. The kinematics modeling and analysis can be divided into the solving process of forward and inverse kinematics. Forward kinematics mainly studies the process of mapping joint space to Cartesian space, that is, the pose information of the terminal axis coordinate system in the base coordinate system is calculated according to the joint Angle of the mechanical arm. The inverse kinematics mainly studies the process of mapping cartesian space to joint space, that is, given the pose information of the end-effector, the joint angles of the manipulator are calculated. The trajectory planning of the robotic arm is based on the forward and inverse kinematics calculation, which carries out the trajectory planning of the robotic arm from the starting point to the target point.
Scrum Framework Based on Agile Methodology in Software Development and Management

With the advancement in technology, the demands for software has also increased. Companies are applying different methods to increase performance and business-related profits. The methodology employed in software development is software development life cycle (SDLC), but with the developing technology and growing market, the software-related requirements changes rapidly. So, another methodology called “Agile” came into existence. This is an improved version of SDLC which increases speed, flexibility, and responsiveness in software development. Scrum is a framework, that is based on Agile methodology, that follows a repetitive and incremental procedure. This paper starts with the introduction of Agile methodology and scrum, followed by procedure and description of Scrum values.
A Theoretical Study on the Genuinely Effective Technology Application in English Language Teaching for Teachers and Students

It is strongly believed that advanced technology has created considerable influences on fields generally and English language teaching and learning particularly. Therefore, a myriad of latest applications, modern devices, and inventive soft wares have flooded the educational market for the last two decades, especially during the time the whole world has suffered from the serious effects of Coronavirus. This paper mainly focuses on the strengths and weaknesses of technology integration in English language teaching and suggesting some practical solutions to overcome the difficulties.
Text Extraction from Hoardings by Hybrid Model

There are various techniques available to detect and extract the text from hoardings. Still it is a challenging task to detect text from images of various sizes, orientation, illuminations and color. With a view to improve on these, a hybrid method of text extraction and detection is proposed. The proposed method uses a symmetry features like Mutual Magnitude Symmetry (MMS), Mutual Direction Symmetry (MDS) and Gradient Vector Symmetry (GVS) to identify text pixel candidates from natural scenes. The proposed method is tested on different datasets like ICDAR, CUTE 80 and also images from mobile phones. Implementation of MMS, MDS, and GVS methods on above datasets has been carried out. Text extraction from hoardings in ICDAR is giving 74% accuracy, CUTE80 is giving 76% and on mobile images 83% of accuracy is achieved.
Advanced Multimedia Platform Based on Big Data and Artificial Intelligence Improving Cybersecurity

The proposed work describes the design of a multimedia platform managing users and implementing cybersecurity. The paper describes in details the use cases of the whole platform embedding Big Data and artificial intelligence (AI) engine predicting network attacks. The platform has been tested by Tree Ensemble algorithm classifying and predicting anomalous server logs of possible attacks. The data logs are collected in Cassandra Big Data System enabling the AI training model. The work has been developed within the framework of a research industry project.
Authorization for Access in Fog Radio Access Networks

As a novel networking paradigm, Fog radio access networks (F-RANs) have gained great attention due to the advantages such as multiple transmission modes, interference prevention, and edge caching. But security guarantee in F-RANs is a vital problem where illegal users may access to F-RANs without proper authorization. To this end, we design an authorization mechanism using attribute based encryption (ABE) to solve the data security issue of edge caching in F-RANs. Through theoretical analysis, we prove that our proposed mechanism is secure. Extensive simulations are carried out to evaluate the performance of our proposed mechanism by comparing state-of-the-art mechanisms. The results show that our proposed mechanism can solve the authorization problem in F-RANs and performs better in terms of computing consumption.
Android based application for visually impaired using deep learning approach

&lt;span&gt;People with visually impaired had difficulties in doing activities related to environment, social and technology. Furthermore, they are having issues with independent and safe in their daily routine. This research propose deep learning based visual object recognition model to help the visually impaired people in their daily basis using the android application platform. This research is mainly focused on the recognition of the money, cloth and other basic things to make their life easier. The convolution neural network (CNN) based visual recognition model by TensorFlow object application programming interface (API) that used single shot detector (SSD) with a pre-trained model from Mobile V2 is developed at Google dataset. Visually impaired persons capture the image and will be compared with the preloaded image dataset for dataset recognition. The verbal message with the name of the image will let the blind used know the captured image. The object recognition achieved high accuracy and can be used without using internet connection. The visually impaired specifically are largely benefited by this research.&lt;/span&gt;
Using Bayesian Networks to Support Managing Technological Risk on Software Projects

Risk management is essential in software project management. It includes activities such as identifying, measuring and monitoring risks. The literature presents different approaches to support software risk management. In particular, the researchers popularly used Bayesian Networks because they can be learned from data or elicited from domain experts. Even though the literature presents many Bayesian networks (BN) for software risk management, none focus on technological risk factors. Given this, this paper presents a BN for managing risks of software projects and the results of a static validation performed through a focus group with eight practitioners. As a result, the practitioners agreed that our proposed to manage technological risks of software projects using BN is valuable and easy to use. Given the successful results, we concluded that the proposed solution is promising.
Principled Interpolation in Normalizing Flows

Generative models based on normalizing flows are very successful in modeling complex data distributions using simpler ones. However, straightforward linear interpolations show unexpected side effects, as interpolation paths lie outside the area where samples are observed. This is caused by the standard choice of Gaussian base distributions and can be seen in the norms of the interpolated samples as they are outside the data manifold. This observation suggests that changing the way of interpolating should generally result in better interpolations, but it is not clear how to do that in an unambiguous way. In this paper, we solve this issue by enforcing a specific manifold and, hence, change the base distribution, to allow for a principled way of interpolation. Specifically, we use the Dirichlet and von Mises-Fisher base distributions on the probability simplex and the hypersphere, respectively. Our experimental results show superior performance in terms of bits per dimension, Fréchet Inception Distance (FID), and Kernel Inception Distance (KID) scores for interpolation, while maintaining the generative performance.
Representation-Learning-Based Fusion Model for Scene Classification Using Convolutional Neural Network (CNN) and Pre-trained CNNs as Feature Extractors

Scene classification plays a crucial role in the travel and tourism industry in promoting travel. In this paper, we propose a fusion approach based on representation learning to perform scene classification using a basic convolutional neural network (CNN) and multiple pre-trained CNNs as feature extractors. We have used the Oliva and Torralba Scene (OT-Scene) dataset consisting of scene images of eight different categories. In this proposed approach we extract image representations from deep learning models such as basic CNN and pre-trained CNN, namely VGG19, MobileNetV2, and RestNet50. We then fuse these representations from several models into different combinations to achieve a more generalized representation or feature vectors. The fused feature vectors and the individual feature vectors are then fed into the artificial neural network (ANN), support vector machine (SVM), and random forest (RF) classifiers separately to compare the results. The results showed the highest scene classification accuracy of 97.4% from specific fused combinations.
Combinatorial and rotational quantum abstract detecting systems

Abstract Quantum abstract detecting systems (QADS) were introduced as a common framework for the study and design of detecting algorithms in a quantum computing setting. In this paper, we introduce new families of such QADS, known as combinatorial and rotational, which, respectively, generalize detecting systems based on single qubit controlled gates and on Grover’s algorithm. We study the algorithmic closure of each family and prove that some of these QADS are equivalent (in the sense of having the same detection rate) to others constructed from tensor product of controlled operators and their square roots. We also apply the combinatorial QADS construction to a problem of eigenvalue decision, and to a problem of phase estimation.
Processing GIS Data Using Decision Trees and an Inductive Learning Method

This paper extends recent work on spatial data mining, with another application of the classification techniques, namely with the Decision tree classifier algorithm.Spatial data mining represents a various and investigated domain because huge amounts of spatial data have been collected, ranging from remote sensing to geographical information system and computer cartography.In this work we used the Weka tool to implement the C4.5 (Quinlan) Decision tree algorithm on a dataset of Geographic Information System (GIS), data collection called Cadastre formed by a parcel plan from the Dolj district of Romania.The results of the experiments highlight several advantages and also some disadvantages of Decision tree in context of spatial data mining, with a favorable accuracy.
H3D-Net: Few-shot high-fidelity 3D head reconstruction

This work has been partially funded by the Spanish government with the projects MoHuCo PID2020-120049RBI00, DeeLight PID2020-117142GB-I00 and Maria de Maeztu Seal of Excellence MDM-2016-0656, and by the Government of Catalonia under 2017 DI 028.