Reviewerly/exHarmony huggingface dataset:
AttnGAN: Realistic Text-to-Image Synthesis with Attentional Generative Adversarial Networks

In this paper, we propose a prototype design for manifold refinement to fine grained text-to-image generation by using Attentional Generative Adversarial Network (AttnGAN) We concentrate on creating realistic images from text descriptions. We have used a collection of Attentional Generative Adversarial Network layers that are able to correctly select the modal meaning at the word-level and sentence-level. Generative Adversarial Networks (GANs) prove to be fundamental structure for many design applications from Game design, Art, Science and Modelling applications. We use GANs for contrastive learning and as a information maximisation approach, and we do extensive research to find the further advancements in image generation. Our prototype is easy to implement and practical; choosing the most relevant word vectors and using those vectors to generate related image sub-regions. The prototype in its current state generates image designs only for the bird species to satisfy the claim for its image generation ability. With due consideration to findings of usability testing, the develpment team in future iterations of the application, hopes to improve the generated image resolution. They plan to provide a choice for created variety of images with further improvements to the image generation algorithm.
Textual Description Generation for Visual Content Using Neural Networks

Various methods in machine learning have noticeable use in generating descriptive text for images and video frames and processing them. This area has attracted the immense interest of researchers in past years. For text generation, various models contain CNN and RNN combined approaches. RNN works well in language modeling; it lacks in maintaining information for a long time. An LSTM language model can overcome this drawback because of its long-term dependency handling. Here, the proposed methodology is an Encoder-Decoder approach where VGG19 Convolution Neural Network is working as Encoder; LSTM language model is working as Decoder to generate the sentence. The model is trained and tested on the Flickr8K dataset and can generate textual descriptions on a larger dataset Flickr30K with the slightest modifications. The results are generated using BLEU scores (Bilingual Evaluation Understudy Score). A GUI tool is developed to help in the field of child education. This tool generates audio for the generated textual description for images and helps to search for similar content on the internet.
Reducing Misclassification Due to Overlapping Classes in Text Classification via Stacking Classifiers on Different Feature Subsets

Correct classification of customer support tickets or complaints can help companies to improve the quality of their services to the customers. One of the challenges in text classification is when certain classes tend to share the same vocabulary. This can result in misclassification by the machine learning algorithm used. The problem is worsened when the dataset is imbalanced. To address this issue, we propose a stacking algorithm based on combining different selected classifiers that operate on different feature subsets; depending on those features that tend to improve the recall and the precision of the overlapped classes. In our approach, first, we train different linear and non-linear classifiers on the full feature set. Second, we use the Chi2 test to determine the best feature set for all our pre-trained classifiers that improve the f1-score for the overlapped class(es). Finally, we train a two-layered stacked model composed of the best base learners obtained from the first step as layer-1 and combine it with a strong meta-learner for the second layer. The experimental results on a real-world dataset from a large IT organization and a public Consumer Complaint database show an improvement in the overall accuracy as well as a reduction in the misclassification rate for the overlapped classes.
User-Defined Smart Contracts Using Answer Set Programming

We present a novel approach to the creation of smart contracts that takes an existing legal document and allows it to be incrementally elaborated into a tested smart contract by domain experts. Smart contracts are currently built with compiled imperative languages, and suffer from lack of agility, elevated risks from errors and security flaws, and high development costs. This paper describes a smart editor that uses a declarative language (Answer Set Programming (ASP)) to represent the business logic of legal documents. The document is incrementally elaborated in a fixed sequence of steps beginning with an ontology discovery step that identifies the explicit and implicit artefacts and applicable constraints. This information is used to generate ASP representations which provide the foundation required for modelling the legal logic. Furthermore, we have achieved the verbalisation of rules built during modelling, and have developed a method of representing artefacts visually which allows logic modelling, model validation and program verification to be visual. During these steps, the original legal document is enhanced with additional embedded information, which results in a tested executable ASP program which can then be used as a smart contract if modifications are made to the blockchain smart contract infrastructure.
Two-mode squeezed state quantisation and semiclassical portraits

Quantisation with Gaussian type states offers certain advantages over other quantisation schemes, in particular, they can serve to regularise formally discontinuous classical functions leading to well defined quantum operators. In this work we define a squeezed state quantisation in two dimensions using several families of squeezed states for one- and two-mode configurations. The completeness relations of the squeezed states are exploited in order to tackle the quantisation and semiclassical analysis of a constrained position dependent mass model with harmonic potential. The effects of the squeezing parameters on the resulting operators and phase space functions are studied, and configuration space trajectories are compared between the classical and semiclassical models.
A Short Introduction To The Concept Of Symbolic Turiyam Matrix

Recently, Turiyam set is introduced to deal with data set beyond three-way fuzzy space. In this process a problem is addressed while precise representation of Turiyam attributes in matrix format for knowledge processing tasks. To resolve this issue, current paper defines concept of symbolic Turiyam matrix by using the symbolic Turiyam set concept. In addition, the paper illustrates several examples to clarify the algebraic structure of these matrices such as addition, multiplication, and symmetry of these matrices.
The effect of quality of education on disruptive innovations: a cross-country analysis

Building on the institutional theory, the study tests the effect of quality of education on disruptive innovations across countries. Fixed and mixed effect models were used to estimate a conceptual framework with 394 observations from a three-year panel data on 151 (42 advanced and 109 developing) countries. Results show that the quality of education is positively associated with disruptive innovations and positively influences quality labour force across countries. Also, the quality labour force mediates a positive association between the quality of education and disruptive innovations. While the developing economies are associated with disruptive innovations, the advanced economies do not. However, the quality of higher education institutions (HEIs) of developing economies is inadequate and unable to adequately supply the competitive human capital that can independently affect disruptive innovations. Thus, the study offers suggestions for policymakers in the institutions of developing economies.
Blockchain Based Smart Contract for Sealed- -Bid Auction

In this growing world, Internet has changed so much to an extent that it turned into a powerful tool in every aspects of our lives.E-auction is one of those things which helps the bidders to take part in an auction online over the air. In a sealed bid third parties need to pay an extra cost to help the buyers and sellers carry out their exchange without any hassle. But there can be a breach of trust by the third parties. Owners of the auction or the company that is auctioning can have direct entry to it when the auction is run on a decentralized platform. When the users auction off something on the chain, the smart contract takes control of the auctioned asset and thereafter it manages the bids associated. In this paper, we execute a smart contract for a verifiable sealed-bid auction on the Ethereum blockchain. The type of auction used is sealed-bid in which the bidders submit their bids privately and each bidder can participate only once. As per the biddings received, the highest bidder wins and pays the highest corresponding highest submitted bid. Additionally, before the auction ends the bidder can withdraw the bid after submitting it. In such a case the bidder will have another chance to place the bid. This smart contract implementation abides by the true essence of a sealed-bid, to be precise, no information about the biddings is leaked to the bidders except for the highest bid
A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning

While reinforcement learning (RL) algorithms are achieving state-of-the-art performance in various challenging tasks, they can easily encounter catastrophic forgetting or interference when faced with lifelong streaming information. In this article, we propose a scalable lifelong RL method that dynamically expands the network capacity to accommodate new knowledge while preventing past memories from being perturbed. We use a Dirichlet process mixture to model the nonstationary task distribution, which captures task relatedness by estimating the likelihood of task-to-cluster assignments and clusters the task models in a latent space. We formulate the prior distribution of the mixture as a Chinese restaurant process (CRP) that instantiates new mixture components as needed. The update and expansion of the mixture are governed by the Bayesian nonparametric framework with an expectation maximization (EM) procedure, which dynamically adapts the model complexity without explicit task boundaries or heuristics. Moreover, we use the domain randomization technique to train robust prior parameters for the initialization of each task model in the mixture; thus, the resulting model can better generalize and adapt to unseen tasks. With extensive experiments conducted on robot navigation and locomotion domains, we show that our method successfully facilitates scalable lifelong RL and outperforms relevant existing methods.
Pareto-Optimal Fairness-Utility Amortizations in Rankings with a DBN Exposure Model

In recent years, it has become clear that rankings delivered in many areas need not only be useful to the users but also respect fairness of exposure for the item producers. We consider the problem of finding ranking policies that achieve a Pareto-optimal tradeoff between these two aspects. Several methods were proposed to solve it; for instance a popular one is to use linear programming with a Birkhoff-von Neumann decomposition. These methods, however, are based on a classical Position Based exposure Model (PBM), which assumes independence between the items (hence the exposure only depends on the rank). In many applications, this assumption is unrealistic and the community increasingly moves towards considering other models that include dependences, such as the Dynamic Bayesian Network (DBN) exposure model. For such models, computing (exact) optimal fair ranking policies remains an open question. In this paper, we answer this question by leveraging a new geometrical method based on the so-called expohedron proposed recently for the PBM (Kletti et al., WSDM'22). We lay out the structure of a new geometrical object (the DBN-expohedron), and propose for it a Carathéodory decomposition algorithm of complexity $O(n^3)$, where n is the number of documents to rank. Such an algorithm enables expressing any feasible expected exposure vector as a distribution over at most n rankings; furthermore we show that we can compute the whole set of Pareto-optimal expected exposure vectors with the same complexity $O(n^3)$. Our work constitutes the first exact algorithm able to efficiently find a Pareto-optimal distribution of rankings. It is applicable to a broad range of fairness notions, including classical notions of meritocratic and demographic fairness. We empirically evaluate our method on the TREC2020 and MSLR datasets and compare it to several baselines in terms of Pareto-optimality and speed.
The Use of Digital Pathology and Artificial Intelligence in Histopathological Diagnostic Assessment of Prostate Cancer: A Survey of Prostate Cancer UK Supporters

There has been particular interest in the deployment of digital pathology (DP) and artificial intelligence (AI) in the diagnosis of prostate cancer, but little is known about the views of the public on their use. Prostate Cancer UK supporters were invited to an online survey which included quantitative and qualitative questions exploring views on the use of DP and AI in histopathological assessment. A total of 1276 responses to the survey were analysed (response rate 12.5%). Most respondents were supportive of DP (87%, 1113/1276) and of testing AI in clinical practice as a diagnostic adjunct (83%, 1058/1276). Respondents saw DP as potentially increasing workflow efficiency, facilitating research, education/training and fostering clinical discussions between clinician and patient. Some respondents raised concerns regarding data security, reliability and the need for human oversight. Among those who were unsure about AI, information was requested regarding its performance and others wanted to defer the decision to use it to an expert. Although most are in favour of its use, some are unsure, and their concerns could be addressed with more information or better communication. A small minority (&lt;1%) are not in favour of the testing of the use of AI in histopathology for reasons which are not easily addressed.
The interaction effect between source text complexity and machine translation quality on the task difficulty of NMT post-editing from English to Chinese: A multi-method study

Abstract This study explores the interaction effect between source text (ST) complexity and machine translation (MT) quality on the task difficulty of neural machine translation (NMT) post-editing from English to Chinese. When investigating human effort exerted in post-editing, existing studies have seldom taken both ST complexity and MT quality levels into account, and have mainly focused on MT systems used before the emergence of NMT. Drawing on process and product data of post-editing from 60 trainee translators, this study adopted a multi-method approach to measure post-editing task difficulty, including eye-tracking, keystroke logging, quality evaluation, subjective rating, and retrospective written protocols. The results show that: 1) ST complexity and MT quality present a significant interaction effect on task difficulty of NMT post-editing; 2) ST complexity level has a positive impact on post-editing low-quality NMT (i.e., post-editing task becomes less difficult when ST complexity decreases); while for post-editing high-quality NMT, it has a positive impact only on the subjective ratings received from participants; and 3) NMT quality has a negative impact on its post-editing task difficulty (i.e., the post-editing task becomes less difficult when MT quality goes higher), and this impact becomes stronger when ST complexity increases. This paper concludes that both ST complexity and MT quality should be considered when testing post-editing difficulty, designing tasks for post-editor training, and setting fair post-editing pricing schemes.
Generalization in transfer learning: robust control of robot locomotion

Abstract In this paper, we propose a set of robust training methods for deep reinforcement learning to transfer learning acquired in one control task to a set of previously unseen control tasks. We improve generalization in commonly used transfer learning benchmarks by a novel sample elimination technique, early stopping, and maximum entropy adversarial reinforcement learning. To generate robust policies, we use sample elimination during training via a method we call strict clipping. We apply early stopping, a method previously used in supervised learning, to deep reinforcement learning. Subsequently, we introduce maximum entropy adversarial reinforcement learning to increase the domain randomization during training for a better target task performance. Finally, we evaluate the robustness of these methods compared to previous work on simulated robots in target environments where the gravity, the morphology of the robot, and the tangential friction coefficient of the environment are altered.
An Improvised Random Forest Model for Breast Cancer Classification

Breast Cancer is considered as the most common cancer in females with high incidence rate. The evolution of modern facilities has helped in reducing the mortality rate, yet the incidence is still the highest among all cancers affecting women. Early diagnosis is a predominant factor for survival. Hence techniques to assist the current modalities are essential. Machine learning techniques have been used so as to produce better prediction and classification models which will aid in better and earlier disease diagnosis and classification. Random Forest is a supervised machine learning classifier that helps in better classification. Random Forests are applied to the Wisconsin breast cancer dataset and the performance of the classifier is evaluated for breast cancer classification. Here in this study an improvised random forest model which uses a cost sensitive learning approach for classification is proposed and it is found to have a better performance than the traditional random forest approach. The model gave an accuracy of 97.51%.
Mission-level Robustness with Rapidly-deployed, Autonomous Aerial Vehicles by Carnegie Mellon Team Tartan at MBZIRC 2020

For robotic systems to succeed in high risk, real-world situations, they have to be quickly deployable and robust to environmental changes, under-performing hardware, and mission subtask failures. These robots are often designed to consider a single sequence of mission events, with complex algorithms lowering individual subtask failure rates under some critical constraints. Our approach utilizes common techniques in vision and control, and encodes robustness into mission structure through outcome monitoring and recovery strategies. In addition, our system infrastructure enables rapid deployment and requires no central communication. This report also includes lessons in rapid field robotic development and testing. We developed and evaluated our systems through real-robot experiments at an outdoor test site in Pittsburgh, Pennsylvania, USA, as well as in the 2020 Mohamed Bin Zayed International Robotics Challenge. All competition trials were completed in fully autonomous mode without RTK-GPS. Our system placed fourth in Challenge 2 and seventh in the Grand Challenge, with notable achievements such as popping five balloons (Challenge 1), successfully picking and placing a block (Challenge 2), and dispensing the most water onto an outdoor, real fire with an autonomous UAV (Challenge 3).
Developing a model to evaluate and improve user experience with hand motions in virtual reality environments

In video games, the evaluation of the user experience (UX) mainly refers to two main groups of aspects, those that refer to the player that is mainly oriented to make the player feel good while playing and those that refer to the video game that is oriented to make the video game easy to understand and play. The aspects considered that are related to the player are engagement, enjoyment, and flow; the aspects related to video game, usability, and dependability. Virtual reality environments today have changed the paradigm in various fields of application, such as health, education, entertainment, among others. Therefore, it is important to observe the effects of handedness with hand movements in virtual reality environments. This work proposes a model to evaluate and improve the user experience considering player and video game aspects, taking into account handedness with hand movements in virtual reality environments. Player and video game aspects can be added to evaluations of the effect of handedness, especially in virtual reality environments, in order to know the user’s behavior in terms of skill, performance, and accuracy, among other features by using a particular hand to perform specific tasks. Next, a case study is presented with two groups of users using a virtual reality environment to perform several user tasks considering the dominant and non-dominant hand. By evaluating the user tasks it is possible to know the levels of engagement, enjoyment, motivation, and usability in a virtual reality environment. Finally, an analysis of results is presented in which several improvements of UX are presented.
Construction of genuinely entangled multipartite subspaces from bipartite ones by reducing the total number of separated parties

Construction of genuinely entangled multipartite subspaces with certain characteristics has become a relevant task in various branches of quantum information. Here we show that such subspaces can be obtained from an arbitrary collection of bipartite entangled subspaces under joining of their adjacent subsystems. In addition, it is shown that direct sums of such constructions under certain conditions are genuinely entangled. These facts are then used in detecting entanglement of tensor products of mixed states and constructing subspaces that are distillable across every bipartite cut, where for the former application we include an example with the analysis of genuine entanglement of a tripartite state obtained from two Werner states.