Reviewerly/exHarmony huggingface dataset:
Open Machine Translation for Low Resource South American Languages (AmericasNLP 2021 Shared Task Contribution)

This paper describes the team ("Tamalli")'s submission to AmericasNLP2021 shared task on Open Machine Translation for low resource South American languages.Our goal was to evaluate different Machine Translation (MT) techniques, statistical and neural-based, under several configuration settings.We obtained the second-best results for the language pairs "Spanish-Bribri", "Spanish-Asháninka", and "Spanish-Rarámuri" in the category "Development set not used for training".Our performed experiments will serve as a point of reference for researchers working on MT with low-resource languages.
Reversible Data Hiding Using Secure Image Transformation Technique

With the gigantic popularity of re-appropriating data in the path of the cloud, it’s essential towards the protection of data and authorizes the cloud employee to easily change the knowledge at the same time. Underneath such requests, Reversible Data Hiding in Encrypted İmages (RDH-EI) attracts associate with an ever-increasing variety of specialists’ decisions concerning the structure for RDH-EI upheld Reversible İmage Transformation (RIT). Two RDH methods, together with customary RDH conspire and unified putting in and scrambling arrangement, data received to insert watermark within the encoded image, which can fulfill numerous wants on image quality and big implanting limit individually.
Half-hour global solar radiation forecasting based on static and dynamic multivariate neural networks

Precise global solar radiation (GSR) measurements in a given location are very essential for designing and supervising solar energy systems. In the case of rarity or absence of these measurements, it is important to have a theoretical or empirical model to compute the GSR values. Therefore, the main goal of this work is to offer, to designers and engineers of solar energy systems, an appropriate and accurate way to predict the half-hour global solar radiation (HHGSR) time series from some available meteorological parameters (relative humidity, air temperature, wind speed, precipitation, and acquisition time vector in half-hour scale). For that purpose, two intelligent models are developed: the first one is a multivariate dynamic neural network with feedback connection, and the second is a multivariate static neural network. The database used to build these models was recorded in Agdal’s meteorological station in Marrakesh, Morocco, during the years of 2013 and 2014, and it was divided into two subsets. The first subset is used for training and validating the models, and the second subset is used for testing the efficiency and the robustness of the developed models. The obtained results, in terms of the statistical performance indicators, demonstrate the efficiency of the developed forecasting models to accurately predict the HHGSR parameter in the city of Marrakesh, Morocco.
Topology Generated Non-Fungible Tokens - Blockchain as infrastructure for a circular economy in architectural design

The paper presents a new digital infrastructure layer for buildings and architectural assets. The infrastructure layer consists of a combination of topology graphs secured on a decentralised ledger. The topology graphs organise non-fungible digital tokens which each represent and correspond to building components, and in the root of the graph to the building itself. The paper presents background research in the relationship of building representation in the form of graphs with topology, of both manifold and non manifold nature. In parallel we present and analyse the relationship between digital representation and physical manifestation of a building, and back again. Within the digital representations the paper analyses the securing and saving of information on decentralised ledger technologies (such as blockchain). We then present a simple sample of generating and registering a non-manifold topology graph on the Ethereum blockchain as an EC721 token, i.e. a digital object that is unique, all through the use of dynamo and python scripting connected with a smart contract on the Ethereum blockchain. Ownership of this token can then be transferred on the blockchain smart contracts. The paper concludes with a discussion of the possibilities that this integration brings in terms of material passports and a circular economy and smart contracts as an infrastructure for whole-lifecycle BIM and digitally encapsulates of value in architectural design.
An incremental path towards a safer OS kernel

Linux has become the de-facto operating system of our age, but its vulnerabilities are a constant threat to service availability, user privacy, and data integrity. While one might scrap Linux and start over, the cost of that would be prohibitive due to Linux's ubiquitous deployment. In this paper, we propose an alternative, incremental route to a safer Linux through proper modularization and gradual replacement module by module. We lay out the research challenges and potential solutions for this route, and discuss the open questions ahead.
Cracking the Quantum Advantage Threshold for Gaussian Boson Sampling

Here we challenge the quantum advantage claimed for the Gaussian Boson Sampling experiment by introducing an approximate polynomial-time algorithm. Our fourth-order approximation gives an accuracy comparable with that of the experiment.
Innovating with Confidence - Embedding AI Governance and Fairness in a Financial Services Risk Management Framework

An increasing number of financial services (FS) companies are adopting solutions driven by artificial intelligence (AI) to gain operational efficiencies, derive strategic insights, and improve customer engagement. However, the rate of adoption has been low, in part due to the apprehension around its complexity and self-learning capability, which makes auditability a challenge in a highly regulated industry. There is limited literature on how FS companies can implement the governance and controls specific to AI-driven solutions. AI auditing cannot be performed in a vacuum; the risks are not confined to the algorithm itself, but rather permeates the entire organization. Using the risk of unfairness as an example, this paper will introduce the overarching governance strategy and control framework to address the practical challenges in mitigating risks AI introduces. With regulatory implications and industry use cases, this framework will enable leaders to innovate with confidence.
ROS-Based GUI Controlled Robot for Indoor Mapping and Navigation

Path planning is one of the key tasks in robotics, which is used for navigation of the robot. Any mobile robot should possess the ability to navigate freely in its environment. This is important to avoid collision with obstacles, move to a certain point as per the function, etc. This research paper reviews the design of an autonomous mobile robot controlled by a graphical user interface(GUI) which possesses the ability to autonomously go to a room in a created environment when it receives the command through GUI. To achieve this, a simple robot is designed in solid works and constructed which is used for testing this application. This function can be applied to use in complex robots such as a service robot, resto bot and firefighting robot. Mapping and autonomous navigation are performed using the linorobot stack. The robot is developed in Linux (Ubuntu 16.04) using the robotic operating system (ROS). The linorobot navigation stack and GUI are separately used till date. In this research work, the results are forwarded when both navigation and GUI are integrated. The performance of the robot is evaluated by testing the ability of the robot to avoid obstacles and reach the destination in different scenarios. The discussion of the scenarios and the performance of the robot are discussed in experimental results.
Multi-Tier Student Performance Evaluation Model (MTSPEM) with Integrated Classification Techniques for Educational Decision Making

In present decade, many Educational Institutions use classification techniques and Data mining concepts for evaluating student records.Student Evaluation and classification is very much important for improving the result percentage.Hence, Educational Data Mining based models for analyzing the academic performances have become an interesting research domain in current scenario.With that note, this paper develops a model called Multi-Tier Student Performance Evaluation Model (MTSPEM) using single and ensemble classifiers.The student data from higher educational institutions are obtained and evaluated in this model based on significant factors that impacts greater manner in student's performances and results.Further, data preprocessing is carried out for removing the duplicate and redundant data, thereby, enhancing the results accuracy.The multi-tier model contains two phases of classifications, namely, primary classification and secondary classification.The First-Tier classification phase uses Naive Bayes Classification, whereas the second-tier classification comprises the Ensemble classifiers such as Boosting, Stacking and Random Forest (RF).The performance analysis of the proposed work is established for calculating the classification accuracy and comparative evaluations are also performed for evidencing the efficiency of the proposed model.
Barricades in the Adoption of Block-Chain Technology in Supply Chain Management: Challenges and Benefits

Blockchain technology, as a distributed digital ledger technology that ensures traceability, security, and transparency is displaying potential for easing some comprehensive supply chain problems. Scholars have started analyzing systematically the potential benefits and effects of block-chain on numerous activities of an organization. This paper presents the barricades in the adoption of blockchain technology in supply chain management. The potential benefits of blockchain adoption such as quality, cost, speed, transparency, durability, and immutability are also discussed in this paper. We present the early literature discussing the use of blockchain in the field of the supply chain to enhance accountability and transparency. This study explains the several mechanisms by which supply chain managers can prepare their organizational structure to adopt the latest technology. It further highlights the mechanisms to achieve supply chain objectives. Part of this paper also discusses how blockchains, a potentially disruptive solution that is on its early evolution, can overcome several potential barricades. Future research directions are proposed which can further provide insights into overcoming barriers and adoption of blockchain technology in the field of supply chain management.
E-Voting using Blockchain

Abstract A vote gives a citizen of a democratic country the power to elect a representative which forms a government which is of the people, by the people, and for the people. Voting forms one of the fundamental pillars of modern democracy. Conducting a transparent, verifiable, and unbiased election is a challenging task for the election commission. Earlier ballot papers were used for conducting elections but it has to face many issues like booth capturing or damaging of ballot papers of the booths where there were fewer chances of winning for a candidate. Currently, EVMs are used for conducting the election in India but there are many reports that EVMs are not fully tamperproof. EVM is not a universal acceptance across the world. Blockchain is a distributed tamperproof technology which can help in conducting election transparent and tamperproof. Here in this paper, an approach for voting using blockchain and Paillier encryption is proposed. It implies the tamperproof property of blockchain and additive homomorphic property of Paillier encryption to build a voting architecture that will make the election process trans-parent and tamperproof. Next, algorithms for registration, voting, and result declaration has been mentioned along with the results of Paillier encryption for voting.
Text and data mining of in-copyright works

How copyright law might be an impediment to text and data mining research.
Evolutionary Computation for Adaptive Quantum Device Design

Abstract As noisy intermediate‐scale quantum (NISQ) devices grow in number of qubits, determining good or even adequate parameter configurations for a given application, or for device calibration, becomes a cumbersome task. An evolutionary algorithm is presented here which allows for the automatic tuning of the parameters of any arrangement of coupled qubits, to perform a given task with high fidelity. The algorithm's use is exemplified with the generation of schemes for the distribution of quantum states and the design of multi‐qubit gates. The algorithm is demonstrated to converge very rapidly, yielding unforeseeable designs of quantum devices that perform their required tasks with excellent fidelities. Given these promising results, practical scalability, and application versatility, the approach has the potential to become a powerful technique to aid the design and calibration of NISQ devices.
Smart Glasses Implementation in Hospitals

Wearable Smart Glasses visualises information projected onto the lens, col-lects data over a wireless network, and uses a program to convert it into text or images to be transmitted to the display. This study discovers the usage of the products that will serve the medical industry, and as a result to design differ-ent products according to different uses, so to customize different prices and willingness of the targeted industry and people to purchase while meet the needs of more consumers. Therefore, the feasibility of using smart glasses in surgery was studied in this study, a technology acceptance model was used to analyze people's acceptance of the use of smart glasses in medical system. Mix mode analysis of the data was conducted through the explanatory study and the cross-sectional study, and relevant data were collected using an online questionnaire. The study found that surgeons using smart glasses could im-prove the success rate of surgery.
Gamification in the formation of digital skills of future teachers

Global digitalization requires changes in all spheres of human life, including education. Modern trends lead to the introduction of various innovative technologies into the educational process, including gamification, which provokes changes in approaches in order to train a new type of teacher. The stability of society and its future development largely depend on the quality of its preparation. In the context of the general digitalization of education, a future teacher must have certain digital skills that underlie digital literacy for successful professional activity. The purpose of this study is to describe an effective experience in the formation of digital skills by the classification of three groups and three levels when using the elements of gamification in teaching future teachers. In the work, general scientific methods were used: analysis of theoretical sources, collection of information, comparison, generalization, questionnaires, and the study of the products of educational activities of students. The study confirmed the effectiveness of the identified conditions for the successful formation of three groups of digital skills among students of educational institutions of higher and secondary vocational education, which showed an increase in their academic performance in the process of studying the disciplines under consideration and the level of their digital literacy.
Matching 3D Facial Shape to Demographic Properties by Geometric Metric Learning: A Part-Based Approach

Face recognition is a widely accepted biometric identifier, as the face contains a lot of information about the identity of a person. The goal of this study is to match the 3D face of an individual to a set of demographic properties (sex, age, BMI, and genomic background) that are extracted from unidentified genetic material. We introduce a triplet loss metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest. The metric learner is trained for multiple facial segments to allow a global-to-local part-based analysis of the face. To learn directly from 3D mesh data, spiral convolutions are used along with a novel mesh-sampling scheme, which retains uniformly sampled points at different resolutions. The capacity of the model for establishing identity from facial shape against a list of probe demographics is evaluated by enrolling the embeddings for all properties into a support vector machine classifier or regressor and then combining them using a naive Bayes score fuser. Results obtained by a 10-fold cross-validation for biometric verification and identification show that part-based learning significantly improves the systems performance for both encoding with our geometric metric learner or with principal component analysis.
Machine Teaching for Human Inverse Reinforcement Learning

As robots continue to acquire useful skills, their ability to teach their expertise will provide humans the two-fold benefit of learning from robots and collaborating fluently with them. For example, robot tutors could teach handwriting to individual students and delivery robots could convey their navigation conventions to better coordinate with nearby human workers. Because humans naturally communicate their behaviors through selective demonstrations, and comprehend others’ through reasoning that resembles inverse reinforcement learning (IRL), we propose a method of teaching humans based on demonstrations that are informative for IRL. But unlike prior work that optimizes solely for IRL, this paper incorporates various human teaching strategies (e.g. scaffolding, simplicity, pattern discovery, and testing) to better accommodate human learners. We assess our method with user studies and find that our measure of test difficulty corresponds well with human performance and confidence, and also find that favoring simplicity and pattern discovery increases human performance on difficult tests. However, we did not find a strong effect for our method of scaffolding, revealing shortcomings that indicate clear directions for future work.
Work-In-Progress-Teaching Invisible Phenomena and Virtual Experiments: Immersion or Augmentation?

This work-in-progress discusses Immersive Virtual Reality and Augmented Reality for teaching invisible phenomena and virtual science experiments in Materials Science. Immersion, coupled with 3D spatial interaction, is useful when the knowledge to be acquired is procedural, to create a sense of scale and to allow easy navigation within a large experimental space. Immersion is also beneficial to transport the learners into the world of the infinitesimally small, when the phenomena occur at atomic scale. Conversely, augmentation is better for experiencing phenomena that can be triggered by interacting with real objects. Deployment, affordability and use in actual teaching are also discussed.
Optimal static mutation strength distributions for the (1 <i>+ λ</i> ) evolutionary algorithm on OneMax

Most evolutionary algorithms have parameters, which allow a great flexibility in controlling their behavior and adapting them to new problems. To achieve the best performance, it is often needed to control some of the parameters during optimization, which gave rise to various parameter control methods. In recent works, however, similar advantages have been shown, and even proven, for sampling parameter values from certain, often heavy-tailed, fixed distributions. This produced a family of algorithms currently known as "fast evolution strategies" and "fast genetic algorithms".
When is the Chernoff Exponent for Quantum Operations Finite?

We consider the problem of testing two hypotheses of quantum operations in a setting of many uses where an arbitrary prior probability distribution is given. The Chernoff exponent for quantum operations is investigated to track the minimal average error probability of discriminating two quantum operations asymptotically. We answer the question, “When is the Chernoff exponent for quantum operations finite?” We show that either two quantum operations can be perfectly distinguished with finite uses, or the minimal discrimination error decays exponentially with respect to the number of uses asymptotically. That is, the Chernoff exponent is finite if and only if the quantum operations can not be perfectly distinguished with finite uses. This rules out the possibility of super-exponential decay of error probability. Upper bounds of the Chernoff exponent for quantum operations are provided.
Numerical estimation of reachable and controllability sets for a two-level open quantum system driven by coherent and incoherent controls

The article considers a two-level open quantum system, whose evolution is governed by the Gorini–Kossakowski– Lindblad–Sudarshan master equation with Hamiltonian and dissipation superoperator depending, correspondingly, on piecewise constant coherent and incoherent controls with constrained magnitudes. Additional constraints on controls’ variations are also considered. The problem is analyzed using Bloch parametrization of system’s density matrix. We adapt the section method for obtaining outer parallelepipedal and pointwise estimations of reachable and controllability sets in the Bloch ball via solving a number of problems for optimizing coherent and incoherent controls with respect to some objective criteria. The differential evolution and dual annealing optimization methods are used. The numerical results show how the reachable sets’ estimations depend on distances between the system’s initial states and the Bloch ball’s center point, final times, constraints on controls’ magnitudes and variations.
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning.

Large scale machine learning and deep models are extremely data-hungry. Unfortunately, obtaining large amounts of labeled data is expensive, and training state-of-the-art models (with hyperparameter tuning) requires significant computing resources and time. Secondly, real-world data is noisy and imbalanced. As a result, several recent papers try to make the training process more efficient and robust. However, most existing work either focuses on robustness or efficiency, but not both. In this work, we introduce GLISTER, a GeneraLIzation based data Subset selecTion for Efficient and Robust learning framework. We formulate GLISTER as a mixed discrete-continuous bi-level optimization problem to select a subset of the training data, which maximizes the log-likelihood on a held-out validation set. We then analyze GLISTER for simple classifiers such as gaussian and multinomial naive-bayes, k-nearest neighbor classifier, and linear regression and show connections to submodularity. Next, we propose an iterative online algorithm GLISTER-ONLINE, which performs data selection iteratively along with the parameter updates, and can be applied to any loss-based learning algorithm. We then show that for a rich class of loss functions including cross-entropy, hinge-loss, squared-loss, and logistic-loss, the inner discrete data selection is an instance of (weakly) submodular optimization, and we analyze conditions for which GLISTER-ONLINE reduces the validation loss and converges. Finally, we propose GLISTER-ACTIVE, an extension to batch active learning, and we empirically demonstrate the performance of GLISTER on a wide range of tasks including, (a) data selection to reduce training time, (b) robust learning under label noise and imbalance settings, and (c) batch-active learning with a number of deep and shallow models. We show that our framework improves upon the state of the art both in efficiency and accuracy (in cases (a) and (c)) and is more efficient compared to other state-of-the-art robust learning algorithms in case (b). The code for GLISTER is at: https://github.com/dssresearch/GLISTER.