Reviewerly/exHarmony huggingface dataset:
Rapid Assisted Visual Search

Designing useful human-AI interaction for clinical workflows remains challenging despite the impressive performance of recent AI models. One specific difficulty is a lack of successful examples demonstrating how to achieve safe and efficient workflows while mitigating AI imperfections. In this paper, we present an interactive AI-powered visual search tool that supports pathologists in cancer assessments. Our evaluation with six pathologists demonstrates that it can 1) reduce time needed with maintained quality, 2) build user trust progressively, and 3) learn and improve from use. We describe our iterative design process, model development, and key features. Through interviews, design choices are related to the overall user experience. Implications for future human-AI interaction design are discussed with respect to trust, explanations, learning from use, and collaboration strategies.
Eternal adiabaticity in quantum evolution

We iteratively apply a recently formulated adiabatic theorem for the strong-coupling limit in finite-dimensional closed and open quantum systems. This allows us to improve approximations to a perturbed dynamics, beyond the standard approximation based on quantum Zeno dynamics and adiabatic elimination. The effective generators describing the approximate evolutions are endowed with the same block structure as the unperturbed part of the generator, and exhibit adiabatic evolutions. This iterative adiabatic theorem reveals that adiabaticity holds eternally, that is, the system evolves within each eigenspace of the unperturbed part of the generator, with an error bounded by $O(1/\ensuremath{\gamma})$ uniformly in time, where $\ensuremath{\gamma}$ is the strength of the unperturbed part of the generator. We prove that the iterative adiabatic theorem reproduces Bloch's perturbation theory in the unitary case, and is therefore a full generalization to open systems. We furthermore prove the equivalence of the Schrieffer-Wolff and des Cloizeaux approaches in the unitary case and generalize both to arbitrary open systems, showing that they share the eternal adiabaticity, and providing explicit error bounds. Finally we discuss the physical structure of the effective adiabatic generators and show that ideal effective generators for open systems do not exist in general.
Domain randomization for neural network classification

Abstract Large data requirements are often the main hurdle in training neural networks. Convolutional neural network (CNN) classifiers in particular require tens of thousands of pre-labeled images per category to approach human-level accuracy, while often failing to generalized to out-of-domain test sets. The acquisition and labelling of such datasets is often an expensive, time consuming and tedious task in practice. Synthetic data provides a cheap and efficient solution to assemble such large datasets. Using domain randomization (DR), we show that a sufficiently well generated synthetic image dataset can be used to train a neural network classifier that rivals state-of-the-art models trained on real datasets, achieving accuracy levels as high as 88% on a baseline cats vs dogs classification task. We show that the most important domain randomization parameter is a large variety of subjects, while secondary parameters such as lighting and textures are found to be less significant to the model accuracy. Our results also provide evidence to suggest that models trained on domain randomized images transfer to new domains better than those trained on real photos. Model performance appears to remain stable as the number of categories increases.
ICT Competency of Mathematics Teachers at Secondary Schools of Nepal

This study aimed to investigate ICT competency of mathematics teachers at secondary schools of Nepal. A cross-sectional survey design was deployed among 336 secondary school teachers of Nepal. The data was analyzed by Mann Whitney U, Kruskal Wallis H, and multiple linear regression. Result showed that teachers’ ICT competency level was found to be proficient in the fundamental concept of computers and the use of Internet. In contrast, it was found to be developing-level in software and hardware. Statistically significant results were found in competencies with respect to age, type of school, culture, and district. Additionally, own laptop, Internet use, work experience, knowledge of software and hardware were significant predictors for ICT competency of teachers. The overall findings clarify that ICT enhancement programs are needed for mathematics teachers at secondary schools in Nepal.
ELITR Multilingual Live Subtitling: Demo and Strategy

This paper presents an automatic speech translation system aimed at live subtitling of conference presentations.We describe the overall architecture and key processing components.More importantly, we explain our strategy for building a complex system for endusers from numerous individual components, each of which has been tested only in laboratory conditions.The system is a working prototype that is routinely tested in recognizing English, Czech, and German speech and presenting it translated simultaneously into 42 target languages.
A Registration Framework for the Comparison of Video and Optical See-Through Devices in Interactive Augmented Reality

In this paper, we designed a registration framework that can be used to develop augmented reality environments, where all the real (including the users) and virtual elements are co-localized and registered in a common reference frame. The software is provided together with this paper, to contribute to the research community. The developed framework allows us to perform a quantitative assessment of interaction and egocentric perception in Augmented Reality (AR) environments. We assess perception and interaction in the peripersonal space through a 3D blind reaching task in a simple scenario and an interaction task in a kitchen scenario using both video (VST) and optical see-through (OST) head-worn technologies. Moreover, we carry out the same 3D blind reaching task in real condition (without head-mounted display and reaching real targets). This provides a baseline performance with which to compare the two augmented reality technologies. The blind reaching task results show an underestimation of distances with OST devices and smaller estimation errors in frontal spatial positions when the depth does not change. This happens with both OST and VST devices, compared with the real-world baseline. Such errors are compensated in the interaction kitchen scenario task. Thanks to the egocentric viewing geometry and the specific required task, which constrain the position perception on a table, both VST and OST have comparable and effective performance. Thus, our results show that such technologies have issues, though they can be effectively used in specific real tasks. This does not allow us to choose between VST and OST devices. Still, it provides a baseline and a registration framework for further studies and emphasizes the specificity of perception in interactive AR.
Combating Cybercrime: Economic and Legal Aspects

Cybercrime threatens the national security of different countries around the world. The growth of cyberattacks destabilizes the international order and disrupts the normal functioning of international relations. The purpose of the academic paper is to analyze the causes and economic consequences of the level of cybercrime in the world and to identify modern legal arrangements to combat cybercrime. In order to achieve the purpose outlined, the following methods have been used, namely: the method of comparison, analysis, element-theoretical method, method of generalization and analogy. It has been established that the level of cybercrime in the world and the economic consequences of its impact tend to increase. It is estimated that in 2020 the total cost of cybercrime and cybersecurity will exceed one trillion US dollars, which is more than 1% of world gross domestic product. The reasons have been determined why the number of cybercrimes are increasing (electronization and computerization of most industries, public sector; low level of operational cooperation; inconsistency of legal policy with the realities of cybercrime; development of cyber-attack mechanism; modernization of cybercrime; obstacles to international cooperation and so forth). The cause and effect interrelationship between the level of cybercrime, cybersecurity and legal methods of counteraction in different countries of the world has been proven. Three interrelated ways of the legal mechanism of counteraction to cybercrime have been offered, namely: the general, organizational and preventive ones. The expediency of international cooperation in the development of global strategies and other measures to combat cybercrime has been emphasized
What Should Conceptual Engineering Be All About?

Conceptual engineering is commonly characterized as the method for assessing and improving our representational devices. Little has been said, however, on how best to construe these representational devices-in other words, on what conceptual engineering should be all about. This paper tackles this problem with a basic strategy: First, by presenting a taxonomy of the different possible subject matters for conceptual engineering; then, by comparatively assessing them and selecting the most conducive one with a view to making conceptual engineering an actionable method, that is, a method that can be applied effectively and consistently to specific case studies. The outcome is that conceptual engineering should be all about concepts on pain of pragmatic inconsistencies otherwise.
Growing synthetic data through differentially-private vine copulas

Abstract In this work, we propose a novel approach for the synthetization of data based on copulas, which are interpretable and robust models, extensively used in the actuarial domain. More precisely, our method COPULA-SHIRLEY is based on the differentially-private training of vine copulas, which are a family of copulas allowing to model and generate data of arbitrary dimensions. The framework of COPULA-SHIRLEY is simple yet flexible, as it can be applied to many types of data while preserving the utility as demonstrated by experiments conducted on real datasets. We also evaluate the protection level of our data synthesis method through a membership inference attack recently proposed in the literature.
Comparative Analysis of Image Classification Algorithms for Face Mask Detection

Background: The COVID-19 pandemic remains a problem in 2021. Health protocols are needed to prevent the spread, including wearing a face mask. Enforcing people to wear face masks is tiring. AI can be used to classify images for face mask detection. There are a lot of image classification algorithm for face mask detection, but there are still no studies that compare their performance.Objective: This study aims to compare the classification algorithms of classical machine learning. They are k-nearest neighbors (KNN), support vector machine (SVM), and a widely used deep learning algorithm for image classification which is convolutional neural network (CNN) for face masks detection.Methods: This study uses 5 and 3 cross-validation for assessing the performance of KNN, SVM, and CNN in face mask detection.Results: CNN has the best average performance with the accuracy of 0.9683 and average execution time of 2,507.802 seconds for classifying 3,725 faces with mask and 3,828 faces without mask images.Conclusion: For a large amount of image data, KNN and SVM can be used as temporary algorithms in face mask detection due to their faster execution times. At the same time, CNN can be trained to form a classification model. In this case, it is advisable to use CNN for classification because it has better performance than KNN and SVM. In the future, the classification model can be implemented for automatic alert system to detect and warn people who are not wearing face masks.
Unmanned Aerial Vehicle for Cleaning and Firefighting Purposes

Fire incidents have been a very common phenomenon for the past few years in Bangladesh. The proposal will be better and faster than any other existing traditional technologies. Again, this can be also used for cleaning high rise buildings, which will reduce hazards of the labors. The project is divided into four essential phases, frame design, flight control system, calibration and PID tuning, and water spraying system. The frame is designed using the "SolidWorks 2018". The components are chosen by calculating the ratings of each component according to requirement. In order to make the drone stable, proper calibration of each sensors is done along with roll and pitch rates. All of these are done using "Mission Planner" software. A pump of 16 liters/minute flow rate with a water hose gun is used for spraying at a target.
Public Libraries and Development across Sub-Saharan Africa: Overcoming a Problem of Perception

Abstract Public libraries and development organizations share many common goals that make them strong potential partners in the Global South. In spite of these commonalities, libraries are often overlooked as development partners. This is unfortunate because these partnerships could allow librarians to more fully and effectively participate in the development of their communities. This paper explores strategies that libraries might employ to make themselves more visible to development organizations. It does so through analysis of interviews with practitioners who have funded or implemented development projects across countries in Africa. We find that these practitioners are open to working more with libraries, but that it will take a lot of work on the part of public libraries to accomplish broad shifts in perception amongst development agencies. Collecting and openly sharing data on the output and impact of public libraries is a first step in this work, but these efforts must also be augmented through increased marketing and networking by libraries. Libraries need to build internal capacity to use data to drive advocacy efforts and attract funding from development donors.
Error mitigation on a near-term quantum photonic device

Photon loss is destructive to the performance of quantum photonic devices and therefore suppressing the effects of photon loss is paramount to photonic quantum technologies. We present two schemes to mitigate the effects of photon loss for a Gaussian Boson Sampling device, in particular, to improve the estimation of the sampling probabilities. Instead of using error correction codes which are expensive in terms of their hardware resource overhead, our schemes require only a small amount of hardware modifications or even no modification. Our loss-suppression techniques rely either on collecting additional measurement data or on classical post-processing once the measurement data is obtained. We show that with a moderate cost of classical post processing, the effects of photon loss can be significantly suppressed for a certain amount of loss. The proposed schemes are thus a key enabler for applications of near-term photonic quantum devices.
Consistent Multiple Graph Embedding for Multi-View Clustering

Graph-based multi-view clustering aiming to obtain a partition of data across multiple views, has received considerable attention in recent years. Although great efforts have been made for graph-based multi-view clustering, it is still challenging to fuse characteristics from various views to learn a common representation for clustering. In this paper, we propose a novel Consistent Multiple Graph Embedding Clustering framework (CMGEC). Specifically, a multiple graph auto-encoder (M-GAE) is designed to flexibly encode the complementary information of multi-view data using a multi-graph attention fusion encoder. To guide the learned common representation maintaining the similarity of the neighboring characteristics in each view, a Multi-view Mutual Information Maximization module (MMIM) is introduced. Furthermore, a graph fusion network (GFN) is devised to explore the relationship among graphs from different views and provide a common consensus graph needed in M-GAE. By jointly training these models, the common representation can be obtained, which encodes more complementary information from multiple views and depicts data more comprehensively. Experiments on three types of multi-view datasets demonstrate CMGEC outperforms the state-of-the-art clustering methods.
BertGCN: Transductive Text Classification by Combining GCN and BERT

In this work, we propose BertGCN, a model that combines large scale pretraining and transductive learning for text classification. BertGCN constructs a heterogeneous graph over the dataset and represents documents as nodes using BERT representations. By jointly training the BERT and GCN modules within BertGCN, the proposed model is able to leverage the advantages of both worlds: large-scale pretraining which takes the advantage of the massive amount of raw data and transductive learning which jointly learns representations for both training data and unlabeled test data by propagating label influence through graph convolution. Experiments show that BertGCN achieves SOTA performances on a wide range of text classification datasets. Code is available at https://github.com/ZeroRin/BertGCN.
Post-Quantum Cryptosystem NTRUEnCrypt and Its Advantage over Pre – Quantum Cryptosystem RSA

Cryptography is inextricably linked to the transfer of data, and in addition to ensuring user authorization; it is designed to guarantee the integrity of the transmitted information and its confidentiality. The cryptographic system NTRUEncrypt is able to provide the necessary level of security at an extremely low cost, while possessing high speeds and low memory requirements. This is the main reason for the attractiveness of the NTRUEncrypt cryptosystem and its widespread use at present. The goal of this work is to develop a software application that implements the NTRUEncrypt cryptosystem in a modern high level C # programming language.
Global Minima of Overparameterized Neural Networks

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 27 December 2019Accepted: 10 February 2021Published online: 13 May 2021Keywordsoptimization, landscape, manifold, eigenvalues of the Hessian, overparameterizationAMS Subject Headings68T99, 51M99Publication DataISSN (online): 2577-0187Publisher: Society for Industrial and Applied MathematicsCODEN: sjmdaq
SimJEB: Simulated Jet Engine Bracket Dataset

Abstract This paper introduces the Simulated Jet Engine Bracket Dataset (SimJEB) [WBM21]: a new, public collection of crowdsourced mechanical brackets and accompanying structural simulations. SimJEB is applicable to a wide range of geometry processing tasks; the complexity of the shapes in SimJEB offer a challenge to automated geometry cleaning and meshing, while categorical labels and structural simulations facilitate classification and regression (i.e. engineering surrogate modeling). In contrast to existing shape collections, SimJEB's models are all designed for the same engineering function and thus have consistent structural loads and support conditions. On the other hand, SimJEB models are more complex, diverse, and realistic than the synthetically generated datasets commonly used in parametric surrogate model evaluation. The designs in SimJEB were derived from submissions to the GrabCAD Jet Engine Bracket Challenge: an open engineering design competition with over 700 hand‐designed CAD entries from 320 designers representing 56 countries. Each model has been cleaned, categorized, meshed, and simulated with finite element analysis according to the original competition specifications. The result is a collection of 381 diverse, high‐quality and application‐focused designs for advancing geometric deep learning, engineering surrogate modeling, automated cleaning and related geometry processing tasks.
Automatic Detection of Fake Profile Using Machine Learning on Instagram

With the arrival of the Internet and social media, at the same time as masses of humans have benefitted from the full-size reassets of records available, there was an full-size boom with inside the upward push of cyber-crimes, mainly targeted closer to women. According to a 2019 file with inside the Economics Times, India has witnessed a 457% upward push in cybercrime with inside the 5 years span among 2011 and 2016. Most speculate that that is because of effect of social media inclusive of Facebook, Instagram and Twitter on our day by day lives. While those simply assist in growing a legitimate social network, advent of consumer debts in those websites normally desires simply an email-id. A actual lifestyles man or woman can create more than one fake IDs and for this reason impostors can effortlessly be made. Unlike the actual international state of affairs in which more than one policies and guidelines are imposed to become aware of oneself in a completely unique manner (as an instance at the same time as issuing one’s passport or driver’s license), with inside the digital international of social media, admission does now no longer require this kind of checks. In this paper, we study the one-of-a-kind debts of Instagram, specifically and try and verify an account as fake or actual the use of Machine Learning strategies specifically Logistic Regression and Random Forest Algorithm.
Sentiment Analysis of COVID-19 Pandemic on the Stock Market

COVID-19 is a dreadful infectious disease, morphed into an economic crisis causing extensive and longstanding ramifications across global markets. Investors continue to hear about COVID-19 and its impact in one corner of the globe or the other for a long time. Though the effects of COVID19 started in December 2019 in Wuhan, China, global markets did not respond actively till W.H.O officially declared on March 11, 2020, that the COVID19 outbreak is a global pandemic. These multi-channel events have eroded investor sentiment, tanking the global stock markets. This article uses a machine learning approach to Twitter to analyze and follow investor sentiment that has guided the market to the new low during the first 150 days of the COVID-19 era. The only respite for recovery of financial markets is the lowering of COVID-19 infected cases for the time being till a vaccine is developed for the virus.
Porting and Execution of Anomalies Detection Models on Embedded Systems in IoT

In the Industry 4.0 era, Microcontrollers (MCUs) based tiny embedded sensor systems have become the sensing paradigm to interact with the physical world. In 2020, 25.6 billion MCUs were shipped, and over 250 billion MCUs are already operating in the wild. Such low-power, low-cost MCUs are being used as the brain to control diverse applications and soon will become the global digital nervous system. In an Industrial IoT setup, such tiny MCU-based embedded systems are equipped with anomaly detection models and mounted on production plant machines for monitoring the machine's health/condition. These models process the machine's health data (from temperature, RPM, vibration sensors) and raise timely alerts when it predicts/detects data patterns that show deviations from the normal operation state.