Reviewerly/exHarmony huggingface dataset:
AI-Based Multilingual Interactive Exam Preparation

Our previous analysis on 26 languages which represent over 2.9 billion speakers and 8 language families demonstrated that cross-lingual automatic short answer grading allows students to write answers in exams in their native language and graders to rely on the scores of the system [1]. With lower deviations than 14% (0.72 points out of 5 points) on the corpus of the short answer grading data set of the University of North Texas [2], our natural language processing models show better performances compared to the human grader variability (0.75 points, 15%). In this paper we describe our latest analysis of the integration and application of a multilingual model in interactive training programs to optimally prepare students for exams. We present a multilingual interactive conversational artificial intelligence tutoring system for exam preparation. Our approach leverages and combines learning analytics, crowdsourcing and gamification to automatically allow us to evaluate and adapt the system as well as to motivate students and increase their learning experience. In order to have an optimal learning effect and enhance the user experience, we also tackle the challenge of explainability with the help of keyword extraction and highlighting techniques. Our system is based on Telegram since it can be easily integrated into massive open online courses and other online study systems and has already more than 400 million users worldwide [3].
Unfinished Paths—From Blockchain to Sustainability in Supply Chains

Blockchain technology has been promised as a solution to social and environmental issues in supply chains. The potential includes reduction of vulnerable party exploitation and avoiding environmentally harmful practices. Yet, it remains unclear how these potential improvements are created and whether blockchain can truly contribute. Therefore, this field study explores and identifies the mechanisms for blockchain technology to facilitate positive social and environmental impacts in supply chains. We applied an explorative qualitative research approach and interviewed blockchain technology implementers and practitioners that allowed a detailed analysis of this problem despite the scarcity of practice data. The results include the development of a middle-range theory that shows barriers and drivers of blockchain-based technologies in supply chains, introduces the concept of blockchain-enabled system, and outlines expected outcomes and impacts. We further identify four impact pathways that describe how blockchain-enabled system create positive impact: (voluntary) market mechanisms, plausibility checks, smart contracts and tokenisation, and peer-to-peer trust. The study contributes by providing insights into “how” blockchain-based technologies in supply chains can lead to social and environmental impacts. The study also furthers the discussion on blockchain technology’s role in supply chain implementation and addresses the yet unresolved problem of measuring the impact of such blockchain-enabled systems.
A Blockchain and Machine Learning based Framework for Efficient Health Insurance Management

Having a health insurance is important for everybody, bearing in mind the increasing medical costs. Medical emergencies can have a severe financial and emotional impact. However, the current insurance system is very expensive and the claim settlement process is excessively lengthy, making it tedious. This results in policyholders not being able to successfully make a claim with their insurance company. In this paper, we focus on developing a fast and cost-effective framework based on blockchain technology and machine learning for the health insurance industry. Blockchain is capable of removing all third-party organisations by forming a smart contract, making the entire process more smooth, secure, and efficient. The contract settles the claim on documents submitted by the claimant. A ridge regression model is used for computing the premiums optimally, based on the total amount claimed under the current policy tenure, along with several other factors. A random forest classifier is applied for predicting the risk that helps in the computation of risk-rated premium rebate.
Unseen Entity Handling in Complex Question Answering over Knowledge Base via Language Generation

Complex question answering over knowledge base remains as a challenging task because it involves reasoning over multiple pieces of information, including intermediate entities/relations and other constraints.Previous methods simplify the SPARQL query of a question into such forms as a list or a graph, missing such constraints as "filter" and "order_by", and present models specialized for generating those simplified forms from a given question.We instead introduce a novel approach that directly generates an executable SPARQL query without simplification, addressing the issue of generating unseen entities.We adapt large scale pre-trained encoder-decoder models and show that our method significantly outperforms the previous methods and also that our method has higher interpretability and computational efficiency than the previous methods.
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss

Recent works in self-supervised learning have advanced the state-of-the-art by relying on the contrastive learning paradigm, which learns representations by pushing positive pairs, or similar examples from the same class, closer together while keeping negative pairs far apart. Despite the empirical successes, theoretical foundations are limited -- prior analyses assume conditional independence of the positive pairs given the same class label, but recent empirical applications use heavily correlated positive pairs (i.e., data augmentations of the same image). Our work analyzes contrastive learning without assuming conditional independence of positive pairs using a novel concept of the augmentation graph on data. Edges in this graph connect augmentations of the same data, and ground-truth classes naturally form connected sub-graphs. We propose a loss that performs spectral decomposition on the population augmentation graph and can be succinctly written as a contrastive learning objective on neural net representations. Minimizing this objective leads to features with provable accuracy guarantees under linear probe evaluation. By standard generalization bounds, these accuracy guarantees also hold when minimizing the training contrastive loss. Empirically, the features learned by our objective can match or outperform several strong baselines on benchmark vision datasets. In all, this work provides the first provable analysis for contrastive learning where guarantees for linear probe evaluation can apply to realistic empirical settings.
Usability Testing of a VR Flight Training Program

Virtual reality (VR) flight training programs are being developed as a low-cost, highly realistic training option and tested to ensure user expectations for skills mastery are met. A usability pilot study was conducted in two experimental courses comparing the training effectiveness of 2D and VR simulation for flight training at a university in the Southwestern United States. The results indicated that system usability was significantly higher in the VR group. There was no significant difference between groups for perceived workload nor user experience. Although both groups reported low symptoms of simulator sickness, users in the VR group reported significantly higher levels of eye strain. Both groups found the simulations to be enjoyable and several users stated that the simulations were beneficial for learning flight maneuvers. The results of the pilot study demonstrated that overall, VR simulation is similar to 2D simulation for flight training in terms of usability and user satisfaction.
Breast Cancer Lesion Detection and Segmentation Based On Mask R-CNN

Breast cancer is an obsession that haunts all women. but early detection for it increases the cure rate, for attain this objective It is very important to create a system to diagnose suspicious masses. On the other hand, is a difficult task due to the fact that breast lumps vary in size and appearance. In this paper, we propose an automatic breast mass segmentation method based on the Mask RCNN model of deep learning using detectron2.our model is trained and testing using the public dataset INbreast. The proposed method achieved results with precision and F1 score 95.87 and 81.05 on INbreast dataset, respectively.
An efficient approach for detecting anomalous events in real‐time weather datasets

Abstract Event detection in real‐time is applied in diverse domains such as detection of fraudulent activities in commercial transactions, detection of faulty systems in industries, and so forth. Businesses and organizations benefit from the actionable information obtained through various techniques available for anomalous event detection. Real‐time event detection is nowadays handled through streaming data frameworks. Traditional approaches effectively handle event detection in real‐time but with more false positives, thus, resulting in false alarms. In this article, an efficient approach comprising two components, an offline model and an online event detection pipeline, is proposed to achieve minimum mean absolute error (MAE). An offline module is developed to investigate a variety of deep learning models that prove suitable for event detection in real‐time. The experiments conducted with PubNub sensors datasets demonstrate that the long short‐term memory unit of recurrent neural networks is the best suitable model for anomalous event detection. The online pipeline module is built using streaming data frameworks to predict the abnormal peaks. It is revealed through the experimental results that the proposed approach efficiently detects anomalous events in real‐time and also eliminates false positives.
A key management scheme using (p, q)-lucas polynomials in wireless sensor network

Wireless Sensor Network (WSN) has witnessed an unpredictable growth for the last few decades. It has many applications in various critical sectors such as real-time monitoring of nuclear power plant, disaster management, environment, military area etc. However, due to the distributed and remote deployment of sensor nodes in such networks, they are highly vulnerable to different security threats. The sensor network always needs a proficient key management scheme to secure data because of resource-constrained nodes. Existing polynomial based key management schemes are simple, but the computational complexity is a big issue. Lucas polynomials, Fibonacci polynomials, Chebychev polynomials are used in Engineering, Physics, Combinatory and Numerical analysis etc. In this paper, we propose a key management scheme using (p, q)-Lucas polynomial to improve the security of WSN. In (p, q)-Lucas polynomial, p represents a random base number while q represents a substitute value of × in the polynomial. The value of p is unique, and q is different according to communication between nodes. Analysis of the proposed method on several parameters such as computational overhead, efficiency and storage cost have been performed and compared with existing related schemes. The analysis demonstrates that the proposed (p, q)-Lucas polynomial based key management scheme outperforms over other polynomials in terms of the number of keys used and efficiency.
Human-Inspired Camera: A Novel Camera System for Computer Vision

Computer vision models aim to emulate biological design so that systems can perform meaningful tasks. We believe that the underlying processes of the human visual system hold the keys to further improving the performance of such computer vision solutions. This exploratory paper investigates the swaying motion of human vision when walking to develop a novel camera system. We successfully demonstrate that this design is able to improve performance in computer vision tasks, such as monocular depth estimation.
Summarizing source code with hierarchical code representation

Code summarization aims to automatically generate natural language descriptions for code, and has become a rapidly expanding research area. Data-driven code summarization models based on neural networks have proliferated in recent few years. Almost all of existing neural models are built upon the granularity of token or AST node. This has several drawbacks: a) Code summarization requires high-level knowledge of code while token representations are limited to provide a global view; b) Such approaches can hardly model the hierarchy of code; c) Long input codes challenge such models to handle long-range dependencies due to the large number of tokens and AST nodes. To address these issues, we propose a novel framework to utilize hierarchical representation of code to generate better summaries. We consider two levels of code hierarchy: token-level and statement-level. Our framework contains a pair of customized encoder-decoder models for tokens and AST of code respectively. Each of them has a hierarchical encoder that aims to extract both token and statement-level code features, and an attentional decoder with the ability to attend to those different levels of representation during decoding. They are then combined to predict summaries via ensemble learning. We conduct extensive experiments to evaluate our models on a large Java corpus. The experimental results show that our approach outperforms several state-of-the-art baselines by a substantial margin. In conclusion, our approach could better learn global information of code and shift attention between important statements during summary generation. With the help of hierarchical attention, the models are able to locate keywords more accurately in a top-down way. Ensemble learning is also proved to be an effective way to benefit from multiple input sources.
Entanglement marginal problems

We consider the entanglement marginal problem, which consists of deciding whether a number of reduced density matrices are compatible with an overall separable quantum state. To tackle this problem, we propose hierarchies of semidefinite programming relaxations of the set of quantum state marginals admitting a fully separable extension. We connect the completeness of each hierarchy to the resolution of an analog classical marginal problem and thus identify relevant experimental situations where the hierarchies are complete. For finitely many parties on a star configuration or a chain, we find that we can achieve an arbitrarily good approximation to the set of nearest-neighbour marginals of separable states with a time (space) complexity polynomial (linear) on the system size. Our results even extend to infinite systems, such as translation-invariant systems in 1D, as well as higher spatial dimensions with extra symmetries.
Ontologies in Cloud Computing—Review and Future Directions

Cloud computing as a technology has the capacity to enhance cooperation, scalability, accessibility, and offers discount prospects using improved and effective computing, and this capability helps organizations to stay focused. Ontologies are used to model knowledge. Once knowledge is modeled, knowledge management systems can be used to search, match, visualize knowledge, and also infer new knowledge. Ontologies use semantic analysis to define information within an environment with interconnecting relationships between heterogeneous sets. This paper aims to provide a comprehensive review of the existing literature on ontology in cloud computing and defines the state of the art. We applied the systematic literature review (SLR) approach and identified 400 articles; 58 of the articles were selected after further selection based on set selection criteria, and 35 articles were considered relevant to the study. The study shows that four predominant areas of cloud computing—cloud security, cloud interoperability, cloud resources and service description, and cloud services discovery and selection—have attracted the attention of researchers as dominant areas where cloud ontologies have made great impact. The proposed methods in the literature applied 30 ontologies in the cloud domain, and five of the methods are still practiced in the legacy computing environment. From the analysis, it was found that several challenges exist, including those related to the application of ontologies to enhance business operations in the cloud and multi-cloud. Based on this review, the study summarizes some unresolved challenges and possible future directions for cloud ontology researchers.
Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration

Recently, much attention has been spent on neural architecture search (NAS), aiming to outperform those manually-designed neural architectures on high-level vision recognition tasks. Inspired by the success, here we attempt to leverage NAS techniques to automatically design efficient network architectures for low-level image restoration tasks. In particular, we propose a memory-efficient hierarchical NAS (termed HiNAS) and apply it to two such tasks: image denoising and image super-resolution. HiNAS adopts gradient based search strategies and builds a flexible hierarchical search space, including the inner search space and outer search space. They are in charge of designing cell architectures and deciding cell widths, respectively. For the inner search space, we propose a layer-wise architecture sharing strategy, resulting in more flexible architectures and better performance. For the outer search space, we design a cell-sharing strategy to save memory, and considerably accelerate the search speed. The proposed HiNAS method is both memory and computation efficient. With a single GTX1080Ti GPU, it takes only about 1 h for searching for denoising network on the BSD-500 dataset and 3.5 h for searching for the super-resolution structure on the DIV2K dataset. Experiments show that the architectures found by HiNAS have fewer parameters and enjoy a faster inference speed, while achieving highly competitive performance compared with state-of-the-art methods. Code is available at: https://github.com/hkzhang91/HiNAS
HTR for Greek Historical Handwritten Documents

Offline handwritten text recognition (HTR) for historical documents aims for effective transcription by addressing challenges that originate from the low quality of manuscripts under study as well as from several particularities which are related to the historical period of writing. In this paper, the challenge in HTR is related to a focused goal of the transcription of Greek historical manuscripts that contain several particularities. To this end, in this paper, a convolutional recurrent neural network architecture is proposed that comprises octave convolution and recurrent units which use effective gated mechanisms. The proposed architecture has been evaluated on three newly created collections from Greek historical handwritten documents that will be made publicly available for research purposes as well as on standard datasets like IAM and RIMES. For evaluation we perform a concise study which shows that compared to state of the art architectures, the proposed one deals effectively with the challenging Greek historical manuscripts.
Performance Estimation in V2X Networks Using Deep Learning-Based M-Estimator Loss Functions in the Presence of Outliers

Recently, 5G networks have emerged as a new technology that can control the advancement of telecommunication networks and transportation systems. Furthermore, 5G networks provide better network performance while reducing network traffic and complexity compared to current networks. Machine-learning techniques (ML) will help symmetric IoT applications become a significant new data source in the future. Symmetry is a widely studied pattern in various research areas, especially in wireless network traffic. The study of symmetric and asymmetric faults and outliers (anomalies) in network traffic is an important topic. Nowadays, deep learning (DL) is an advanced approach in challenging wireless networks such as network management and optimization, anomaly detection, predictive analysis, lifetime value prediction, etc. However, its performance depends on the efficiency of training samples. DL is designed to work with large datasets and uses complex algorithms to train the model. The occurrence of outliers in the raw data reduces the reliability of the training models. In this paper, the performance of Vehicle-to-Everything (V2X) traffic was estimated using the DL algorithm. A set of robust statistical estimators, called M-estimators, have been proposed as robust loss functions as an alternative to the traditional MSE loss function, to improve the training process and robustize DL in the presence of outliers. We demonstrate their robustness in the presence of outliers on V2X traffic datasets.
A network-based CNN model to identify the hidden information in text data

With the development of the internet and big data, the missing or hidden information identification of text data has become an imperative task. At present, the challenge in the hidden information study is judging whether there is hidden information and where it exists. In this paper, hidden information refers to the words that do not appear in a sentence, however, they have certain correlations with the existing words or sentence and have a great influence on the comprehension of a sentence or part of the text data. This paper focuses on discovering the key and influential hidden information in the text data. A keyword-based hidden information extraction framework is proposed in this paper to search hidden entities, with the assumption that the importance of hidden objects is reflected by the keywords in the text data. A network-based Convolution Neural Network (CNN) model is developed to identify the hidden information related to keywords. The model is based on the results of CNN, and cosine similarity is used to judge whether there is hidden information in the source text data or not. We primarily form the word co-occurrence network of text, select the words with the highest degree as keywords, and generate random walk paths on the network. Besides, we use the random walk path where the last word is the keyword to train CNN. In the experimental section, the proposed model is applied to the dataset in 20Newgroups. The results show that the proposed model can effectively identify the hidden information associated with the keywords in the source text data, and the detection accuracy of keywords can reach 98%–99% achieved by CNN.
Clifford Fuzzy Support Vector Machine for Regression and Its Application in Electric Load Forecasting of Energy System

Electric load forecasting is a prominent topic in energy research. Support vector regression (SVR) has extensively and successfully achieved good performance in electric load forecasting. Clifford support vector regression (CSVR) realizes multiple outputs by the Clifford geometric algebra which can be used in multistep forecasting of electric load. However, the effect of input is different from the forecasting value. Since the load forecasting value affects the energy reserve and distribution in the energy system, the accuracy is important in electric load forecasting. In this study, a fuzzy support vector machine is proposed based on geometric algebra named Clifford fuzzy support vector machine for regression (CFSVR). Through fuzzy membership, different input points have different contributions to deciding the optimal regression hyperplane. We evaluate the performance of the proposed CFSVR in fitting tasks on numerical simulation, UCI data set and signal data set, and forecasting tasks on electric load data set and NN3 data set. The result of the experiment indicates that Clifford fuzzy support vector machine for regression has better performance than CSVR and SVR of other algorithms which can improve the accuracy of electric load forecasting and achieve multistep forecasting.