Reviewerly/exHarmony huggingface dataset:
A new HSI denoising method via interpolated block matching 3D and guided filter

A new hyperspectral images (HSIs) denoising method via Interpolated Block-Matching and 3D filtering and Guided Filtering (IBM3DGF) denoising method is proposed. First, inter-spectral correlation analysis is used to obtain inter-spectral correlation coefficients and divide the HSIs into several adjacent groups. Second, high-resolution HSIs are produced by using adjacent three images to interpolate. Third, Block-Matching and 3D filtering (BM3D) is conducted to reduce the noise level of each group; Fourth, the guided image filtering is utilized to denoise HSI of each group. Finally, the inverse interpolation is applied to retrieve HSI. Experimental results of synthetic and real HSIs showed that, comparing with other state-of-the-art denoising methods, the proposed IBM3DGF method shows superior performance according to spatial and spectral domain noise assessment. Therefore, the proposed method has a potential to effectively remove the spatial/spectral noise for HSIs.
Simultaneous completion and spatiotemporal grouping of corrupted motion tracks

Abstract Given an unordered list of 2D or 3D point trajectories corrupted by noise and partial observations, in this paper we introduce a framework to simultaneously recover the incomplete motion tracks and group the points into spatially and temporally coherent clusters. This advances existing work, which only addresses partial problems and without considering a unified and unsupervised solution. We cast this problem as a matrix completion one, in which point tracks are arranged into a matrix with the missing entries set as zeros. In order to perform the double clustering, the measurement matrix is assumed to be drawn from a dual union of spatiotemporal subspaces. The bases and the dimensionality for these subspaces, the affinity matrices used to encode the temporal and spatial clusters to which each point belongs, and the non-visible tracks, are then jointly estimated via augmented Lagrange multipliers in polynomial time. A thorough evaluation on incomplete motion tracks for multiple-object typologies shows that the accuracy of the matrix we recover compares favorably to that obtained with existing low-rank matrix completion methods, specially under noisy measurements. In addition, besides recovering the incomplete tracks, the point trajectories are directly grouped into different object instances, and a number of semantically meaningful temporal primitive actions are automatically discovered.
A method of determining molecular excited-states using quantum computation

A method is presented in which the ground-state subspace is projected out of a Hamiltonian representation. As a result of this projection, an effective Hamiltonian is constructed where its ground-state coincides with an excited-state of the original problem. Thus, low-lying excited-state energies can be calculated using existing hybrid quantum classical techniques and variational algorithm(s) for determining ground-state. The method as formulated is shown to be fully valid for the $$\hbox{H}_2$$ and LiH molecules. The primary restriction with our technique is the number of terms required in the projection operator, therefore we explore arguments to reduce the number of terms and discuss applicability to different classes of Hamiltonians.
Information Security and Privacy – Challenges and Outlook

The ongoing demand for new and faster technologies continues to leave consumers and business users to face the constant challenge of updating systems and software. This unrelenting pace of technological evolution has not always been matched with a commensurate focus on security and privacy matters. In particular, the obligatory move to embrace cloud and IoT - that frequently result in the collection and analysis of large data lakes has raised challenges for sovereign data protection and privacy legislation where data at rest can change overnight with mergers and acquisitions of service providers. This chapter examines the role of IFIP Technical Committee 11 (and its 14 underlying Working Groups) in this ever-changing and evolving domain. The discussion provides an outline of key issues in information security when viewed from technical, organisational and human perspectives, which collectively represent the breadth of areas within which TC-11 and its Working Groups are seeking to make contributions. The chapter as a whole gives a clear sense of the challenges involved in achieving and maintaining security and privacy, alongside insights into the ways that they are being tackled within IFIP activities.
MODELS AND METHODS OF ACTUALIZATION EDUCATIONAL PROGRAMS AND CONTENT IN THE FRAMEWORK OF BUILDING SMART LEARNING ENVIRONMENT

The purpose of the study is to improve the quality of the process of training specialists in the process of updating educational programs and educational content, taking into account the changing requirements of professional standards and employers in the transition to an innovative economy. To achieve the goal, the following tasks were solved: methods for monitoring employers' requirements, educational and professional standards, models of life cycles of educational programs and electronic resources with stages of updating, methods for updating educational programs with adaptive adjustment to the requirements of employers in an intelligent educational environment. The authors investigated the problems of monitoring the requirements of employers and adapting electronic educational resources and programs to them, the features of their modernization in the continuous process of training specialists. New models for managing the processes of updating and personalizing educational content and training programs are considered. A methodology for adapting educational programs and content has been implemented using the tools of the Smart Learning Environment. The proposed models and methods make it possible to take into account the range of external conditions of the labor market and employers' requirements for educational programs and resources, build geotagged models to assess the territorial distribution of requirements for the competencies of specialists in the regions, taking into account the location of higher educational institutions in related fields.
Effect of interactive vs. passive virtual reality on pain threshold and tolerance

Scientific evidence suggests that virtual reality (VR) could potentially help patients tolerate painful medical procedures and conditions. The aim of this study was to evaluate the efficacy of virtual reality on pain tolerance and threshold.A within-subjects experimental study design was conducted on 53 female students at Qassim University in Saudi Arabia. Each participant completed three rounds of assessment: one baseline (no VR) and two VR immersion (passive and interactive) in random order sequence. During each round, participants submerged their non-dominant hand into an ice bath; pain threshold and tolerance were measured as outcomes and analyzed using repeated measures ANOVA.Participants had both higher pain threshold and tolerance during interactive and passive VR rounds in comparison to the non-VR baseline assessment (p<0.05). Participants had greater pain tolerance during the interactive VR condition compared to the passive VR condition (p<0.001).VR experiences increase pain threshold and tolerance with minimal side effects, and the larger effects were demonstrated using interactive games. Interactive VR gaming should be considered and tested as a treatment for pain.
Variational Bayesian‐based adaptive distributed fusion target tracking with unknown sensor measurement losses

A new variational Bayesian-based adaptive distributed fusion unscented Kalman filter (ADFUKF-VB) algorithm is proposed for the problem of state estimation in a distributed fusion target tracking system with unknown sensor measurement losses. First, the Bernoulli random variables are introduced as the judgement factors of the measurement losses in each local unscented Kalman filter (UKF) of the distributed fusion target tracking system, and the probability density function (PDF) of the measurement loss probability is modelled as a Beta distribution. Then, the variational Bayesian (VB) approach is used to infer the estimated variables in each local UKF. The measurement losses are judged according to the expectations of the Bernoulli random variables, and three ways are adopted to estimate the state vector based on the obtained judgement results. Finally, the covariance intersection (CI) fusion strategy is used to achieve the fusion of all local state estimates. The simulation results show that the proposed ADFUKF-VB algorithm can accurately judge whether the measurement is lost and estimate the unknown measurement loss probability, moreover, effectively improve the accuracy of state estimation compared with the existing filtering algorithms with unknown measurement losses.
Variance-Aware Machine Translation Test Sets

We release 70 small and discriminative test sets for machine translation (MT)evaluation called variance-aware test sets (VAT), covering 35 translationdirections from WMT16 to WMT20 competitions. VAT is automatically created by anovel variance-aware filtering method that filters the indiscriminative testinstances of the current MT test sets without any human labor. Experimentalresults show that VAT outperforms the original WMT test sets in terms of thecorrelation with human judgement across mainstream language pairs and testsets. Further analysis on the properties of VAT reveals the challenginglinguistic features (e.g., translation of low-frequency words and proper nouns)for competitive MT systems, providing guidance for constructing future MT testsets. The test sets and the code for preparing variance-aware MT test sets arefreely available at https://github.com/NLP2CT/Variance-Aware-MT-Test-Sets .
Generative Adversarial Networks for Creating Synthetic Free-Text Medical Data: A Proposal for Collaborative Research and Re-use of Machine Learning Models.

Restrictions in sharing Patient Health Identifiers (PHI) limit cross-organizational re-use of free-text medical data. We leverage Generative Adversarial Networks (GAN) to produce synthetic unstructured free-text medical data with low re-identification risk, and assess the suitability of these datasets to replicate machine learning models. We trained GAN models using unstructured free-text laboratory messages pertaining to salmonella, and identified the most accurate models for creating synthetic datasets that reflect the informational characteristics of the original dataset. Natural Language Generation metrics comparing the real and synthetic datasets demonstrated high similarity. Decision models generated using these datasets reported high performance metrics. There was no statistically significant difference in performance measures reported by models trained using real and synthetic datasets. Our results inform the use of GAN models to generate synthetic unstructured free-text data with limited re-identification risk, and use of this data to enable collaborative research and re-use of machine learning models.
Design and Fabrication of Silicon Gratings for the Optical Addressing of Trapped Ion Qubits

Grating couplers are fabricated for the optical addressing of trapped ion qubits, where their respective feasibility is evaluated. From the obtained results, optical addressing of 1 to 2 ions is possible along various axes.
Transfer Learning via Optimal Transportation for Integrative Cancer Patient Stratification

The Stratification of early-stage cancer patients for the prediction of clinical outcome is a challenging task since cancer is associated with various molecular aberrations. A single biomarker often cannot provide sufficient information to stratify early-stage patients effectively. Understanding the complex mechanism behind cancer development calls for exploiting biomarkers from multiple modalities of data such as histopathology images and genomic data. The integrative analysis of these biomarkers sheds light on cancer diagnosis, subtyping, and prognosis. Another difficulty is that labels for early-stage cancer patients are scarce and not reliable enough for predicting survival times. Given the fact that different cancer types share some commonalities, we explore if the knowledge learned from one cancer type can be utilized to improve prognosis accuracy for another cancer type. We propose a novel unsupervised multi-view transfer learning algorithm to simultaneously analyze multiple biomarkers in different cancer types. We integrate multiple views using non-negative matrix factorization and formulate the transfer learning model based on the Optimal Transport theory to align features of different cancer types. We evaluate the stratification performance on three early-stage cancers from the Cancer Genome Atlas (TCGA) project. Comparing with other benchmark methods, our framework achieves superior accuracy for patient outcome prediction.
Achieve efficient position-heap-based privacy-preserving substring-of-keyword query over cloud

The cloud computing technique, which was initially used to mitigate the explosive growth of data, has been required to take both data privacy and users’ query functionality into consideration. Symmetric searchable encryption (SSE) is a popular solution to supporting efficient keyword queries over encrypted data in the cloud. However, most of the existing SSE schemes focus on the exact keyword query and cannot work well when the user only remembers the substring of a keyword, i.e., substring-of-keyword query. This paper aims to investigate this issue by proposing an efficient and privacy-preserving substring-of-keyword query scheme over cloud. First, we employ the position heap technique to design a novel tree-based index to match substrings with corresponding keywords. Then based on the tree-based index, we introduce our substring-of-keyword query scheme, which contains two consecutive phases. The first phase queries the keywords that match a given substring, and the second phase queries the files that match a keyword in which people are really interested. In addition, detailed security analysis and experimental results demonstrate the security and efficiency of our proposed scheme.
Study on Intelligent Image Recognition of Non-linear Short Pointer SF6 Meter Readings

Currently widely used SF6 inflatable equipment pressure gauge are short pointer and the pointer is not connected to the center of the dial (non-linear pointer), The position of the hands on this dial cannot be identified using traditional computer vision techniques. In order to solve the problem, This paper proposes a method for SF6 pressure gauge pointer and reading recognition based on digital image processing and Mask-RCNN neural network image segmentation technology. The method first pre-processes the SF6 pressure gauge image and Canny edge detection, while using Mask-RCNN network to extract the pointer feature information and scale feature information, and uses the SF6 pressure gauge pointer feature and scale feature to calculate the pressure gauge reading. The effectiveness of the algorithm is verified through the scale identification of 180 short pointer SF6 pressure meters actually operated in a 220kV substation with 100% accuracy.
Classification Of Positive And Negative Fake Online Reviews Using Machine Learning Techniques

E-Commerce is one of the most flourishing businesses in today’s world. A large part of the population, especially in urban areas, is switching towards e-commerce websites to fulfill all of their shopping requirements, whether groceries, electrical appliances, clothing, etc. In an online purchase, product review is considered a significant factor in deciding the right choice of product. Therefore, e-commerce businesses are primarily dependent on product reviews. Due to the lack of authenticity of the reviewer information, while posting a review of any product or service online, the presence of fake reviews is increasing day by day. The presence of these fake reviews of various products or services impacts the customers and the sellers. The customers might choose the wrong brand of a product or service, while the sellers might face low sales of their high-quality products because of these fake reviews. This paper used different machine learning approaches to detect fake reviews of services on e-commerce sites. We have further categorized the fake reviews into positive and negative based on the reviewer’s rating.
Gold price prediction in covid-19 era

As one of the most frequently traded commodities in the world, gold has been hugely impacted by the COVID-19 crisis. In this study, we try to apply a famous Deep Learning method for time series analysis, namely the Long Short-Term Memory (LSTM) networks, for future gold price prediction. However, rather than using a complex network architecture, we propose simple three layers LSTM networks that were trained on 4,219 training records and tested on 1,055 test records. We found that the Root Mean Square Error (RMSE) value for the prediction results is 39.94162, while the Mean Absolute Percentage Error(MAPE) value is 17.66144. Moreover, the R2 score of the prediction results could reach 97.242%, which is considered high and comparable with other more complex networks’ architectures available in the literature. © Muk Publications.
Research on Project-driven Teaching Mode Based on Software Development Model

This paper conducts a comprehensive analysis based on the typical research of the traditional project-driven teaching model of software programming practice courses. According to the software programming course's own software engineering discipline attributes and the typical development model in enterprise software development, a life cycle model theory based on the software engineering discipline is proposed. As a basis, we can flexibly choose different software development models according to the different scales of project cases used in the classroom, and design teaching links scientifically and rationally according to the typical development process of different models of software engineering. Thereby, a teaching process design plan for software programming courses is formed, which meets the actual needs of the company's position and can reflect the dominant position of students, which is conducive to the cultivation of applied talents with soft engineer qualities in universities.
Adapting Soft Robotics Outreach to Teacher-Delivered Curriculum in the Virtual Classroom (Work in Progress)

He is the founder of the Harvard Biodesign Lab, which brings together researchers from the engineering, industrial design, medical and business communities to develop smart medical devices and translate them to industrial partners in collaboration with the Wyss Institute's Advanced Technology Team.Conor's research projects focus on wearable robotics to assist the disabled and able-bodied, as well as on tools for minimally invasive diagnosis and treatment of disease
Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus

Recent literature has underscored the importance of dataset work for machine learning, and part of this work involves addressing documentation for datasets that have been used widely but documented sparsely. This paper aims to help address debt for BookCorpus, a popular text dataset for training large language models. Notably, researchers have used BookCorpus to train OpenAI's GPT-N models and Google's BERT models, even though little to no exists about the dataset's motivation, composition, collection process, etc. We offer a preliminary datasheet that provides key context and information about BookCorpus, highlighting several notable deficiencies. In particular, we find evidence that (1) BookCorpus likely violates copyright restrictions for many books, (2) BookCorpus contains thousands of duplicated books, and (3) BookCorpus exhibits significant skews in genre representation. We also find hints of other potential deficiencies that call for future research, including problematic content, potential skews in religious representation, and lopsided author contributions. While more work remains, this initial effort to provide a datasheet for BookCorpus adds to growing literature that urges more careful and systematic for machine learning datasets.
A Chinese Machine Reading Comprehension Dataset Automatic Generated Based on Knowledge Graph

Machine reading comprehension (MRC) is a typical natural language processing (NLP) task and has developed rapidly in the last few years. Various reading comprehension datasets have been built to support MRC studies. However, large-scale and high-quality datasets are rare due to the high complexity and huge workforce cost of making such a dataset. Besides, most reading comprehension datasets are in English, and Chinese datasets are insufficient. In this paper, we propose an automatic method for MRC dataset generation, and build the largest Chinese medical reading comprehension dataset presently named CMedRC. Our dataset contains 17k questions generated by our automatic method and some seed questions. We obtain the corresponding answers from a medical knowledge graph and manually check all of them. Finally, we test BiLSTM and BERT-based pre-trained language models (PLMs) on our dataset and propose a baseline for the following studies. Results show that the automatic MRC dataset generation method is considerable for future model improvements.
Incorporating blockchain and internet of thing: The context of renewable energy certificate on smart grid

Blockchain and the Internet of Things (IoT) complements each other within the space of decentralization, real time monitoring, and data sharing and security. In this paper we explored the integration of blockchain and IoT within the context of smart grid. Current REC system lacks visibility and transparency which are very critical to the success of smart grids. Conventional smart grid system makes it difficult to proof whether a buyer has truly received an energy certificate or not. Therefore, we proposed a blockchain and IoT based system for autonomous monitoring of energy sources and tracking of RECs on smart grid. Our system provides the visibility and transparency on smart grid networks that is imperative to safeguarding RECs market. Results based on extensive experiments illustrate the efficiency and scalability of our system.
Robust Stochastic Bayesian Games for Behavior Space Coverage

A key challenge in multi-agent systems is the design of intelligent agents solving real-world tasks in close interaction with other agents (e.g. humans), thereby being confronted with a variety of behavioral variations and limited knowledge about the true behaviors of observed agents. The practicability of existing works addressing this challenge is being limited due to using finite sets of hypothesis for behavior prediction, the lack of a hypothesis design process ensuring coverage over all behavioral variations and sample-inefficiency when modeling continuous behavioral variations. In this work, we present an approach to this challenge based on a new framework of Robust Stochastic Bayesian Games (RSBGs). An RSBG defines hypothesis sets by partitioning the physically feasible, continuous behavior space of the other agents. It combines the optimality criteria of the Robust Markov Decision Process (RMDP) and the Stochastic Bayesian Game (SBG) to exponentially reduce the sample complexity for planning with hypothesis sets defined over continuous behavior spaces. Our approach outperforms the baseline algorithms in two experiments modeling time-varying intents and large multidimensional behavior spaces, while achieving the same performance as a planner with knowledge of the true behaviors of other agents.