Reviewerly/exHarmony huggingface dataset:
Redeeming Reset Indifferentiability and Applications to Post-quantum Security

Indifferentiability is used to analyze the security of constructions of idealized objects, such as random oracles or ideal ciphers. Reset indifferentiability is a strengthening of plain indifferentiability which is applicable in far more scenarios, but has largely been abandoned due to significant impossibility results and a lack of positive results. Our main results are: Under weak reset indifferentiability, ideal ciphers imply (fixed size) random oracles, and domain shrinkage is possible. We thus show reset indifferentiability is more useful than previously thought. We lift our analysis to the quantum setting, showing that ideal ciphers imply random oracles under quantum indifferentiability. Despite Shor’s algorithm, we observe that generic groups are still meaningful quantumly, showing that they are quantumly (reset) indifferentiable from ideal ciphers; combined with the above, cryptographic groups yield post-quantum symmetric key cryptography. In particular, we obtain a plausible post-quantum random oracle that is a subset-product followed by two modular reductions.
Research on AI-assisted Hybrid Teaching for English Writing

In order to improve the quality of English writing teaching, this paper first describes the research background of the hybrid teaching mode under the background of artificial intelligence, and then discusses the application of the hybrid teaching mode in English writing teaching under the background of artificial intelligence from four aspects of pre-class, in-class, after-class and diversified teaching evaluation, exploring the auxiliary effects of artificial intelligence on English writing teaching and students' autonomous learning. It is proposed that the application of artificial intelligence technology can effectively assist teachers to complete composition correction and data analysis more efficiently, help solve the problem of students' low self-efficacy in writing learning, and is expected to cultivate students' self-awareness of English writing and lifelong learning.
Grievance Redressal System

An effective and efficient response to the complaints is an essential index of organization’s performance. In colleges if anyone wants to complaint about something they have to write it down on a paper and submit which is not environment friendly. So, for solving this problem we developed Grievance redressal system. Here anyone associated with college being a student, faculty or working staff can easily file their complaint. To make this window more secure we mandated registration and login before one can file their complaint. These complaint are going to be solved by competent authority. The presented model for the Complaint Redressal System will have the ability to minimize people’s dissatisfaction and on the other hand it can encourage people to participate in improving the quality of the service provided.
An Enhanced Personal Profile Ontology for Software Requirements Engineering Tasks Allocation

The availability of a web application for allocating software requirements engineering tasks to qualify personnel requires personal profile ontology (PPO) which includes both static and dynamic features. Several personal profile ontologies have been developed and deployed, but the personnel information represented is static, leaving out fundamental and dynamic properties of the personal data suitable for task handling in applications such as allocating tasks during the software requirement engineering processes. Personal profile is often modified for several purposes, calling for augmentation and annotation when needs arise. The resume is one resulting extract from personal profile and often contain slightly different information based on needs. The urgent preparation of a resume may introduce bias and incorrect information for the sole aim of projecting the personnel as being qualified for the available job. This work is aimed at providing an enhanced personal profile ontology for software requirements engineering task allocation that captures both static and dynamic properties of personal data. A mixed approach of existing ontologies like Methontology and Neon have been followed in the creation of this ontology. The enhanced personal profile ontology (e-PPO) is a constraint-based semantic data model tested using Protégé inbuilt reasoner with its updated plugins. Upon application of e-PPO, an abridged resumes otherwise referred to the smart resumes will be obtained from the populated ontology with instances, and this will aid in the decision and selection of the most qualified personnel for any queried software requirements engineering task.
STC-NAS: Fast neural architecture search with source-target consistency

Neural architecture search (NAS) has shown very promising results for automatically designing network models. Most existing cell-based NAS approaches generate the target network model from a source super-network, which usually confront inconsistency issues. In this paper, we propose a new NAS method named STC-NAS, a fast neural architecture search with source-target consistency, so that not only the performance of the searched target model is improved but also the search process is boosted. Specifically, during the search phase, we sample the source super-network to let the samples be consistent with the target model. Moreover, we leverage the Jensen-Shannon divergence to ensure the samples are optimized in the direction of being more similar to the target model. Experimental results demonstrate that our method needs only 0.059 GPU-days to search on CIFAR-10. Benefited from its efficiency, STC-NAS can directly search the target super-network on the target task datasets, achieving 2.42% test error on CIFAR-10, 16.45% test error on CIFAR-100, and 24.2% test error on ImageNet datasets.
Solving Traveling Salesman Problems Using Ising Models with Simulated Bifurcation

Many combinatorial optimization problems can be solved by numerically simulating classical nonlinear Hamiltonian systems based on the Ising model. Solving the traveling salesman problem (TSP) using the Ising model requires a quadratically increasing number of spins with strict constraints. Unlike classical simulated annealing, simulated bifurcation (SB) can update the states of spins in parallel. This feature can potentially accelerate the convergence of Hamiltonian in the Ising model by taking advantage of modern multi-core processors. As an improved SB algorithm, the ballistic SB (bSB) algorithm is considered for solving the TSP in this paper. The TSP is converted to an Ising problem with external magnetic fields. bSB is then expanded by introducing a time-dependent factor. Experiments on benchmark datasets show that the bSB-based Ising solver offers superior performance in solution quality and convergence speed.
Federated Learning via Conditional Mutual Learning for Alzheimer’s Disease Classification on T1w MRI

Data-driven deep learning has been considered a promising method for building powerful models for medical data, which often requires a large amount of diverse data to be sufficiently effective. However, the expensive cost of collecting and the privacy constraints lead to the fact that existing medical datasets are small-scale and distributed. Federated learning via model distillation is a data-private collaborative learning where the model can leverage all available data without direct sharing. The data knowledge is shared by distillation through the multi-site average prediction scores on the public dataset. However, the average consensus is suboptimal to individual client due to data domain shift in MRI data caused by acquisition protocols, recruitment criteria, etc. In this work, we propose a federated conditional mutual learning (FedCM) to improve the performance by considering the clients’ local performance and the similarity between clients. This work is the first federated learning on multi-dataset Alzheimer’s disease classification by 3DCNN using T1w MRI. Our method achieves the best recognition rates comparing with FedMD and other frameworks. Further visualization and relevance ranking on the region of interests (ROI) in human brains implies that the left hemisphere may have greater relevance than the right hemisphere does. Several potential regions are listed for future investigation.
Implicit Discourse Relation Classification Based on Semantic Graph Attention Networks

Theimplicit discourse relation classification is of great importance to discourse analysis. It aims to identify the logical relation between sentence pair. Compared with the linear network model, the graph neural network has a more complex structure to capture cross-sentence interactions. Therefore, this article proposes a semantic graph neural network for implicit discourse relation classification. Specifically, we design a semantic graph to describe the syntactic structure of sentences and semantic interactions between sentence pair. Then, convolutional neural network (CNN) with different convolutional kernels to extract the multi-granularity semantic features. The experimental results on Penn Discourse TreeBank 2.0 (PDTB 2.0) prove that our work performed well.
Empirical likelihood confidence interval for sensitivity to the early disease stage

Abstract Disease status can naturally be classified into three or more ordinal stages rather than just being binary stages. Many works have been done for the estimation and inference procedure regarding three ordinal disease stages, which are non‐disease, early disease, and full disease stages. The early disease stage can be very important for therapeutic intervention and prevention potentiality. As a diagnostic measure, sensitivity to the early disease stage is often used. In this article, we propose confidence intervals for the sensitivity to early disease stage based on given target specificity for non‐disease stage and target sensitivity to full disease stage using both empirical likelihood (EL) and adjusted EL procedures. We compare the performance of the proposed EL confidence intervals with other procedures in our simulation study. The proposed procedures are further applied to Alzheimer's Disease Neuroimaging Initiative data set.
Large-Batch and Multi-Structure Group Targets Tracking Based on Serial GLMB

In recent years, with the improvement of sensor resolution, tracking a large number of group targets has become more and more popular. When tracking the group targets, the number of posterior components of the GLMB filter will increase exponentially, and the dependence exists between the group targets, which makes it more difficult to directly track them with GLMB algorithm. To solve this problem, a serial GLMB tracking algorithm is proposed in this paper, and the experimental simulation results show the effectiveness of our method. To evaluate the performance of the proposed tracker, wo also introduce a new method of applying the optimal subpattern assignment(OSPA) metric.
Photon routing based on non-chiral interaction between atoms and waveguides

Abstract The photon router plays an essential role in the optical quantum network. However, conventional routers generally couple photons chirally into waveguides to achieve complete transmission from the input port to the required port. Here, we use non-chiral photon-atom interactions for targeted routing. The system consists of two V-type three-level atoms and two parallel waveguides. In addition, the two atoms are driven by external coherent fields, respectively. With a real-space Hamiltonian, the probability of photon transmitted to four ports can be obtained. The study shows that a single photon input from the left port of the waveguide-a can be deterministically transferred to any of the four ports of the two waveguides by adjusting the detuning of the atom and the driving field on the atom, as well as the distance between the two atoms.
Identification of Significantly Expressed Gene Mutations for Automated Classification of Benign and Malignant Prostate Cancer

Among males, prostate cancer (Pca) is the cancer type with the highest prevalence and the second leading cause of cancer deaths. The current screening methods for prostate cancer lack effectiveness such as prostate-specific antigen (PSA) and digital rectal exam (DRE). Machine learning models have been used to predict Pca progression, Gleason score, and laterality. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machines (SVM), Decision Trees, Logistic Regression, K-Nearest Neighbors, Random Forest and AdaBoost for detecting malignant prostate cancers from benign ones. Moreover, different feature extracting strategies are proposed to improve the detection performance and identify potential genomic biomarkers. The results show the Lasso feature set yielded high performance from the models with SVM achieving exemplary classification accuracy of 97%. The Lasso and SVM combination reported many significant biomarker genes and gene mutations including but not restricted to CA2320112, CA2328529, and CA2436168.
Vision-Based Augmented Reality System for Middle Ear Surgery: Evaluation in Operating Room Environment

Augmented reality (AR) solely based on image features is achievable in operating room conditions and its precision is compatible with otological surgery.The objective of this work was to evaluate the performance of a vision-based AR system for middle ear surgery in the operating room conditions.Nine adult patients undergoing ossicular procedures were included in this prospective study. AR was obtained by combining real-time video from the operating microscope with the virtual image obtained from the preoperative computed tomography (CT)-scan. Initial registration between the video and the virtual CT image was achieved using manual selection of six points on the tympanic sulcus. Patient-microscope movements during the procedure were tracked using image-feature matching algorithm. The microscope was randomly moved at an approximated speed of 5 mm/s in the three axes of space and rotation for 180 seconds. The accuracy of the system was assessed by calculating the distance between each fiducial point selected on the video image and its corresponding point on the scanner.AR could be obtained for at least 3 minutes in seven out of nine patients. The overlay fiducial and target registration errors were 0.38 ± 0.23 mm (n = 7) and 0.36 ± 0.15 mm (n = 5) respectively, with a drift error of 1.2 ± 0.5 μm/s. The system was stable throughout the procedure and achieved a refresh rate of 12 fps. Moderate bleeding and introduction of surgical instruments did not compromise the performance of the system.The AR system yielded sub-millimetric accuracy and remained stable throughout the experimental study despite patient-microscope movements and field of view obtrusions.
ON THE CONSISTENCY OF THE LEAST SQUARES ESTIMATOR IN MODELS SAMPLED AT RANDOM TIMES DRIVEN BY LONG MEMORY NOISE: THE RENEWAL CASE

In this study, we prove the strong consistency of the least squares estimator in a random sampled linear regression model with long-memory noise and an independent set of random times given by renewal process sampling. Additionally, we illustrate how to work with a random number of observations up to time T = 1. A simulation study is provided to illustrate the behavior of the different terms, as well as the performance of the estimator under various values of the Hurst parameter H.
An Improved Fuzzy Adaptive Firefly Algorithm-Based Hybrid Clustering Algorithms

The implication of firefly and fuzzy firefly optimization algorithms has been greatly witnessed in clustering techniques and extensively used in applications such as Image segmentation. Parameters such as step factor and attractiveness have been kept constant in these algorithms, which affect the convergence rate and accuracy of the clustering process. Though fuzzy adaptive firefly algorithm tackled this problem by making those parameters an adaptive one, issues such as low convergence rate, and provision of non-optimal solutions are still there. To tackle these issues, this paper proposed a novel fuzzy adaptive fuzzy firefly algorithm that significantly improves the accuracy and convergence rate while comparing with the existing optimization algorithms. Further, the proposed algorithm fused with existing hybrid clustering algorithms involving fuzzy set, intuitionistic fuzzy set, and rough set resulted in eight novel hybrid clustering algorithms which lead to better performance in optimizing the selection of initial centroids. To validate the proposal, experimental studies have been conducted on datasets found in bench-marking data repositories such as UCI, and Kaggle. The performance and accuracy evaluation of proposed algorithms have been carried out with the aid of seven accuracy measures. Results clearly indicate the improved accuracy and convergence rate of the proposed algorithms.
Study on the location of thunderclouds based on irradiance characteristics under lightning meteorological conditions

In the past decade, lightning activity on land has increased by more than 12% worldwide. With the deepening of the construction of “smart grid”, a large number of renewable energy sources are connected to the grid, and the grid structure and scale are more complex than before. The impact of lightning cannot be ignored. The main purpose of this paper is to prove that the irradiance characteristic curve under lightning conditions is different from that under other weather conditions from the perspective of big data, so as to provide a basis for applying the irradiance monitoring curve of photovoltaic power station to the location of thunder clouds. Firstly, through the observation of the unionized irradiance curve characteristics of a photovoltaic power station in one year under the lightning meteorological conditions using the clear sky and outer earth irradiance model as the reference value, the differences between the two kinds of irradiance curves under the lightning meteorological conditions and other meteorological conditions are described qualitatively from the perspective of graph. Secondly, from the perspective of data, the characteristics of photovoltaic output curve under lightning meteorological conditions are quantitatively analyzed, which is abstracted and defined as the characteristic curve of lightning irradiance. Finally, according to the characteristics defined by the characteristic curve of lightning irradiance, the curve with characteristics of lightning irradiance is screened from the historical data of irradiance, and the cloud and ground lightning monitoring information is used to judge whether there are thunder clouds over the photovoltaic power station in this period, and the accuracy is verified. It is considered that there are thunderclouds over the photovoltaic power plant with lightning irradiance characteristics, and the location of thunderclouds is consistent with the geographic longitude and latitude of the photovoltaic power plant. Therefore, the role of photovoltaic power plants in positioning thundercloud is realized.
A novel Confidential Consortium Blockchain framework for peer to peer energy trading

Abstract The vital dependence of peer to peer (P2P) energy trading frameworks on creative Internet of Things (IoT) has been making it more vulnerable against a wide scope of attacks and performance bottlenecks like low throughput, high latency, high CPU, memory use, etc. This hence compromises the energy exchanging information to store, share, oversee, and access. Blockchain innovation as a feasible solution, works with the rule of untrusted members. To alleviate this threat and performance issues, this paper presents a Blockchain based Confidential Consortium (CoCo) P2P energy trading system that works on the trust issues among the energy exchanging networks and limits performance parameters. It reduces the duplicate validation by creating a trusted network on nodes, where participants identities are known and controlled. A Java-script-based smart contract is sent over the Microsoft CoCo system with Proof of Elapsed Time (PoET) consensus protocol. Also, a functional model is designed for the proposed framework and the performance bench-marking has been done considering about latency, throughput, transaction rate control, success and fail transaction, CPU and memory usage, network traffic. Additionally, it is shown that PoET’s performance is superior to proof of work (PoW) for multi-hosting conditions. The measured throughput and latency moving toward database speeds with more flexible, business-specific confidentiality models, network policy management through distributed governance, support for non-deterministic transactions, and reduced energy consumption.
Privacy Preserving Outsourced Data Integration from Inner Product Encryption

The multiparty private set intersection (MPSI) protocol is jointly implemented by multiple parties to obtain the intersection of private datasets indexed by matching attributes. MPSI can be applied to achieve privacy preserving data integration. In this paper, we focus on privacy preserving data integration for two datasets with an outsourced cloud that is in charge of obtaining set intersection. Our main techniques include Bloom filter and function-hiding inner product encryption. Finally, the proposed construction is conceptually-simple.
Design and Practice of Software Architecture in Agile Development

Abstract Software architecture design is a very critical part of the software development process. In order to be able to respond to changes in user requirements in software development in a timely manner, a convenient and lightweight development model should be adopted. Agile development adopts a human-oriented, step-by-step development process that can quickly respond to changes in software requirements, adjust the design of software architecture in a timely manner and adapt to software development in a dynamic environment. Based on the analysis and comparison of traditional development process and agile development process, this paper analyzes the key technologies in agile architecture design and proposes a software architecture design method based on agile development mode.
Outcomes of COVID-19 contact tracing in hospital healthcare workers: a retrospective cohort study

To determine risk-group-specific rate of becoming COVID-19-positive among healthcare workers having had contact with COVID-19 cases.The retrospective cohort study was conducted at the Ege University Hospital, Bornova, Turkey, and comprised all healthcare workers who had come into contact with COVID-19 cases between March 11 and May 31, 2020. The contacts were classified as low-risk, medium-risk and high-risk using the guidelines of the Turkish Ministry of Health. The outcome measures were the incidence of infection among contacts and the incubation period and serial interval among the new cases. Data was analysed using SPSS 23.Of the 845 cases, 312(37%) had high risk, 263(31%) medium and 270(32%) low. Overall, there were 490(58%) females, 355(41%) males, 565(67%) aged <40 years, and 277(33%) aged >40 years. Of the total, 27(3.20%) healthcare workers tested COVID-19-positive and distribution among the risk-based groups was significant (p=0.037). There was a significantly increased risk of incidence among repeated contacts, no mask use, and the source being a colleague (p<0.05).The detection of high-risk contacts was found to be important for controlling COVID-19 infection in a hospital setting.