Reviewerly/exHarmony huggingface dataset:
Extracting Lifestyle Factors for Alzheimer's Disease from Clinical Notes Using Deep Learning with Weak Supervision

Since no effective therapies exist for Alzheimer's disease (AD), prevention has become more critical through lifestyle factor changes and interventions. Analyzing electronic health records (EHR) of patients with AD can help us better understand lifestyle's effect on AD. However, lifestyle information is typically stored in clinical narratives. Thus, the objective of the study was to demonstrate the feasibility of natural language processing (NLP) models to classify lifestyle factors (e.g., physical activity and excessive diet) from clinical texts. We automatically generated labels for the training data by using a rule-based NLP algorithm. We conducted weak supervision for pre-trained Bidirectional Encoder Representations from Transformers (BERT) models on the weakly labeled training corpus. These models include the BERT base model, PubMedBERT(abstracts + full text), PubMedBERT(only abstracts), Unified Medical Language System (UMLS) BERT, Bio BERT, and Bio-clinical BERT. We performed two case studies: physical activity and excessive diet, in order to validate the effectiveness of BERT models in classifying lifestyle factors for AD. These models were compared on the developed Gold Standard Corpus (GSC) on the two case studies. The PubmedBERT(Abs) model achieved the best performance for physical activity, with its precision, recall, and F-1 scores of 0.96, 0.96, and 0.96, respectively. Regarding classifying excessive diet, the Bio BERT model showed the highest performance with perfect precision, recall, and F-1 scores. The proposed approach leveraging weak supervision could significantly increase the sample size, which is required for training the deep learning models. The study also demonstrates the effectiveness of BERT models for extracting lifestyle factors for Alzheimer's disease from clinical notes.
Applicability of Scrum Methods in Software Development Process

Scrum is an agile software development methodology or framework used primarily for software development projects with the goal of delivering new software capability every 2-4 weeks. It is one of the approaches that influenced the Agile Manifesto, which articulates a set of values and principles to guide decisions on how to develop higher-quality software faster. Scrum is widely used by software development teams. In fact it's the most popular agile methodology. The paper has been carried with the distinct objectives of examine and gain insights into the current Scrum methods and practices, understanding the strengths and weaknesses of Scrum methods and various issues of their applicability.
An Improved Collaborative Filtering Recommendation Algorithm Based on Retroactive Inhibition Theory

Collaborative filtering (CF) is the most classical and widely used recommendation algorithm, which is mainly used to predict user preferences by mining the user’s historical data. CF algorithms can be divided into two main categories: user-based CF and item-based CF, which recommend items based on rating information from similar user profiles (user-based) or recommend items based on the similarity between items (item-based). However, since user’s preferences are not static, it is vital to take into account the changing preferences of users when making recommendations to achieve more accurate recommendations. In recent years, there have been studies using memory as a factor to measure changes in preference and exploring the retention of preference based on the relationship between the forgetting mechanism and time. Nevertheless, according to the theory of memory inhibition, the main factors that cause forgetting are retroactive inhibition and proactive inhibition, not mere evolutions over time. Therefore, our work proposed a method that combines the theory of retroactive inhibition and the traditional item-based CF algorithm (namely, RICF) to accurately explore the evolution of user preferences. Meanwhile, embedding training is introduced to represent the features better and alleviate the problem of data sparsity, and then the item embeddings are clustered to represent the preference points to measure the preference inhibition between different items. Moreover, we conducted experiments on real-world datasets to demonstrate the practicability of the proposed RICF. The experiments show that the RICF algorithm performs better and is more interpretable than the traditional item-based collaborative filtering algorithm, as well as the state-of-art sequential models such as LSTM and GRU.
Research Status and Prospect of Blockchain Technology in Agriculture Field

Abstract Agriculture 4.0 is the era of integrating intelligent technologies in agriculture. Problems such as low informatization, food safety, high management cost and imbalance between supply and demand in agriculture have greatly hindered the development of agriculture. The various properties of blockchain technology can make up for the lack of agricultural mechanism, and the fusion of the two is a hot issue in the application of blockchain. Blockchain technology has already had some application cases in the field of agriculture. Based on the research status of Chinese and foreign scholars in this field, this paper firstly introduces the basic overview of blockchain. Then, with agricultural supply chain and agricultural product traceability as the core, it describes the application of blockchain technology in the agricultural field, and further explores solutions to different application problems. Finally, combined with the practical application of “agriculture + blockchain”, relevant Suggestions are proposed to provide reference for cross-disciplinary research.
An Efficient Generic Review Framework for Assessment of Learners Ability using KNN Algorithm

Nowadays online review and recommendation system plays major role for maintaining quality of any kind of product with various domain.The customer has to evaluate the product and provides the positive as well as negative reviews based on their interest.The education domain has various stages of the evaluation namely staff related, student related and organization related and so on.Normally the review is restricted to the particular college or university because of their own protocols and system.The proposed framework is to implement the generic review system based on the respective configuration.There are various templates introduced to get the accurate review with good evaluation result.This framework is modeled using the layer of abstraction.User interface layer provides the complete support for the user who uses the system with customized design.Review layer handles all data in different by retrieving and storing the credentials for further review process.Application configuration layer is used to provide the template for assigning credentials and authorization with complete configuration of the system.The final reports are taken for the evaluation process for taking corrective action for further improvement.The classification of the review is carried out in order to achieve high level of accuracy.The main objective of the proposed framework is to classify the student review using KNN (K Nearest Neighbor) algorithm with high efficiency.
A Deep Learning Method Based Receiver Design

The design of the communication receiver for recovering the transmitted signals in conventional works is usually divided into several independent processing blocks, respectively corresponding to specific sub-tasks. In contrast, we propose an end-to-end receiver design based on deep learning (DL) technique in this paper. The proposed DL-based scheme feeds the received signals (including the pilots and transmitted data) at the receive antenna into the neural network as the input, the DL model is trained as a communication receiver to directly output the originally transmitted bits. The proposed DL-based scheme jointly solves the different tasks such as the carrier frequency offset estimation, channel state information estimation, and signal detection using a single neural network, which achieves performance gains. To reveal the performance improvements achieved by the proposed novel scheme, we provided numerical simulations compared with benchmarks. We further verified its feasibility in practical implementation via software-defined radio and illustrated the strong robustness.
Efficient training of energy-based models via spin-glass control

Abstract We introduce a new family of energy-based probabilistic graphical models for efficient unsupervised learning. Its definition is motivated by the control of the spin-glass properties of the Ising model described by the weights of Boltzmann machines. We use it to learn the Bars and Stripes dataset of various sizes and the MNIST dataset, and show how they quickly achieve the performance offered by standard methods for unsupervised learning. Our results indicate that the standard initialization of Boltzmann machines with random weights equivalent to spin-glass models is an unnecessary bottleneck in the process of training. Furthermore, this new family allows for very easy access to low-energy configurations, which points to new, efficient training algorithms. The simplest variant of such algorithms approximates the negative phase of the log-likelihood gradient with no Markov chain Monte Carlo sampling costs at all, and with an accuracy sufficient to achieve good learning and generalization.
Chinese named entity recognition model based on BERT

Nowadays, most deep learning models ignore Chinese habits and global information when processing Chinese tasks. To solve this problem, we constructed the BERT-BiLSTM-Attention-CRF model. In the model, we embeded the BERT pre-training language model that adopts the Whole Word Mask strategy, and added a document-level attention. Experimental results show that our method achieves good results in the MSRA corpus, and F1 reaches 95.00%.
Simple multi-dataset detection

How do we build a general and broad object detection system? We use all labels of all concepts ever annotated. These labels span diverse datasets with potentially inconsistent taxonomies. In this paper, we present a simple method for training a unified detector on multiple large-scale datasets. We use dataset-specific training protocols and losses, but share a common detection architecture with dataset-specific outputs. We show how to automatically integrate these dataset-specific outputs into a common semantic taxonomy. In contrast to prior work, our approach does not require manual taxonomy reconciliation. Experiments show our learned taxonomy outperforms a expert-designed taxonomy in all datasets. Our multi-dataset detector performs as well as dataset-specific models on each training domain, and can generalize to new unseen dataset without fine-tuning on them. Code is available at https://github.com/xingyizhou/UniDet.
Correction to: PIXL: Planetary Instrument for X-Ray Lithochemistry

A Correction to this paper has been published: https://doi.org/10.1007/s11214-021-00801-2
Automatic tumor segmentation in 3D automated breast ultrasound using convolutional neural network

Automatic breast ultrasound (ABUS) imaging provides complementary information when other imaging modalities (i.e., mammography) are not conclusive in the task of tumor detection. It enables sectional plane visualization, simplified temporal comparison and coronal depiction, and features higher reproducibility than conventional ultrasound imaging. Although the 3D ABUS acquisition significantly reduces the acquisition time and cost, the manual segmentation of tumor on 3D ABUS could be time-consuming and labor-intensive due to its high slice number. This work aims to develop a deep-learning-based method to automatically segment the breast tumor on 3D ABUS. We integrated mask scoring-based self-correlation strategy into the R-CNN-based method to force the final segmented tumor contour to be more reasonable. We tested the performance of the proposed method using 3D ABUS of 40 patients who are confirmed with breast tumor through four-fold cross validation test. The comparison between our results and the ground truth contours was quantified by metrics including the Dice similarity coefficient (DSC), Jaccard index (JAC), 95% Hausdorff distance (HD95). The mean DSC, JAC, and HD95 were 0.85 &plusmn; 0.10, 0.75 &plusmn; 0.14, and 1.65 &plusmn; 1.20 (mm), respectively.
Fully automated segmentation of brain tumor from multiparametric MRI using 3D context u-net with deep supervision

Gliomas are very heterogenous set of tumors that grow within the substance of brain and often mix with normal brain tissues. Due to its histologic complexity and irregular shapes, multiparametric magnetic resonance imaging is used to accurately diagnose brain tumor and their subregions. Current practice requires physicians to manually segment these regions on a large image dataset, which can be a very time consuming and complicated task especially with large variations among different tumor regions. Automatic segmentation of brain tumors in multimodal MRI holds a great potential in developing an effective treatment plan and improving brain tumor radiotherapy workflow. Despite continuous investigations on DL-based brain tumor segmentation, irregular shapes and histologic complexities of brain tumors introduces a major challenge in developing an effective automatic segmentation method. In this study, we develop a novel context U-net with deep supervision to segment both the whole brain tumor and their subregions of tumors. The context module was formed by an inception-like structure to extract more information regarding the brain tumors. The deep supervision in the encoder path was achieved by adding up the segmentation outputs at different levels of the network. We evaluated our method on Brain Tumor Segmentation Challenge (BraTS) 2019 training dataset in which 80% was used for training while the remaining 20% was used for performance testing. Our method achieved Dice similarity coefficients (DSC) were 0.8693, 0.8013 and 0.7782 for the whole tumor (WT), tumor core (TC) and enhancing tumor (ET), respectively. The results attained by our proposed network suggested that this technique could be used for segmentation of brain tumors and their subregions to facilitate the brain tumor radiotherapy workflow.
Quantum multicast schemes of different quantum states via non-maximally entangled channels with multiparty involvement*

Due to the unavoidable interaction between the quantum channel and its ambient environment, it is difficult to generate and maintain the maximally entanglement. Thus, the research on multiparty information transmission via non-maximally entangled channels is of academic value and general application. Here, we utilize the non-maximally entangled channels to implement two multiparty remote state preparation schemes for transmitting different quantum information from one sender to two receivers synchronously. The first scheme is adopted to transmit two different four-qubit cluster-type entangled states to two receivers with a certain probability. In order to improve success probabilities of such multicast remote state preparation using non-maximally entangled channels, we put forward the second scheme, which deals with the situation that is a synchronous transfer of an arbitrary single-qubit state and an arbitrary two-qubit state from one sender to two receivers. In particular, its success probability can reach 100% in principle, and independent of the entanglement degree of the shared non-maximally entangled channel. Notably, in the second scheme, the auxiliary particle is not required.
Stream-Based Data Sampling Mechanism for Process Object

Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reflected. Recently, how to find appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of finding c hanges o f p rocess o bjects. There i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to find a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to find changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.
3D Reconstruction with circular light field by using 3D hough transformation

Aiming at the issue of incomplete trajectories in the 2D epipolar image of circular light field, this paper proposes a 3D reconstruction method by using 3D Hough transformation. This method computes 3D point clouds by computing the parameters of feature trajectories in 3D image volume. By analyzing the 3D distribution of circular light field trajectories, binary curves in image volume are extracted, and their local orientation are further estimated by the 3D structure tensor. The 3D Hough space generation and the parameter selection method are proposed to the 3D curves detection. The parameters of these curves are converted to 3D point clouds on each view and then merged to final 3D reconstruction. The ambiguity of Hough transformation solution on 2D epipolar image is overcome by the 3D analyzing method. The experiments are carried out on both synthetic and real datasets. The experiment results show that this method can improve the reconstruction performance compared with the state-of-the-art in circular light field.
Improved ResNet-Based Image Classification Technique for Malaria Detection

Malaria is a communicable disease instigated by mosquitoes and is life-threatening. As per the World Health Organization (WHO), there are 216 million cases of malaria spread across 91 countries. According to the report dated December 2019, there are 228 million people affected by malaria in 2018 in contrast to 231 million ones in 2017. The traditional way of diagnosing malaria is by visually inspecting blood samples of Red Blood Cells (RBCs) infected by the parasite under a microscope. However, this method is time-consuming as the diagnosis is entirely manual and is based on the expertise of the examiner. Other alternate diagnostic methods include Automatic Image Recognition (AIR) technologies based on Machine Learning (ML). These methods when applied to blood smear yield non-optimal results. To overcome the drawbacks associated with these techniques, the proposed system uses a Residual Neural Network (ResNet) architecture for detecting malaria from blood sample images. Classification is performed on the augmented image dataset to achieve more accuracy. The model offers 93.25% diagnostic accuracy on the training data and 87.35% accuracy on the validation data.
Design and development SST Prototype for Yemeni paper currency

Self-service terminal equipment generally, compose of group of integrated systems, which operate together to offer services to human in the economic aspect. The conception of the self-service store terminal equipment facilitates person's life, greatly. The self- service terminal equipment that exist in Yemen are rarely found, because of their extremely expensive cost. Additionally, that material can't deal with all denominations of Yemeni paper currency; just two denominations can be treated. The proposed system is a Mechatronics' system which typifies a prototype of SST equipment which is able to arrangement with all denominations of Yemeni paper currency. The principal function of the proposed scheme is building up an SST prototype with simple social system which is able to recognize and detect four denominations of Yemeni paper currency (1000, 500, 200 and 100 Yr), then store them in the specified box for each denomination. The proposed scheme is composed of four integrated systems as work together concurrently. The entered paper currency is fed into the detection place to be determined whether it is fake or genuine, then if it is genuine, it will be recognized to identify its denomination, finally it is stored in the specified box according to the currency denomination. The identification scheme has been designed to distinguish the dominant colour in each denomination using color sensor, while the detection scheme is designed using the UV - light detector. The developed system has been tested using 10 samples of each denomination for checking the accuracy and speed of operation. The prototype has achieved a high accuracy with short time of processing.
Harmonization between Renewable Energy and Cloud Computing towards Green Computing A Case Study: Data Center at The University Of Jordan

The use of high-performance computing in massive data centers (DC) and massive clusters is growing and consequently large number of processor chips is required. This increases their energy required to operate the DC and hence the amounts of emitted pollutants will increase as more electrical power is required to cover the increase in the power input. In addition more heat is generated leading to an increase in the devices and hence efficient cooling is required. To minimize these problems, green cloud computing has become essential. In this work, a study was conducted to investigate the potential of utilization solar PV energy and cloud computing in the data center at the University of Jordan. Various scenarios were suggested for a green cloud computing solution among which and to cut down the running cost of the power consumed, a net metering solar PV hybrid system was designed, such that this system will supply the annual power load. To further cut down the cost, the conventional servers were replaced by cloud servers as an alternative system. Using this alternative system, it was found that the total of required PV panels was reduced by almost 57%. Finally, it was found that the payback periods of both systems is 20 months.
Economic Structure Analysis Based on Neural Network and Bionic Algorithm

In this article, an in-depth study and analysis of economic structure are carried out using a neural network fusion release algorithm. The method system defines the weight space and structure space of neural networks from the perspective of optimization theory, proposes a bionic optimization algorithm under the weight space and structure space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects. In the shallow neuroevolutionary, the improved genetic algorithm (IGA) based on elite heuristic operation and migration strategy and the improved coyote optimization algorithm (ICOA) based on adaptive influence weights are proposed, and the shallow neuroevolutionary method based on IGA and the shallow neuroevolutionary method based on ICOA are applied to the weight space of backpropagation (BP) neural networks. In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search (NAS), and the GA-based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. The neural network fusion bionic algorithm used has the application value of exploring the spatial structure and dynamics of the socioeconomic system, improving the perception of the socioeconomic situation, and understanding the development law of society, etc. The idea is also verifiable through the present computer technology.