Ranking Information Research Articles

Truthful Mechanisms for Medical Surplus Product Allocation

Problem definition: We analyze a resource allocation problem faced by medical surplus recovery organizations (MSROs) that recover medical surplus products to fulfill the needs of underserved healthcare facilities in developing countries. The objective of this study is to identify implementable strategies to support recipient selection decisions to improve MSROs’ value provision capability. Academic/practical relevance: MSRO supply chains face several challenges that differ from those in traditional for-profit settings, and there is a lack of both academic and practical understanding of how to better match supply with demand in this setting where recipient needs are typically private information. Methodology: We propose a mechanism design approach to determine which recipient to serve at each shipping opportunity based on recipients’ reported preference rankings of different products. Results: We find that when MSRO inventory information is shared with recipients, the only truthful mechanism is random selection among recipients, which defeats the purpose of eliciting information. Subsequently, we show that (1) eliminating inventory information provision enlarges the set of truthful mechanisms, thereby increasing the total value provision; and (2) further withholding information regarding other recipients leads to an additional increase in total value provision. Finally, we show that under a class of implementable mechanisms, eliciting recipient valuations has no value added beyond eliciting preference rankings. Managerial implications: (1) MSROs with large recipient bases and low inventory levels can significantly improve their value provision by appropriately determining the recipients to serve through a simple scoring mechanism; (2) to truthfully elicit recipient needs information to support the recipient selection decisions, MSROs should withhold inventory and recipient-base information; and (3) under a set of easy-to-implement scoring mechanisms, it is sufficient for MSROs to elicit recipients’ preference ranking information. Our findings have already led to a change in the practice of an award-winning MSRO.

Supplemental Nutrition Assistance Program (SNAP)-Authorized Grocery, Convenience, Dollar, and Restaurant or Delivery Service Settings Are Associated With Increased Obesity Prevalence in Virginia.

United States Department of Agriculture Supplemental Nutrition Assistance Program (SNAP) participants use SNAP-authorized stores for dietary purchases. Relationships between obesity prevalence and access to grocery and varied nontraditional (eg, dollar, drug, and convenience) SNAP settings are underexplored. This research aimed to determine the association of a full range of SNAP-authorized stores with obesity prevalence in Virginia. The SNAP Retailer Locator was used to cross-sectionally identify authorized stores, and county health ranking information was applied based on store location. Virginia, United States. The SNAP-authorized stores, classified among store categories: grocery or supermarket; drug; mass merchandiser; supercenter; convenience; dollar; club; other; nonfood store; farmers markets; and independent grocery stores. County-level obesity prevalence with income and rurality as potential confounders. Multiple linear regression was used to determine associations between county-level adult obesity prevalence and available SNAP-authorized store formats (P < .05 a priori). Store format was a predictor of obesity prevalence in Virginia in simple and adjusted models (R2 = 0.035, P < .0001 and R2 = 0.434, P < .0001, respectively). Grocery store or supermarket access was associated with obesity. The SNAP-authorized convenience, dollar, and nonfood stores were associated with a 0.3, 0.5, and 1.3 increase in county obesity prevalence, respectively (P < .05). Research, practice, and health policy approaches to improve grocery, convenience, dollar, and restaurant or delivery service settings may favorably influence community obesity prevalence in Virginia.

On the nature of information access evaluation metrics: a unifying framework

We provide a uniform, general, and complete formal account of evaluation metrics for ranking, classification, clustering, and other information access problems. We leverage concepts from measurement theory, such as scale types and permissible transformation functions, and we capture the nature of evaluation metrics in many tasks by two formal definitions, which lead to a distinction of two metric/tasks families, and provide a comprehensive classification of the tasks that have been proposed so far. We derive some theorems to analyze the suitability (or otherwise) of some common metrics. Within our model we can derive and explain the theoretical properties and drawbacks of the state of the art metrics for multiple tasks. The main contributions of this paper are that, differently from previous studies, the formalization is well grounded on a solid discipline, it is general as it can take into account most effectiveness metrics as well as most existing tasks, and it allows to derive important consequences on metrics and their limitations.

Improved Kaplan-Meier Estimator in Survival Analysis Based on Partially Rank-Ordered Set Samples.

This study presents a novel methodology to investigate the nonparametric estimation of a survival probability under random censoring time using the ranked observations from a Partially Rank-Ordered Set (PROS) sampling design and employs it in a hematological disorder study. The PROS sampling design has numerous applications in medicine, social sciences and ecology where the exact measurement of the sampling units is costly; however, sampling units can be ordered by using judgment ranking or available concomitant information. The general estimation methods are not directly applicable to the case where samples are from rank-based sampling designs, because the sampling units do not meet the identically distributed assumption. We derive asymptotic distribution of a Kaplan-Meier (KM) estimator under PROS sampling design. Finally, we compare the performance of the suggested estimators via several simulation studies and apply the proposed methods to a real data set. The results show that the proposed estimator under rank-based sampling designs outperforms its counterpart in a simple random sample (SRS).

An integrated retrieval framework for similar questions: Word-semantic embedded label clustering – LDA with question life cycle

Question retrieval is an extremely important research field in Community Question Answering (CQA). Most existing question retrieval methods depend on semantic analysis of questions, whose effectiveness suffers from the short texts of the noise words in the question corpus. In order to recommend the questions with more advanced knowledge to users, the influence of the questions’ popularity should be considered during retrieving questions. To make retrieved questions with both similar semantics and high popularity, we propose an Integrated Retrieval Framework for Similar Questions named Word-semantic Embedded Label Clustering – LDA with Question Life Cycle (WELQLC-QR), consisting of Word-semantic Embedded Label Clustering – LDA (WEL) and Question Life Cycle Optimization Similar Question List Strategy (QLC). Firstly, WEL is proposed for question retrieval from the perspective of semantic matching. It not only overcomes the problem of over-generalization of the semantic information extracted by topic models when facing short questions with multi-levels labels, but also avoids the influence of noise vocabularies during semantic extracting of the questions. Then, based on the internal factors (i.e., the number of comments and answers to the question) and external factors (i.e., programming language ranking information) of questions, QLC constructs a popularity-predicted model to optimize the similar question set searched by WEL, making the final retrieval results both semantically similar and popular. Finally, experiments are conducted on CQADupStack dataset, and results show that the MRR@N of WELQLC-QR model has an average increase of 8.99%, 8.3%, 4.74% and 3.56% compared with that of L-LDA, LC-LDA, BM25 and Word2vec, respectively.

A novel blockchain based framework to secure IoT-LLNs against routing attacks

Routing attacks in the Internet of Things environment (IoT) can result in degraded network performance and often denial of service. Low Power and Lossy Network (LLN) is that segment in IoT which comprises constrained devices like sensors and RFIDs. IPv6 Routing Protocol over Low Power and Lossy Networks (RPL) is the standard routing protocol proposed by IETF for routing in IoT-LLNs. RPL efficiently organizes and maintains the IoT-LLNs and also provides certain security features capable of countering external attacks. However, RPL is vulnerable to many insider attacks. In RPL supported IoT-LLNs, the control messages carry the LLN configuration information. The sensor nodes participating in the routing process are allowed to disseminate the configuration information to organize and maintain the LLNs. Two important configuration information carried in the control message, namely, the rank and the version number, play a major role in the topological formation of IoT-LLNs. A participating node may purposely advertise a false rank or version number information to instigate a routing attack. Such attacks degrade the performance of the IoT-LLNs and increase the consumption of network resources. In this paper, we propose a layered model of IoT routing security to analyze the vulnerabilities associated with each phase of the routing process. We explore how to leverage the inherent features of blockchain to enhance routing security in IoT-LLNs. To this end, we propose a blockchain-based framework with a smart contract for generating real-time alerts to efficiently identify the sensor nodes involved in the tampering of LLN configuration information.

SAT-637 Patient-Centered Glycemic Management of Type 2 Diabetes with Mobile Applications

BACKGROUND: Ehealth apps have 3.7 billion downloads yearly. The accessibility of diabetes mobile applications allows for patient self-management of diabetes. Renal and cardiovascular complications,1 which play a role in diabetic patient outcomes, are newly highlighted by the 2019 American Diabetes Association’s Standards of Medical Care in Diabetes.2 Purpose: To evaluate free Android mobile apps using the Diabetes Self-Management Education and Support (DSMES) and the 2019 American Diabetes Association (ADA) guidelines for renal and cardiovascular complications. Methods: Using the search term “diabetes,” Google Play store was accessed. Inclusion criteria: 1) Apps with downloads 1M-100,000; 2) free; 3) DSMES criteria; and 4) medication adherence. Exclusion criteria: 1) Purely educational factual apps on diabetes; 2) no ranking or download information. Google displayed a list of 10 free apps in 2019. The 2019 apps were analyzed for DSMES criteria, renal and cardiovascular complications (ADA 2019), and medication adherence via push notification. Results: The top 10 mobile apps in descending order were: 1) mySugr, 2) Onetouch Reveal, 3) OneDrop Diabetes Management, 4) Diabetes: M, 5) Diabetes, 6) Ontrack Diabetes, 7) Health2Sync, 8) Diabetes Connect, 9) Glucose Buddy Diabetes Tracker, 10) Blood Glucose Tracker. All of the mobile apps had the functionality of tracking blood glucose levels. 8/10 had the ability of tracking HbA1C levels. The percent of DSMES incorporation within the apps ranged from 18.2%-81.8%. None of the apps used all the DSMES guidelines or 2019 ADA. Only 1/10 of the mobile apps had the ability to track the presence of heart palpitation and retina/eye issues recommendations. None of the apps had the ability to track cardiovascular and renal complications. 7/10 of the apps had medication reminders (sound notification) and 4/10 of the apps had push notifications. Conclusion: eHealth mobile apps could be a powerful tool for patient self-management of diabetes. Currently, none of the apps incorporate all of the DSMES or ADA guidelines regarding comorbidities and complications of diabetes. Despite these shortcomings, these apps provide an introduction to the concept of patient-centered tracking of health data. We look for future improvements as more physicians use the apps and provide feedback to the app developers and eHealth commerce space.

A General Framework for Deep Supervised Discrete Hashing

With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefiting from recent advances in deep learning, deep hashing methods have shown superior performance over the traditional hashing methods. However, there are some limitations of previous deep hashing methods (e.g., the semantic information is not fully exploited). In this paper, we develop a general deep supervised discrete hashing framework based on the assumption that the learned binary codes should be ideal for classification. Both the similarity information and the classification information are used to learn the hash codes within one stream framework. We constrain the outputs of the last layer to be binary codes directly, which is rarely investigated in deep hashing algorithms. Besides, both the pairwise similarity information and the triplet ranking information are exploited in this paper. In addition, two different loss functions are presented: $${l_2}$$ loss and hinge loss, which are carefully designed for the classification term under the one stream framework. Because of the discrete nature of hash codes, an alternating minimization method is used to optimize the objective function. Experimental results have shown that our approach outperforms current state-of-the-art methods on benchmark datasets.

HERA

Partial label learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with this type of problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this article, we propose a novel PLL approach named HERA, which simultaneously incorporates the HeterogEneous Loss and the SpaRse and Low-rAnk procedure to estimate the labeling confidence for each instance while training the desired model. Specifically, the heterogeneous loss integrates the strengths of both the pairwise ranking loss and the pointwise reconstruction loss to provide informative label ranking and reconstruction information for label identification, whereas the embedded sparse and low-rank scheme constrains the sparsity of ground-truth label matrix and the low rank of noise label matrix to explore the global label relevance among the whole training data, for improving the learning model. Comprehensive ablation study demonstrates the effectiveness of our employed heterogeneous loss, and extensive experiments on both artificial and real-world datasets demonstrate that our method achieves superior or comparable performance against state-of-the-art methods.

From experiment results to a constraint hierarchy with the ‘Rank Centrality’ algorithm

Rank Centrality (RC; Negahban, Oh, &amp; Shah 2017) is a rank-aggregation algorithm that computes a total ranking of elements from noisy pairwise ranking information. I test RC as an alternative to incremental error-driven learning algorithms such as GLA-MaxEnt (Boersma &amp; Hayes 2001; Jäger 2007) for modeling a constraint hierarchy on the basis of two-alternative forced-choice experiment results. For the case study examined here, RC agrees well with GLA-MaxEnt on the ordering of the constraints, but differs somewhat on the distance between constraints; in particular, RC assigns more extreme (low) positions to constraints at the bottom of the hierarchy than GLA-MaxEnt does. Overall, these initial results are promising, and RC merits further investigation as a constraint-ranking method in experimental linguistics.

From experiment results to a constraint hierarchy with the ‘Rank Centrality’ algorithm

Rank Centrality (RC; Negahban, Oh, &amp; Shah 2017) is a rank-aggregation algorithm that computes a total ranking of elements from noisy pairwise ranking information. I test RC as an alternative to incremental error-driven learning algorithms such as GLA-MaxEnt (Boersma &amp; Hayes 2001; Jäger 2007) for modeling a constraint hierarchy on the basis of two-alternative forced-choice experiment results. For the case study examined here, RC agrees well with GLA-MaxEnt on the ordering of the constraints, but differs somewhat on the distance between constraints; in particular, RC assigns more extreme (low) positions to constraints at the bottom of the hierarchy than GLA-MaxEnt does. Overall, these initial results are promising, and RC merits further investigation as a constraint-ranking method in experimental linguistics.

Investigation of the unique solutions in bilinear decomposition of multiset chemical data

Multiset data analysis can be a proper way to increase the accuracy of multivariate curve resolution (MCR) results, which is dropped due to rotational ambiguity. Resolution theorems and databased-uniqueness introduce the conditions for obtaining the unique solutions. However, the conditions for resolution theorems are dependent on the estimation of local rank information. With multiset analysis, there is a possibility to design the calibration data sets that provide conditions for resolution theorems. Therefore, correspondence constraint, which express the presence or absence of the components in each data set, can be applied in simultaneous analysis to implement the local rank information. Furthermore, simultaneous analysis even without applying correspondence can improve the accuracy of results and in some cases obtain the unique solution based on databased-uniqueness. The results can be explained by the duality concept, which explains the relationship between row and column vector spaces. Two simulated examples and one real excitation-emission fluorescence data were analyzed to explore the unique solutions. The results illustrate how a proper design of multiset data can lead to a unique solution in MCR methods while providing further information about the system. Therefore, not only multiset analysis can improve the results in well-designed data sets, but also can provide the situation to apply correspondence and lead to a unique resolution.

A novel reliability sensitivity analysis method based on directional sampling and Monte Carlo simulation

Local reliability sensitivity and global reliability sensitivity are required in reliability-based design optimization, since they can provide rich information including variable importance ranking and gradient information. However, traditional Monte Carlo simulation is inefficient for engineering application. A novel numerical simulation method based on Monte Carlo simulation and directional sampling is proposed to simultaneously estimate local reliability sensitivity and global reliability sensitivity. By suitable transformation, local reliability sensitivity and global reliability sensitivity can be estimated simultaneously as by-products of reliability analysis for Monte Carlo simulation method. The key is how to efficiently classify Monte Carlo simulation samples into two categories: failure samples and safety samples. Directional sampling method, a classical reliability analysis method, is more efficient than Monte Carlo simulation for reliability analysis. A novel strategy based on nearest Euclidean distance is proposed to approximately screen out failure samples from Monte Carlo simulation samples using directional sampling samples. In the proposed method, local reliability sensitivity and global reliability sensitivity are by-products of reliability analysis using the directional sampling method. Different from existing methods, the proposed method does not introduce hypotheses and does not require additional gradient information. The advantages of the Monte Carlo simulation and directional sampling are well integrated in the proposed method. The accuracy and the efficiency of the proposed method for local reliability sensitivity and global reliability sensitivity are demonstrated by four numerical examples and two engineering examples including the headless rivet and the wing box structure.

PERBEDAAN KUALITAS CITRA MRI LUMBAL SEKUEN TIWI TSE POTONGAN SAGITAL DENGAN DAN TANPA PENGGUNAAN SENSITIVITY ENCODING (SENSE) PADA KASUS HERNIA NUCLEUS PULPOSUS

Background: Parallel imaging is a time-reduction technique that uses phased-array coils. Phased array coils measure and process signals from a piece, then combine pieces to form an image of a larger area of anatomy. Commonly used imaging parallel technique is one of them is sensitivity encoding or SENSE. This research is the difference of image quality MRI Lumbal sequence T1WI TSE of sagittal pieces with and without the use of SENSE in the case of hernia nucleus pulposus (HNP). The aim of this research is to know the difference between SNR, anatomy information and better image quality and anatomical information. MRI Lumbal sequence T1WI TSE sagittal pieces with and without the SENSE in HNP.Methods: The type of this research is quantitative with an experimental approach, The research was conducted in RS dr. Saiful Anwar Malang. The research was taken from Lumbal MRI examination in 9 patients with predetermined exclusion and inclusion criteria of T1WI TSE sectional pieces with and without the use of SENSE in HNP. Image assessment was done by two radiologists. Analysis of data used paired T-test and Wilcoxon test with alpha 5%.Results: The results showed that there was a difference in image quality of MRI Lumbal T1WI TSE sequence of sagittal pieces with and without the use of SENSE in HNP with p-value = 0.001, and anatomic information with p-value = 0.001. Sensitivity encoding resulted in better image quality and anatomical information, with mean values at SNR 181,4333, and rank information anatomy 11,00.Conclusion: There is a difference in SNR and anatomical information between the use of SENSE andwithout the use of SENSE on sagittal lumbar MRI. The use of SENSE is capable of producing quality MRIimagery (SNR) and Anatomical Information on lumbar MRI lumbar sequences of sagittal slices.

Regional geochemical zonation of cultivated floodplains–Application of multi-element associations for soil quality evaluation along the Ohře (Eger) River, Czech Republic

Floodplains are dynamic ecosystems wherein intensive farming can meet increased environmental concentrations and bioavailability of a number of trace elements. Therefore, the primary objective of this study was to investigate the partial digestion of selected trace elements in surficial soil samples (depth up to 40 cm) across a gradient of environmental conditions along the Ohře River and determining the elements of high priority, while also exploring their geographic variation. The established agrochemical testing of Czech soils provides a valuable resource to determine the geochemical differences of multi-element associations in cultivated areas (arable crops and hop fields) in periodically inundated areas in the catchment with a number of potential pollution sources. The topsoil samples were digested using the nitric acid (2 mol/L HNO3), analysed for trace elements (Be, Cd, Co, Cr, Cu, Ni, Pb, V, and Zn) using inductively coupled plasma mass spectrometry (ICP-MS) and atomic absorption spectroscopy (AAS), and for their total Hg content. Data (n = 130) were modelled against the sample location and cultivation using the log-ratio approach. The explanatory and inferential statistical tools (the Mantel's correlation test, PCA, MANOVA) adapted for compositions, combined with a simple spatial ranking and land-use information defined effective discriminating subcomposition (Cu–Hg–Ni) that were able to describe the changes of geochemical domains in floodplains and retained a decent representation (46%) of the entire compositional variability in our dataset. Mapping the Cu–Hg–Ni subcomposition using the RGB composite enabled to display the high-dimensional multivariate dataset with reduced information and enabled to discriminate spatial domains representing geochemical signatures related to changes in element inputs along the watercourse. The analysis of our results within the compositional framework showed a changing pattern of relative enrichment/depletion of the HNO3–extractable trace elements in which a regional contrast between groups of the chalcophile elements (Cu, Pb, Zn, and partly Cd), siderophile transition metals (Cr, Co, Ni, and V) and single elements (Hg, Be) was source-apportioned to the effect of point pollution sources (Hg, Ni, Cr, Co) as well as diffuse sources (Cu, Zn, Pb, Cd).

Secure and privacy-preserving multi-keyword ranked information retrieval from encrypted big data

Cloud deployment raises some security challenges to the confidentiality of data, and the privacy of users. These challenges, along with the pressing demand for adopting big data technologies, together call for the development of stronger encryption algorithms. But encrypting the data makes it difficult to retrieve the most matching documents with respect to the query keywords. Therefore, the authors propose a solution for the ranked encrypted information retrieval, using the modified homomorphic encryption scheme (MHE) still preserving user's privacy. The scheme efficiently utilises the processing power of the cloud server to compute the similarity scores, leaving the decryption and ranking to the client side, thus ensuring the security of the data. Vector space model and term frequency-inverse document frequency (TF-IDF) concepts are used for similarity matching. The execution is then accelerated using a Hadoop cluster and is found to be accurate, efficient, scalable and practical for real world applications.

Rank-One Matrix Approximation With ℓ p -Norm for Image Inpainting

In the problem of image inpainting, one popular approach is based on low-rank matrix completion. Compared with other methods which need to convert the image into vectors or dividing the image into patches, matrix completion operates on the whole image directly. Therefore, it can preserve latent information of the two-dimensional image. An efficient method for low-rank matrix completion is to employ the matrix factorization technique. However, conventional low-rank matrix factorization-based methods often require a prespecified rank, which is challenging to determine in practice. The proposed method factorizes an image matrix as a sum of rank-one matrices so that it does not require rank information in advance as it can be automatically estimated by the algorithm itself when the algorithm has satisfactorily converged. In our study, matching pursuit is applied to search for the best rank-one matrix at each iteration. To be robust against impulsive noise, the residual error between the observed and estimated matrices is minimized by $\ell _p$ -norm with $0 . Then the resultant $\ell _p$ -norm minimization is solved by the iteratively reweighted least squares method. The proposed model is beneficial for the robustness against outliers, and does not require rank information. Experimental results verify the effectiveness and higher accuracy of the proposed method with comparison to several state-of-the-art matrix completion-based image inpainting approaches.

Secure and privacy-preserving multi-keyword ranked information retrieval from encrypted big data

Cloud deployment raises some security challenges to the confidentiality of data, and the privacy of users. These challenges, along with the pressing demand for adopting big data technologies, together call for the development of stronger encryption algorithms. But encrypting the data makes it difficult to retrieve the most matching documents with respect to the query keywords. Therefore, the authors propose a solution for the ranked encrypted information retrieval, using the modified homomorphic encryption scheme (MHE) still preserving user's privacy. The scheme efficiently utilises the processing power of the cloud server to compute the similarity scores, leaving the decryption and ranking to the client side, thus ensuring the security of the data. Vector space model and term frequency-inverse document frequency (TF-IDF) concepts are used for similarity matching. The execution is then accelerated using a Hadoop cluster and is found to be accurate, efficient, scalable and practical for real world applications.

Analysis of Correlation between Real-time Sales Ranking and Information Provided by Mobile Movie Platform: Focus on Non-descriptive Information in Google Play Store’s Best-selling Movies

The cinema circuit is facing a digital, network, and mobile age, which expands nontheater accessibility to movies. Application platforms are situated as the most competitive business model that provide digital content such as games, music, books, and movies. Consumers can acquire content-related information not just offline, but online as well. Therefore, item information provided by application platforms is required. The information provided by application platforms consists of richly descriptive information such as storyline summary, consumer reviews, and related articles, while non-descriptive normative information covers data such as sales ranking, release date, genre, rental or purchase cost, domestic/foreign classification, consumer rating, number of consumer ratings, film rating, and so on. In this study, we surveyed and analyzed statistically the correlation between real-time sales ranking and other comparable non-descriptive information.

Few-Shot Breast Cancer Metastases Classification via Unsupervised Cell Ranking.

Tumor metastases detection is of great importance for the treatment of breast cancer patients. Various CNN (convolutional neural network) based methods get excellent performance in object detection/segmentation. However, the detection of metastases in hematoxylin and eosin (H&E) stained whole-slide images (WSI) is still challenging mainly due to two aspects. (1) The resolution of the image is too large. (2) lacking labeled training data. Whole-slide images generally stored in a multi-resolution structure with multiple downsampled tiles. It is difficult to feed the whole image into memory without compression. Moreover, labeling images for the pathologists are time-consuming and expensive. In this paper, we study the problem of detecting breast cancer metastases in the pathological image on patch level. To address the abovementioned challenges, we propose a few-shot learning method to classify whether an image patch contains tumor cells. Specifically, we propose a patch-level unsupervised cell ranking approach, which only relies on images with limited labels. The main idea of the proposed method is that when cropping a patch A from the WSI and further cropping a sub-patch B from A, the cell number of A is always larger than that of B. Based on this observation, we make use of the unlabeled images to learn the ranking information of cell counting to extract the abstract features. Experimental results show that our method is effective to improve the patch-level classification accuracy, compared to the traditional supervised method. The source code is publicly available at https://github.com/fewshot-camelyon.