Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fuzzy Set Based Approach for Rating Bias

In recommender systems, the user uncertain preference results in unexpected ratings. This paper makes an initial attempt in integrating the influence of user uncertain degree into the matrix factorization framework. Specifically, a fuzzy set of like for each user is defined, and the membership function is utilized to measure the degree of an item belonging to the fuzzy set. Furthermore, to enhance the computational effect on sparse matrix, the uncertain preference is formulated as a side-information for fusion. Experimental results on three real-world datasets show that the proposed approach produces stable improvements compared with others.
Multilayer feature descriptors fusion CNN models for fine‐grained visual recognition

Fine‐grained image classification is a challenging topic in the field of computer vision. General models based on first‐order local features cannot achieve acceptable performance because the features are not so efficient in capturing fine‐grained difference. A bilinear convolutional neural network (CNN) model exhibits that a second‐order statistical feature is more efficient in capturing fine‐grained difference than a first‐order local feature. However, this framework only considers the extraction of a second‐order feature descriptor, using a single convolutional layer. The potential effective classification features of other convolutional layers are ignored, resulting in loss of recognition accuracy. In this paper, a multilayer feature descriptors fusion CNN model is proposed. It fully considers the second‐order feature descriptors and the first‐order local feature descriptor generated by different layers. Experimental verification was carried out on fine‐grained classification benchmark data sets, CUB‐200‐2011, Stanford Cars, and FGVC‐aircraft. Compared with the bilinear CNN model, the proposed method has improved accuracy by 0.8%, 1.1%, and 5.5%. Compared with the compact bilinear pooling model, there is an accuracy increase of 0.64%, 1.63%, and 1.45%, respectively. In addition, the proposed model effectively uses multiple 1×1 convolution kernels to reduce dimension. The experimental results show that the multilayer low‐dimensional second‐order feature descriptors fusion model has comparable recognition accuracy of the original model.
Part-based Tracking via Discriminative Correlation Filters

In order to better deal with the partial occlusion issue, part-based trackers are widely used in visual object tracking recently. However, it is still difficult to realize fast and robust tracking, due to complicated online training and updating process. Correlation filters have been used in visual object tracking tasks recently because of their high efficiency. However, the traditional correlation filter based tracking methods do not deal with occlusion well. In this paper, we propose a novel tracking method which tracks objects based on parts with multiple correlation filters. The Bayesian inference framework and a structural constraint mask are adopted to enable our tracker to be robust to various appearance changes. Additionally, a discriminative part selection scheme is adopted to further improve performance and accelerate our method. Experimental results demonstrate that our multiple part tracker can significantly improve tracking performance on benchmark datasets.
Genetic Algorithm & Tabu Search Attack on The Mono-Aiphanetic Substitution Cipher

With the exponential growth of networked system and application such as Commerce, the demand for effective internet security is increasing. Cryptology is the science and study of systems for secret communication. It consists of two complementary fields of study: cryptography and cryptanalysis. In this paper, we propose a cryptanalysis method based on Genetic Algorithm and Tabu Search to break a Mono-Alphabetic Substitution Cipher. We will also compare and analyze the performance of these algorithms in automated attacks on a Mono-Alphabetic Substitution Cipher. The use of Tabu Search is a largely unexplored area in the field of Cryptanalysis. A generalized version of these algorithms can be used for attacking other cipher as well.
Proceedings of the BioNLP Shared Task 2011 Workshop

The requirements of improved access to the massive amount of scientific literature in biomedical domain - through applications such as semantic search, assisted pathway annotation, and the automatic identification of specific biomolecular reactions for database curation support - place continuing demands on the development of methods and resources for advanced biomedical information extraction and text mining. The BioNLP Shared Task series seeks to advance this development through an increased focus on detailed structured representations of extracted information, novel corpus resources with fully text-bound annotation, and precise task definitions, support and evaluation. The BioNLP Shared Task 2011 is the second in the series, following up on the first event organized in 2009. Seeking to build on the success of the previous event, the task was organized as a collaboration between several groups in Asia, Europe and the US who defined in total eight specific tasks involving diverse challenges, including in addition to structured event extraction also relation extraction and supporting tasks such as coreference resolution. The main theme of the 2011 event was generalization, and the main tasks further broadened on the 2009 setup in three aspects: text types, subject domains, and novel event extraction targets. The task attracted broad interest from the community, and a total of 46 final submissions were received from 24 groups, maintaining the 2009 task participation numbers while nearly doubling its number of submissions. In addition to the continued interest from the biomedical text mining community, we were glad to welcome the participation of many new groups from academia and industry. The submissions demonstrated substantial progress at the established event extraction task and showed that event extraction methods generalize well, among other aspects, to full papers, new subject domains such as infectious diseases and bacterial interactions, and new sets of events such as protein post-translational modifications.
Spreadsheet Implementation Technology: Basics and Extensions

Spreadsheets are used daily by millions of people for tasks that range from organizing a list of addresses to carrying out complex economic simulations. Spreadsheet programs are easy to learn and convenient to use because they have a clear visual model and a simple efficient underlying computational model. Yet although the basic spreadsheet model could be extended, improved, or otherwise experimented with in many ways, there is no coherently designed, reasonably efficient open source spreadsheet implementation that is a suitable platform for such experiments. This book fills the gap, teaching users how to experiment with and implement innovative spreadsheet functionality and introducing two software platforms for doing so. Along the way, it draws on and illustrates software technologies and computer science topics that range from object-oriented programming to compiler technology. Spreadsheet Implementation Technology surveys a wide range of information about spreadsheets drawn from user experience, the scientific literature, and patents. After summarizing the spreadsheet computation model and the most important challenges for efficient recalculation, the book describes Corecalc, a core implementation of essential spreadsheet functionality suitable for practical experiments, and Funcalc, an extension of Corecalc that allows users to define their own functions without extraneous programming languages or loss of efficiency. It also shows the advantages of automatic function specialization and offers a user's manual for Funcalc. The Corecalc and Funcalc software is downloadable free of charge.
A Variable Step Size LMS Adaptive Filtering A lgorithm

The paper based on the LMS algorithm that normalized LMS (NLMS) algorithm enlarges the dynamic range of input and variable step-size LMS algorithm (VSSLMS)which uses auto-correlation of errors can provide fast convergence, eliminate the influence of independent noise. This paper combinates their advantages, and the new algorithm can perform well both in the environment of low SNR and environment with large dynamic range of input. Meanwhile, it manages to use MATLAB to realize the Refrained Variable Step-size Normalized LMS Algorithm.
Time Series Shapelet Extraction Based on Principal Component Analysis

Shapelet provides a fast classification method in time series classification, but the extraction of time series Shapelet is so slow that it restricts the application of the Shapelet. In order to speed up the extraction of time series Shapelet, an improved method is proposed based on the principal component analysis. Firstly, it uses the principal component analysis(PCA) to reduce the dimension of time series data set and chooses the reduced data to represent the original data. Secondly, it can extract the most discriminatory Shapelet sequence from the reduced data. Lastly, the experimental results show that the improved method improves the speed of the extraction and ensures the accuracy of classification.
Detection of Agreement and Disagreement: An investigation of linguistic coordination and conversational features

Detection of Agreement and Disagreement: An investigation of linguistic coordination and conversational features Maria Alexandropoulou Chair of the Supervisory Committee: Gina-Anne Levow, Assistant Professor Department of Linguistics The focus of this thesis is detection of agreement and disagreement in multiparty conversations using existing transcripts from the ICSI corpus. We use an unsupervised lexicon-based method to create our baseline and then follow a supervised approach to study the effect on performance of different feature sets. The feature sets we are interested in are the following: a. lexicosyntactic, b. Dialog act tag-based features, c. Linguistic style coordination features and d. Conversational features (baseline, individual and non-individual ones). The results enabled us to study the presence of coordination in agreeing and disagreeing statements and showed that the performance can improve when adding further features on top of the lexicosyntactic ones. Additionally, we saw that non-individual conversational features can improve the performance of agreement detection while individual conversational ones appear to have a similar effect on disagreement detection.
Clinical and Molecular Predictors of Response to Immune Checkpoint Inhibitors in Patients with Advanced Esophagogastric Cancer

Purpose: Immune checkpoint inhibitors (ICI) are effective in only a minority of patients with esophagogastric cancer (EGC). Here, we aimed to identify predictors of durable clinical benefit to ICI in EGC. Experimental Design: Patients with advanced EGC treated with ICIs at Memorial Sloan Kettering Cancer Center (New York, NY) were identified. Clinicopathologic variables were assessed. In patients profiled by MSK-IMPACT–targeted sequencing, outcomes were correlated with tumor genomic features. Results: One-hundred sixty-one patients were treated with ICIs (110 with anti–PD-1/PD-L1 antibodies and 51 with anti–CTLA-4 and PD-1/PD-L1 antibodies). The median progression-free survival (PFS) and overall survival (OS) were 1.7 and 4.9 months, respectively. Greater number of disease sites (≥3), liver metastases, treatment with ≥3 prior therapies and ECOG performance status ≥2 were associated with poorer PFS and OS. Patients treated with combination ICI and those with PD-L1–positive tumors had improved outcomes. There was no difference in outcomes between patients treated with antibiotics during or in the 2 months preceding ICI treatment versus those who were not. Occurrence of irAEs was associated with improved OS. In genomically profiled tumors (n = 89), survival was associated with increasing tumor mutation burden (TMB). However, in multivariable analyses and when microsatellite unstable (MSI) patients were excluded, a significant association was no longer observed. Conclusions: In patients with advanced EGC, heavily pretreated patients, those with high-volume disease and/or poor PS were less likely to benefit from ICI. irAEs were associated with improved OS. TMB correlated with improved survival, but this association was not observed when MSI-high patients were excluded.
Impaired Amino Acid and TCA Metabolism and Cardiovascular Autonomic Neuropathy Progression in Type 1 Diabetes

While diabetes is characterized by hyperglycemia, nutrient metabolic pathways like amino acid and tricarboxylic acid (TCA) cycle are also profoundly perturbed. As glycemic control alone does not prevent complications, we hypothesized that these metabolic disruptions are responsible for the development and progression of diabetic cardiovascular autonomic neuropathy (CAN). We performed standardized cardiovascular autonomic reflex tests and targeted fasting plasma metabolomic analysis of amino acids and TCA cycle intermediates in subjects with type 1 diabetes and healthy control subjects followed for 3 years. Forty-seven participants with type 1 diabetes (60% female and mean ± SD age 35 ± 13 years, diabetes duration 13 ± 7 years, and HbA1c 7.9 ± 1.2%) had lower fumarate levels and higher threonine, serine, proline, asparagine, aspartic acid, phenylalanine, tyrosine, and histidine levels compared with 10 age-matched healthy control subjects. Higher baseline fumarate levels and lower baseline amino acid levels—asparagine and glutamine—correlate with CAN (lower baseline SD of normal R-R interval [SDNN]). Baseline glutamine and ornithine levels also associated with the progression of CAN (lower SDNN at 3 years) and change in SDNN, respectively, after adjustment for baseline HbA1c, blood glucose, BMI, cholesterol, urine microalbumin-to- creatinine ratio, estimated glomerular filtration rate, and years of diabetes. Therefore, significant changes in the anaplerotic flux into the TCA cycle could be the critical defect underlying CAN progression.
Relationships between Hearing Loss and the Prevalences of Cataract, Glaucoma, Diabetic Retinopathy, and Age-Related Macular Degeneration in Korea

This study was conducted using the database of the Korea National Health and Nutrition Examination Survey to determine whether age-related eye diseases such as cataract, glaucoma, diabetic retinopathy (DR), and age-related macular degeneration (AMD), are related to hearing loss. 12,899 participants ≥ 40 years of age were included. The weighted prevalence of diabetic retinopathy was not significantly different between the normal hearing group and hearing-impaired group, but the weighted prevalences of cataract, glaucoma, early AMD, and late AMD were significantly different in the two groups. The odds ratio for cataract in the hearing-impaired group was 1.373 (1.118–1.687). The odds ratios of glaucoma, DR, early AMD, and late AMD were not significantly different in the hearing-impaired group. Age was significantly associated with the presence of concurrent cataract and hearing impairment by 6.574-fold per decade. Significant factors that increased the risk of concurrent glaucoma and hearing impairment were age, male gender, and triglyceride. Age, ex-smoker, systolic BP elevation, BMI decline, and fasting blood sugar significantly predicted the presence of concurrent DR and hearing loss. In early AMD, age and triglyceride, and in late AMD, age and systolic BP elevations increased the risk of concurrent AMD and hearing impairment.
Prehypertension and risk of cardiovascular diseases: a meta-analysis of 47 cohort studies.

OBJECTIVE To assess the association of prehypertension (SBP 120-139 mmHg and/or DBP 80-89 mmHg) and total cardiovascular diseases (CVDs), coronary heart disease (CHD), myocardial infarction (MI), and stroke. METHODS PubMed, Embase, and Web of Science were searched for articles published up to 7 November 2018. Normal range BP was considered SBP less than 120 mmHg and DBP less than 80 mmHg. RRs and 95% CIs were pooled using fixed-effects models. Meta-regression was conducted to estimate the heterogeneity among subgroups. RESULTS We included 27 articles (47 studies including 491 666 study participants) in the analysis. Prehypertension was associated with total CVDs (RR 1.40, 95% CI 1.34-1.46), CHD (1.40, 1.28-1.52), MI (1.86, 1.50-2.32), and stroke (1.66, 1.56-1.76). Risk of total CVDs, MI, and stroke was increased with low-range prehypertension (low-range: SBP 120-129 mmHg and/or DBP 80-84 mmHg) versus normal BP - RR 1.42 (95% CI 1.29-1.55), 1.43 (1.10-1.86), and 1.52 (1.27-1.81), respectively - and risk of total CVDs, CHD, MI, and stroke was increased with high-range prehypertension (high-range: SBP 130-139 mmHg and/or DBP 85-89 mmHg) - RR 1.81 (95% CI 1.56-2.10), 1.65 (1.13-2.39), 1.99 (1.59-2.50), and 1.99 (1.68-2.36), respectively. The population-attributable risk for the association of total CVDs, CHD, MI, and stroke with prehypertension was 12.09, 13.26, 24.60, and 19.15%, respectively. CONCLUSION Prehypertension, particularly high-range, is associated with increased risk of total CVDs, CHD, MI, and stroke. Effective control of prehypertension could prevent more than 10% of CVD cases.
ForMAAD: towards a model driven approach for agent based application design

Current trends in multi-agent systems development show a move towards adopting the Model Driven Architecture (MDA) approach to improve the development process and the quality of the agent-based software. Our work has two main contributions. First, it presents a reformulation of the For MAAD methodology in terms of the MDA paradigm by using the AML language. Second, it proposes a translation of each model to a formal language, Temporal Z that integrates linear temporal logic to the Z notation, in order to guarantee a formal verification of the models. Furthermore, we make extensions to the StarUML tool to support the proposed models and use the transition rules. Our work is illustrated by developing an agent-based solution for the air traffic control problem.
Modeling Impacts of Highway Circular Curve Elements on Heavy-Duty Diesel Trucks’ CO2 Emissions

Heavy-duty trucks contribute a significant component of all transportation in cargo terminals, such as Shaanxi Province, China. The emissions from these vehicles are the primary source of carbon emissions during highway operations. While several studies have attempted to address emission issues by improving traffic operations, a few focused on the relationship between emissions and highway geometric design, especially for heavy-duty trucks. The primary goal of this research was to understand the impact of circular curve on carbon dioxide (CO2) emissions produced by heavy-duty diesel trucks. Firstly, appropriate parameters were specified in MOVES (motor vehicle emission simulator) model according to the geometrical characteristics. Fuel consumption, speed and location data were collected by hiring five skilled drivers on the automotive proving ground located at Chang’an University, Shaanxi Province. The associated carbon emission data were derived from fuel consumption data by applying the IPCC (Intergovernmental Panel on Climate Change) method. After this, the applicability of MOVES model was verified by the field experiment. Moreover, a multiple regression model for CO2 emissions incorporated with roadway segment radius, circular curve length, and initial vehicle speed was established with data generated by the MOVES model. The proposed CO2 emission model was also verified by field experiment with relative error of 6.17%. It was found that CO2 emission had monotone decreasing property with radius increasing, and the minimum radius that influenced diesel CO2 emission was 550 m. The proposed quantitative CO2 emission model can provide a reference for low-carbon highway design, leading to environment-friendly transportation construction.
Protein supplementation combined with low-intensity resistance training in geriatric medical patients during and after hospitalisation: a randomised, double-blind, multicentre trial

Abstract Sarcopenia (loss of muscle mass/strength) burdens many older adults – hospitalised older adults being particularly vulnerable. Treating the condition, protein supplementation (PrS) and resistance training (RT) may act synergistically. Therefore, this block-randomised, double-blind, multicentre intervention study, recruiting geriatric patients > 70 years from three medical departments, investigated the effect of PrS combined with RT during hospitalisation and 12 weeks after discharge. Participants were randomly allocated (1:1) to receive PrS (totally 27·5 g whey protein/d, about 2000 kJ/d) or isoenergetic placebo-products (< 1·5 g protein/d) divided into two servings per d to supplement the habitual diet. Both groups were engaged in a standardised, progressive low-intensity RT programme for the lower extremities (hospital: supervised daily/after discharge: self-training 4×/week). From April 2016 to September 2017, 2351 patients were screened, 462 were eligible, and 165 included. Fourteen were excluded and ten dropped out, leaving 141 participants in the intention-to-treat analysis. The average total protein intake during hospitalisation/after discharge was 1·0 (interquartile range (IQR) 0·8, 1·3)/1·1 (IQR 0·9, 1·3) g/kg per d (protein-group) and 0·6 (IQR 0·5, 0·8)/0·9 (IQR 0·6, 1·0) g/kg per d (placebo group). Both groups improved significantly for the primary and secondary endpoints of muscle mass/strength, functional measurements and quality of life, but no additional effect of PrS was seen for the primary endpoint (30-s chair stand test, repetitions, median changes from baseline: (standard test: 0 (IQR 0, 5) (protein group) v. 2 (IQR 0, 6) (placebo group) and modified test: 2 (IQR 0, 5) (protein group) v. 2 (IQR −1, 5) (placebo group)) or any secondary endpoints (Mann–Whitney U tests, P > 0·05). In conclusion, PrS increasing the total protein intake by 0·4 and 0·2 g/kg per d during hospitalisation and after discharge, respectively, does not seem to increase the adaptive response to low-intensity RT in geriatric medical patients.
Vehicle Driver Monitoring through the Statistical Process Control

This paper proposes the use of the Statistical Process Control (SPC), more specifically, the Exponentially Weighted Moving Average method, for the monitoring of drivers using approaches based on the vehicle and the driver’s behavior. Based on the SPC, we propose a method for the lane departure detection; a method for detecting sudden driver movements; and a method combined with computer vision to detect driver fatigue. All methods consider information from sensors scattered by the vehicle. The results showed the efficiency of the methods in the identification and detection of unwanted driver actions, such as sudden movements, lane departure, and driver fatigue. Lane departure detection obtained results of up to 76.92% (without constant speed) and 84.16% (speed maintained at ≈60). Furthermore, sudden movements detection obtained results of up to 91.66% (steering wheel) and 94.44% (brake). The driver fatigue has been detected in up to 94.46% situations.