Miracle-dz/UniFAR-FTUs huggingface dataset:
Customer‐focused performance and the dynamic model for competence building and leveraging

In today's turbulent environment, customers are playing a more important role in competition, which can be reflected by customers as co‐producer, value co‐producer, or co‐developer of knowledge and competencies, etc. Accordingly, business priority should be given to what customers really value. Unlike previous studies, which emphasize market performance mainly from the internal or firm's perspective, this paper proposes that firms should prioritize customer‐focused performance, defined totally from an external perspective of targeted customers. The paper examines the important role of customer‐focused performance and its interactive relationships with other dimensions of the overall performance system, and goes further to analyze the components and dynamics of customer‐focused performance. Finally, attention is given to the dynamic competence building and leveraging process and its key elements, which determines the customer‐focused performance in perspective of resource‐based views. Important propositions are presented and future implications discussed.
CSP Bounded Model Checking of Preprocessed CTL Extended with Events Using Answer Set Programming

Model checking is a mainstream method for formal verification of communicating sequential processes (CSP). Existing CSP Model Checkers are incapable of verifying multiple properties concurrently in one run of a model checker. In addition, the properties to be verified are described with CSP in most model checkers, which is good for refinement checking, but leads to limited description power and weak generality. In order to tackle the two problems, answer set programming(ASP), which is completely free of sequential dependencies, is used to construct a CSP bounded model checking framework, where the CSP model checking problem is turned into a computation problem of answer sets. CTL is extended with events to describe the properties to be verified. In addition, preprocessing technique of properties is proposed for the sake of reducing the expense spending on replicated verification of the same sub formulas. An ASP based description system is constructed for complete description of various CSP processes and automatic generation of parallel processes. We integrated all the methods into a CSP model checker - ACSPChecker. The feasibility and efficiency of our methods are illustrated by the experiments with a classic concurrency problem - dining philosophers problem.
A PAPR-Aware Dual-Mode Subgigahertz CMOS Power Amplifier for Short-Range Wireless Communication

A peak-to-average power ratio (PAPR)-aware dual-mode power amplifier (PA) for short-range wireless communication in the subgigahertz band is proposed in this brief. By carefully choosing the bias, the dual-mode PA achieves the measured output P1 dB (OP1 dB) of 13.6 dBm with a PAE of 25.5%. Furthermore, a dynamic load modulation network is utilized to optimize the PA's efficiency at back-off power. A power-control loop is proposed to detect the input signal PAPR in real time and flexibly reconfigure the PA's operation modes. With these proposed techniques, the PAE has been improved by ×2.61 at 7-dB back-off power. The measurement shows that the subgigahertz-band PA archives an adjacent channel leakage ratio (ACLR1) of -37.3 dBc and an average/peak error vector magnitude of -30.4 dB/-20.5 dB at 8.9-dBm output power for ~5-dB-PAPR 16-quadrature-magnitude-modulation LTE signals with 5-MHz bandwidth with the power control loop enabled. The PAPR-aware dual-mode PA, powered by a 2-V supply, occupies a 2.52-mm2 die area in a 180-nm CMOS.
Prevalence of Listeria monocytogenes, Yersinia enterocolitica, Staphylococcus aureus, and Salmonella enterica Typhimurium in meat and meat products using multiplex polymerase chain reaction

Aim: The objective of the study was to investigate the occurrence of Listeria monocytogenes, Yersinia enterocolitica, Staphylococcus aureus, and Salmonella enterica Typhimurium in meat and meat products using the multiplex polymerase chain reaction (PCR) method. Materials and Methods: The assay combined an enrichment step in tryptic soy broth with yeast extract formulated for the simultaneous growth of target pathogens, DNA isolation and multiplex PCR. A total of 1134 samples including beef (n=349), chicken (n=325), pork (n=310), chevon (n=50), and meat products (n=100) were collected from different parts of Kerala, India. All the samples were subjected to multiplex PCR analysis and culture-based detection for the four pathogens in parallel. Results: Overall occurrence of L. monocytogenes was 0.08 % by cultural method. However, no L. monocytogenes was obtained by multiplex PCR method. Yersinia enterocolitica was obtained from beef and pork samples. A high prevalence of S. aureus (46.7%) was found in all types of meat samples tested. None of the samples was positive for S. Typhimurium. Conclusion: Multiplex PCR assay used in this study can detect more than one pathogen simultaneously by amplifying more than one target gene in a single reaction, which can save time and labor cost.
Shilling Attacks Detection in Recommender Systems Based on Target Item Analysis

Recommender systems are highly vulnerable to shilling attacks, both by individuals and groups. Attackers who introduce biased ratings in order to affect recommendations, have been shown to negatively affect collaborative filtering (CF) algorithms. Previous research focuses only on the differences between genuine profiles and attack profiles, ignoring the group characteristics in attack profiles. In this paper, we study the use of statistical metrics to detect rating patterns of attackers and group characteristics in attack profiles. Another question is that most existing detecting methods are model specific. Two metrics, Rating Deviation from Mean Agreement (RDMA) and Degree of Similarity with Top Neighbors (DegSim), are used for analyzing rating patterns between malicious profiles and genuine profiles in attack models. Building upon this, we also propose and evaluate a detection structure called RD-TIA for detecting shilling attacks in recommender systems using a statistical approach. In order to detect more complicated attack models, we propose a novel metric called DegSim’ based on DegSim. The experimental results show that our detection model based on target item analysis is an effective approach for detecting shilling attacks.
Diminished survival in patients with inner versus outer quadrant breast cancers.

PURPOSE To evaluate the role of breast tumor location on survival in patients with breast cancer. PATIENTS AND METHODS We evaluated tumor location within the breast on breast cancer-specific survival (BCSS) and overall survival (OS) in patients with invasive breast cancer using the Surveillance, Epidemiology, and End-Results (SEER) registries in the United States. Effects on survival were evaluated according to age, stage, tumor site, tumor size, grade, axillary lymph node status, extent of surgery, and radiotherapy (RT). A multivariate model was used with complete data on 45,880 patients. The median follow-up was 59 months. RESULTS Patients with outer tumor location demonstrated superior BCSS on Kaplan-Meier analysis for both local stage (node-negative, P <.001) and regional stage disease (node-positive, P =.0002). For BCSS, the hazard ratio (HR) for inner quadrant location compared with outer quadrant was 1.31 (95% confidence interval [CI], 1.19 to 1.37; P <.001); and for OS, the HR was 1.12 (95% CI, 1.05 to 1.17; P <.001). When ER and PR status were included in the model, the HR for inner quadrant location compared with outer quadrant was 1.27 for BCSS (95% CI, 1.16 to 1.40; P <.001) and 1.11 for OS (95% CI, 1.03 to 1.19; P =.004). Patients treated by lumpectomy that received RT had a superior OS compared with patients that did not receive RT in both local (HR, 0.52; 95% CI, 0.48 to 0.61; P <.001) and regional (HR, 0.63; 95% CI, 0.56 to 0.72; P <.001) stage disease. Mastectomy patients with local stage disease that received RT had a diminished OS (HR, 1.24; 95% CI, 1.02 to 1.50; P =.033). CONCLUSION On multivariate analysis, incorporating data on age, stage, tumor site, tumor size, grade, ER and PR status, axillary lymph node status, extent of surgery, and RT, this SEER registry-based study demonstrates that medial tumor location adversely impacts BCSS and OS.
N-gram and Neural Language Models for Discriminating Similar Languages

This paper describes our submission to the 2016 Discriminating Similar Languages (DSL) Shared Task. We participated in the closed Sub-task 1 with two separate machine learning techniques. The first approach is a character based Convolution Neural Network with an LSTM layer (CLSTM), which achieved an accuracy of 78.45% with minimal tuning. The second approach is a character-based n-gram model of size 7. It achieved an accuracy of 88.45% which is close to the accuracy of 89.38% achieved by the best submission.
A Unified Model for Evolutionary Multiobjective Optimization and its Implementation in a General Purpose Software Framework: ParadisEO-MOEO

This paper gives a concise overview of evolutionary algorithms for multiobjective optimization. A substantial number of evolutionary computation methods for multiobjective problem solving has been proposed so far, and an attempt of unifying existing approaches is here presented. Based on a fine-grained decomposition and following the main issues of fitness assignment, diversity preservation and elitism, a conceptual global model is proposed and is validated by regarding a number of state-of-the-art algorithms as simple variants of the same structure. The presented model is then incorporated into a general-purpose software framework dedicated to the design and the implementation of evolutionary multiobjective optimization techniques: ParadisEO-MOEO. This package has proven its validity and flexibility by enabling the resolution of many real-world and hard multiobjective optimization problems.
Defending Suspected Users by Exploiting Specific Distance Metric in Collaborative Filtering Recommender Systems

Collaborative filtering recommender systems (CFRSs) are critical components of existing popular e-commerce websites to make personalized recommendations. In practice, CFRSs are highly vulnerable to "shilling" attacks or "profile injection" attacks due to its openness. A number of detection methods have been proposed to make CFRSs resistant to such attacks. However, some of them distinguished attackers by using typical similarity metrics, which are difficult to fully defend all attackers and show high computation time, although they can be effective to capture the concerned attackers in some extent. In this paper, we propose an unsupervised method to detect such attacks. Firstly, we filter out more genuine users by using suspected target items as far as possible in order to reduce time consumption. Based on the remained result of the first stage, we employ a new similarity metric to further filter out the remained genuine users, which combines the traditional similarity metric and the linkage information between users to improve the accuracy of similarity of users. Experimental results show that our proposed detection method is superior to benchmarked method.
The Man Who Tells You He Understands Series Will Lie to You About Other Things as Well

My good friend Scott Ludwig, a font of southern aphorisms, is known to have observed “A man who tells you he is in charge of his household will lie to you about other things as well. ” While it is beyond my skill set to assess the universality of this obvious generalization, I am confident that anyone representing that they truly understand the “series” as utilized in certain LLC, limited partnership and business/statutory trust acts is at a minimum overestimating their capabilities. Simply put, consequent to the disparate elements of series between the various acts and the many unresolved questions, understanding is simply at this time not possible.
A Publicly Available Annotated Corpus for Supervised Email Summarization

Annotated email corpora are necessary for evaluation and training of machine learning summarization techniques. The scarcity of corpora has been a limiting factor for research in this ﬁeld. We describe our process of creating a new annotated email thread corpus that will be made publicly available. We present the trade-offs of the different annotation methods that could be used.
EULAR recommendations for the treatment of systemic sclerosis: a report from the EULAR Scleroderma Trials and Research group (EUSTAR)

Purpose: The optimal treatment of systemic sclerosis (SSc) is a challenge because the pathogenesis of SSc is unclear and it is an uncommon and clinically heterogeneous disease affecting multiple organ systems. The aim of the European League Against Rheumatism (EULAR) Scleroderma Trials and Research group (EUSTAR) was to develop evidence-based, consensus-derived recommendations for the treatment of SSc. Methods: To obtain and maintain a high level of intrinsic quality and comparability of this approach, EULAR standard operating procedures were followed. The task force comprised 18 SSc experts from Europe, the USA and Japan, two SSc patients and three fellows for literature research. The preliminary set of research questions concerning SSc treatment was provided by 74 EUSTAR centres. Results: Based on discussion of the clinical research evidence from published literature, and combining this with current expert opinion and clinical experience, 14 recommendations for the treatment of SSc were formulated. The final set includes the following recommendations: three on SSc-related digital vasculopathy (Raynaud’s phenomenon and ulcers); four on SSc-related pulmonary arterial hypertension; three on SSc-related gastrointestinal involvement; two on scleroderma renal crisis; one on SSc-related interstitial lung disease and one on skin involvement. Experts also formulated several questions for a future research agenda. Conclusions: Evidence-based, consensus-derived recommendations are useful for rheumatologists to help guide treatment for patients with SSc. These recommendations may also help to define directions for future clinical research in SSc.
Learning Hand-eye Coordination for a Humanoid Robot using SOMs

Hand-eye coordination is an important motor skill acquired in infancy which precedes pointing behavior. Pointing facilitates social interactions by directing attention of engaged participants. It is thus essential for the natural flow of human-robot interaction. Here, we attempt to explain how pointing emerges from sensorimotor learning of hand-eye coordination in a humanoid robot. During a body babbling phase with a random walk strategy, a robot learned mappings of joints for different arm postures. Arm joint configurations were used to train biologically inspired models consisting of SOMs. We show that such a model implemented on a robotic platform accounts for pointing behavior while humans present objects out of reach of the robot’s hand.
Toward better feature weighting algorithms: a focus on Relief

Feature weighting algorithms try to solve a problem of great importance nowadays in machine learning: The search of a relevance measure for the features of a given domain. This relevance is primarily used for feature selection as feature weighting can be seen as a generalization of it, but it is also useful to better understand a problem's domain or to guide an inductor in its learning process. Relief family of algorithms are proven to be very effective in this task. Some other feature weighting methods are reviewed in order to give some context and then the different existing extensions to the original algorithm are explained. One of Relief's known issues is the performance degradation of its estimates when redundant features are present. A novel theoretical definition of redundancy level is given in order to guide the work towards an extension of the algorithm that is more robust against redundancy. A new extension is presented that aims for improving the algorithms performance. Some experiments were driven to test this new extension against the existing ones with a set of artificial and real datasets and denoted that in certain cases it improves the weight's estimation accuracy.
UStore: A Low Cost Cold and Archival Data Storage System for Data Centers

Recent trend in cloud computing demands vast and ever increasing storage capacity for data centers. For many cloud service providers, much of the storage capacity demand is driven by cold and archival data, such as user uploaded contents, system logs, and backups. In this paper, we describe UStore, a hard disk based storage system designed for such workloads. We make the assumption that most data centers are already populated with computer servers and networking gears, and propose a solution to attach additional disks to these servers reliably at extremely low cost. The main component of UStore is a novel fat tree interconnect fabric to connect hard disks to existing servers and network infrastructure. To reduce cost, UStore leverages the mature commodity USB 3.0 technology to build the fabric, which has extremely low amortized cost per disk while still providing sufficient throughput to satisfy cold and archival workload. The software of the UStore system abstracts the system's physical topology and provides a consistent view of the storage capacity to the upper layer services such as distributed file systems or backup services. In a sense, UStore can be regarded as external USB hard disks designed for data centers.
Prevalence of and Risk Factors for Dry Eye Symptom in Mainland China: A Systematic Review and Meta-Analysis

Purpose. To evaluate the pooled prevalence rate and risk factors of dry eye symptoms (DES) in mainland China. Methods. All the published population-based studies investigating the prevalence of DES in China were searched and evaluated against inclusion criteria. A systematic review and meta-analysis were performed. Results. Twelve out of the 119 identified studies were included in the meta-analysis. The pooled prevalence of DES in China was 17.0%. Female individuals, subjects living in the Northern and Western China, and over 60 years of age had significantly higher prevalent rates (21.6%, 17.9%, 31.3%, and 34.4%, resp.) compared with their counterparts. Patients with diabetes were also found to be more vulnerable to DES. Conclusions. The pooled prevalence rate of DES in mainland China was lower than that in other Asian regions and countries. A remarkable discrepancy in the prevalence in different geographic regions was noted. Aging, female gender, and diabetes were found to be risk factors for DES in China.
Everyday functioning and cognitive correlates in healthy older adults with subjective cognitive concerns

Abstract Objective: Few studies have examined functional abilities and complaints in healthy older adults (HOAs) with subjective cognitive concerns (SCC). The aims of this study were to assess everyday functioning in HOAs reporting high and low amounts of SCC and examine cognitive correlates of functional abilities. Method: Twenty-six HOAs with high SCC and 25 HOAs with low SCC, as well as their knowledgeable informants, completed the Instrumental Activities of Daily Living-Compensation (IADL-C), a questionnaire measure of everyday functioning. Results: After controlling for depression, the high-SCC group self-reported significantly more everyday difficulties on the IADL-C, including all subdomains. Compared to the low-SCC group, informants for the high-SCC group endorsed more difficulties on the IADL-C and specifically the social skills subdomain. For the high-SCC group, poorer self-report of everyday functioning was related to poorer executive functioning and temporal order memory. Conclusions: These findings indicate that there may be subtle functional changes that occur early in the spectrum of cognitive decline in individuals with high SCC, and these functional changes are evident to informants. Further work is needed to investigate whether individuals with both SCC and functional difficulties are at an even higher risk for progression to mild cognitive impairment.
Excavatolide B Modulates the Electrophysiological Characteristics and Calcium Homeostasis of Atrial Myocytes

Severe bacterial infections caused by sepsis always result in profound physiological changes, including fever, hypotension, arrhythmia, necrosis of tissue, systemic multi-organ dysfunction, and finally death. The lipopolysaccharide (LPS) provokes an inflammatory response under sepsis, which may increase propensity to arrhythmogenesis. Excavatolide B (EXCB) possesses potent anti-inflammatory effects. However, it is not clear whether EXCB could modulate the electrophysiological characteristics and calcium homeostasis of atrial myocytes. This study investigated the effects of EXCB on the atrial myocytes exposed to lipopolysaccharide. A whole-cell patch clamp and indo-1 fluorimetric ratio technique was employed to record the action potential (AP), ionic currents, and intracellular calcium ([Ca2+]i) in single, isolated rabbit left atrial (LA) cardiomyocytes, with and without LPS (1 μg/mL) and LPS + EXCB administration (10 μM) for 6 ± 1 h, in order to investigate the role of EXCB on atrial electrophysiology. In the presence of LPS, EXCB-treated LA myocytes (n = 13) had a longer AP duration at 20% (29 ± 2 vs. 20 ± 2 ms, p < 0.05), 50% (52 ± 4 vs. 40 ± 3 ms, p < 0.05), and 90% (85 ± 5 vs. 68 ± 3 ms, p < 0.05), compared to the LPS-treated cells (n = 12). LPS-treated LA myocytes showed a higher late sodium current, Na+/Ca2+ exchanger current, transient outward current, and delayed rectifier potassium current, but a lower l-type Ca2+ current, than the control LA myocytes. Treatment with EXCB reversed the LPS-induced alterations of the ionic currents. LPS-treated, EXCB-treated, and control LA myocytes exhibited similar Na+ currents. In addition, the LPS-treated LA myocytes exhibited a lower [Ca2+]i content and higher sarcoplasmic reticulum calcium content, than the controls. EXCB reversed the LPS-induced calcium alterations. In conclusion, EXCB modulates LPS-induced LA electrophysiological characteristics and calcium homeostasis, which may contribute to attenuating LPS-induced arrhythmogenesis.
A brief survey of web data extraction tools

In the last few years, several works in the literature have addressed the problem of data extraction from Web pages. The importance of this problem derives from the fact that, once extracted, the data can be handled in a way similar to instances of a traditional database. The approaches proposed in the literature to address the problem of Web data extraction use techniques borrowed from areas such as natural language processing, languages and grammars, machine learning, information retrieval, databases, and ontologies. As a consequence, they present very distinct features and capabilities which make a direct comparison difficult to be done. In this paper, we propose a taxonomy for characterizing Web data extraction fools, briefly survey major Web data extraction tools described in the literature, and provide a qualitative analysis of them. Hopefully, this work will stimulate other studies aimed at a more comprehensive analysis of data extraction approaches and tools for Web data.