Miracle-dz/UniFAR-FTUs huggingface dataset:
Supporting Many-Objective Software Requirements Decision: An Exploratory Study on the Next Release Problem

The decision of which requirements should be satisfied in the next release is crucial to software company. The next release problem, a family of requirements selection decision, aims to maximize profits by satisfying requirements to balance customer profits and development costs. However, due to diverse practical scenarios, solutions to the next release problem have to face many different objectives. In this paper, we propose an exploratory study on the many-objective next release problem with five evolutionary optimization algorithms. The goal of this study is to use the experimental results to assist project managers to make the requirements decision in the scenario of many decision objectives. This study focuses on four research questions, including the effectiveness of optimization, the significance of results, the distribution of metric values, and the correlation between metrics. We design the study to explore five objective functions of the next release problem, including the maximum of customer profits, the minimum of requirements costs, the fairness of requirements selection, etc. The study is conducted on 12 benchmark instances from three real-world projects via evaluating six metrics. Our study suggests that among algorithms in comparison, either eMOEA or IBEA is the best choice for the many-objective next release problem.
Pure-Exploration Bandits for Channel Selection in Mission-Critical Wireless Communications

In emergency communications, guaranteeing ultrareliable and low-latency communication is challenging yet crucial to save human lives and to coordinate the operations of first responders. To address this problem, we introduce a general approach for channel selection in mission-critical communications, i.e., choose channels with the best quality timely and accurately via channel probing. Since the channel conditions are dynamic and initially unknown to wireless users, choosing channels with the best conditions is nontrivial. Thus, we adopt online learning methods to let users probe channels and predict the channel conditions by a restricted time interval of observation. We formulate this problem as an emerging branch of the classic multiarmed bandit (MAB) problem, namely the pure-exploration bandit problem, to achieve a tradeoff between sampling time/resource budget and the channel selection accuracy (i.e., the probability of selecting optimal channels). The goal of the learning process is to choose the “optimal subset” of channels after a limited time period of channel probing. We propose and evaluate one learning policy for the single-user case and three learning policies for the distributed multiuser cases. We take communication costs and interference costs into account, and analyze the tradeoff between these costs and the accuracy of channel selection. Extensive simulations are conducted and the results show that the proposed algorithms can achieve considerably higher channel selection accuracy than previous exploration bandit approaches and classic MAB methods.
Poker Face Influence: Persuasive Robot with Minimal Social Cues Triggers Less Psychological Reactance

Applications of social robotics in different domains such as education, healthcare, or as companions to people living alone, often entail that robots will act as persuasive agents. However, persuasive attempts can give rise to psychological reactance where people have negative thoughts and emotions that limit adherence to the persuader. To understand the phenomenon of reactance to robotic persuaders, we investigate the effect of social cues of an artificial agent on psychological reactance and compliance. Participants in a laboratory experiment played a decision-making game in which persuasive attempts were delivered in one of three forms: as a persuasive-text, spoken by a social robot (the SocibotTM) displaying minimal social cues, or by the same robot displaying enhanced social cues. Our results suggest that a social robot with minimal social cues invokes the lowest reactance. Remarkably, exploratory analyses indicate cross-gender effects (between robot and user) upon invoking lower psychological reactance and female participants have higher compliance than male participants.
Impact of Donor Hepatectomy Time During Organ Procurement in Donation After Circulatory Death Liver Transplantation: The United Kingdom Experience

Background. No data exist to evaluate how hepatectomy time (HT), in the context of donation after cardiac death (DCD) procurement, impacts short- and long-term outcomes after liver transplantation (LT). In this study, we analyze the impact of the time from aortic perfusion to end of hepatectomy on outcomes after DCD LT in the United Kingdom. Methods. An analysis of 1112 DCD donor LT across all UK transplant centers between 2001 and 2015 was performed, using data from the UK Transplant Registry. Donors were all Maastricht Category III. Graft survival after transplantation was estimated using Kaplan-Meier method and logistic regression to identify risk factors for primary nonfunction (PNF) and short- and long-term graft survivals after LT. Results. Incidence of PNF was 4% (40) and in multivariate analysis only cold ischemia time (CIT) longer than 8 hours (hazard ratio [HR], 2.186; 95% confidence interval [CI], 1.113–4.294; P = 0.023) and HT > 60 minutes (HR, 3.669; 95% CI, 1.363–9.873; P = 0.01) were correlated with PNF. Overall 90-day, 1-, 3-, and 5-year graft survivals in DCD LT were 91.2%, 86.5%, 80.9%, and 77.7% (compared with a donation after brain death cohort in the same period [n = 7221] 94%, 91%, 86.6%, and 82.6%, respectively [P < 0.001]). In multivariate analysis, the factors associated with graft survival were HT longer than 60 minutes, donor older than 45 years, CIT longer than 8 hours, and recipient previous abdominal surgery. Conclusions. There is a negative impact of prolonged HT on outcomes on DCD LT and although HT is 60 minutes or longer is not a contraindication for utilization, it should be part of a multifactorial assessment with established prognostic donor factors, such as age (>45 y) and CIT (>8 h) for an appropriately selected recipient.
Trust based mechanism for Securing IoT Routing Protocol RPL against Wormhole &Grayhole Attacks

Internet of Things is attracting a lot of interest in the modern world and has become a part of daily life leading to a large scale of distribution of Low power and Lossy Networks (LLN). For such networks constrained by low power and storage, IETF has proposed RPL an open standard routing protocol. However RPL protocol is exposed to a number of attacks which may degrade the performance and resources of the network leading to incorrect output. In this paper, to address Wormhole and Grayhole attack we propose a light weight Trust based mechanism. The proposed method uses direct trust which is computed based on node properties and Indirect Trust which is based on opinion of the neighboring nodes. The proposed method is energy friendly and does not impose excessive overhead on network traffic.
An Optimized Hetrojunction Dopingless TFET

The concept of dopingless TFET (DL TFET) is an potential solution methodology against problems due to RDFs. However. , the low ON-state current ($I_{ON}$) is still a problem in physically doped TFET and it becomes severe for DL TFET due to the existence of barrier at the gap between source and gate electrodes. Alternate solution against this problem is only heterojunction devices. These devices has got inherent high value subthreshold Swing (SS). This work focus on optimizing heterojunction dopingless TFET for obtaining low value SS. Methodology adopted is introducing a Metal Layer into oxide region between Source and Gate in DLTFET using Germanium and GaAs. Performance of various devices are r performance are compared. Optimized structure provides a ION in range of $0.1 \mu\mathrm{A}$, IOFF in the range of 0.39 fA and threshold voltage of about 0.7V is obtained.
Intra-familial Transmission of Chronic Hepatitis B Infection: A Large Population-Based Cohort Study in Northern Iran.

AIM The aim of this study was to investigate the intra-familial transmission of chronic hepatitis B (CHB) in Golestan province, that has the highest prevalence of CHB in Iran. METHODS The Golestan Cohort Study (GCS) is a population-based prospective study of 50045 individuals, 40 years or older, initially set-up to study upper GI cancers in Northern Iran. In 2008, a baseline measurement of hepatitis B surface antigen (HBsAg) on the stored serum of all GCS participants identified 3505 HBsAg+ individuals. In 2011, we assessed HBV serological markers in 2590 initially HBsAg+ individuals and their first-degree relatives including spouses (1454) and children (3934). RESULTS The median (IQR) age of spouses and children were 52 (12) and 25 (12) years respectively. Out of 5388 family members, 2393 (44.5%) had no HBV markers, indicating susceptibility to infection. Of these, 378 (15.8%) were fully-vaccinated children with no apparent response to primary immunization. HBsAg was positive in 2.2% (n = 33) of spouses and 8.2% (n = 325) of children (overall rate of 6.6%). HBcAb was positive in 761 (52.3%) and 914 (23%) spouses and children, respectively. The rate of spontaneous loss of HBsAg (HBsAg-, HBsAb+ and HbcAb+) was 41.3% and 13.9% in spouses and children, respectively. A higher rate of HBsAg+ children (10.2%) was found in families in which the mother was positive for HBsAg compared with families where the father was positive for HBsAg (6.3%) (P < 0.001). When both parents were positive for HBsAg, the rate of HBsAg positivity was high (23.5%, P < 0.001). Despite high virus exposure rates between spouses (52.6 %), the prevalence of HBsAg positivity among them was very low (2.3 %). CONCLUSION Sexual and parent-to-child transmission are important routes of CHB spread in this population from northern Iran despite the fact that 24 years have passed since the beginning of hepatitis B vaccination in infants. Low percentage of HBsAg positivity in spouses is related to high HBsAg clearance rate among them.
Should I Use Laptop or Smartphone? a Usability Study on an Online Learning Application

Nowadays, people increasingly use smartphones for numerous activities including learning. This research aims to investigate the usability of an online learning application and compare if there is any difference in usability between usage in desktop environment and mobile environment. The research analyzed system usability according to the Nielsen's usability model and further investigated how learnability, efficiency, errors, and attractiveness of the application in both website version and mobile app version could influence user satisfaction. The research used Duolingo, an online learning application for foreign languages, and involved Gen Y and Gen Z populations in Jakarta, Indonesia as respondents. The results show that website usage in desktop environment scored higher in learnability compared to the mobile app version; while mobile app usage scored higher in attractiveness compared to the website version. It was also observed that user satisfaction in website and mobile app were influenced by different usability factors. Efficiency and errors were found to have significant influence on user satisfaction in desktop website usage, while only attractiveness showed significant influence on user satisfaction in mobile app usage.
Evolution of Cancer Pharmacological Treatments at the Turn of the Third Millennium

The medical history of cancer began millennia ago. Historical findings of patients with cancer date back to ancient Egyptian and Greek civilizations, where this disease was predominantly treated with radical surgery and cautery that were often ineffective, leading to the death of patients. Over the centuries, important discoveries allowed to identify the biological and pathological features of tumors, without however contributing to the development of effective therapeutic approaches until the end of the 1800s, when the discovery of X-rays and their use for the treatment of tumors provided the first modern therapeutic approach in medical oncology. However, a real breakthrough took place after the Second World War, with the discovery of cytotoxic antitumor drugs and the birth of chemotherapy for the treatment of various hematological and solid tumors. Starting from this epochal turning point, there has been an exponential growth of studies concerning the use of new drugs for cancer treatment. The second fundamental breakthrough in the field of oncology and pharmacology took place at the beginning of the ‘80s, thanks to molecular and cellular biology studies that allowed the development of specific drugs for some molecular targets involved in neoplastic processes, giving rise to targeted therapy. Both chemotherapy and target therapy have significantly improved the survival and quality of life of cancer patients inducing sometimes complete tumor remission. Subsequently, at the turn of the third millennium, thanks to genetic engineering studies, there was a further advancement of clinical oncology and pharmacology with the introduction of monoclonal antibodies and immune checkpoint inhibitors for the treatment of advanced or metastatic tumors, for which no effective treatment was available before. Today, cancer research is always aimed at the study and development of new therapeutic approaches for cancer treatment. Currently, several researchers are focused on the development of cell therapies, anti-tumor vaccines, and new biotechnological drugs that have already shown promising results in preclinical studies, therefore, in the near future, we will certainly assist to a new revolution in the field of medical oncology.
Dynamic genome and transcriptional network‐based biomarkers and drugs: precision in breast cancer therapy

Despite remarkable progress in medium‐term overall survival benefit in the adjuvant, neoadjuvant and metastatic settings, with multiple recent targeted drug approvals, acquired resistance, late relapse, and cancer‐related death rates remain challenging. Integrated technological systems have been developed to overcome these unmet needs. The characterization of structural and functional noncoding genome elements through next‐generation sequencing (NGS) systems, Hi‐C and CRISPR/Cas9, as well as computational models, allows for whole genome and transcriptome analysis. Rapid progress in large‐scale single‐biopsy genome analysis has identified several novel breast cancer driver genes and oncotargets. The exploration of spatiotemporal tumor evolution has returned exciting while inconclusive data on dynamic intratumor heterogeneity (ITH) through multiregional NGS and single‐cell DNA/RNA sequencing and circulating genomic subclones (cGSs) by serial circulating cell‐free DNA NGS to predict and overcome intrinsic and acquired therapeutic resistance. This review discusses reliable breast cancer genome analysis data and focuses on two major crucial perspectives. The validation of ITH, cGSs, and intrapatient genetic/genomic heterogeneity as predictive biomarkers, as well as the valid discovery of novel oncotargets within patient‐centric genomic trials, encouraging early drug development, could optimize primary and secondary therapeutic decision‐making. A longer‐term goal is to identify the individualized landscape of both coding and noncoding key mutations. This progress will enable the understanding of molecular mechanisms perturbating regulatory networks, shaping the pharmaceutical controllability of deregulated transcriptional biocircuits.
Insulin receptor signaling regulates renal collecting duct and intercalated cell antibacterial defenses

People with diabetes mellitus have increased infection risk. With diabetes, urinary tract infection (UTI) is more common and has worse outcomes. Here, we investigate how diabetes and insulin resistance impact the kidney’s innate defenses and urine sterility. We report that type 2 diabetic mice have increased UTI risk. Moreover, insulin-resistant prediabetic mice have increased UTI susceptibility, independent of hyperglycemia or glucosuria. To identify how insulin resistance affects renal antimicrobial defenses, we genetically deleted the insulin receptor in the kidney’s collecting tubules and intercalated cells. Intercalated cells, located within collecting tubules, contribute to epithelial defenses by acidifying the urine and secreting antimicrobial peptides (AMPs) into the urinary stream. Collecting duct and intercalated cell–specific insulin receptor deletion did not impact urine acidification, suppressed downstream insulin-mediated targets and AMP expression, and increased UTI susceptibility. Specifically, insulin receptor–mediated signaling regulates AMPs, including lipocalin 2 and ribonuclease 4, via phosphatidylinositol-3-kinase signaling. These data suggest that insulin signaling plays a critical role in renal antibacterial defenses.
Advanced Dietary Patterns Analysis Using Sparse Latent Factor Models in Young Adults.

Background Principal components analysis (PCA) has been the most widely used method for deriving dietary patterns to date. However, PCA requires arbitrary ad hoc decisions for selecting food variables in interpreting dietary patterns and does not easily accommodate covariates. Sparse latent factor models can be utilized to address these issues. Objective The objective of this study was to compare Bayesian sparse latent factor models with PCA for identifying dietary patterns among young adults. Methods Habitual food intake was estimated in 2730 sedentary young adults from the Training Interventions and Genetics of Exercise Response (TIGER) Study [aged 18-35 y; body mass index (BMI; in kg/m2): 26.5 ± 6.1] who exercised <30 min/wk during the previous 30 d without restricting caloric intake before study enrollment. A food-frequency questionnaire was used to generate the frequency intakes of 102 food items. Sparse latent factor modeling was applied to the standardized food intakes to derive dietary patterns, incorporating additional covariates (sex, race/ethnicity, and BMI). The identified dietary patterns via sparse latent factor modeling were compared with the PCA derived dietary patterns. Results Seven dietary patterns were identified in both PCA and sparse latent factor analysis. In contrast to PCA, the sparse latent factor analysis allowed the covariate information to be jointly accounted for in the estimation of dietary patterns in the model and offered probabilistic criteria to determine the foods relevant to each dietary pattern. The derived patterns from both methods generally described common dietary behaviors. Dietary patterns 1-4 had similar food subsets using both statistical approaches, but PCA had smaller sets of foods with more cross-loading elements between the 2 factors. Overall, the sparse latent factor analysis produced more interpretable dietary patterns, with fewer of the food items excluded from all patterns. Conclusion Sparse latent factor models can be useful in future studies of dietary patterns by reducing the intrinsic arbitrariness involving the choice of food variables in interpreting dietary patterns and incorporating covariates in the assessment of dietary patterns.
Semi-Automated Feature Traceability with Embedded Annotations

Engineering software amounts to implementing and evolving features. While some engineering approaches advocate the explicit use of features, developers usually do not record feature locations in software artifacts. However, when evolving or maintaining features – especially in long-living or variant-rich software with many developers – the knowledge about features and their locations quickly fades and needs to be recovered. While automated or semi-automated feature-location techniques have been proposed, their accuracy is usually too low to be useful in practice. We propose a semi-automated, machine-learning-assisted feature-traceability technique that allows developers to continuously record feature-traceability information while being supported by recommendations about missed locations. We show the accuracy of our proposed technique in a preliminary evaluation, simulating the engineering of an open-source web application that evolved in different, cloned variants.
Social Network Performance Analysis and Content Engagement on Indonesia's E-Commerce

The prosperousness of open data has the advantage to provide strategies for a business to find a new highly effective approach to understand market behavior. Performance is a recap of results over a given period of time. The effort to stay competitive among similar e-commerce sites is measured by their performance in the content characteristics strategy to engage the customer on social network services or social media, such as Facebook. Most companies use similar strategy to support Social Customer Relationship Management (SCRM) effort and their content engagement value. E-commerce is included in the sector which also utilizes social media to interact with their customers. Hence, using social media data provides a new practical way to measure SCRM effort. The more active SCRM networks generally have positive impact on the company performance. In this paper, we use Social Network Analysis (SNA) method and descriptive analysis to evaluate the SCRM and to measure content performance based on engagement value (EV). We use Tokopedia and Bukalapak as case studies, during observation period from 1st September 2017 to 30th September 2017 on Facebook page.
The Costs and Beliefs Implied by Direct Stock Ownership

This paper develops a structural model of the costs and beliefs required to rationalize household direct stock ownership. In the model, households believe they can learn information about individual stock returns through costly research. The model provides a novel explanation for many empirical features of household portfolios. Further, the model identifies the distributions of both household research costs and household beliefs about the predictability of individual stock returns. Identification depends only on households JEL Classification: G02, G11
Clinicopathologic features of anaplastic myxopapillary ependymomas

Myxopapillary ependymomas (MPE) are considered benign (World Health Organization (WHO) grade I) neoplasms with favorable prognosis. However, malignant behavior occurs in a small subset. To our knowledge, only five anaplastic MPEs have been reported without consensus on diagnostic criteria. We retrieved 14 anaplastic MPEs from the pathology archives of six institutions. Each tumor included at least two of the following features: ≥5 mitoses per 10 high power fields, Ki‐67 labeling index (LI) ≥10%, microvascular proliferation (MVP) and spontaneous necrosis. These features were typically encountered in the foci of hypercellularity and reduced mucin. There were eight male and six female patients (age range 6–57 years, median = 16.5). Ten tumors displayed anaplasia at initial resection, and 4 were anaplastic at a second surgery for recurrence (ranging from 9 months to 14 years following initial resection). The Ki‐67 LI ranged between 8% and 40% in the anaplastic foci and <3% in the foci of classic MPE. There was documented cerebrospinal fluid (CSF) dissemination in seven cases, recurrence following an anaplastic diagnosis in three cases and bone or soft tissue invasion in two cases. One patient suffered lung metastases. Two cases evaluated by targeted next‐generation sequencing and one evaluated by fluorescence in situ hybridization (FISH) showed nonspecific chromosomal gains. We conclude that although rare, anaplastic MPE occurs in both pediatric and adult patients, similar to other ependymomas. At a minimum, closer follow‐up is recommended, given the concern for aggressive biologic potential. Further study is needed to determine WHO grading criteria and genetic indicators of tumor progression.