Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentence diagramming by non-experts: as good as Treebank?

The last 10-15 years have seen enormous advances in the accuracy and usefulness of syntactic parsing, specifically in the form of stochastic parsers whose models are fit via supervised learning from an annotated treebank. For English, the most commonly used resource (by far) is the Penn Treebank, which consists of constituent tree annotations for 40,000 sentences from the Wall Street Journal, collected in the early 90’s (Marcus, Santorini, and Marcinkiewicz 1994). While there has been an incredible amount of work developing parsers for this dataset, there has been very little work on creating new syntactically annotated corpora, in part because the task is perceived as very difficult. If the goal of research in syntactic analysis is to create effective parsers, this imbalance of data collection vs. algorithmic research is troubling. First, the WSJ’s genre of English may be overly narrow to support the goal of creating broad-coverage English parsers – for example, the corpus contains very few questions. Second, it may be the case that creating more training data will be the simplest route toward creating better syntactic parsers. We are interested in exploring whether it is possible to have online annotators provide syntactic annotations rich enough to support training a parser. If so, and costs can be reduced to the range of $1/sentence, then it would be very feasible to create corpora of similar size to the Penn Treebank, and also may additionally become possible to conduct small annotation projects for new domains when necessary. This would have enormous utility for natural language processing, given that errors in syntactic analysis are often a bottleneck in applied systems.
Application of Response Surface Methodology in Development and Optimization of Stability Indicating RP-HPLC Method for Determination of Tolvaptan in Bulk and Formulation

Design of Experiment assisted stability indicating RP-HPLC was developed and optimized using response surface methodology for determination of Tolvaptan. Mobile phase was developed and optimized using Design of Experiment with response surface methodology. Acetonitrile and phosphate buffer with pH 5.5 (70:30% V/V) was optimized as mobile phase. The flow rate was maintained at 1ml/min. Stress studies were performed as per guidelines. Method was validated in accordance with regulatory requirements and results were within specified limits of regulatory guidelines. Tolvaptan was eluted at 3.24 min. It shows linearity from 2.5-15 µg/ml. Coefficient of correlation was 0.999, LOD and LOQ values were 1.0871 (µg/ml) and 3.2942 (µg/ml). Precision was determined with % RSD of 0.8669 and 0.9709%, mean percentage Recovery value was found to be 99.88 ±1.2. All stress degradation products are very well resolved from drug peak which indicate suitability indicating nature of the developed method. Design of Experiment technique can help in fast and economical optimization of mobile phase which inturn will save time for method development. The developed method is simple, accurate, sensitive which can be utilised as stability indicating method for identification of degradation products in routine analysis of the drug.
Lycopene Rich Drink: Consumers Acceptance and Purchase Intention in Malaysia

This study had been carried out to assess the level of acceptance and purchase intention towards new functional beverages developed by the Malaysia Agricultural Research Development Institute (MARDI). The Lycopene Rich Drink (LRD) had been assessed towards 400 respondents in 4 Zone in Malaysia which is Northern, Southern, Western, and Eastern using stratified sampling. The results revealed that the consumers had well accepted the LRD based on the highest mean score acceptance (mean=4.16) compared to the commercial (mean=3.15) beverages in the market. Taste and color are the most preferred attributes that influenced the purchase intention. The logistic regression analysis had predicted almost 95% of the respondents' intent to purchase. Marital status and number of households are the sociodemographic factors that show a significant relationship based on the Chi-Square analysis. The findings of this study are beneficial for the beverages industry players towards exploiting the opportunity of health additional benefit offered.
Detecting Missed Security Operations Through Differential Checking of Object-based Similar Paths

Missing a security operation such as a bound check has been a major cause of security-critical bugs. Automatically checking whether the code misses a security operation in large programs is challenging since it has to understand whether the security operation is indeed necessary in the context. Recent methods typically employ cross-checking to identify deviations as security bugs, which collects functionally similar program slices and infers missed security operations through majority-voting. An inherent limitation of such approaches is that they heavily rely on a substantial number of similar code pieces to enable cross-checking. In practice, many code pieces are unique, and thus we may be unable to find adequate similar code snippets to utilize cross-checking. In this paper, we present IPPO (Inconsistent Path Pairs as a bug Oracle), a static analysis framework for detecting security bugs based on differential checking. IPPO defines several novel rules to identify code paths that share similar semantics with respect to an object, and collects them as similar-path pairs. It then investigates the path pairs for identifying inconsistent security operations with respect to the object. If one path in a path pair enforces a security operation while the other does not, IPPO reports it as a potential security bug. By utilizing on object-based path-similarity analysis, IPPO achieves a higher precision, compared to conventional code-similarity analysis methods. Through differential checking of a similar-path pair, IPPO eliminates the requirement of constructing a large number of similar code pieces, addressing the limitation of traditional cross-checking approaches. We implemented IPPO and extensively evaluated it on four widely used open-source programs: Linux kernel, OpenSSL library, FreeBSD kernel, and PHP. IPPO found 154, 5, 1, and 1 new security bugs in the above systems, respectively. We have submitted patches for all these bugs, and 136 of them have been accepted by corresponding maintainers. The results confirm the effectiveness and usefulness of IPPO in practice.
HPV vaccination in women treated for Cervical Intraepithelial Neoplasia grade 2 or 3: evidence-based recommendation from the Multisociety Italian Guidelines for cervical cancer prevention

Objective: Women treated for Cervical Intraepithelial Neoplasia (CIN) grade 2 or 3 are at increased risk of CIN and cervical cancer. Human Papillomavirus (HPV) vaccination is effective in preventing CIN in women who are not infected by HPV. Some studies suggested that vaccination may reduce the risk of CIN2 or 3 in women treated for CIN. A working group including all Italian scientific societies involved in tackling cervical cancer developed a recommendation on vaccination against HPV for women treated for CIN2 or CIN3. Data sources, methods of study selection: The group conducted a systematic review of the literature published from January 2006 to May 2019. Evidence on safety outcomes was retrieved by a recent Cochrane Review on vaccination in the general population. To develop the final recommendation, evidences were appraised and integrated by a Panel of Experts using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Evidence to Decision framework. Tabulation, integration and results: Six eligible studies were included. Four were RCTs and two were cohort studies, with different timing of vaccination. An additional study, published in October 2019, was taken into consideration after external review. A reduction of 70% CIN2+ in the treatment group was estimated; the vaccine was considered safe. Conclusion: The working group recommends the use of HPV vaccination in women treated for CIN2 or 3. The strong recommendation is based on large estimated desirable effects and trivial anticipated undesirable effects (moderate certainty of evidence), negligible costs and savings (no studies included), and a positive judgment in terms of feasibility, acceptability, and impact on equity. Keywords
Study on RCA and HFMEA in Patient Safety with Measurable Outcomes: A Case Scenario

Introduction: Safety concerns of outborn neonatal infant babies during transportation to the NICU had been a concern. Better team coordination and improvement in the technical competency of the care providers helped implement the ambulance policy. Methods: With the background of the Donabedian model and Juran's Trilogy concepts, process improvisation was performed. The RCA (Root Cause Analysis) and HFMEA (Healthcare Failure Mode and Effects Analysis) as tools were used for better deliverables. Results: The measurable outcomes in terms of time taken for departure before and after were compared, which showed considerable significance. By measuring the baby temperatures, a significant difference was seen in clinical aspects. Discussion: Lack of infrastructure, equipment for neonatal transportation was a challenge that required prioritization of budget allocation. The procurement policy of equipment had to be relooked for user-friendly strategies. Lack of trained staff and doctors, which were major issues, required an intense training and development module. The challenges of hypothermia, ventilation issues during transportation of neonates were of major concern, were tackled using quality tools. Application: To minimize the mortality and morbidity of neonates, infants by Specialized Pediatric Critical Care Transport (SPCCT) ambulance policy was implemented. The value addition was to minimize the risks and prevention of hazards. Conclusion: Improvement in the Technical competency of nurses and caregivers was observed. Nurses were well exposed to handling transporting sick babies. Logistics, equipment handling was performed properly. With better team coordination, the babies were safe. The trust and confidence among the members of the community at large improved. The patient footfall increased in numbers.
A Survey of Digital Image Watermarking Techniques Based on Discrete Cosine Transform

- In this study, a literature review is presented to examine the Discrete Cosine Transform (DCT) based digital image watermarking techniques since 2010, apart from the very basic articles. General features of the digital watermarking, frequency domain & spatial domain watermarking techniques, DCT and various watermarking researches with DCT in last decade were stated. The studies were classified according to the application methods of DCT, features of the watermarking technique, watermark extraction method, robustness, and usage area. Results have shown that DCT has increased the robustness of watermarking by performing with a union of several methods from simple to complex. In this case, the main purpose of this study can be considered as making a compilation of DCT techniques and preparing an information note that can be a reference for those working in this field. For the future studies to improve watermarking with DCT, a combination between machine learning and optimization techniques would be preferred.
Are plasma Brain-Derived Neurotrophic Factor or reproductive hormones related to depression in PCOS patients?: a prospective cohort study

Department of Psychiatry, Haeundae Paik Hospital, College of Medicine, Inje University, 48108 Busan, Republic of Korea Paik Institute for Clinical Research, Inje University, 47392 Busan, Republic of Korea Department of Convergence Biomedical Science, College of Medicine, Inje University, 47392 Busan, Republic of Korea Department of Obstetrics and Gynecology, Haeundae Paik Hospital, College of Medicine, Inje University, 48108 Busan, Republic of Korea
Health related quality of life scales in women diagnosed with gynecological and breast cancer: the role of resilience. A systematic review

Objective: Resilience and health-related quality of life are factors to be valued today in all types of patients for their relationship to wellbeing and health. Any stressful situation can significantly impact quality of life and resilience and there are numerous scales to rate these aspects. The main objective of this review is to describe the most used health-related quality of life and resilience scales in gynecological and breast cancer patients to highlight the limitations. Data sources: A review of literature in Pubmed, MEDLINE, Cochrane Database and Google Scholar was carried out to identify articles on health-related quality of life in oncological patients published in English between 2000 and 2020. Methods of study selection: The review was done following the PRISMA guidelines. Tabulation: A total of 460 papers were identified using MeSH terms but finally, according to the inclusion and exclusion criteria, we evaluated 41. Integration and results: Questionnaires have a good performance to quantify quality of life and resilience in oncological patients in general. However, most publications were not focused on patients with gynecological cancer. Conclusions: Due to the particularities of the group of patients with gynecologic and breast cancer secondary to their treatment significantly affecting several areas and domains, it is necessary to validate specific scales for them in order to offer these patients the correct management of their disease at all levels. The role of resilience, premature and iatrogenic menopause and mutilating surgeries are essential to understand the uniqueness of health-related quality of life in gynecological and breast cancer patients.
A Simple Framework for Cross-Domain Few-Shot Recognition with Unlabeled Data

Most existing works in few-shot learning rely on meta-learning the model on a large base dataset which is typically from the same domain as the target dataset. We tackle the problem of cross-domain few-shot learning where there is a large shift between the base and target domain. We propose a simple solution to utilize unlabeled images from the novel/base dataset. We calculate pseudo soft-label from the weakly-augmented version of the unlabeled image and compare it with the strongly augmented version. We also minimize the supervised cross-entropy loss for the labeled base dataset at the same time. We show that the proposed network learns representation that can be easily adapted to the target domain even though it has not been trained with target-speciﬁc classes during the pretraining phase. Our model outperforms the current state-of-the art method by 2.7% for 5-shot and 3.6% for 1-shot classiﬁcation in the BSCD-FSL benchmark.
Proposal of reference values for transverse cerebellar diameter in Korean fetuses and comparison with other countries

Department of Radiological Science, College of Health Sciences, Catholic University of Pusan, 46252 Busan, Republic of Korea Departments of Radiology, Masan University, 51217 Gyeongsangnam-do, Republic of Korea Department of Obstetrics and Gynecology, Ilsin Christian Hospital, 48724 Busan, Republic of Korea Department of Radiological Science, College of Nursing, Healthcare Sciences & Human Ecology, Dong-eui University, 47340 Busan, Republic of Korea
CHATBOT: DESIGN, ARCHITECUTRE, AND APPLICATIONS

A chatbot, also known as a dialogue system or a conversational agent, is a computer program that can imitate a conversation with a user [61]. In the past decade, chatbot technology has evolved tremendously, thanks to the growing popularity of artificial intelligence and machine learning. Chatbots are proving themselves as a valuable tool in many situations and are visible in almost every aspect of our daily lives. This paper intends to address the design, architecture, and applications of chatbots. We will discuss the evolution of chatbots, present a technical overview of the chatbot system and the technologies that support it, and address the applications and potential implications of chatbots on the wider world.
Labor adjustment and productivity dynamics: cross-country evidence and policy implications

Labor productivity, measured as output per hour worked, is more procyclical in countries with lower employment volatility. To capture this new stylized fact, we propose a business cycle model with employment adjustment costs, variable hours per worker and labor effort. We show that variations in work effort help to replicate the procyclicality of labor productivity observed in many OECD countries. In contrast, the constanteffort model fails to replicate the observed cross-country pattern in the data. By implication, labor market deregulation – a reduction in firms' employment adjustment costs – reduces the cyclicality of labor productivity by more when effort can vary. Variable effort is thus relevant for evaluating the effect of such a reform. More fundamentally, we argue that the cyclicality of labor productivity in itself is not a reliable indicator of which type of shock – technology or demand – drives the business cycle. SUERF Policy Briefs No 177, September 2021
Validation of a quick and simple chromatographic method for simultaneous quantification of sertraline, escitalopram, risperidone and paliperidone levels in the human plasma

Simultaneous quantification of multiple psychiatric drugs is important for the therapeutic drug monitoring of psychiatric patients. In addition, it would be highly advantageous if the method could be simple, straightforward, and not time-consuming. A 200 µl plasma sample was deproteinized, drugs were separated by a ZORBAX Eclipse XDB-Phenyl column with the mobile phase composed of acetonitrile and 0.1 % formic acid in water (60:40, v/v), and recorded in the MRM mode by using a positive electrospray source with tandem mass spectrometry detection. The dynamic range was 2-256 ng/ml for all the analyzed drugs, except escitalopram (8-256 ng/ml). Quality control samples were prepared in quintuplicates in three relevant concentrations for each drug. Coefficients of determination (R2 ) were higher than 0.99, while the relative difference between nominal and measured concentrations (RE) and CV were lower than 15% for all targets. High performance liquid chromatography coupled with the mass detector (HPLC-MS/MS) method for simultaneous determination of sertraline, escitalopram, risperidone and paliperidone in human plasma was validated with respect to selectivity, linearity, accuracy, precision, matrix effect and stability. This method has significant advantages in terms of low sample volume (200 µl), short preparation time (3 hours) and short runtime per sample (4 minutes).
Color Constancy Using Faces

This paper presents an empirical study on the problem of solving color constancy using faces. We investigate the effectiveness of using the human face in a scene as the source of chromatic information to achieve color constancy. State-of-the-art algorithms are evaluated to see whether the chromatic information provided by faces may help to estimate the scene illuminant. We create a dataset containing 472 pairs of images of 472 people, and each pair includes one image with a neutral-color reference card and the other image without the card. The neutral-color reference card can be used to retrieve the ‘ground-truth’ illuminant of the scene. The dataset covers diverse scenes with various lighting conditions, and the face regions of the people in the images are manually labeled. Based on the dataset, we show that the color on the face highly correlates with the scene illuminant. The results of empirical evaluation on the dataset suggest that, when face information is available, better performance of color constancy can be achieved by inferring the scene illuminant from the face instead of the whole scene.
The Energy Consumption of Passenger Vehicles in a Transformed Mobility System with Autonomous, Shared and Fit-For-Purpose Electric Vehicles in the Netherlands

This article explores the tank-to-wheel energy consumption of passenger transport at full adoption of fit-for-purpose shared and autonomous electric vehicles. The energy consumption of passenger transport is increasing every year. Electrification of vehicles reduces their energy consumption significantly but is not the only disruptive trend in mobility. Shared fleets and autonomous driving are also expected to have large impacts and lead to fleets with one-person fit-for-purpose vehicles. The energy consumption of passenger transport in such scenarios is rarely discussed and we have not yet seen attempts to quantify it. The objective of this study is to quantify the tank-to-wheel energy consumption of passenger transport when the vehicle fleet is comprised of shared autonomous and electric fit-for-purpose vehicles and where cheap and accessible mobility leads to significantly increased mobility demand. The approach consists of four steps. First, describing the key characteristics of a future mobility system with fit-for-purpose shared autonomous electric vehicles. Second, estimating the vehicle miles traveled in such a scenario. Third, estimating the energy use of the fit-for-purpose vehicles. And last, multiplying the mileages and energy consumptions of the vehicles and scaling the results with the population of the Netherlands. Our findings show that the daily tank-to-wheel energy consumption from Dutch passenger transport in full adoption scenarios of shared autonomous electric vehicles ranges from 700 Wh to 2200 Wh per capita. This implies a reduction of 90% to 70% compared to the current situation. Full adoption of shared autonomous electric vehicles could increase the vehicle-miles-travelled and thus energy use of passenger transport by 30% to 150%. Electrification of vehicles reduces energy consumption by 75%. Autonomous driving has the potential of reducing the energy consumption by up to 40% and implementing one-person fit-for-purpose vehicles by another 50% to 60%. For our case study of the Netherlands, this means that the current 600 TJ/day that is consumed by passenger vehicles will be reduced to about 50 to 150 TJ/day at full adoption of SAEVs.
Learning Laplacian Matrix from Graph Signals with Sparse Spectral Representation

In this paper, we consider the problem of learning a graph structure from multivariate signals, known as graph signals . Such signals are multivariate observations carrying measurements corresponding to the nodes of an unknown graph, which we desire to infer. They are assumed to enjoy a sparse representation in the graph spectral domain, a feature which is known to carry information related to the cluster structure of a graph. The signals are also assumed to behave smoothly with respect to the underlying graph structure. For the graph learning problem, we propose a new optimization program to learn the Laplacian of this graph and provide two algorithms to solve it, called IGL-3SR and FGL-3SR. Based on a 3-step alternating procedure, both algorithms rely on standard minimization meth-ods –such as manifold gradient descent or linear programming– and have lower complexity compared to state-of-the-art algorithms. While IGL-3SR ensures convergence, FGL-3SR acts as a relaxation and is signiﬁcantly faster since its alternating process relies on multiple closed-form solutions. Both algorithms are evaluated on synthetic and real data. They are shown to perform as good or better than their competitors in terms of both numerical performance and scalability. Finally, we present a probabilistic interpretation of the proposed optimization program as a Factor Analysis Model.