Miracle-dz/UniFAR-FTUs huggingface dataset:
Betalains, the nature-inspired pigments, in health and diseases

Abstract Betalains are unique nitrogen-containing pigments found exclusively in families of the Caryophyllales order and some higher order fungi, where they replace anthocyanin pigments. Betalains, consisting of betacyanins and betaxanthins are generally used as color additives in food. This review discusses on the favorable effects of acute and chronic consumption of betalains, whose edible sources consist primarily of red beetroots (Beta vulgaris) and prickly pears (fruit of the Opuntia genus of cacti). Moreover, it encompasses in vivo and in vitro studies about the bioavailability and bioaccessibility of betanin and indicaxanthin. It seems that treatment with betalains and betalain-rich diets is not only nontoxic but could also prove to be a promising alternative to supplement therapies in oxidative stress-, inflammation-, and dyslipidemia-related diseases such as stenosis of the arteries, atherosclerosis, hypertension, and cancer, among others. Due to its toxicological safety, accessibility, low price, biodegradability, and potentially advantageous biological effects on health, the incorporation of betalains in food manufacturing and related industries could pave the way to overcome current concerns over the health risks of artificial colors. Nevertheless, further studies using pure betalains are required to gain a deeper understanding of their precise biological functions.
CMUML System for KBP 2015 Cold Start Slot Filling

In this paper, we present an overview of the CMUML system for KBP 2015 English Cold Start Slot Filling (SF) task.The CMUML 2015 SF system aggregates the output of several semantic analysis sub-components that read nat-ural language at the sentence level. These sub-components, which we refer to as micro-readers , have different reading capabilities. In addition, we ran a fraction of the queries on our 2014 CRF-based system. We also used our 2014 rule-based system
Lossless steganography: An approach for hiding text under image cover

Steganography helps in secret communication in such a way that the unintended person is prevented from detection of the existence of the secret data or information. The goal of this paper is to introduce a technique to encrypt a text message and embed it with an cover image[1] file. The main idea is to encrypt the message by reversing, swapping and circular right shifting with randomly generated key and embedding each data bit to the pixel matrix of the image cover starting from 1st bit position(1,1,1). Whenever we want to extract the encrypted text from that cover image and decrypt the text too, the pixel values (embedded with text) of the image are converted into its decimal equivalent and print the character value of those decimal bit values and after that circular left shifting with the random key value(generated at the time of encryption), swapping and reversing are done to get the decrypted as well as the original message. This is lossless[2] encryption as there is no any chance to lose information. The main advantage of this method is minimal distortion of the image and lesser complexity. The complexity of the method is O(n3).
Automating Web Application Testing from the Ground Up: Experiences and Lessons Learned in an Industrial Setting

Automating web application testing is a very tricky process due to inherent complexity, dynamic behavior(s) in web pages, differences in the way browsers render the same content (especially on different form factors), and so on. Yet manual testing is not a practical option, and such automation is a must in the interests of effectiveness and efficiency, because of the large number of browsers/devices that users can choose from, and also given the rapid software development cycles of today. This paper discusses our efforts at Varidesk to automate web tests against our main website – which offers many features and content, but is also a true eCommerce site where users from around the globe can purchase a very broad variety of active workspace solutions that we offer. Our solution was developed in-house, from the ground up, and leveraged and extended freely available automation and test libraries such as Selenium WebDriver and NUnit respectively. We talk about the challenges we faced and how we overcame them, as well as provide technical insights on real-world concerns such as managing test brittleness, and integrating the web tests into an existing Continuous Integration and Continuous Deployment (CI/CD) pipeline. Part of the novelty of this paper is that we are also transparent on the rationale behind our decision to build versus buy, and how we managed resources, especially in terms of cost. We also present lessons learned, and encouraged by the success that we have observed, hope that the results will be beneficial to academia and practitioners alike.
A Forensic Examination of Online Search Facility URL Record Structures

The use of search engines and associated search functions to locate content online is now common practice. As a result, a forensic examination of a suspect's online search activity can be a critical aspect in establishing whether an offense has been committed in many investigations. This article offers an analysis of online search URL structures to support law enforcement and associated digital forensics practitioners interpret acts of online searching during an investigation. Google, Bing, Yahoo! , and DuckDuckGo searching functions are examined, and key URL attribute structures and metadata have been documented. In addition, an overview of social media searching covering Twitter, Facebook, Instagram, and YouTube is offered. Results show the ability to extract embedded metadata from search engine URLs which can establish online searching behaviors and the timing of searches.
Applying a physiologic partograph to Consortium on Safe Labor data to identify opportunities for safely decreasing cesarean births among nulliparous women

BACKGROUND The timing of hospital admission for women with spontaneous labor onset and the criteria used to assess active labor progress and diagnose labor dystocia may significantly influence women's risk for primary cesarean birth. Our aims were to assess associations of labor status at admission (i.e., preactive or active) and active labor progress (i.e., dystocic or physiologic) with oxytocin augmentation, cesarean birth, and adverse neonatal outcome rates. METHODS A sample of low-risk, nulliparous women admitted to hospitals for spontaneous labor onset was extracted from the Consortium on Safe Labor (n = 27 077). Binomial logistic regression was used to assess associations between labor classifications and outcomes. RESULTS At admission, 68.0% of women were in preactive labor and 32.0% were in active labor. Cesarean rates for these groups were 18.0% and 7.2%, respectively (adjusted odds ratio [AOR] 2.69; 95% CI 2.45-2.96). Oxytocin augmentation and adverse neonatal outcomes were more likely for women admitted in preactive labor. Among women admitted in active labor, 9.3% experienced labor dystocia and 90.7% progressed physiologically. Cesarean rates for these groups were 20.4% and 5.9%, respectively (AOR 3.02; 95% CI 2.45-3.73). Nearly half of the cesareans performed for dystocia among women admitted in active labor occurred when cervical dilation was physiologic. Oxytocin augmentation and adverse neonatal outcomes were more likely when active labor was dystocic. CONCLUSIONS Adoption of evidence-based, standardized approaches for diagnosing active labor onset, assessing labor progress, and diagnosing dystocia may safely decrease oxytocin augmentation and cesarean birth rates in the United States.
Community Detection by Information Flow Simulation

Community detection remains an important problem in data mining, owing to the lack of scalable algorithms that exploit all aspects of available data - namely the directionality of flow of information and the dynamics thereof. Most existing methods use measures of connectedness in the graphical structure. In this paper, we present a fast, scalable algorithm to detect communities in directed, weighted graph representations of social networks by simulating flow of information through them. By design, our algorithm naturally handles undirected or unweighted networks as well. Our algorithm runs in $\mathcal{O}(|E|)$ time, which is better than most existing work and uses $\mathcal{O}(|E|)$ space and hence scales easily to very large datasets. Finally, we show that our algorithm outperforms the state-of-the-art Markov Clustering Algorithm (MCL) in both accuracy and scalability on ground truth data (in a number of cases, we can find communities in graphs too large for MCL).
Plastid Envelope-Localized Proteins Exhibit a Stochastic Spatiotemporal Relationship to Stromules

Plastids in the viridiplantae sporadically form thin tubules called stromules that increase the interactive surface between the plastid and the surrounding cytoplasm. Several recent publications that report observations of certain proteins localizing to the extensions have then used the observations to suggest stromule-specific functions. The mechanisms by which specific localizations on these transient and sporadically formed extensions might occur remain unclear. Previous studies have yet to address the spatiotemporal relationship between a particular protein localization pattern and its distribution on an extended stromules and/or the plastid body. Here, we have used discrete protein patches found in several transgenic plants as fiducial markers to investigate this relationship. While we consider the inner plastid envelope-membrane localized protein patches of the GLUCOSE 6-PHOSPHATE/PHOSPHATE TRANSLOCATOR1 and the TRIOSE-PHOSPHATE/ PHOSPHATE TRANSLOCATOR 1 as artifacts of fluorescent fusion protein over-expression, stromule formation is not compromised in the respective stable transgenic lines that maintain normal growth and development. Our analysis of chloroplasts in the transgenic lines in the Arabidopsis Columbia background, and in the arc6 mutant, under stromule-inducing conditions shows that the possibility of finding a particular protein-enriched domain on an extended stromule or on a region of the main plastid body is stochastic. Our observations provide insights on the behavior of chloroplasts, the relationship between stromules and the plastid-body and strongly challenge claims of stromule-specific functions based solely upon protein localization to plastid extensions. One sentence summary Observations of the spatiotemporal relationship between plastid envelope localized fluorescent protein fusions of two sugar-phosphate transporters and stromules suggest a stochastic rather than specific localization pattern that questions the idea of independent functions for stromules.
Mining LMS students' data on online task-based master degree studies

The use of information and communication technologies (ICT) in the educational processes has brought about a series of changes in students' behaviour while raising new challenges for teaching. The analysis of the data provided by the learning management systems (LMS) is essential to know the behaviour of students and improve educational techniques. This behaviour may vary depending on the level of studies and the educational methodology used, and their knowledge allows determining various parameters to guide or implement improvements in the learning process. The purpose of this study is to know the behaviour of students when connecting to the LMS in online studies that follow the task-based methodology at the master level.
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis

This paper describes the system used and results obtained for team FOI DSS at SemEval-2018 Task 1: Affect In Tweets. The team participated in all English language subtasks, with a method utilizing transfer learning from LSTM nets trained on large sentiment datasets combined with embeddings and lexical features. For four out of five subtasks, the system performed in the range of 92-95% of the winning systems, in terms of the competition metrics. Analysis of the results suggests that improved pre-processing and addition of more lexical features may further elevate performance.
On energy minimizers of the diblock copolymer problem

The original formula in [12] is given for the whole space. The expression here on a bounded domain first appeared in Nishiura and Ohnishi [10]. The two unlike monomer units are represented byu = 1 and u = 1 respectively. The connectivity of the monomers in a chain leads to the long range interaction (/ 2)|( ) 1/2 (u m)| 2 in the free energy. Here is viewed as a positive operator, and ( ) 1/2 is the square root of its inverse. The parameter is proportional to the inverse of the square root of the total chain length of the copolymer. ( 2 /2)|ru| 2 represents the interfacial energy density at bonding points. The parameter is proportional to the thickness of interfaces between the two monomers. m stands for the mass ratio of the two monomer units.
UMDuluth-CS8761 at SemEval-2018 Task 2: Emojis: Too many Choices?

In this paper, we present our system for assigning an emoji to a tweet based on the text. Each tweet was originally posted with an emoji which the task providers removed. Our task was to decide out of 20 emojis, which originally came with the tweet. Two datasets were provided - one in English and the other in Spanish. We treated the task as a standard classification task with the emojis as our classes and the tweets as our documents. Our best performing system used a Bag of Words model with a Linear Support Vector Machine as its’ classifier. We achieved a macro F1 score of 32.73% for the English data and 17.98% for the Spanish data.
SenseLE: Exploiting Spatial Locality in Decentralized Sensing Environments

Generally, smart devices, such as smartphones, smartwatches, or fitness trackers, communicate with each other indirectly, via cloud data centers. Sharing sensor data with a cloud data center as intermediary invokes transmission methods with high battery costs, such as 4G LTE or WiFi. By sharing sensor information locally and without intermediaries, we can use other transmission methods with low energy cost, such as Bluetooth or BLE. In this paper, we introduce Sense Low Energy (SenseLE), a decentralized sensing framework which exploits the spatial locality of nearby sensors to save energy in Internet-of-Things (IoT) environments. We demonstrate the usability of SenseLE by building a real-life application for estimating waiting times at queues. Furthermore, we evaluate the performance and resource utilization of our SenseLE Android implementation for different sensing scenarios. Our empirical evaluation shows that by exploiting spatial locality, SenseLE is able to reduce application response times (latency) by up to 74% and energy consumption by up to 56%.
Automatic nonrigid registration of MR and PET images

Registration of functional PET and MR images is a necessary step for combining functional information from PET images with anatomical information in MR images. But, the methods published are non-automatic or rigid body transformation. In this paper, we present a method that mapping PET image onto the MR image with automatic non-rigid transformation. The method is largely composed of two parts. First part is the segmentation and extracting the features of both MR and PET by using FCM and morphological methods. And, second part is the non-rigid mapping of PET image onto the PET template and MR image onto MR template. The templates are made from 20 PET images and 20 MR images each other. And, the MR template is registered with PET template. In non-rigid mapping, we use Bayesian framework in which statistical information on the imaging process is combined with prior information on expected template deformations to make inferences about the parameters of the deformation field. The method newly defines intensity similarity between the deforming scan and the target brain. Intensity similarity combined with prior information is used to generate deformation field. We applied our algorithm to PET and T1-weighted MR images from many patients. The registered images were validated by physicians. And we got the satisfactory results.
Cryptanalysis on the HHSS Obfuscation Arising From Absence of Safeguards

Indistinguishability Obfuscation (<inline-formula> <tex-math notation="LaTeX">$iO$ </tex-math></inline-formula>) is a hopeful tool which obfuscates a program with the least-possible leakage, and produces various applications including functional encryption and deniable encryption. Recently, Halevi <italic>et. al.</italic> proposed a state-of-the-art obfuscator implementation, called HHSS obfuscation, in ACM-CCS’17. In this paper, we describe a polynomial time distinguishing attack on HHSS obfuscation. In other words, we show that there exist two functionally equivalent branching programs but obfuscated programs are actually distinguishable. This attack implies that HHSS obfuscation fails to achieve a general purpose of <inline-formula> <tex-math notation="LaTeX">$iO$ </tex-math></inline-formula> security. The idea of the attack is quite simple; we multiply a left kernel vector of the branching program <inline-formula> <tex-math notation="LaTeX">${\mathcal P}$ </tex-math></inline-formula> to an evaluation of obfuscated matrix, which yields a small value when the program <inline-formula> <tex-math notation="LaTeX">${\mathcal P}$ </tex-math></inline-formula> is obfuscated. Our attack algorithm is also applicable even if evasive functions are obfuscated.
Study of significance of spectral and wavelet energy measures to detect the electrical onset of seizure

The objective of this study is to assess the utility of spectral and wavelet energy measures in detecting electrical onset of seizure in patients with temporal lobe epilepsy (TLE). The scalp-recorded EEG data of 20 seizures from 11 TLE patients is used for this study. The spectral and wavelet energy in same set of frequency bands are calculated for each 4 s windowed EEG signal. Among the 14 measures, 3–6 Hz and 6–12 Hz band spectral and wavelet energy increases at electrical onset in 60% and 90% of the recorded seizures respectively. The spectral and wavelet energy in 1–3 Hz band increases in 40% of the recorded seizures. This study identifies the correlation between spectral and wavelet energy in same frequency bands. Hence the simple and efficient spectral energy measures are selected as significant features for the design of automated seizure detection system.
Dual-drug nanomedicine with hydrophilic F127-modified magnetic nanocarriers assembled in amphiphilic gelatin for enhanced penetration and drug delivery in deep tumor tissue

Introduction Deep penetration of large-sized drug nanocarriers into tumors is important to improve the efficacy of tumor therapy. Methods In this study, we developed a size-changeable “Trojan Horse” nanocarrier (THNC) composed of paclitaxel (PTX)-loaded Greek soldiers (GSs; ~20 nm) assembled in an amphiphilic gelatin matrix with hydrophilic losartan (LST) added. Results With amphiphilic gelatin matrix cleavage by matrix metalloproteinase-2, LST showed fast release of up to 60% accumulated drug at 6 h, but a slow release kinetic (~20%) was detected in the PTX from the GSs, indicating that THNCs enable controllable release of LST and PTX drugs for penetration into the tumor tissue. The in vitro cell viability in a 3D tumor spheroid model indicated that the PTX-loaded GSs liberated from THNCs showed deeper penetration as well as higher cytotoxicity, reducing a tumor spheroid to half its original size and collapsing the structure of the tumor microenvironment. Conclusion The results demonstrate that the THNCs with controlled drug release and deep penetration of magnetic GSs show great potential for cancer therapy.
Role of PPAR‐α agonist fenofibrate in the treatment of induced benign prostatic hyperplasia with dysplastic changes in rats

Nearly all men who reach average life expectancy have prostate disease. The most common is benign prostatic hyperplasia (BPH). Peroxisome proliferator‐activated receptor alpha (PPARα) had protective effect in different models, but still, there are no studies explain its role in BPH. So that we investigated the effect of fenofibrate (FEN) on induced BPH by testosterone propionate (TP) (3 mg/kg/day for 4 weeks) subcutaneous injection followed by FEN (300 mg/kg/day) was given orally for 4 weeks. We measured prostate weights changes, prostatic tissue superoxide dismutase (SOD), and malondialdehyde (MDA) levels. Prostate‐specific antigen (PSA), dihydrotestosterone (DHT), and total antioxidant capacity (TAC) in serum were determined. The mRNA gene expressions of proliferating cell nuclear antigen (PCNA), PPARα, and glutathione peroxidase (GPx) in prostatic tissue were also measured by quantitative real‐time polymerase chain reaction. In addition, the histopathological changes and activated caspase3 immunoexpression were evaluated. Our results showed that TP succeeded in induction of BPH, which was detected by significant increase in prostate weights, prostatic tissue MDA, serum levels of DHT, PSA, and mRNA gene expression of PCNA but significant decrease in PPARα and GPx gene expression. Moreover, TAC in serum and SOD level in prostate tissue decreased. The histopathological examination showed typical changes of BPH with dysplastic changes with marked decrease in activated caspase3 immunoexpression indicating marked suppression of the apoptotic process. FEN significantly improved all disturbed parameters of BPH model. Moreover, there are no dysplastic changes with co‐administration of FEN to BPH induced group.
NTNU at SemEval-2018 Task 7: Classifier Ensembling for Semantic Relation Identification and Classification in Scientific Papers

The paper presents NTNU’s contribution to SemEval-2018 Task 7 on relation identification and classification. The class weights and parameters of five alternative supervised classifiers were optimized through grid search and cross-validation. The outputs of the classifiers were combined through voting for the final prediction. A wide variety of features were explored, with the most informative identified by feature selection. The best setting achieved F1 scores of 47.4% and 66.0% in the relation classification subtasks 1.1 and 1.2. For relation identification and classification in subtask 2, it achieved F1 scores of 33.9% and 17.0%,
A Modified K-Means Algorithm for Big Data Clustering

Amount of data is getting bigger in every moment and this data comes from everywhere; social media, sensors, search engines, GPS signals, transaction records, satellites, financial markets, ecommerce sites etc. This large volume of data may be semi-structured, unstructured or even structured. So it is important to derive meaningful information from this huge data set. Clustering is the process to categorize data such that data are grouped in the same cluster when they are similar according to specific metrics. In this paper, we are working on k-mean clustering technique to cluster big data. Several methods have been proposed for improving the performance of the k-means clustering algorithm. We propose a method for making the algorithm less time consuming, more effective and efficient for better clustering with reduced complexity. According to our observation, quality of the resulting clusters heavily depends on the selection of initial centroid and changes in data clusters in the subsequence iterations. As we know, after a certain number of iterations, a small part of the data points change their clusters. Therefore, our proposed method first finds the initial centroid and puts an interval between those data elements which will not change their cluster and those which may change their cluster in the subsequence iterations. So that it will reduce the workload significantly in case of very large data sets. We evaluate our method with different sets of data and compare with others methods as well. Keywords—Big Data, K-means, Clustering, Clustering Algorithm, Modified K-means.