Miracle-dz/UniFAR-FTUs huggingface dataset:
pyPI: Potential Intensity Calculations in Python

3 pyPI Codebase & Examples 3 3.1 Potential Intensity Module: pi.py Description . 3 3.1. 1 Inputs & Outputs . 3 3.1. 2 Adjustable Parameters . 4 3.1. 3 Output Flags & Handling Missing Data . 6 3.2 Sample Data . 8 3.3 pyPI Validation . 9 3.4 Sample Analyses: PI Annual Means . 9 3.5 Sample Analyses: PI Seasonal Cycles . 9 3.6 Sample Analyses: Decomposition . 14
Inhibition of the Photocatalytic Degradation of Benzoic and Salicylic Acid by Nontoxic Aromatic Compounds

This work studies the photocatalytic degradation of benzoic (HBz) and salicylic acid (HSal) in the presence of nontoxic aromatic compounds (phenylalanine, tyrosine, tryptophan, and sodium benzenesulfonate). The purpose is to find compounds able to compete for electron‐transfer processes in photocatalysis: the failure to do this is one of the main drawbacks of currently adopted sunscreens. Sodium benzene sulfonate (SBS) proved to be quite effective in inhibiting electron transfer as demonstrated by its effect on the photocatalytic degradation of HBz and HSal. Furthermore, a mixture of SBS and 1,3‐butanediol was able to inhibit the photocatalytic degradation of both phenol and HBz. This fact indicates that the mixture is able to block both the hydroxylation pathway (responsible for phenol degradation) and the electron‐transfer one (HBz degradation). The simultaneous inhibition of the degradation of both phenol and HBz has not been observed in commercial sunscreen treatments. This work thus shows that the use of nontoxic aromatic compounds can be a way to improve sunscreen performance, and that the photocatalytic degradation test of phenol+HBz (or phenol+HSal) is the most appropriate way to find the best performing treatments.
CHARACTERIZATION OF FUSARIUM SOLANI, CAUSAL AGENT OF PEA WILT AND ITS BIO-MANAGEMENT

Pea (Pisum Sativum L.) is an important winter crop in Pakistan. The quality and production of pea are deteriorated by several biotic and abiotic factors. Among the biotic factors, Fusarium solani is one of the major limiting factors in pea production. In current studies, a total of two fields located in Koont Research Farm, PMAS-Arid Agriculture University Rawalpindi, and National Agricultural Research Centre Islamabad (NARC) were surveyed for the determination of Fusarium wilt disease incidence. Total eighteen isolates were collected during the field survey; a pathogenicity test was carried out to confirm the degree of virulence among all pathogens. A total of 03 plant essential oils such as Neem (Azadirachta indica), Eucalyptus (Eucalyptus globulus Labill) and Clove (Syzygium aromaticum (L.) at 200, 400 and 600 ppm were tested against F. solani causing Fusarium wilt of pea. Results revealed that eucalyptus essential oil at all applied concentrations showed the maximum effectiveness against mycelial growth of F. solani under vitro condition followed by neem and clove essential oil as compared to control. Keeping in view the above mentioned, eucalyptus essential oil can be used as bio fungicide against Fusarium solani causing Fusarium wilt of Pea.
Functional characterization of DLK1/MEG3 locus on chromosome 14q32.2 reveals the differentiation of pituitary neuroendocrine tumors

Pituitary neuroendocrine tumors (PitNETs) represent the neoplastic proliferation of the anterior pituitary gland. Transcription factors play a key role in the differentiation of PitNETs. However, for a substantial proportion of PitNETs, the etiology is poorly understood. According to the transcription data of 172 patients, we found the imprinting disorders of the 14q32.2 region and DLK1/MEG3 locus associated with the differentiation of PitNETs. DLK1/MEG3 locus promoted somatotroph differentiation and inhibited tumor proliferation in PIT1(+) patients, furthermore, the level of DLK1 played a critical role in the trend of somatotroph or lactotroph differentiation. Anti-DLK1 monoclonal antibody blockade or siMEG3 both indicated that the DLK1/MEG3 significantly promoted the synthesis and secretion of GH/IGF-1 and inhibited cell proliferation. In addition, loss of DLK1 activated the mTOR signaling pathway in high DLK1-expressing and PIT1(+) GH3 cell lines, a mild effect in the low DLK1-expressing and PIT1(+) MMQ cell lines and no effect in the PIT1(-) ATT20 cell line. These findings emphasize that expression at the DLK1/MEG3 locus plays a key role in the differentiation of PitNETs, especially somatotroph adenomas, and provide potential molecular target data for patient stratification and treatment in the future.
Identifying Social Networks of Programmers using Text Mining for Code Similarity Detection

The availability of code in many online repositories and collaborating platforms has posed new challenges in source code attribution not only for plagiarism detection but also in other settings such as in the use of insecure copied code in commercial application, etc. The sophistication of different type of attacks in the code sequence used especially by the students requires more effective code similarity detection algorithms. In this paper, a novel source code detection method is proposed that can identify programmers' social network based on advanced pattern detection text mining techniques. The proposed methodology has significant advantages against existing methods since ARPaD algorithm can detect all common patterns between all possible code sequences in one run. Therefore, the computational time is massively reduced to 0(mn log $n$). In order to assess the performance of the methodology, a new dataset was created by assigning to 46 students a code project with specific instructions. The assessment results have been visualized, producing the social network graphs of possible collaboration teams.
Genuine Geruchssignaturen der Natur – Perspektiven aus der Lebensmittelchemie für die Biotechnologie

Die Geruchsqualitaten naturlicher Duftobjekte werden durch die geruchsstoffinduzierten Aktivierungsmuster olfaktorischer Rezeptoren definiert. Die Meta-Analyse in diesem Aufsatz zeigt, dass von den bislang ca. 10 000 in Lebensmitteln identifizierten fluchtigen Verbindungen nur ca. 230 zu den genuinen Schlusselgeruchsstoffen zahlen und der typische Geruch von Lebensmitteln durch das charakteristische Verhaltnis von nur 3–40 dieser Verbindungen kodiert wird. Dies lasst auf eine Coevolution naturlicher Geruchsstoffmolekule unserer Nahrung und der etwa 400 humanen Geruchsrezeptorproteine schliesen. Der Aufsatz gibt einen Uberblick uber die genuinen Signaturen naturlicher Geruchsobjekte und die chemische Geruchskodierung von mehr als 220 Lebensmitteln. Es werden biotechnologische Perspektiven fur die Herstellung biomimetischer Rekombinate diskutiert, mit denen erstmals die authentische Rekonstruktion naturlicher Geruchssignaturen zur Verwendung in Aromen und Parfums, biomedizinischen Applikationen, virtuellen Umgebungen oder humanoiden bioelektronischen Nasen moglich wird.
Modality Selection for Classification on Time-series Data

In this paper, we investigate the optimal modality selection problem for time-series data in the context of late fusion. Multimodal emotion or action recognition is used as a testbed. Widely-used features and classifier are used for each modality drawn from five benchmark datasets. We experimented with four widely-used late fusion methods. From the classification accuracies obtained for all possible combinations of modalities in each dataset, we observe that the accuracy does not always improve with increase in number of modalities. We further show that expected information gain increases monotonically with classification accuracy in an useful interval and hence, can be used for selecting a subset of modalities for late fusion to achieve a high classification accuracy.
WP-70-2017 Illiquidity and Investment Decisions : A Survey Working

Roméo Tédongap Professor of Finance Ernest Tafolong Senior Quantitative Analyst Investing in illiquid assets has become increasingly popular among individual and institutional investors. Illiquid assets provide higher returns and interesting diversification alternatives for asset managers. To the contrary of traditional assets that investors are familiar with, the alternative investments require deep knowledge of their intrinsic properties. This knowledge is necessary to value the underlying and determine their share in portfolios. As the last financial crisis showed, misgauging the implications of illiquidity can have severe consequences. The current article surveys the existing literature that enhances our understanding of illiquid assets, their attractiveness, their valuation, and most importantly, their optimal allocation in investment portfolios.
Dyscalculia: What We Must Know about Students' Learning Disability in Mathematics?

Dyscalculia is one of the less well-known learning problems in mathematics due to lack of exposure and study. Children with dyscalculia usually face arithmetic and symbolic number comparison issues, with about 3-6 percent of individuals affected. The lack of wide-ranging study and inconsistency in the condition's characterizations through studies have impeded progress in identifying the root causes of dyscalculia and how best to handle it. This problem can be more serious because it can prolong up to adulthood. Therefore, this paper will discuss the general aspects related to dyscalculia problems and their effects on children in their lives. This paper also explains the signs and symptoms that are needed to understand children who may have dyscalculia. Finally, this paper discusses what treatments or methods can be used significantly to help children improve their mastery and mathematical skills, including treatment for co-occurring issues. The implications are society should be aware of possible problems with their children related to dyscalculia and should always increase their initiative to use various methods to address the symptoms of dyscalculia, especially for children who also have other learning problems such as dyslexia and ADHD. Besides, increased knowledge of this distinction going forward, combined with longitudinal observational studies, provides tremendous potential to deepen our understanding of the condition and establish successful educational approaches.
Financial Decision and Intellectual Capital and Managerial Ownership: Evidence from Indonesian Manufacturing Companies

This study aims to analyze the effect of investment decisions, financing decisions, dividend policy, intellectual capital, and managerial ownership on the firms' value. The sample used in this study was manufacturing companies listed on the Indonesian Stock Exchange from 2013-2017. The analysis technique used is Panel Regression. The result shows that dividend policy, intellectual capital, and managerial ownership variables positively affect the firms’ value. In contrast, the variable's investment decisions and financing decisions have a negative and not significant impact on the firm's value.
Seed: A Segmentation-Based Egocentric 3D Point Cloud Descriptor for Loop Closure Detection

Place recognition is essential for SLAM system since it is critical for loop closure and can help to correct the accumulated drift and result in a globally consistent map. Unlike the visual slam which can use diverse feature detection methods to describe the scene, there are limited works reported to represent a place using single LiDAR scan. In this paper, we propose a segmentation-based egocentric descriptor termed Seed by using a single LiDAR scan to describe the scene. Through the segmentation approach, we first obtain different segmented objects, which can reduce the noise and resolution effect, making it more robust. Then, the topological information of the segmented objects is encoded into the descriptor. Unlike other reported approaches, the proposed method is rotation invariant and insensitive to translation variation. The feasibility of proposed method is evaluated through the KITTI dataset and the results show that the proposed method outperforms the state-of-the-art method in terms of accuracy.
Impact of Uncertainty on Cash Holding: Evidence from Manufacturing Firms of Pakistan

AUTHORS This study investigates the impact of uncertainty on the cash holdings of manufacturing firms of Pakistan. It also explores the impact of uncertainty on cash holdings for financially constrained firms. We employ dividend payout ratio as a proxy for classification of financially constrained firms. In order to mitigate the problem of endogeneity and to take into account the dynamic nature of the panel dataset, we employ the twostep system-GMM using unbalanced panel dataset of 301 firms covering the period of 2001-2015. The results reveal that cash holdings of firms increase with both macroeconomic and firmspecific uncertainty. However, the increase in cash holding during higher macroeconomic uncertainty is more than that of firm specific uncertainty. The reason behind is that firm specific uncertainty can be hedged while macroeconomic uncertainty cannot be evaded. The results also suggest that financially constrained firms further increase their cash holding while facing both sorts of uncertainty. Muhammad Akram International Institute of Islamic Economics, International Islamic University, Pakistan muhammad.akram@iiu.edu. pk Abdul Rashid International Institute of Islamic Economics, International Islamic University, Pakistan abdulrashid@iiu.edu. pk Uzair Anjum International Institute of Islamic Economics International Islamic University, Pakistan Email: uzairanjum123@gmail.com
Applications of Machine Learning in Cyber Security

Machine learning techniques have been applied in many areas of science due to their unique properties like adaptability, scalability, and potential to rapidly adjust to new and unknown challenges. Cyber security is a fast-growing field demanding a great deal of attention because of remarkable progresses in social networks, cloud and web technologies, online banking, mobile environment, smart grid, etc. Diverse machine learning methods have been successfully deployed to address such wide-ranging problems in computer security. This paper discusses and highlights different applications of machine learning in cyber security. This study covers phishing detection, network intrusion detection, testing security properties of protocols, authentication with keystroke dynamics, cryptography, human interaction proofs, spam detection in social network, smart meter energy consumption profiling, and issues in security of machine learning techniques itself
Eucalyptus spp. volume determined through geospatial interpolation Volume de Eucalyptus spp. por meio de interpolação geoespacial

Planning and managing forest resources is fundamental for increasing the productivity of forest products and byproducts. This study evaluates the use of the ordinary kriging interpolator in the estimation of standing wood volume in Eucalyptus spp. plantations in Paragominas city, Pará State, Brazil. For this, we used data from the precut forest inventory, processed by the ordinary kriging method. Measurements took place in 27 georeferenced fields, with plots of 504 m2, where trees were rigorously cubed. Geostatistical modeling was performed using ordinary kriging (spherical semivariogram model) with isotropic and anisotropic interpolators, seeking to map the volume of the area. To this end, experimental semivariograms were obtained through the Geostatistical Analyst tool of ArcGIS 10.1 software. Interpolation presented satisfactory results regarding plot volume mapping, in which the average volume fitted by ordinary kriging was statistically similar to the average volume obtained by the forest inventory, 11.3517 m3 and 11.3842 m3, respectively, showing efficiency with an adjusted coefficient of determination of 0.94. The use of ordinary kriging interpolation proved to be effective in plot volume estimation in the forest stands under study. Additional keywords: forest resources; geostatistics; ordinary kriging; planning.
A Movie Recommendation System using Collaborative Approach and Cuttlefish Optimization

— The Internet is a living entity, always changing and evolving. In addition to an evolving Internet, the usage of devices and systems has tremendously increased which leads to the need of recommended systems. The recommended system is one in which the user will receive the intended or appropriate information according to the user choices over the Internet. The proposed method combines the collaborative filtering technique, which carry out recommendations by referring to the public, who has the same taste. The System uses K-means clustering by adopting nature inspired cuttlefish optimization algorithm, which works on two parameters namely reflection and visibility. The system is built with the help of IMDB dataset for movie choices. It is also compared with the existing system which uses cuckoo search optimization and the results have been analyzed.
Test-Retest Reliability of Surface Electromyographic Measurements in Athletes With Anterior Cruciate Ligament Reconstruction: A Voluntary Response Index Analysis

Introduction: Voluntary Response Index (VRI) is used as a measure of motor control to study abnormalities of voluntary movements. This study aimed to evaluate the reliability of voluntary response index analysis in subjects with Anterior Cruciate Ligament Reconstruction (ACLR). Materials and Methods: Using surface electromyography, the VRI components of both groups of 15 ACLR and 15 healthy controls were assessed during the functional task (sit-stand-sit). The outcome variables of VRI included the magnitude and similarity index.Results: In sit to stand phase, high reliability was found (ICC=0.80-0.89) for the magnitude and similarity index in both groups. In the standing phase, high to very high reliability was found for the magnitude and similarity index in both groups (ICC=0.75-0.91). In stand to sit phase, high to very high reliability was found (ICC=0.78-0.92) for the magnitude and similarity index in both groups.Conclusion: Surface electromyographic measurements of VRI analysis demonstrated high to very high reliability in athletes with ACLR during the functional task (sit-stand-sit). The results of the current study showed that the VRI analysis in athletes after ACLR was a reliable method and can be used to evaluate motor control before and after ligament injury in these patients.
Phase behavior of poly(ethylene−1-butene) in subcritical and supercritical propane : ethyl branches reduce segment energy and enhance miscibility

On the basis of experimental batch-cell data, increasing short-chain branch density is found to reduce the cloud-point pressure of poly(ethylene-1-butene) solutions in propane, by as much as a factor of 3. This trend is captured by a SAFT approximation via an effective. segment energy that depends on the branch density. Increasing branch density is found to decrease the effective segment energy and, hence, to make it closer to that of propane, which is consistent with the enhanced miscibility of branchy poly(ethylene-1-butenes) in propane
k-Truss Network Community Detection

We review the k-truss algorithm for community detection in networks. The k-truss is an efficient clustering algorithm that holds advantageous properties for many network applications. In this paper, we compare the k-truss performance against other, more well-known community detection algorithms. The k-truss is uniformly more computationally efficient than the Louvain and Clauset-Newman-Moore algorithms in terms of speed and memory with comparable modularity. Potential applications are discussed.
Automatic Detection of Respiratory Symptoms Using a Low Power Multimodal CNN Processor

—The ongoing impact concerning the COVID-19 pulmonary infection has once again highlighted the importance of machine aided diagnosis. This paper proposes a scalable hardware involving end-to-end multimodal Deep Convolutional Neural Networks (DCNN) for respiratory symptom and disease detection. Our analysis provides evidence that detection accuracy improves up to 5% when additional information relating to demography or physiology of the subject is appended to the deep learning framework. The proposed hardware is implemented on Artix-7 FPGA and consumes 245 mW with energy e ﬃ ciency of 7.3 GOPS / W, which is 4.3 × higher than the existing work.
Multimodal and Multitask Classiﬁcation Using Artiﬁcial Neural Networks

We used brand name, product description, and image to classify fashion inventory (clothing and accessories) into 3 levels of nested categories. To ﬁnd a model with optimal classiﬁ-cation accuracy, we experimented with the following: varying input data sources from text to images to both; embedding text with a bag-of-words representation vs. long-short-term memory RNN; varying classiﬁcation model architecture. In our experiments, we achieved the best result ( > 99 % accuracy for the top category) by training the multitask feedfor-ward neural network on both text and image data.
Weight and Condition Index seasonal variation of Testudo hermanni (Reptilia, Testudinidae) at the “Bosco della Mesola” reserve (Po River Delta, Northern Italy)

Testudo hermanni Gmelin, 1789, commonly known as Hermann’s tortoise, is present in Northern Italy, being the northernmost part of its areal. However, the original distribution of this species in Italy has been greatly fragmented, and a few disjunct populations are known. Here we present a database comprised by more than 3,600 observations of weight, morphometry and behaviour, which were recorded in nearly 25 years of research (1992-2016) monitoring a T. hermanni population in a Mediterranean wooded biotope, the “Bosco della Mesola” reserve. The database was used to investigate the health status of the population: the good status of specimens from the focal population was confirmed and changes experienced during the year are described in detail, with special reference to female individuals. The use of Condition Index and the availability of data for such a large timespan allowed us to depict a fine-grained picture of life history traits and physiology of this poikilotherm species in this particular habitat; the importance of large, curated database like the one that is presented here should not be underestimated when trying to characterize and predict ecological changes in warming environments.
Visualization for Cyber Security and Security Analysts’ Use of Visualizations: Is There a Gap?

Many research threads in visualization for cybersecurity aim to equip practitioners with novel visualization techniques and systems to facilitate the exploration and analysis of data in their organizations. It is not clear, however, the extent to which security practitioners are directly or indirectly beneﬁting from current research efforts in data visualization and visualization for cybersecurity. To explore this question, we propose an interview study protocol, adapted from previous works that identiﬁed gaps and opportunities between visualization research and practitioner workﬂows and tools. Our interview targets security practitioners who use visualizations as part of their daily work, focusing on considerations behind their visualization-related choices, preferences