Miracle-dz/UniFAR-FTUs huggingface dataset:
DCRL: Approach for Pattern Recognition in Price Time Series using Directional Change and Reinforcement Learning

Developing an intelligent pattern recognition model for electronic markets has been a vital research direction in the field. Ongoing research continues for intelligent learning algorithms capable of recognizing and classifying price patterns and hence providing investors and market analysts with better insights into price time-series. In this paper, an adaptive intelligent Directional Change (DC) pattern recognition model with Reinforcement Learning (RL) is proposed, so called DCRL model. Compared with traditional analytical approaches that uses fixed time interval and specified features of the market, the DCRL is an alternative intelligent approach that samples price time-series using an event-based time interval and RL. In this model, the environment’s behavior is incorporated into the RL process to automate the identification of directional price changes. The DCRL learns the price time-series representation by adaptively selecting different price features depending on the current state. DCRL is evaluated using Saudi stock market data with different price trends. A series of analyses demonstrate the effective analytical performance in detecting price changes and the extensive applicability of the DCRL model. Keywords—Machine learning; reinforcement learning; directional-change event; pattern recognition; stock market
The Influence of Executive Compensation Gap on Earnings Management from the Perspective of Media Supervision: Evidence from China

To pursue higher compensation, the agent’s earnings management behavior may damage the principal’s interests. Can managers whose compensation reaches the expected level seek benefits for the company through earnings management? This study takes China’s A-share listed companies from 2014 to 2018 as samples. The conclusions show that managers with higher compensation levels will carry out earnings management in favor of the company while taking their own interests into consideration. For companies with stronger profitability, the higher the managers’ compensation is, the more they are inclined to reduce accrued earnings in the current period to further reduce taxes and fees. For companies with weaker profitability, managers with higher compensation tend to choose to increase real earnings to further optimize financial indicators. It has been found through further research that high pressure generated by media attention can make well-paid executives restrain the above earnings management behavior, which serves as an effective method to protect investors’ rights and interests.
Unsupervised Clustering of Comments Written in Albanian Language

Now-a-days, social media and communications in social media have become very important for services providers and those play a key role in service quality improvement as well as in decision making. The services consumers’ discussions usually are written in their local languages and extracting important knowledge sometimes is very hard and problematic. In this field the natural language processing techniques are helpful, but different languages have their specifics and difficulties, and some languages are not prosperous enough in the techniques and methods on NLP, especially the local speaking of the language. In this scientific paper, we have tried to solve such a problem for the Albanian language spoken in Kosovo. Namely, for a dataset of the comments, written in Albanian language in Kosovo (local speaking), collected from the social media, by use of unsupervised clustering techniques, to make clustering regarding the topic of discussion in the comment. In this research, the different techniques of text feature extraction (vectorization and others) and clustering algorithms (K-means, Spectral, Agglomerative, etc.), are used with the idea to find and define more appropriate techniques for the Albanian language. In this paper are shown the results of the conducted experiments as well as discussions about what to use in case of the Albanian language and other languages similar or in group with Albanian (those which have a weak NLP). Keywords—Unsupervised clustering; k-means; spectral; agglomerative; vectorization; Albanian language
A Cryptographic Technique for Communication among IoT Devices using Tiger192 and Whirlpool

—The heterogeneous standards and operational platforms of IoT devices, introduce additional security loopholes into the network thereby increasing the attack surface for the IoT. Most of the devices used in these IoT systems are not secure by design. Such vulnerable devices pose a great threat to the IoT system. In recent times, there have been a lot of research works on improving existing mechanisms for securing IoT data at both the software and hardware levels. Although there exist cryptographic research solutions to secure data at the node level in IoT systems, there is not a lot of these security solutions that target securing both the IoT data and validating IoT nodes. The Authors propose a cryptographic solution that uses double hashing to provide improved security for IoT node data and validating nodes in IoT system. A cryptographic mechanism that is composed of the Tiger192 cryptographic hash and the whirlpool hash function is proposed in authenticating IoT data and validating devices in this paper. The use of digital ledger technology and cryptographic double hashing algorithm provided enhanced security, privacy, and integrity of data among IoT nodes. It also assured the availability of IoT data.
Amylase Production by Aspergillus niger and Penicillium Species by Solid-State and Submerged Cultivation Using Two Food Industrial Wastes

Amylase enzymes are starch degrading enzymes and have received a great deal of attention due to their perceived technology importance and economic benefit. Amylase enzymes are considered important enzymes used in starch processing industries for the hydrolysis of polysaccharides like starch into simple sugar constituents. This enzyme is also involved in the commercial production of glucose. Solid-state cultivation and submerged cultivation have tremendous potentials for enzyme amylase production by using different solid substrates like rice bran, wheat bran, coconut oil cake, and groundnut oil cake which are rich in starch. These agro-industrial wastes are considered cheap raw materials for the production of amylase. Wastewater from the industry like brewery can also be used as a liquid substrate for submerged cultivation. It may have the possibility of depurination of wastewater. In the present study, Aspergillus niger and Penicillium species were isolated and their amylase activity was determined by the starch hydrolysis method. Enzyme production was done by using coconut oil cake as a substrate for solid-state fermentation and brewery wastewater as a substrate for submerged fermentation. The enzyme produced by the organisms was extracted and enzyme assay was done by the Dinitrisalicilic method (DNS method). The protein estimation was done by Lowry Folin’s method. The qualitative assay was carried out by performing Gas Chromatography-Mass Spectroscopy (GC-MS).
Intra- and interspecific discrimination of Scorpaena species from the Aegean, Black, Mediterranean and Marmara seas

This study was conducted to discriminate five Scorpaena species and populations of each species according to morphometric characters. A total of 1865 fish specimens were collected from the eight locations in the four Turkish seas: Antalya, Balıkesir, Çanakkale, Hatay, İzmir, Marmara Ereğlisi, Ordu and Şile. In the study, 26 morphometric traits were measured for intra- and interspecific discrimination of five Scorpaena species. The data were subjected to analysis of variance, principal components analysis (PCA) and canonical discriminant analysis. As results of the PCA, 10 traits for S. maderensis and S. scrofa, 12 traits for S. elongata and 13 traits for S. notata and S. porcus were found to be important for intraspcific discrimination. The overall classification scores of intraspecific discrimination were determined as 94.6% for S. elongata, 90.5% for S. maderensis, 96.7% for S. notata, 96.5% for S. porcus and 92.2% for S. scrofa. The PCA indicated that 13 morphometric measurements among the 26 traits are important in the interspecific discrimination of five Scorpaena species. The cross-validated canonical discriminant analysis was correctly classified as 97.4% at the Scorpaena species level. The discrimination of correctly classified species ranged from 94.8% to 100%. Finally, we demonstrated that the morphometric characters examined in the present study can be used successfully in the intra- and interspecific discrimination of Scorpaena species from different habitats.
A Study on Burden of Prehypertension in Youth (or Pediatric Hypertension) in West Bengal, India

Abstract Hypertension is a silent threat to the developing countries in recent times. The aim of this article was to determine the burden of prehypertension and hypertension among school-going children and the risk factors associated with those conditions. A cross-sectional study was conducted among school-going children (6–18 years) in seven schools of Burdwan, West Bengal, India, selected by stratified random sampling from March 2017 to August 2018. Anthropometric and blood pressure measurements were obtained along with sociodemographic parameters. Prehypertension and hypertension were defined as per American Pediatric Society's definition. Mean age of the study population was 11.3 ± 3.8 years (n = 604). Prevalence of prehypertension and hypertension was estimated to be 5% and 4.6%, respectively. Both prehypertension and hypertension were more common among children aged > 15 years (10.3% and 15.5%). In logistic regression, the independent determinants of hypertension were higher socioeconomic condition, increasing age, obesity, increased intake of junk food, parental hypertension, and obesity among first-degree relatives. Proper preventive measures are the need of the hour to tackle the emerging epidemic at its root. Periodic measurements of blood pressure at regular intervals are advisable at community levels to recognize high-risk children, control obesity, and prevent irreversible end organ damages.
Novel Small Molecule Growth Inhibitor Affecting Bacterial Outer Membrane Reduces Extraintestinal Pathogenic <named-content content-type='genus-species'>Escherichia coli</named-content> (ExPEC) Infection in Avian Model

Avian pathogenic Escherichia coli (APEC), a subgroup of extraintestinal pathogenic E. coli (ExPEC), causes colibacillosis in chickens and is reportedly implicated in urinary tract infections and meningitis in humans. A major limitation for the current ExPEC antibiotic therapy is the development of resistance, and antibacterial drugs that can circumvent this problem are critically needed. Here, we evaluated eight novel membrane-affecting anti-APEC small molecule growth inhibitors (GIs), identified in our previous study, against APEC infection in chickens. Among the GIs tested, GI-7 (the most effective), when administered orally (1 mg/kg of body weight), reduced the mortality (41.7%), severity of lesions (62.9%), and APEC load (2.6 log) in chickens. Furthermore, GI-7 administration at an optimized dose (60 mg/liter) in drinking water also reduced the mortality (14.7%), severity of lesions (29.5%), and APEC load (2.2 log) in chickens. The abundances of Lactobacillus and oleate were increased in the cecum and serum, respectively, of GI-7-treated chickens. Pharmacokinetic analysis revealed that GI-7 was readily absorbed with minimal accumulation in the tissues. Earlier, we showed that GI-7 induced membrane blebbing and increased membrane permeability in APEC, suggesting an effect on the APEC membrane. Consistent with this finding, the expression of genes essential for maintaining outer membrane (OM) integrity was downregulated in GI-7-treated APEC. Furthermore, decreased levels of lipopolysaccharide (LPS) transport (Lpt) proteins and LPS were observed in GI-7-treated APEC. However, the mechanism of action of GI-7 currently remains unknown and needs further investigation. Our studies suggest that GI-7 represents a promising novel lead compound that can be developed to treat APEC infection in chickens and related human
Empirical Validation of WebQMDW Model for Quality-based External Web Data Source Incorporation in a Data Warehouse

In recent years, World Wide Web has emerged as the most promising external data source for organizations’ Data Warehouses for valuable insights required in comprehensive decision making to gain a competitive edge. However, when the Data Warehouse uses external data sources from the Web without quality evaluation, it can adversely impact its quality. Quality models have been proposed in the research literature to evaluate and select Web Data sources for their integration in a Data Warehouse. However, these models are only conceptually proposed and not empirically validated. Therefore, in this paper, the authors present the empirical validation conducted on a set of 57 subjects to thoroughly validate the set of 22 quality factors and the initial structure of the multi-level, multi-dimensional WebQMDW quality model. The validated and restructured WebQMDW model thus obtained can significantly enhance the decision-making in the DW by selecting high-quality Web Data Sources. Keywords—Data warehouse; external data sources; web data sources; quality evaluation model; quality model validation
Fortification and Stability of Iodine in Bread to Mitigate Iodine Deficiency Disorder

The core objective of this study was to prepare iodized bread by using iodized salt and potassium iodide (KI) to fulfill Recommended Daily Intake (RDI) of iodine. Triplicate level of each fortificant was added in separate treatments in different concentrations with an aim that 2 slices of bread provide RDI of iodine. The prepared samples were analyzed for stability of iodine by spectrophotometric method and for sensory attributes by panel of judges. Results showed good retention of iodine in bread with 15-20% loss of iodine in final product after processing. A slight level of potassium was increased in treatments in which KI was used while other minerals profile was not affected by fortificants and showed no significant behaviour after examining the results statistically.
CITIZEN PARTICIPATION & DIGITAL TOOLS TO IMPROVE PEDESTRIAN MOBILITY IN CITIES

Abstract. In this work, we present a framework supported by mobile and web apps and able to propose personalized pedestrian routes that match user mobility profile considering mobility impediments factors. We explain how these later have been defined using a pedestrian-centric approach based on travel experiences as perceived in the field by senior citizens. Through workshops, six main factors that may influence pedestrian route choices were revealed: passability, obstacle in path, surface problem, security, sidewalk width, slope. These categories were used to build digital tools and guide a citizen participatory approach to collect geolocated points of obstacle documented with walkability information (picture, category, impact score, free comment). We also involved citizens to evaluate these information and especially senior referents for validation. Finally we present how we connect these points of obstacle with a pedestrian network based on OpenStreetMap to configure a routing cost function. The framework has been partially deployed in 2020 with limited people due to the pandemic. Nonetheless, we share lessons learned from interaction with citizens in the design of such a framework whose underlying workflow is reproducible. We plan to further assess its relevance and sustainability in the future.
NDT_A_336126 3195..3203

1Neuroscience Research Institute, Gachon University, Incheon, Republic of Korea; 2Department of Biomedical Engineering, College of Health Science, Gachon University, Incheon, Republic of Korea; 3Gachon Advanced Institute for Health Sciences & Technology, Gachon University, Incheon, Republic of Korea; 4Department of Psychiatry, Gachon University College of Medicine, Gil Medical Center, Incheon, Republic of Korea Background: Previous studies suggested a link between serotonergic neurotransmission and impaired insight in schizophrenia. In this study, we examined the relationship between serotonin transporter (SERT) availability in regions of the prefrontal cortex (dorsolateral, ventrolateral, ventromedial, and orbitofrontal cortices) and insight deficits in antipsychotic-free patients with schizophrenia using high-resolution positron emission tomography (PET) with [C]DASB. Methods: Nineteen patients underwent [C]DASB PET and 7-Tesla magnetic resonance imaging scans. To assess SERT availability, the binding potential with respect to non-displaceable compartment (BPND) was derived using the simplified reference tissue model. Patients’ level of insight was assessed using the Insight and Treatment Attitude Questionnaire (ITAQ). The relationship between ITAQ scores and [C]DASB BPND values was examined using the region-of-interest (ROI)and voxel-based analyses with relevant variables as covariates. The prefrontal cortex and its four subregions were selected as a priori ROIs since the prefrontal cortex has been implicated as the critical neuroanatomical substrate of impaired insight in schizophrenia. Results: The ROI-based analysis revealed that the ITAQ illness insight dimension had significant negative correlations with the [C]DASB BPND in the left dorsolateral, left orbitofrontal, and bilateral ventrolateral prefrontal cortices. The ITAQ treatment insight dimension had significant negative correlations with the [C]DASB BPND in the bilateral dorsolateral, left orbitofrontal, and bilateral ventrolateral prefrontal cortices. The ITAQ total score showed significant negative correlations with the [C]DASB BPND in the bilateral prefrontal cortex and three subregions (dorsolateral, ventrolateral, and orbitofrontal cortices). A supplementary voxel-based analysis corroborated a significant negative association between the ITAQ score and the [C]DASB BPND in the prefrontal cortices. Conclusion: Our study provides in vivo evidence of significant negative correlations between insight deficits and prefrontal SERT availability in patients with schizophrenia, suggesting significant involvement of prefrontal serotonergic signaling in impaired insight, one of the core symptoms of schizophrenia.
Genetic Algorithm and Ensemble Learning Aided Text Classification using Support Vector Machines

Text classification is one of the areas where machine learning algorithms are used. The size of the dataset and the methods used for converting the textual words into vectors play a major role in classifying them. This paper proposes a heuristic based approach to classify the documents using Genetic Algorithm aided Support Vector Machines (SVM) and Ensemble Learning approach. The real valued representation of the textual data into vectors is done on applying Term Frequency – Inverse Document Frequency (TF-IDF) and Bi-Normal Separation (BNS). However, in this paper, the common data misclassification issue in SVM is overcome by introducing two algorithms that adds weightage to accurate classification. The first algorithm applied BNS and TF-IDF along with ensemble learning and constructs a voting classifier for classifying the textual documents. The results produced justify that TF-IDF produces good results with voting classifier than BNS for classification. Henceforth TF-IDF is applied in the subsequent approach for vector generation. Secondly, genetic algorithm is applied along with OneVsRest strategy in SVM to overcome the drawback of multiclass multilabel classification. The results show that Genetic algorithm improves the accuracy of classification even with a very small labelled dataset, as genetic algorithm applies the process of Mutation and Cross over across many generations to understand the pattern of right classification. Keywords—Genetic algorithm; ensemble learning; support vector machines; text classification
Methods and Architectural Patterns of Storage, Analysis and Distribution of Spatio-temporal Data

The work describes the key principles of the process of building digital spatial data infrastructures for effective decision-making in the management of natural systems and for the sustainable development of the regional economy. The following reference points are considered in detail: increasing the accuracy of the deep learning and neural networks algorithmic and software for the process of analyzing spatial data, developing storage systems for large spatio-temporal data by developing new physical and logical storage models, introducing effective geoportal technologies and developing new architectural patterns for presentation and further dissemination of spatio-temporal using modern web technologies. The plan for working out a scientific problem of development of methods and architectural patterns of storage, analysis and distribution of spatio-temporal data determined the structure of the article. The first section concretizes the criteria of efficiency of information processes in the digital spatial data infrastructure (SDI), the second section discusses algorithmic support of the process of analysis of spatial data, the third – integration of spatial data, and finally, the final section – implementation and projectoriented use of geoportal systems. Keywords—Spatial data infrastructure; deep learning; neural networks; spatial data; geoportals
Inhibition of Staphylococcus aureus enterotoxin genes by using plant extracts

Introduction: Enterotoxigenic Staphylococcus aureus is an important pathogen that causes septicemia and bacteremia and is often associated with serious complications, such as endocarditis and osteomyelitis. Some Staphylococcus enterotoxins require only minute quantities to be toxic in humans. The present study focused on investigation how to remove this problematic issue. Objectives: This study was conducted to inhibit S. aureus enterotoxin genes that obtained from positive blood culture bottles of patients at the pediatric hospital in Sulaimania city. Methods: Twenty five isolates of S. aureus were isolated among 100 positive blood culture bottles and determined the strains that produce enterotoxins through culture method. Then, the enterotoxin genes that located on plasmids were cured by two medicinal plants (Eugenia caryophyllata and Cinnamomum zeylanicum). Results: The results showed that nine out of 25 isolates were released enterotoxins from which the plasmid encoding enterotoxin genes were confirmed in four of them. And, two of the isolates were transferred to recipient DH10B E. coli isolate successfully. Methanol extracts of (E. caryophyllata and C. zeylanicum) were used at sub minimum inhibition concentration as curing agents. Conclusion: Methanol extracts of (E. caryophyllata and C. zeylanicum) have grate effect on eliminating the plasmidsencoding enterotoxin gene of S. aureus.