Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effectiveness of a Religious Counseling Program According to Albert Ellis' Theory in Treating Depression among Syrian Students in Karak Qasabeh’s Schools, Jordan

The study aimed to identify the effectiveness of a religious counseling program according to the Albert Ellis’s theory and its effect in treating depression among Syrian students in Karak Qasabeh’s schools, Jordan. To achieve the objectives of the study, the quasiexperimental approach was used, whereby the study individuals were randomly assigned to two groups: a control group who does not receive the counseling program and the other is experimental group to which the counseling program is applied. Pre and post-test of the depression variable were performed for both groups, and follow-up was measured for the experimental group only. The study population and its sample consisted of (60) Syrian students in Karak Qasabeh’s schools during the year 2019/2020, and they were divided into two groups, the experimental group of (30) students, and the control group of (30) students. The results of the study showed the effectiveness of a religious counseling program according to the theory of Albert Ellis in treating depression among Syrian students in Karak Qasabeh’s schools.
Friend Recommendation System for Social Networks: A Semantic and Profile based Approach

Social networking services which are currently on existence, recommend friends to users based on their social graphs. This graph is based on pre-existing user relationships. This approach does not reflect a user’s preferences on friend selection in real life. A new semantic based friend recommendation system for social networks is proposed, where recommendation is based on their life styles. The recommendation system takes advantage of Smartphone sensors to discover life styles of users from user-centric sensor data. In order to improve the accuracy of the life style detected, the system uses similarity of the content extracted from the messages, the video and audio files stored in the Smartphone, and also the details of applications installed in the Smartphone. The proposed system measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. The system is implemented on the Android-based Smartphones and its performance is evaluated.
Methods and apparatus for cooperative file distribution with target data delivery rate

Methods and apparatus are provided for cooperative file distribution with a target data delivery rate. A file is provided in a cooperative file distribution system by obtaining a target delivery rate for the at least one file; obtaining at least a portion of the file from one or more unreliable data sources at an unreliable data delivery rate; and obtaining at least a portion of the file from one or more reliable data sources based on a throttle rate if the unreliable data delivery rate is below the target delivery rate. The one or more unreliable data sources comprise one or more of a sender of the file and one or more peers. The one or more reliable data sources comprise one or more of one or more web servers and one or more storage proxies. The throttle rate can be adjusted to substantially maintain the target delivery rate. The target delivery rate can be dynamically determined based on one or more business rules.
Comparison of the Effect of Two Methods of Breastfeeding and Oral Glucose 10% on the Severity of Venipuncture-Induced Pain and Physiological Indicators in Hospitalized Infants: A Randomized Clinical Trial Study

Background: The incidence of pain in ill infants hospitalized in the neonatal ward is unavoidable. The aim of the present study was to compare the effects of two methods of breastfeeding and oral glucose 10% on the severity of venipuncture-induced pain and physiological indicators in infants hospitalized in the neonatal ward of Seyyed Al-Shohada Hospital in Kerman in 2019.Methods: The present study is a clinical trial study with three groups and with a pre-test and a post-test. It was conducted on 120 neonates who met the inclusion criteria. Infants were selected by a convenient sampling method, and they were randomly divided into three groups. The study instruments included a neonatal demographic questionnaire, and neonatal infant pain scale and a checklist for recording physiological indicators. Then, the data were analyzed using SPSS25 software.Results: The results of the study showed that the mean score of pain severity before the intervention was the same and zero in the three groups (P > 0.05), but during venipuncture and after that, a statistically significant difference was observed among three groups (P < 0.05). The highest mean pain during venipuncture was observed in the control group (86.95) and the lowest mean pain was observed in the breastfeeding intervention group (35.91). After venipuncture, the lowest mean pain was observed in the breastfeeding intervention group (40.40). Also, there was no significant difference between the mean physiological indicators (temperature and heart rate) in different stages of the venipuncture (before, during, and after) in three groups (P > 0.05). Conclusion: Based on the results of the study, breastfeeding is more effective than oral glucose 10% in reducing the severity of infant pain during venipuncture. Breastfeeding is an easy and inexpensive and available method that can be easily implemented with proper training for the mother and it can be used as a method of reducing infant pain by nurses in neonatal intensive care units to ensure the normal growth and development of the infant and prevent the physical effects and discomfort of infants.
Analytical Workspace, Kinematics, and Foot Force Based Stability of Hexapod Walking Robots

Many environments are inaccessible or hazardous for humans. Remaining debris after earthquake and fire, ship hulls, bridge installations, and oil rigs are some examples. For these environments, major effort is being placed into replacing humans with robots for manipulation purposes such as search and rescue, inspection, repair, and maintenance. Mobility, manipulability, and stability are the basic needs for a robot to traverse, maneuver, and manipulate in such irregular and highly obstructed terrain. Hexapod walking robots are as a salient solution because of their extra degrees of mobility, compared to mobile wheeled robots. However, it is essential for any multi-legged walking robot to maintain its stability over the terrain or under external stimuli. For manipulation purposes, the robot must also have a sufficient workspace to satisfy the required manipulability. Therefore, analysis of both workspace and stability becomes very important. An accurate and concise inverse kinematic solution for multi-legged robots is developed and validated. The closed-form solution of lateral and spatial reachable workspace of axially symmetric hexapod walking robots are derived and validated through simulation which aid in the design and optimization of the robot parameters and workspace. To control the stability of the robot, a novel stability margin based on the normal contact forces of the robot is developed and then modified to account for the geometrical and physical attributes of the robot. The margin and its modified version are validated by comparison with a widely known stability criterion through simulated and physical experiments. A control scheme is developed to integrate the workspace and stability of multi-legged walking robots resulting in a bio-inspired reactive control strategy which is validated experimentally.
Cloudlet Placement and Task Allocation in Mobile Edge Computing

Mobile edge computing (MEC) offers a way to shorten the cloud servicing delay by building the small-scale cloud infrastructures, such as cloudlets at the network edge, which are in close proximity to end users. On one hand, it is energy consuming and costly to place each cloudlet on each access point (AP) to process the requested tasks. On the other hand, the service provider should provide delay-guaranteed service to end users, otherwise they may get revenue loss. In this paper, we first model how to calculate the task completion delay in MEC and mathematically analyze the energy consumption of different equipments in MEC. Subsequently, we study how to place cloudlets on the network and allocate each requested task to cloudlets and public cloud with the minimum total energy consumption without violating each task’s delay requirement. We prove that this problem is NP-hard and propose a Benders decomposition-based algorithm to solve it. We also present a software-defined network (SDN)-based framework to deploy the proposed algorithm. Extensive simulations reveal that the proposed algorithm can achieve an (close-to-)optimal performance in terms of energy consumption and acceptance ratio compared with two benchmark heuristics.
Image forming apparatus, and image forming method

According to one embodiment, an image forming apparatus includes a data adding part and an image forming part. The data adding part acquires first identification data from a first storage part provided in a colorant container to contain a colorant and storing the first identification data intrinsic to the colorant container, and adds first additional data based on the first identification data to image data to be formed on a sheet. The image forming part forms an image on the sheet based on the image data to which the first additional data is added.
The Creation of You: A Holistic Approach to Design for AI-Augmented Brand Experiences Through Individual Differentiation

If brands that operate in the digital context do not start building their own front-end AI enabled channels, they risk falling behind on competition (White, Addison a Fritz, 2018). But as these (often traditional) organisations implement AI-systems in customer-facing practices, it slowly becomes the new front-office brand ambassadors of the brand (Wilson, Daugherty a Bianzino, 2017). But even though many organisations are starting to invest and implement AI-solutions they do not seem to fully comprehend its impact on their brand and business (Capgemini Digital Transformation Institute, 2017. And, in a similar way that people can be annoyed or pleased by an interaction with a customer service representative, customer-facing AI also has the potential to shape the brand experience of users. This is due to the fact that people tend to judge and anthropomorphise interactions with such AI-systems, which will ultimately affect their perception of the brand and its performance. Therefore, in order for customer-facing AI to add value to the business as well as the consumer, AI-systems should be designed in such a way that they fit with the brand, and benefit the brand experience of the customer. Based on this, the following research question has been formulated: How to augment the brand experience of users in service-driven brands through customer-facing Artificial Intelligence? Furthermore, as this project has been performed in the context of Deloitte Digital, the aim is twofold. First, this thesis will explore how to design for AI-augmented brand experiences by defining brand experience, and the potential of Artificial Intelligence in it. Secondly, these insights should be presented in such a way that they are applicable for Deloitte Digital designers and strategists, so it can be implemented within their existing design processes. Hereby, the insights from each chapter are made actionable,and are translated into concrete add-on design tools and methods that can be used within the context of the client. These tools are illustrated and validated with a concrete client usecase (Volksbank).
An Improved Efficient Data Transmission using Key Management in Mobile Ad-Hoc Network

Mobile Ad Hoc Networks (MANET) is a system that consists of mobile nodes such as laptops, sensors, etc. interfacing without the assistance of access points, bridges, etc. several research have made in terms of routing, synchronization, bandwidth considerations, power consumption, etc. Our focus is only to concentrate on multipath routing techniques is one of the most challenging issue that occurs due in dynamic topology of ad hoc networks. Based on the network conditions, the routing protocols are selected but it faces some difficulty while selecting the exact protocol. Hence, this paper provides a detail comparison between two routing protocols namely, Ad-hoc On-Demand Multipath Distance Vector Routing (AODV) and Dynamic Source Routing. Finally, the best protocol is selected with high transmission rate, low delay and less energy consumption and implemented in the key management applications and also efficient simple MANET based data transmission module is made with improved efficiency and limited delay. Keywords--MANETs, Multi path routing, Ad-hoc OnDemand Multipath Distance Vector Routing, Hybrid Multi-rate
Genome-wide identification and expression analysis of the cytokinin gene family in Brassica oleracea L. reveals its role in clubroot resistance

Background: cytokinins have important functions in regulating plant growth and response to abiotic stress. cytokinin family genes have been described in several plant species, but a comprehensive analysis of the cytokinin family genes in Brassica oleracea has not been reported to date, especially their roles in dealing with the invasion of P. brassicae. Results: Cytokinins are a class of phytohormones that promote cell division and differentiation and are thought to affect plant immunity to multiple pathogens. To reveal the mechanisms of the Brassica oleracea cytokinin family genes in response to clubroot disease, a total of 36 cytokinin genes were identified using a genome-wide search method. Phylogenetic analysis classified these genes into three groups. They were distributed unevenly across nine chromosomes in B. oleracea, and 15 of them did not contain introns. The results of colinear analysis showed that each cytokinin gene in the B. oleracea genome had at least one homologous gene in the Arabidopsis genome. A cis-element analysis indicated that these genes possessed several stress response cis-elements. The heatmap of the cytokinin gene family showed that these genes were expressed in various tissues and organs. Five and eight genes were up- and downregulated, respectively, in the susceptible material after inoculation. In addition, two and one genes were up- and downregulated, respectively, in resistant material. This may indicate that these cytokinin genes play important roles in the host plant response to clubroot disease. In addition, the results provide insights for better understanding the role of cytokinin in the B. oleracea–P. brassicae interaction. Conclusions: Our results are helpful to elucidate the role of cytokinin family genes in cabbage response to infection by P. brassicae, and lay a foundation for further study on the function of these genes. Keywords: Brassica oleracea, genome-wide, cytokinin family genes, clubroot
Evidence-based guidelines for developing automated conservation assessment methods

Assessing species’ risk of extinction is a vital first step in setting conservation priorities. However, assessment endeavours like the IUCN Red List of Threatened Species still have significant gaps in their coverage of some taxonomic groups. Automated assessment (AA) methods are gaining popularity to rapidly fill these gaps, taking advantage of improvements in computing and digitally available information. However, implicit choices made when developing and reporting automated assessment methods could prevent their successful adoption or, even worse, their predictions could lead to poor allocation of conservation resources.We systematically explored how the choice of data cleaning, taxonomic group, training sample, and automation method affected predicted threat status. We used occurrence records from GBIF to generate assessments for three distinct taxonomic groups using four different automated assessment methods. We measured each method’s performance and coverage after applying increasingly stringent cleaning to the input occurrence data. We used these results to build evidence-based guidelines for developing and reporting automated assessments.Automatically cleaned data from GBIF resulted in comparable performance to occurrence records cleaned manually by an expert. However, all types of data cleaning removed species and limited the coverage of automated assessments. This limitation was more severe for some groups of species than others. Overall, machine learning-based methods performed well on all taxonomic groups, even with minimal data cleaning.We recommend using a machine learning-based method on minimally cleaned data to get the best compromise between performance and species coverage. However, our results demonstrate that the optimal data cleaning, training sample, and automation method depends on the focal group of species. Therefore, we recommend evaluating new AA methods across multiple groups and providing additional context with extinction risk predictions for users to make informed decisions.
Mining Social Networking Sites: A Specific area of Facebook

Now a days facebook is the specific social network site for communicating more people, to develop learning process & research area. Although mining and analysis are needed area of social network so, we are showing useful part in this paper that are related to communication with people, collection of data and uses of social network’s tool and techniques . During our research work we found several tools are available for collecting data and analyzing fan pages. For the purpose of our research work, we used social network site such as Facebook, in this platform we created one page related to blog who did the panacea for us. In this research paper we have applied online social media network for extracting data. Uniform node detection has used for finding common properties among audience as well as Regular equivalence nodes are using for calculating uniformity. Effective user detection, Graph structure, sampling framework are an efficient way that are explained in our paper. Breadth first search & Uniform are best approaches that has used . Graph API has major role to collect data and for the purpose of analyzing data, we used Facebook crawling process. Whenever for growing audience and know about audience information , we have explained about Facebook Insight, Like Alyzer , Sociograph , Agorapulse, Quintly , Brandwatch, So Trender , Brand 24 , Social Bakers, Rival IQ, Unmetric etc. that are more appropriate and accurate tools .
Sensitivity analysis for publication bias in meta-analyses

We propose sensitivity analyses for publication bias in meta-analyses. We consider a publication process such that "statistically significant" results are more likely to be published than negative or "nonsignificant" results by an unknown ratio, eta. Our proposed methods also accommodate some plausible forms of selection based on a study's standard error. Using inverse-probability weighting and robust estimation that accommodates non-normal population effects, small meta-analyses, and clustering, we develop sensitivity analyses that enable statements such as: "For publication bias to shift the observed point estimate to the null, 'significant' results would need to be at least 30-fold more likely to be published than negative or 'nonsignificant' results. " Comparable statements can be made regarding shifting to a chosen non-null value or shifting the confidence interval. To aid interpretation, we describe empirical benchmarks for plausible values of eta across disciplines. We show that a worst-case meta-analytic point estimate for maximal publication bias under the selection model can be obtained simply by conducting a standard meta-analysis of only the negative and "nonsignificant" studies; this method sometimes indicates that no amount of such publication bias could "explain away" the results. We illustrate the proposed methods using real-life meta-analyses and provide an R package, PublicationBias.
Penetrating the fog: analytics in learning and education

Nell’era di Internet, delle tecnologie mobili e dell’istruzione aperta, la necessita di interventi per migliorare l’efficienza e la qualita dell’istruzione superiore e diventata pressante. I big data e il Learning Analytics possono contribuire a condurre questi interventi, e a ridisegnare il futuro dell’istruzione superiore. Basare le decisioni su dati e sulle evidenze empiriche sembra incredibilmente ovvio. Tuttavia, l’istruzione superiore, un campo che raccoglie una quantita enorme di dati sui propri “clienti”, e stata tradizionalmente inefficiente nell’utilizzo dei dati, spesso operando con notevole ritardo nell’analizzarli, pur essendo questi immediatamente disponibili. In questo articolo, viene evidenziato il valore delle tecniche di analisi dei dati per l’istruzione superiore, e presentato un modello di sviluppo per i dati legati all’apprendimento. Ovviamente, l’apprendimento e un fenomeno complesso, e la sua descrizione attraverso strumenti di analisi non e semplice; pertanto, l’articolo presenta anche le principali problematiche etiche e pedagogiche connesse all’utilizzo delle tecniche di analisi dei dati in ambito educativo. Cionondimeno, il Learning Analytics puo penetrare la nebbia di incertezza che avvolge il futuro dell’istruzione superiore, e rendere piu evidente come allocare le risorse, come sviluppare vantaggi competitivi e, soprattutto, come migliorare la qualita e il valore dell’esperienza di apprendimento.
A Survey and Evaluation of the Potentials of Distributed Ledger Technology for Peer-to-Peer Transactive Energy Exchanges in Local Energy Markets

The unpredictability and intermittency introduced by Renewable Energy Sources (RESs) in power systems may lead to unforeseen peaks of energy production, which might differ from energy demand. To manage these mismatches, a proper communication between prosumers (i.e., users with RESs that can either inject or absorb energy) and active users (i.e., users that agree to have their loads changed according to the system needs) is required. To achieve this goal, the centralized approach used in traditional power systems is no longer possible because both prosumers and active users would like to take part in energy transactions, and a decentralized approach based on transactive energy systems (TESs) and Peer-to-Peer (P2P) energy transactions should be adopted. In this context, the Distributed Ledger Technology (DLT), based on the blockchain concept arises as the most promising solution to enable smart contracts between prosumers and active users, which are safely guarded in blocks with cryptographic hashes. The aim of this paper is to provide a review about the deployment of decentralized TESs and to propose and discuss a transactive management infrastructure. In this context, the concept of Proof of Energy is proposed as a novel consensus protocol for P2P energy exchanges managed by DLT. An application of the proposed infrastructure considering a Virtual Power Plant (VPP) aggregator and residential prosumers endowed with a new transactive controller to manage the electrical storage system is discussed.
Aircraft Detection in Aerial Image Based on BOVW of Affine Invariance Features

Aircraft detection is one of the applications of high resolution remote-sensing images. Conventional method of aircraft detection use deep and complicated network, which needs highly computing cost. In this letter, an efficient and effective aircraft detection framework based on BOVW features and cascade AdaBoost classifier has been proposed. A variety of affine invariant features, which represent the complicate structure of aircraft, are extracted from sliding windows, and the direction estimation of aircraft is introduced to align the aircraft before detection. Besides, an accurate NMS algorithm is designed to make the target location more accurate. The proposed method is evaluated with recent advanced detection methods. The superior experimental outcome indicates that our framework achieves better accuracy as well as efficiency.