Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural network natural language processing tools for identifying personal priorities in the project performers selection in the field of smart agriculture

The project implementation effectiveness in creating digital smart agriculture systems depends on the correct selection of performers. Considering personal priorities makes it possible to increase the validity of decision-making regarding the employment of specific individuals for the implementation of IT projects in the agro-industrial sector. Personal priorities are internal, hidden characteristics that have an effect on the process of long-term joint work and interaction in various situations that arise in the team. The identification of the analyzed individual’s personal priorities is proposed to be reduced to solving the classification problem based on the analysis of person’s text Internet traces using neural network technologies of natural language processing. As a training sample, it is proposed to use a set of text document vectors and the corresponding marks of personal priority classes. In the process of identifying the personal priorities classes, it is required to create an appropriate text array based on parsing and processing of text messages published on the Internet by the analyzed person. Next, from the resulting text array, a text vector of the document must be formed, which then needs to be submitted to the input of the neural network. It is assumed that the mark of the analyzed individual’s personal priority class will be displayed in the output layer of neurons.
Hypoplastic Models for Soil-Structure Interfaces - Modelling and Implementation

The consideration of interfaces is an important issue when modelling the holistic global structural behaviour of geotechnical engineering structures. The most prominent example is the shaft friction of axial loaded piles and anchors. In this thesis, a stochastic assessment scheme was proposed and applied. This scheme was modified to take into account the special considerations for the assessment of interface models. Based on the assessment and a state-of-the-art review, theoretical considerations were used and a novel scheme was developed. This scheme uses reformulated mathematical operators as well as reduced stress and strain rate tensors, based on existing constitutive equations, to model interfaces. Shear stress mobilization and the volumetric behaviour are predicted more accurately, and the bearing behaviour of frictional contacts can be modelled in a better way. By using the novel scheme, an older hypoplastic interface model for granular interfaces was enhanced, and three different hypoplastic clay interface models were proposed. In addition to the theoretical constitutive model formulation, an implementation method was developed. This allowed a user-friendly implementation of advanced constitutive models as interface models using the capabilities of a commercial finite element software package. This concept was exemplary applied to various 3D boundary value problems and the benefits of such advanced hypoplastic interface model are discussed.
Development of a Bioactive Sauce: Effect of the Packaging and Storage Conditions

Consumers’ interest in a high-quality healthy diet is creating a growing trend in the food industry, focusing on the design and development of new products rich in bioactive compounds. This work involves the formulation of a vegetable sauce obtained from a mixture of pumpkin and pepper, the study of the evolution of bioactive compounds, quality and sensory parameters during storage at 4 and 25 °C, the influence of the packaging materials (PVC, PE/PA, and PS), and the migration degree. Antioxidant activity, polyphenols, carotenoids, and brown pigments contents were studied at 25 °C. Overall migration of the containers and the evolution of the physicochemical parameters and sensory attributes of the sauce were analyzed. All plastic materials showed an overall migration lower than the limit of EU and Mercosur Regulations. PVC better preserved polyphenols, antioxidant activity, and carotenoids until 50, 10, and 30 days, respectively, and lower development of brown pigments was observed. Higher storage temperatures favored undesirable changes in sensory attributes before 50 days of storage. PVC can be used to achieve greater conservation of the sensory attributes of sauce, regardless of the storage temperature. It could be considered the best material to preserve the bioactive properties and sensory attributes of the sauce until 30 days.
A REVIEW ON AN EFFECTIVE AND EFFICIENT APPROACH OF SOFTWARE CLONE DETECTION METHOD

: Making copy existing code portion and pasting them without modifications or with modifications into other sections of code is very common process in software development. The code which is copied is called a cloning of the software and the process is called software cloning. Software developers tend to copy the existing functionality from the source code and paste it somewhere else either intentionally or unintentionally. The increasing use of open source software and its variants increased code reuse, too. The existing codes can be modified according to new requirements thereby facilitating and advancing open source development. Clone management tools should be similar and integrated into development environments, to help designers understand the behavior of integration patterns. Combine a management tool with integrated discovery and friendly clones recognized by the developer that will help engineers identify clones as they grow over time. The discovery of software clones will help raise awareness of clones that exist within systems and are aimed at preventing the creation of new clones. We believe that the results of our systematic review will be useful in any case for a researcher who wants to continue research in any field related to software clones such as clone management, clone detection, clone analysis, software quality clones, etc. This paper presents a systematic review of existing literature performed to analyze and report the findings of a clone-based study
RECENT ADVANCES, METHODS AND OPPORTUNITIES OF DEEP LEARNING IN COMPUTATIONAL AND SYSTEMS BIOLOGY

: Deep learning depicts a class of AI calculations that are fit for joining crude contributions to layers of moderate provisions. These calculations have as of late shown noteworthy outcomes across an assortment of areas. Science and medication are information-rich disciplines; however, the information are perplexing and frequently poorly comprehended. Consequently, profound learning strategies might be especially appropriate to tackle issues of these fields. We look at uses of deep figuring out how to an assortment of biomedical issues patient characterization, central organic cycles and therapy of patients - and talk about whether profound learning will want to change these errands or then again if the biomedical circle presents exceptional difficulties. Following from a broad writing survey, we track down that profound learning still can't seem to reform biomedicine or absolutely resolve any of the most squeezing difficulties in the field, yet encouraging advances have been made on the earlier cutting edge. Even though upgrades over past baselines have been unassuming as a rule, the new advancement demonstrates that profound learning techniques will give significant intends to accelerating or helping human examination. However, progress has been made connecting a particular neural organization's forecast to enter highlights, seeing how clients ought to decipher these models to make testable speculations about the framework under investigation stays an open test. Moreover, the restricted measure of named information for preparing presents issues in certain areas, as do legitimate and protection limitations on work with delicate wellbeing records. Regardless, we predict deep picking up empowering changes at both seat and bedside with the possibility to
Modular Class Data Sharing in OpenJDK

Today, a vast majority of big data processing platforms are implemented in JVM-based languages such as Java and Scala. The main reasons for the popularity of the JVM are: the write once, run everywhere paradigm through the use of platform independent bytecode, automatic garbage collection, bytecode interpretation for fast start-up, JIT-compilation of hot code paths for fast JVM warm-up and runtime code optimization. While these features are well suited for traditional Java applications in embedded systems, application servers and enterprise middleware, the characteristics of big data processing applications force us to examine the efficiency of existing JVM features. Modern big data platforms dwarf the size of the user programs that implement the actual application requiring the loading of 1000s of framework classes per JVM to run a task. Furthermore, these applications instantiate mostly the same identical classpath over and over thus multiplying the time required to load the classes and the memory required to store the class metadata in the JVM. The overhead due to classloading is particularly large when the task length is small, which is common in big data processing pipelines. The issue of classloading overhead is partly addressed by the introduction of OpenJDK Class Data Sharing (CDS), a technique to persist the metadata of classes loaded during the execution of an application in a memory mapped file, so that any subsequent run of the application can simply map this file into memory and reuse the loaded classes. We propose two approaches for utilizing CDS in Hadoop and Spark: BestFit CDS and Classpath CDS representing the two extreme possibilities for sharing. BestFit CDS provides minimum classloading time and potential duplication while Classpath CDS provides zero duplication at the cost of higher classloading time. We show that per-JVM classloading overhead in Hadoop can be as high as 49% for small tasks and further show that the use of BestFit CDS can provide a 30% to 50% improvement in JVM execution time. Further, we identify the key limitations in the current implementation of CDS and propose Modular CDS to improve the scalability and modularity of sharing. With the use of synthetic workloads, we show that the speedup due to Modular CDS lies between that of Classpath and BestFit CDS while at the same time achieving minimum archive size and no duplication of across archives.
A consumer cultural paradox: exploring the tensions between traditional and international education

Purpose – In a globalizedworld, consumers embrace mutually conflicting culturalvalues ratherthan making exclusive,either/orchoices.Asaresult,theyexperiencemultipletensions,a phenomenonthatcan beidentified as the consumer cultural paradox. Despite clear interest in the influence of local/global culture on consumers, knowledge of how conflicting cultural elements shape consumer behavior remains limited. To address these issues, the current article seeks to identify higher- and lower-level tensions inherent in the consumer cultural paradox. Design/methodology/approach – Using in-depth interviews, the authors investigate tensions experienced byChineseconsumersofinternationalprivateeducationservices.Thisstudyappliesaparadoxlens,atension-basedconceptualapproachthatiswellsuitedforstudyingconsumerparadoxes. Findings – Ten lower-level tensions of the consumer cultural paradox arise in the focal international service context; these tensions in turn form three higher-level tensions. Originality/value – Thestudyisamongthefirstinmarketingtouseaparadoxlensandempiricalresearchto delineate multiple dimensions of the consumer cultural paradox, then categorize them into lower and higher- level tensions. The findings offer theoretical and managerial implications, in that recognizing the multiple tensions experienced by consumers allows scholars and marketers to gain a better understanding of how consumers perceive and evaluate services from different cultures.
Deep Learning Approach Based Optical Edge Detection Using ENZ Layers

|Metamaterials oﬀer a chance to design (cid:12)lms that could achieve optical diﬀerentiation due to their special properties. Layered (cid:12)lm would be the simplest case considering the easy-fabrication and compactness. Instead of performing the optical diﬀerentiation at the Fourier plane, Green-function based multi-layers are used to achieve optical diﬀerentiation. In this work, epsilon-near-zero (ENZ) material is utilized to realize the optical diﬀerentiation owning to the special optical properties that the re(cid:13)ection increases with the increase of incident angle, which (cid:12)ts the characteristics of optical diﬀerentiation. In addition, deep learning is also used in this work to simplify the design of ENZ layers to achieve the optical diﬀerentiation, and further realize the optical edge detection. Simulations based on the Fresnel diﬀraction are carried out to verify that our (cid:12)lms designed by this method could realize the optical detection under diﬀerent cases.
probability of military aggression of

Kostenko O.V. The probability of military aggression of autonomous AI: assumptions or imminent reality (analyzing the facts of russian war against Ukraine). The application of modern technologies with artificial intelligence in all spheres of human life is growing exponentially. There is a real concern that this growth could become uncontrollable. The lack of public, state and international control over artificial intelligence technologies creates largescale risks of using such software and hardware that inevitably affect or, conversely, intentionally harm humanity. The events of recent weeks and Russia’s war against democratic Ukraine confirm the thesis that the uncontrolled use of AI, especially in the military sphere, can lead to complete noncompliance with the moral norms of the use of controlled AI or the spontaneous emergence of aggressive autonomous AI. The development of legal regulation of the use of technologies with artificial intelligence is being carried out rather slow, in comparison to the rapid development of AI technologies, which simultaneously cover all areas of public relations. Therefore, control over the creation and use of AI should be carried out not only by technical regulation (requirements, technical standards, regulations, assessments of compliance with technical standards, control of compliance with technical regulations) but also by the developing of standard laws and specific changes in the information, civil, criminal and other spheres of law, by creating comprehensive legislation and the state supervisory authority in this area.
Developing a rapid and economical DNA extraction protocol for an important medicinal plant Artemisia judaica without using liquid nitrogen

: Aims: Artemisia judaica in Saudi Arabia are exposed to serious threats due to heavy human impacts such as uncontrolled tourism, overgrazing and uncontrolled collection, mining, quarrying and natural drought. These activities warrant careful conservation strategies that would allow inclusion of these plants into development plans of the Saudi Arabia economy in order to ensure their sustainable use. The evaluation of the genetic diversity by using ISSR and RAPD PCR requires a high quality of intact DNA. Therefore the aim of this study was to establish a rapid DNA isolation protocol for Artemisia species without using Liquid Nitrogen. Results : In case of Artemisia species, the DNA protocol was optimized by using solution based method without using Liquid Nitrogen . The quality of DNA was checked on 0.7% agarose gel containing ethidium bromide at a final concentration of 0.5 mg/ml . This provides a very rapid and sensitive means of estimating the nucleic acid concentration. Similarly, the quantity and purity of DNA extracted from Artemisia species analyzed by using NanoDrop1000. The λ 260/ λ 280 determined for Artemisia DNA samples were in a range of 1.83-2.0 and concentration (ug/ml) (502.7 to 1288.5 ng/µl) indicating a good quality DNA from our method. The protocol was simple, reliable and operative in normal laboratory condition in two hours. Conclusion: The developed protocol proved to be simple, reliable and operative in normal laboratory condition yielding intact DNA from Artemisia species having good quantity (170 to 1200 ng/µl) and quality ( A 260/280 ratio - 1.64 to 1.95) completed in two 2 to 3 hours .
Sonic Hedgehog in Astyanax cavefish: multiple consequences on forebrain development and evolution

Ventral midline Sonic Hedgehog (Shh) signalling is crucial for growth and patterning of the embryonic forebrain. Here, we report how enhanced Shh midline signalling affects the evolution of telencephalic and diencephalic neuronal patterning in the blind cavefish Astyanax mexicanus , a teleost fish closely related to zebrafish. A comparison between cave- and surface-dwelling forms of Astyanax shows that cavefish display larger Shh expression in all anterior midline domains throughout development. This does not affect global forebrain regional patterning, but has several important consequences on specific regions and neuronal populations. First, we show expanded Nkx2.1a expression and higher levels of cell proliferation in the cavefish basal diencephalon and hypothalamus. Second, we uncover an Nkx2.1b-Lhx6 -GABA-positive migratory pathway from the subpallium to the olfactory bulb, which is increased in size in cavefish. Finally, we observe heterochrony and enlarged Lhx7 expression in the cavefish basal forebrain. These specific increases in olfactory and hypothalamic forebrain components are Shh-dependent and therefore place the telencephalic midline organisers in a crucial position to modulate forebrain evolution through developmental events, and to generate diversity in forebrain neuronal patterning.
Evidence of (cid:2) -Cell Dedifferentiation in Human Type 2 Diabetes

Context: Diabetesisassociatedwithadeficitofinsulin-producing (cid:2) -cells.Animalstudiesshowthat (cid:2) -cells become dedifferentiated in diabetes, reverting to a progenitor-like stage, and partly con-verting to other endocrine cell types. Objective: To determine whether similar processes occur in human type 2 diabetes, we surveyed pancreatic islets from 15 diabetic and 15 nondiabetic organ donors. Design: We scored dedifferentiation using markers of endocrine lineage, (cid:2) -cell-specific transcription factors, and a newly identified endocrine progenitor cell marker, aldehyde dehydrogenase 1A3. Results: By these criteria, dedifferentiated cells accounted for 31.9% of (cid:2) -cells in type 2 diabetics vs 8.7% in controls, and for 16.8% vs 6.5% of all endocrine cells ( P (cid:2) . 001). The number of aldehyde dehydrogenase1A3-positive/hormone-negativecellswas3-foldhigherindiabeticscomparedwith controls. Moreover, (cid:2) -cell-specific transcription factors were ectopically found in glucagon- and somatostatin-producing cells of diabetic subjects. Conclusions: The data support the view that pancreatic (cid:2) -cells become dedifferentiated and convert to (cid:3) - and (cid:4) -“like” cells in human type 2 diabetes. The findings should prompt a reassessment of goals in the prevention and treatment of (cid:2) -cell dysfunction. ( J Clin Endocrinol Metab 101: 1044–1054, 2016)
Re-Ranking BERT; Revisiting Passage Re-Ranking with BERT on MS MARCO

Faculty of Science Department of Computer Science Master of Data Science Re-Ranking BERT; Revisiting Passage Re-Ranking with BERT on MS MARCO by Tom JANSSEN GROESBEEK In this thesis, the task of passage ranking using the MS MARCO passage ranking dataset is examined. Given an input query and candidate passages retrieved by the baseline ranker BM25 model, the task is to re-rank these passages by relevance to the query. Currently, the relevance labels provided by the dataset are assumed to be incomplete as not every query-passage pair is assessed on relevancy. The hypothesis of this work is that there are more relevant passages per query and an online assessment is organized in order to gather these additional labels. With these new relevance labels, the performances of the BM25 ranker and the state-of-the-art BERT model on the passage ranking task are re-examined. Both models show increased performances on the passage ranking task when they are evaluated with the additional relevance labels. While originally BERT outperforms the BM25 ranker, utilizing the new relevance labels shows that BM25 achieves equal and in some settings even better performance. Additionally, both models are evaluated using multigraded relevance labels and the results of this evaluation show that they perform equally well in ranking multigraded passages, but that BERT does not improve the rankings of BM25.
Hunting for Direct Translations across Wikipedia Articles

—This paper deals with a problem in the area of Cross-Language Plagiarism Detection. In particular, it presents a system, able to detect portions of a Wikipedia page, which have been obtained by translating a Wikipedia page on the same semantic content but written in a different language. The problem is relevant in the context of Wikipedia pages maintenance, and could be of interest in other areas such as news comparison in different languages. We discuss the problem, the system and its implementation and briefly present its evaluation.
Structural Reforms During the Greek Economic Adjustment Programme: The Amalgamation of Municipalities

Amalgamation reforms have extensively been implemented in several countries as policies meant to improve local government service provision and reduce costs based on scale economies. However, their effectiveness has proved ambiguous in practice. We investigate the impact of a large-scale amalgamation, which took place in 2010 and reduced the number of municipalities substantially, on per-capita current costs and investments of Greek municipalities, using data for the 2005-2018 period. We find very weak evidence that current costs are affected by the amalgamation reform, while the amalgamation of municipalities is associated with a significant decrease in per capita investment in the range of 11 to 31%. This effect is robust, persistent, and associated with a substantial decline in GDP per capita of about 13% over the sample period.
A Review Of Abrasive Material And Advancements In Abrasive Flow Machining(AFM)

- Abrasive flow machining is a process to deburr, polish and radius difficult-to-reach surfaces. In this process small quantities of work material are removed by flowing semi-solid, abrasive dispersed putty (for sealing glass) or dough under pressure through or across work piece material. Number of cycles, Extrusion pressure, flow volume, grit size, media, and work piece geometry are the prior machining parameters that improve the surface finish properties. Recently majority of researchers are focusing to create hybrid processes by merging abrasive flow machining process with other non-conventional processes. By implementation of hybrid processes materials with complicated shapes which are difficult to machine have been possible. It finds application in modular head of hip joint, extrusion die, aerospace, pharmaceutical and automobile industry. This paper presents the past research on different developments in hybrid AFM processes, different rheological properties of abrasive flow machining medium, findings of different process parameters and also review the finishing results of various materials such as brass, aluminum and mild steel. This paper also gives directions to the future aspects of abrasive flow machining by including organic abrasive laden media which will be beneficial to protect our environment. _____________________________________________________________________________________________________