Reviewerly/exHarmony huggingface dataset:
Behavior of major ions and heavy metals risk assessment in spring and surface water on the southwest slope of Mount Cameroon (Western Africa)

The coastal flanks of Mt Etinde is wedged between the sea and the southwestern slope of Mt Cameroon.The only fresh water resources available for multipurpose uses in this area are mountain springs and streams that drain volcanic materials.To access the sources, processes and assess the ecological risk associated with hydrochemicals and heavy metals in these mountain water resources, 18 water samples were collected in different seasons at 4 locations between may 2009 and december 2010 for major ions and heavy metals determination.The sampled waters ranged from neutral to slithly alkaline, weakly mineralized and dominated by Ca-Mg-HCO 3 water type irrespective of season.The hydrochemical ratios revealed ions chemistry mostly influenced by geogenic source of silicate rock weathering and atmospheric inputs.Sampled waters had low concentration of heavy metals which are less mobile in the aqueous phase, controlled by hydraulic condition.Geochemical processes of mineral dissolution and reverse ion exchange coupled with hydrodynamic processes of flushing at the weathering front and soil leaching are the main controls on the water chemistry in the area.Despite agricultural and other anthropogenic activities, quality indices in the study area indicate the absence of anthropogenic inputs.
The Moderating Effects of Demographic Factors: The Usage of M-Learning Services among Jordanian Students

This study attempts to suggest a model that will increase the usage of M-Learning Services in developing countries. This study intends to study the aspect of Jordanian university students’ acceptance of M-learning services by looking into the students’ intention to use & the Continued Usage Intention of the M-learning services. The proposed model may facilitate the understanding of how certain factors can affect the level of usage of M-Learning Services in Jordan. The main objective of this study is identifying the effects of Effort Expectancy (EE), Facilitating Condition (FC), Performance Expectancy (PE), Social Influence (SI), Culture Factors (FC), Quality of Service (QoS) and Student Readiness (SR) on the Usage Behaviour (UB) of m-learning services. Determining the significant factors influencing students’ acceptance is aimed at reducing students’ resistance to the use of m-learning system. This study applied a quantitative research methodology, which includes a numerical measurement and analysis of the factors which influence acceptance. The findings of this study practically contribute to solving the research problem, namely students' acceptance of m-learning. The descriptive statistics revealed that, respondents indicated of this study have expertise and good experience with using m-learning in Jordan. More importantly, the study has offered results of PLS-SEM analysis which was obtained from evaluating hypotheses testing, the measurement model, and structural model.
Simulation of three-spin evolution under XX Hamiltonian on quantum processor of IBM-Quantum Experience

We simulate the evolution of three-node spin chain on the quantum processor of IBM Quantum Experience using the diagonalization of XX-Hamiltonian and representing the evolution operator in terms of CNOT operations and one-qubit rotations. We study the single excitation transfer from the first to the third node and show the significant difference between calculated and theoretical values of state transfer probability. Then, we propose a method reducing this difference by applying the two-parameter transformation including the shift and scale of the calculated probabilities. We demonstrate the universality of this transformation inside of the class of three-node evolutionary systems governed by the XX-Hamiltonian.
A model of access to information among Nigerian rice farmers

Facilitating access to information has been and will continue to be a dynamic process in light of the growing importance of information. Many studies have explored the socio-economic characteristics or attributes of farmers that affect access to information. Studies on models for their prediction are often lagged, particularly in developing countries. In this article, data on access to information and other attributes was collected from 1920 randomly selected rice farmers in Nigeria using structured interviews. Half of the farmers (49.0%) had access to information on rice farming and more than half (64.9%) belonged to a farmers group. The majority of the rice farmers were married (90.7%) and educated (73.1%). A logistic regression model with a 54.4% correct prediction showed age, education and membership of a farmers group to be predictors of access to information. Librarians should ascertain the attributes of rice farmers to enable them to repackage information for easy access.
Symbolic Regression for Approximating Graph Geodetic Number

Graph properties are certain attributes that could make the structure of the graph understandable. Occasionally, standard methods cannot work properly for calculating exact values of graph properties due to their huge computational complexity, especially for real-world graphs. In contrast, heuristics and metaheuristics are alternatives proved their ability to provide sufficient solutions in a reasonable time. Although in some cases, even heuristics are not efficient enough, where they need some not easily obtainable global information of the graph. The problem thus should be dealt in completely different way by trying to find features that related to the property and based on these data build a formula which can approximate the graph property. In this work, symbolic regression with an evolutionary algorithm called Cartesian Genetic Programming has been used to derive formulas capable to approximate the graph geodetic number which measures the minimal-cardinality set of vertices, such that all shortest paths between its elements cover every vertex of the graph. Finding the exact value of the geodetic number is known to be NP-hard for general graphs. The obtained formulas are tested on random and real-world graphs. It is demonstrated how various graph properties as training data can lead to diverse formulas with different accuracy. It is also investigated which training data are really related to each property.
A step towards Improving Knowledge Tracing

The advancements in learning analytics and artificial intelligence have shown potential to transform traditional modalities of education. One such advancement relates to the use of educational data to track students’ knowledge state [1] . In the field of Artificial Intelligence in Education knowledge tracing is a well-established area where a machine models the students’ knowledge as they interact with coursework. Effective modeling of student knowledge can have a high impact on the provision of adaptive learning. In fact, lately, research on knowledge tracing is intensifying with a particular focus on the utilisation of new machine learning algorithms for modelling the students’ knowledge levels and for the prediction of performance on future tasks and assessment questions [2] . In the case of question-level assessment, knowledge tracing provides an interpretation of the learner’s current knowledge level and models their mastery of the skill or knowledge component to which future questions are related [3] .
Geochemical Characteristics of the Campano-Maastrictian Sediments of the Anambra Basin, Southeastern Nigeria - Implication For Provenance, Paleodepositional Environment, Maturity and Tectonic Setting

Abstract The present study investigates the Anambra Basin shales to determine the provenance and maturity of the sediments using standard geochemical techniques. Twelve (12) representative samples recovered from shale sequences of The Mamu Formation and Nkporo Group of The Anambra Basin were studied to determine the sediment provenance, paleoenvironment, diagenetic conditions, maturity as well as the tectonic setting. To consider in detail and establish the inherent constituents of the Major minerals, Trace and Rare Earth elements, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) analyses techniques was employed. The detrital minerals determined are Al2O3 (18.27% and 21.16%), TiO2 (1.73% and 1.63%) and Fe2O3 (2.78% \ and 2.85%), for Nkporo Group and Mamu Formation respectively. The enrichment of SiO2, Al2O3 and TiO2 (1.14, 1.94, 3.67 respectively) supported by Chemical Index of Alteration (CIA) of 93.54 &amp; 39.55 and Rb/Sr ratio of 0.57 &amp; 0.40, indicate that the Anambra Basin sediments are matured. TiO2/AL2O3 binary plots, Th/Co Vs La/Sc crossplots, Th-Sc-Zr triplots and Cr, Ni concentration suggest mixed provenance of felsic to mafic source rocks for these sediments. From the log (K2O/Na2O) Vs SiO2 crossplots, a passive margin tectonic setting was determined for these sediments.
Secure and Efficient Application of Manet Using RSA Using Vedic Method Combine With Visual Cryptography and Identity Based Cryptography Technique

Security and rapid transmission of data could be very important a part of military devices. Propose technique present the stable, green and rapid way to send pictures by using the Identity Based Cryptography and Visual Cryptography. In this application Identity Based Cryptography is used with Visual cryptography. In Identity primarily based cryptography the RSA Cryptosystem is used to generate public and private key by using Ancient Indian Mathematics for fast mathematical calculation. RSA is the most secure and general algorithm. Implementation of it by using the Vedic multiplication could be very green in time period of area, speed compared to its cutting-edge mathematics implementations. The regenerations of public and private key are adopted to make the system greater steady from diverse attacks.
Cognitive Approach for Creation of Visual Security Codes

Modern cryptography allows to apply personal features or cognitive parameters in creation of security protocols. This allow to define personally oriented security cryptosystems widely oriented for different application like services management, and big data secure exploration. We can also create thematic-based visual codes used for personal authentication. In such procedures it is possible to use contactless devices while selecting proper visual parts, necessary for authentication. In this paper will be proposed such approach dedicated for creation of security procedures, which use visual paths. Passing such paths requires special cognitive skills, and abilities of following commands during authentication.
The Effectiveness of Pre Reading Plan (PReP)Technique to Improve Studentâ€™s Reading Comprehensionof Narrative Text

The present study intended to investigate the effectiveness of pre reading plan (PReP) technique to improve studentâ€™s reading comprehension of narrative text at one senior high school in Pinrang. How is the studentsâ€™ reading comprehension. This study is aimed to know the studentsâ€™ reading comprehension before and after applying Pre Reading Plan Technique and to finding outis applying Pre Reading Plan Technique able to improve studentsâ€™ reading comprehension. Beside that, the purpose of writing this article is to describe how teaching of reading through the PReP technique. The article support by a literature reviewÂ of PReP technique theory in teaching reading. The information about of PReP technique supported by expert theory, it can be used as a source of information about English teaching technique especially reading that can be applied by teachers in high school.Â This study employed a quantitatif research design which is a case study using pre-experimental designÂ which applied (one group pre-test and post-test design). The study found out that the studentsâ€™ reading comprehension had increased after doing the learning process by usingpre reading plan (PReP) technique. So, it can be concluded that the pre reading plan (PReP) technique is effective to improve studentâ€™s reading comprehension of narrative text. This study also provides a set of recommendation to avoid the overlapped researches in education technology.
Improved real-time joint object detection and road segmentation multi-task network

To realize real-time road scene understanding, jointly detecting objects and segmenting road areas, an improved multi-task network was proposed. Based on SE-ResNeXt, dilated convolution was added to expand the image receptive field and improve the performance of the encoder. In terms of object detection, a coarse-fine optimization network was proposed, using high-level features to further refine the low-level rough estimated results, and the self-attention module was used to adaptively adjust the detection results of different scales from a global perspective view. For road detection, a pyramid pooling model was added to obtain global information, and a jump connection mode was used to combine multi-level features. A channel adjustment module was added to adjust the relationship between different channels. Experiments show that these strategies can significantly improve the detection results while increasing a small amount of reasoning time. And generalization experiment proves the effectiveness of the method. Two tasks were trained together, resulting in mutual promotion.
A Kind of Syntax Parsing Algorithm Based on The Push-down Automaton

Natural language processing is an important branch of artificial Intelligence. Syntax parsing algorithms are basic algorithms of natural language processing. The Context free grammar can be transferred to the Graibach normal form. According to the grammar in the form of the Graibach normal form, the push-down automaton can be constructed. The syntax parsing algorithm can be constructed based on the push-down automaton. The syntax parsing can be completed by using the push-down automaton and the syntax parsing algorithm based on the push-down automaton.
Classification of Texts Using LSTM and LDA

With the approach of the Fourth Industrial Revolution, information has become a powerful resource for society and the economy to operate and develop. In particular, as customized algorithms have become an essential service in most systems, the importance of big data-based information processing technology is also deepening. However, human languages have extreme variability compared to programming languages, so interpretation and processing are difficult. Efficient measures need to be taken to extract the desired information from these unstructured data. Therefore, in this paper, we identify and develop a more effective analytical system by classifying papers that are subdivided into five categories within the topic of 'technique' into LSTM and LDA techniques after learning.
State Conditional Filtering

In many dynamical state estimation problems, not all the values that the state can take have the same importance; hence, missing to deliver an appropriate estimate has more severe consequences for certain state values than for others. In many applications, such important state values correspond to events that have low a priori probability to happen (e.g., unsafe situations or conditions that one tries to avoid by design). Provably, Kalman filtering techniques are inadequate to correctly estimate such rare events. In this article, a new state estimation paradigm is introduced to build confidence regions that contain the true state value, whatever this value is, with a user-chosen probability. Among regions having this property, an algorithm is introduced that generates in a Gaussian setup the region that satisfies a minimum-volume condition.
Cold-Start Hospital Length of Stay Prediction Using Positive-Unlabeled Learning

Accurately predicting the in-hospital length of stay (LOS) at the time of admission can positively impact healthcare metrics. Machine learning (ML) techniques have been used to predict hospital patients’ LOS based on their demographic and clinical characteristics. During the regular steady-state operation, traditional supervised-learning classification algorithms can be used for this purpose to inform hospital planning. However, when there are sudden changes to the admission and patient statistics, such as during the onset of a pandemic or the establishment of a new hospital, these approaches break down because reliable data for training ML models becomes available only gradually over time. This paper shows how LOS predictions can be improved during such a cold-start transition period by utilizing the positive-unlabelled (PU) learning techniques. Experimental results from using two PU learning algorithms with inpatient data from New York state hospitals support the proposed approach.
INSTITUTIONALITY OF EDUCATIONAL DISCOURSE IN THE CONDITIONS OF DIGITALIZATION

The article features a comparative analysis of the traditional pedagogical and modern educational discourse. The central aspect of this comparison is the institutional factor in the context of digitalization. Pedagogical discourse traditionally belongs to institutional types of discourse where participants act within the framework of certain social roles: the teacher transfers knowledge and norms of social behavior to the student. In modern educational discourse we are talking about the transfer of information. The authors consider the following forms of modern educational discourse: the use of digital media in classrooms, online courses with tutors, online courses with trainers, educational accounts on social networks, online classes in zoom interactive program. The study revealed that in the first case, digitalization does not affect the degree of institutionalization. Online courses with tutors, which are characterized by a rather rigid structure and clearly defined functions of tutors, demonstrate higher degree of institutionalization compared to traditional pedagogical discourse. In other cases, digitalization has led to a lower degree of institutionalization: learning using Internet technologies is generally more personalized, participants use rather informal language, use smiles and emojis to express their emotions. The authors believe that this trend will remain, since the emotional component is an important factor in maintaining motivation to learn.
Construction of the Course System Based on Teacher Certification and Virtual Simulation Technology

In response to the spirit of teacher certification, the major of electromechanical technology education promoted actively the secondary teacher certification of Vocational and technical. This paper discusses the reform ideas and contents under the guidance of teacher certification. The course system has been greatly improved in the aspects of construction mechanism, system framework, evaluation and so on. Finally, the certification spirit has implemented in the Special field building. The reform of course system will continuously summarize and improve.
State Of Art on Ontologies for Improving the Living Conditions of Seniors: Positioning Towards Learning Systems For Seniors

Older people are fragile people who need the support of other younger people and even help from new technologies to improve their living conditions. The issue of improving the living conditions of senior citizens through intelligent computer systems is discussed in this article. We present a state of the art on intelligent computer systems techniques, in particular ontologies for improving the living conditions of seniors, in order to highlight some shortcomings and take a position accordingly.
Energy Efficiency for HDFS

The rapidly increasing growth of big data is leading to increased usage of distributed computing and clusters as this can enable tasks to be split and processed in parallel resulting in higher computational efficiency. Environments like Apache Hadoop use Distributed file systems like the HDFS to support large clusters of typically commodity hardware to achieve the same. Commodity hardware present in HDFS consumes significant energy even when not in an active state, and considering our fragile ecosystem, reduction of the carbon footprint and conservation of energy in these environments is the focus of our work. We have evolved on a dynamic approach to Hadoop Distributed File Systems (HDFS) that provides an energy efficient paradigm for Hadoop clusters by segregating the servers in the cluster to two “Zones”, a “Hot-Zone”, and a “Cold-Zone”. We use energy efficient storage devices (SSDs), to store data that is frequently used for the hot zone. The Cold-Zone would have lesser energy efficient devices (HDDs) and would be used to store data that is not frequently used. The Hot-Zone would always be in an active state to compute while the cold zone would have the HDDs to be in a low power consumption state. We have also enhanced the block placement policy for HDFS to support dynamic block transfer among the two zones.
Unraveling Learner Interaction Strategies in VeriSIM for Software Design Diagrams

In the past, unraveling learner interaction data in TELE was a challenge. However, the advent of LA has helped in uncovering latent information in log data to scaffold learning. This paper focuses on learner interaction in VeriSIM, a TELE, to teach software design diagrams. The learners’ performance in the system is used to categorize them into three groups, namely, "full scorers", "partial scorers", and "give uppers". Our analysis found that the full scorers spend a significantly higher duration per action than the give-uppers in an introductory challenge presented in the learning environment. Further analysis unravels the strategies used by consistent and inconsistent learners, and it was observed that the learner interaction strategies evolve with increasing difficulty levels as they navigate through the challenges.
Photonic Spiking VCSEL Neurons using Multi-Frequency Switching

We propose an approach to generate neuron-like spikes of VCSELs by multi-frequency switching. A frequency modulation is realized by numerical simulation to obtain stable spiking response of up to 1 Gbps.
Designing a Bank-Based Flexible Performance Evaluation System (Study: Bank Shahr) (in Persian)

Given the limitations of the existing performance evaluation models for organizations with dynamic internal and external conditions, this study aims to provide a flexible performance evaluation model with adaptability to intra- and extra-organizational changes. The present study first forms a database of criteria related to banking activities. After gathering the expertschr('39') opinions, we select 25 general concepts. By using a Fuzzy cognitive map (FCM) and weighted by the Fuzzy Analytic Network Process (FANP), we identify nine effective criteria. Creating organizational justice for all colleagues, meeting customer needs as soon as possible, emphasizing proper employee ethics with the customer, and doing research and developing for products and services are the most important criteria. The results show that the proposed flexible model can be used as a tool to extract performance evaluation criteria in accordance with the organizationchr('39')s requirements and utilized in the performance appraisal process. The novelties of the present study are updating the existing criteria in the database based on the results of evaluations and management decisions and changing strategies, using this model to select appropriate criteria in each period of performance evaluation based on organizational conditions.