Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Depression using Temporal Text Analysis in Social Network Messages

In recent years, depression has gained increasing attention. As with other disorders, early detection of depression is an essential area of study, since severe depression can result in suicide. Thus, this study develops, implements, and analyzes a computational model based on natural language processing to identify the depression tendencies of Twitter users over time based on their tweets. Consequently, an F-measure of 83.58 % was achieved by analyzing both the textual content and the emotion of the papers. With these data, it is possible to determine whether constant fluctuation of emotions or the message in the text is a more accurate indicator of depression.
AppTree: An Intelligent Platform for Discovering the World of Plants

——“AppTree” is an intelligent platform to bring researchers, visitors, and all interested people closer to the oldest and most attractive botanical garden at the University of Tehran. AppTree can scan the QR-Barcode of each plant in person by smartphone or search various plants on the website and get all the useful knowledge about them. Also, the ability of AppTree is the recognition of different plants which don’t have labels. The plant recognition part is a machine learning module that can identify more than 100 different species of plants and give the user details about them. This novel platform is based on Android and Web-app and the identification of new plants type is done by machine learning approach. We utilized VGG19, a deep CNN, to classify images and to identify unlabeled plants. The classification accuracy, F1-score, recall, and precision were 98.25, 93.16%, 88.21%, and 94.85%, respectively, on the plant dataset of the University of Tehran. The proposed method was compared with other deep learning architectures such as AlexNet, AlexNetOWTBn, and GoogLeNet on the same dataset and obtained higher performance. Our AppTree platform has achieved considerable success and easily can be extended to use in other botanical gardens.
A Constrained Joint-Equation Estimation of at-a-station Hydraulic Geometry

Hydraulic geometry describes the relations between a stream’s discharge and its width, depth and velocity as a system. The continuity equation implies two cross-equation constraints, one of which is that the hydraulic exponents should sum to one. Traditional literature, using one-at-a-time estimation methods, have either ignored the constraints, or force the exponents to sum to unity by arbitrarily manipulating the estimates. By using a systematic approach called Seemingly Unrelated Regression(SUR), we are able to jointly estimate the relations and impose the constraints. The unrestricted and restricted estimates are computed from SUR method and Ordinary Least Squares(OLS). It is found that SUR and OLS yield identical unrestricted results when the sets of regressors are identical. Although SUR estimates are asymptotically at least as efficient as OLS, due to the finite sample sizes of our data, the restricted estimates from SUR generally have larger standard errors than the restricted OLS.
An Empirical Study of the Performance of Code Similarity in Automatic Program Repair Tool

Recently, code similarity has been used in several automated program repair (APR) tools. It suggests that similarity has a great contribution to APR. However, code similarity is not fully utilized in some APR tools. For example, SimFix only uses structure similarity (Deckard) and name (variable and method) similarity to rank candidate code blocks that are used to extract patches and do not use similarity in patch filtering. In this paper, we combine the tool with longest common sequence (LCS) and term frequency-inverse document frequency (TFIDF) to rank candidate code blocks and filter incorrect patches. Then we design and set up a series of experiments based on the approach and collect the rank of the correct patch and time cost for each selected buggy program. In the candidate ranking phase, LCS and TFIDF improve the rank of the block, which contains the correct patch for several bugs. In the patch validation phase, LCS filters out 68% of incorrect patches on average. It shows that code similarity can greatly improve the performance of APR tools.
RePersian A Fast Relation Extraction Tool in Persian

— The task of extracting semantic relations from raw data is called relation extraction. One of the most important fields in open information extraction is the automatically extraction of relations in any domain, especially in web mining. There are many works and approaches for relation extraction in English and other languages. Some of these approaches are based on parsing trees. Dependency parsing in the Persian language is difficult and time-consuming, since Persian is a low resource language and has also a dependency grammar and lexical structure, which affects also the speed of relations extraction in Persian. In this paper we will introduce a fast relation extraction method in Persian called RePersian. RePersian is dependent on part-of-speech (POS) tags of a sentence and special relation patterns, which are extracted by analyzing sentence structures in Persian. For finding relation patterns, RePersian searches through POS-tags that are given in regular expression forms. By matching the correct POS pattern to a relation pattern, RePersian extracts the semantic relations in a sentence. We appraise RePersian in two different scenarios on the Dadegan Persian dependency tree dataset. RePersian had on average the precisions 78.05%, 80.4% and 54.85% in finding the first argument on a relation, the second argument and the right relation between them.
Artificial Neural Networks with Enhanced Nearest Neighbor algorithm for Heart Disease Prediction

Heart failure is the root cause of the increased mortality rate happening now in the current scenario. Many health care services have been investing in the research forefronts to improvise the predictive methodology of cardio arrests and cardio related diseases to avoid this major health hazard. The improvised predictive methodology by the research forefronts with timely response helps to provide proper medical aid before the condition gets severe. The forecast methods use artificial intelligence which includes the neural networks and fuzzy techniques. The neural network algorithm makes use of the datasets and predicts the future datasets under various conditions. The fuzzy technique implements the data sets performed by the neural algorithms provides automated training to the system which in turn makes the system reliable to device any decisions on their own under any critical circumstances. To contribute in providing the solution, this paper analyzes the invisible medical information of medications in-taken for various medical issues and their effects on the patients along with the patient’s medical history are taken as dataset inputs to the classification with association along with the nearest neighbor which process the possibilities and facts that bares the key reason for cardiac arrest. To avoid critical circumstances in the last minute of cardiac arrest, the neural algorithms forecast the cardiac related hazards on time which could help them to survive even in critical condition with proper medication and intensive care. There are 14 Attributes such as gender, cardiac acute type, pressure level, body glucose level, age factor, cholesterol and body fat contents, BMI, medications are the key factors taken as the primary source for the input datasets. The interpreted data sets are analyzed and visualized as patterns. The nearest neighbor algorithm is enhanced for recognizing the patterns and grouping the similarities and dissimilarities which later formed as data subsets for the forecast. To improvise the efficiency of results 14 attributes are taken as input datasets and by the process of neural algorithm, it is then reduced to 6 attributes results 99.1% accuracy with 0.9% of noise figures.
Teaching Ethical Decision-Making in Counselor Education Teaching Ethical Decision-Making in Counselor Education

Abstract How and when counseling graduate students are taught to apply ethical codes and principles varies. In this article the authors review foundational concepts and proposed practices for ethics education in graduate counseling programs. The authors present strategies for teaching ethical decision-making that focus on the intersection of personal values, principle and virtue ethics, and self-reflection. Recommendations are provided for content delivery and activities and assignments that encourage self-reflection of values in conjunction with the application of decision-making skills and models. Implications for future research are addressed.
Deep Learning Visual Methods for Identifying Abandoned Houses

Abstract Housing abandonment contributes to neighborhood decline and disinvestment. Abandonment has plagued large metropolitan areas for decades, yet quantifying the scope and impact of abandonment has proven costly and elusive. This study introduces an innovative approach to detect and measure abandoned houses using technology innovations without requiring significant resource commitments. It presents a system of detecting abandoned houses leveraging deep learning models for image classification, building an ensemble model that considers both global and local contexts to identify abandoned structures. This study takes imagery and structure data from multiple sources and uses transfer learning in a three-stage ensemble approach to identify abandoned houses. Four deep learning models are constructed for this study: the ResNet-50 model, an incremental knowledge model, a hybrid approach, and a check model. Results from the different models are compared and analyzed to identify the visual characteristics of houses that improve or degrade each method’s accuracy. The methodology presented herein is scalable and could be applied in other neighborhoods and communities. The data generated by this method will empower communities and cities to design more effective strategies to address housing abandonment.
On the role played by NURBS weights in isogeometric structural shape optimization

Finite element based shape optimization is a classical approach for solving realistic problems, such as those encountered in industry. However, some difficulties may arise from the divide between the parametrization of the shape using Non-Uniform Rational B-Spline (NURBS) functions used in CAD tools and the finite element representation of the geometry using piecewise linear functions used in solvers. An alternate approach based on isogeometric analysis has been recently proposed for which the solver is also based on NURBS functions. In this framework, the locations of NURBS control points are usually considered as optimization variables whereas the corresponding control weights are frozen. However, this a priori choice of weights imposes a severe limitation of the shapes that could be found by the optimization algorithm. In this work, we present and experiment a shape optimization algorithm where the weights are taken as optimization variables in addition to control points.
Extreme Precipitation and Atmospheric Rivers in a Model of Pliocene Climate

A Senior Thesis presented to the faculty of the Department of Geology and Geophysics, Yale University, in partial fulfillment of the Bachelor’s Degree. Abstract While it is understood that overall precipitation will increase as the climate warms, our knowledge of how warmer temperatures will affect the intensity, frequency, and regional distribution of the heaviest precipitation events is less established. The mid-Pliocene warm period (mPWP), or mid-Piacenzian, is an example of a warm past climate with concentrations of atmospheric CO 2 comparable to those of the present. It is often invoked as a possible analog for the long-term effects of climate change. This study analyzes output from one model of mid-Piacenzian climate—the University of Toronto Version of the Community Climate System Model Version 4 (UofT-CCSM4)—to examine changes in patterns of extreme precipitation in the Pliocene. It focuses on midlatitude regions where atmospheric rivers (ARs) play a major role in transporting water vapor, and uses vertically-integrated vapor transport anomalies to identify changes in AR behavior. Globally, there is an overall increase in the intensity and frequency of the heaviest precipitation events in the Pliocene simulation, particularly of the very heaviest events. Regionally, changes in the hydrologic cycle are influenced by a combination of thermodynamic and dynamic factors. Two strongly AR-influenced areas—the Pacific coast of North America and the Pacific coast of Chile—experience distinctly different patterns of change in extreme precipitation. These differences can largely be explained by changes in AR behavior in the Pliocene simulation, highlighting the effects of ARs on regional patterns of precipitation. This study also has implications for the design of future experiments using Pliocene climate models, and for further efforts to better constrain Pliocene conditions using proxy data.
Underwater Robotics Semantic Parser Assistant

Semantic parsing is a means of taking natural language and putting it in a form that a computer can understand. There has been a multitude of approaches that take natural language utterances and form them into lambda calculus expressions -- mathematical functions to describe logic. Here, we experiment with a sequence to sequence model to take natural language utterances, convert those to lambda calculus expressions, when can then be parsed, and place them in an XML format that can be used by a finite state machine. Experimental results show that we can have a high accuracy model such that we can bridge the gap between technical and nontechnical individuals in the robotics field.
Cell Adhesion as Dynamic Interplay of Lock-and-Key, Generic and Elastic Forces(Physics of Non-Equilibrium Systems: Self-Organized Structures and Dynamics Far from Equilibrium)

The selectivity of cell-cell and cell-tissue adhesion is determined by specific short range forces between cell surface proteins. Long range entropic interfacial forces (mediated by repeller molecules and membrane undulations) and adhesion-induced elastic stresses in the cell envelope serve the fine control of the strength and duration of adhesion. The initial step of cell adhesion exhibits typical features of a first order wetting transition resulting in the formation of tight adhesion domains by lateral phase separation of receptors. External lift forces can cause shrinking and unbinding of adhesion sites if the receptors are immobile but induce domain growth if they are mobile. Strong adhesion domains (resisting nano-Newton forces) can form by commitment of some 10,000 receptors enabling cells to control adhesion strength rapidly by varying the receptor and repeller densities on cell surfaces through endocytosis and exocytosis. The adhesion domains can function as constraint reaction spaces facilitating the local assembly of actin stress fibers and control cell signalling processes as shown for the activation of immunological responses by immunological synapses.
Unconventional Entreats for the Anticipation of Solar Irradiance

: At present, the world is putting a pioneering whack to escalate their energy generation capacity to meet the global demand in increase in electricity requirement. The solar energy provides a sustainable model for energy production, when the world is shifting to renewable energy from fossils. The installation of solar photovoltaic panels has increased in recent years to a significant percentages upgrade as the Sun is most viable form of alternate energy that can be harnessed easily and most efficiently. But, the efficiency of the power output via solar modules is still a big challenge to be worked upon. A large number of external and climatic conditions to affect the generation and prediction of output power from the PV cell module. The emitting challenge that are being faced by operators of alternate energy producers as in for operation, planning and power scheduling can be minimized to a certain level by predicting or forecasting the photovoltaic module output on the basis of short term and long-term basis. The anticipation of the yield of the PV module can play a major key role for the execution and weighing of the fiscal and monetarily consummation of the power system. The solar irradiance anticipation is one major step for solar power yield anticipation which is dependent on various factors such as cloud and climatic condition over the region. The meteorological transcript of the suburb for a prolongated span of term is imperative precondition of anticipation of PV module power yield as time series problem. The appositeness of Machine Learning Algorithm complies as a momentous factor in this field. Here in the chapter disparate Machine Learning algorithms are implied for the study of prediction of solar irradiance.
Relativistic Effects in the Spectra of Atomic Systems

Quantum electrodynamics of atoms and ions energy levels transition probabilities and the line shape theory parity violation effects in the multicharged ions atomic system in the external fields spectra of the multicharged ions in the external constant fields spectra of the multicharged ions in oscillating electric fields S-matrix based perturbation theory for energy and width of bound states S-matrix based theory of atomic states in external oscillating fields.
The quest for ﬂorigen: a review of recent progress

The photoperiodic induction of ﬂowering is a systemic process requiring translocation of a ﬂoral stimulus from the leaves to the shoot apical meristem. In response to this stimulus, the apical meristem stops producing leaves to initiate ﬂoral development; this switch in morphogenesis involves a change in the identity of the primordia initiated and in phyllotaxis. The physiological study of the ﬂoral transition has led to the identiﬁcation of several putative ﬂoral signals such as sucrose, cytokinins, gibberellins, and reduced N-compounds that are translocated in the phloem sap from leaves to the shoot apical meristem. On the other hand, the genetic approach developed more recently in Arabidopsis thaliana allowed the discovery of many genes that control ﬂowering time. These genes function in ‘cascades’ within four promotive pathways, the ‘photoperiodic’, ‘autonomous’, ‘vernalization’, and ‘gibberellin’ pathways, which all converge on the ‘integrator’ genes SUPPRESSOR OF OVEREXPRESSION OF CO 1 (SOC1) and FLOWERING LOCUS T ( FT ). Recently, several studies have highlighted a role for a product of FT as a component of the ﬂoral stimulus or ‘ﬂorigen’. These recent advances and the proposed mode of action of FT are discussed here.
Accounting for disruptive developments in strategic planning of shipping in the Arctic

The changing climate gradually opens the way to the Arctic waters for commercial shipping. This requires long-term investments in infrastructure and hence strategic planning of shipping activities both by governments and the global maritime industry to take into account various global and regional factors influencing the economics of shipping. Many of these factors are inherently uncertain and subject to disruptive developments, such as natural disasters or marine accidents. Therefore, such extreme events and disruptive developments should be accounted for when formulating strategic policy documents that will guide the development of shipping in the Arctic.
Automating Idea Unit Segmentation and Alignment for Assessing Reading Comprehension via Summary Protocol Analysis

In this paper, we approach summary evaluation from an applied linguistics (AL) point of view. We provide computational tools to AL researchers to simplify the process of Idea Unit (IU) segmentation. The IU is a segmentation unit that can identify chunks of information. These chunks can be compared across documents to measure the content overlap between a summary and its source text. We propose a full revision of the annotation guidelines to allow machine implementation. The new guideline also improves the inter-annotator agreement, rising from 0.547 to 0.785 (Cohen’s Kappa). We release L2WS 2021, a IU gold standard corpus composed of 40 manually annotated student summaries. We propose IUExtract; i.e. the first automatic segmentation algorithm based on the IU. The algorithm was tested over the L2WS 2021 corpus. Our results are promising, achieving a precision of 0.789 and a recall of 0.844. We tested an existing approach to IU alignment via word embeddings with the state of the art model SBERT. The recorded precision for the top 1 aligned pair of IUs was 0.375. We deemed this result insufficient for effective automatic alignment. We propose “SAT”, an online tool to facilitate the collection of alignment gold standards for future training.
2 2 N ov 2 00 1 Analysis of SU ( 3 ) c ⊗ SU ( 3 ) L ⊗ U ( 1 ) X local gauge theory

Six different models, straightforward extensions of the standard model to SU(3)c ⊗ SU(3)L ⊗U(1)X , which do not contain particles with exotic electric charges are presented. Two of the models are one family and four are three family models. In two of the three family models one of the families transforms different from the others, and in the other two all the three families are different.