Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Ambiguous Nature of Theory in Educational Design-based Research – Reflecting and Structuring from an IS Perspective

Background: The lack of commonly accepted models of educational Design-based Research (DBR) hinders the effectiveness of knowledge transfer and theory development in this field. Such models are well established in information systems (IS) research. The structured approaches to DBR in IS research have the potential to inform educational research practice for facilitating the interplay between theoretical and practical advancements. Method: We compare existing approaches to identify compatibility in terms of objectives and structured process designs in the two fields based on a literature review. Having established common ground, we examine the role of theory as a result of DBR and identify potential for synthesis of existing concepts. Finally, we transfer an DBR contribution types model from IS and evaluate its applicability by reflecting it against outcomes in educational DBR. Findings: We show a compatibility of the goals and characteristics of DBR in both disciplines and identify a common underlying understanding that enables a transfer of concepts and models. The nature of DBR outcomes in educational research is found to be ambiguous, in particular with respect to the role of theory. This hampers the development of generalizable and transferable findings. DBR in IS provides more coherent models of the role of theory and the processes that lead to its development and validation. We show that these models can inform the implementation of educational DBR. Contribution: The paper contributes to the advancement of educational DBR by transferring models from IS research after thoroughly examining the compatibility of DBR in both fields. The more structured approach to examine and assess DBR outcomes enables to improve the generalizability and transferability of findings in educational research.
Safe Control Synthesis With Uncertain Dynamics and Constraints

This paper considers safe control synthesis for dynamical systems with either probabilistic or worst-case uncertainty in both the dynamics model and the safety constraints. We formulate novel probabilistic and robust (worst-case) control Lyapunov function (CLF) and control barrier function (CBF) constraints that take into account the effect of uncertainty in either case. We show that either the probabilistic or the robust (worst-case) formulation leads to a second-order cone program (SOCP), which enables efficient safe and stable control synthesis. We evaluate our approach in PyBullet simulations of an autonomous robot navigating in unknown environments and compare the performance with a baseline CLF-CBF quadratic programming approach.
Determinants of Household Heads Willingness to Pay for Labelled and Certified Moringa Products: Evidence from GamoGofa, Segen Peoples and South Omo Zones

The main objective of this study was estimating the mean willingness to pay of household heads for the certified and labelleled moringa leaf tea product in ArbaMinch, Sawula, Karat and Jinka Town. A contingent valuation method survey was applied to a sample of 397 household heads using the multi bounded discrete choice format which contained a matrix of the five response options with different level of certainty. The survey also included socio-economic, demographic, and environmental characteristics, which are expected to determine the households’ willingness to pay for the certified and labeled moringa leaf tea product. The survey data was analyzed by using ordered logit model. The result of the study contained descriptive and econometric analysis. The descriptive analysis revealed that household heads are generally willing to have certified and labeled moringa product. The econometric analysis result was shown that household heads’ WTP is found to be significant determined by education, monthly income; religion and prevalence of diseases in the households etc. Generally, the finding of this study would suggest that the value of certified and labeled moringa leaf tea product could have better welfare effect if it can be supplied by considering the demand side of the product.
RetroTRAE: retrosynthetic prediction with Transformer Retrosynthetic reaction pathway prediction through neural 1 machine translation of atomic environments

7 Designing efficient synthetic routes for a target molecule remains a major challenge in organic syn-8 thesis. Atom environments are ideal, stand-alone, chemically meaningful building blocks providing 9 a high-resolution molecular representation. Our approach mimics chemical reasoning, and predicts 10 reactant candidates by learning the changes of atom environments associated with the chemical re-11 action. Through careful inspection of reactant candidates, we demonstrate atom environments as 12 promising descriptors for studying reaction route prediction and discovery. Here, we present a new 13 single-step retrosynthesis prediction method, viz. RetroTRAE, being free from all SMILES-based 14 translation issues, yields a top-1 accuracy of 58.3% on the USPTO test dataset, and top-1 accuracy 15 reaches to 61.6% with the inclusion of highly similar analogs, outperforming other state-of-the-art 16 neural machine translation-based methods. Our methodology introduces a novel scheme for frag-17 mental and topological descriptors to be used as natural inputs for retrosynthetic prediction tasks. 18
Binary D ecision Diagrams by Shared Rewdting

A bstract. In this paper we propose a uniform description of basic BDD theory and algorithms by means of term rewriting. Since a BDD is a DAG instead of a tree we need a notion of shared rewriting and develop appropriate theory. A rewriting system is presented by which canonical fol·ms can be obtained. Various reduction strategies give rise to differ ent algorithms. A . layer-wise strategy is proposed having the same time complexity as the traditional apply-algorithm, and the lazy strategy is studied, which resembles the e. 'Cisting up-one-algorithm. We show that these algorithms have incomparable performance.
CHANG-ES. XV. Large-scale magnetic field reversals in the radio halo of NGC 4631

Aims. NGC 4631 is an interacting galaxy which exhibits one of the largest gaseous halos observed among edge-on galaxies. We aim to examine the synchrotron and polarization properties of its disk and halo emission with new radio continuum data. Methods. Radio continuum observations of NGC 4631 were performed with the Karl G. Jansky Very Large Array at C-band (5.99 GHz) in the C and D array configurations, and at L-band (1.57 GHz) in the B, C, and D array configurations. The Rotation Measure Synthesis algorithm was utilized to derive the polarization properties. Results. We detected linearly polarized emission at C-band and L-band. The magnetic field in the halo is characterized by strong vertical components above and below the central region of the galaxy. The magnetic field in the disk is only clearly seen in the eastern side of NGC 4631, where it is parallel to the plane of the major axis of the galaxy. We detected for the first time a large-scale, smooth Faraday depth pattern in a halo of an external spiral galaxy, which implies the existence of a regular (coherent) magnetic field. A quasi-periodic pattern in Faraday depth with field reversals was found in the northern halo of the galaxy. Conclusions. The field reversals in the northern halo of NGC 4631, together with the observed polarization angles, indicate giant magnetic ropes with alternating directions. To our knowledge, this is the first time such reversals are observed in an external galaxy.
Disentangling Controlled Effects for Hierarchical Reinforcement Learning

Exploration and credit assignment are still challenging problems for RL agents under sparse rewards. We argue that these challenges arise partly due to the intrinsic rigidity of operating at the level of actions. Actions can precisely deﬁne how to perform an activity but are ill-suited to describe what activity to perform. Instead, controlled effects describe transformations in the environment caused by the agent. These transformations are inherently composable and temporally abstract, making them ideal for descriptive tasks. This work introduces CEHRL 1 , a hierarchical method leveraging the compositional nature of controlled effects to expedite the learning of task-speciﬁc behavior and aid exploration. Borrowing counterfactual and normality measures from causal literature, CEHRL learns an implicit hierarchy of transformations an agent can perform on the environment. This hierarchy allows a high-level policy to set temporally abstract goals and, by doing so, long-horizon credit assignment. Experimental results show that using effects instead of actions provides a more efﬁcient exploration mechanism. Moreover, by leveraging prior knowledge in the hierarchy, CEHRL assigns credit to few effects instead of many actions and consequently learns tasks more rapidly.
User Review Mining: An Approach for Software Requirements Evolution

As users of internet-based software applications increase, functional and non-functional problems for software applications are quickly exposed to user reviews. These user reviews are an important source of information for software improvement. User review mining has become an important topic of intelligent software engineering. This study proposes a user review mining method for software improvement. User review data collected by crawling on the app review page is analyzed to check user satisfaction. It analyzes the sentiment of positive and negative that users feel with a machine learning method. And it analyzes user requirement issues through topic analysis based on structural topic modeling. The user review mining process proposed in this study conducted a case study with the a non-face-to-face video conferencing app. Software improvement through user review mining contributes to the user lock-in effect and extending the life cycle of the software. The results of this study will contribute to providing insight on improvement not only for developers, but also for service operators and marketing.
The Political Roots of China's Housing Vacancies

China has a vast number of unsold and unoccupied homes. Can incentives faced by local politicians explain this puzzle? In China, local governments are monopolists in selling land use-rights, which is a key input for the housing market. City managers, the leader of local governments, are promoted based on the performance of the local economy. They also use land revenues to fund local government expenditures. I develop and estimate a dynamic, infinite-horizon model of land supply. In this model, forwardlooking city managers choose how much land to make available for the private sector, in order to both boost local economic growth and generate local government revenues. Due to political concerns such as achieving GDP growth rate targets, city managers tend to allocate more land to the private sector than the revenue-maximizing level. By estimating the structural model of equilibrium land supply with manager-city-year level data, I infer city managers’ weights on career advancement relative to land revenues. Using the parameter estimates from the model, I then estimate the impact of political incentives on land allocation. Overall, city managers sell 7.4% more land between 2003 and 2012 relative to the counterfactual in which city managers have no GDP growth incentives. This land oversupply is 12.5% in smaller cities and 3.7% in larger cities. Finally, converting land to housing construction, the estimated impacts explain 23.2% to 27.7% of unsold homes. ∗Email: liut@g.harvard. edu. I am grateful to my advisors Edward Glaeser, Ariel Pakes, Raj Chetty, Elie Tamer, Amanda Pallais, and Eric Maskin for their guidance and support. This paper also benefited immensely from conversations with Sarah Armitage, Matthew Backus, Panle Jia Barwick, Augustin Bergeron, Cuicui Chen, Shani Cohen, David Cutler, Pedro Degiovanni, Liz Engle, Myrto Kalouptsidi, Larry Katz, Robin Lee, Jing Li, Shushu Liang, Ethan Matlin, Jeffrey Miron, Erica Moszkowski, Frank Pinter, Devesh Raval, Tobias Salz, Gregor Schubert, Audrey Tiew, Larry White, David Yang, Hanbin Yang, Ron Yang, Ran Zhuo, David Zhang and participants at Harvard IO and Labor/Public workshops. This research was funded by the Lab for Economic Applications and Policy at Harvard University. All errors are my own.
THE POTENTIAL OF JUTEORC (CORCHORUS OLITORIUS L.), A GEOTEXTILE MATERIAL, UNDER SALINE SOILLESS CULTURE CONDITIONS IN TURKEY

The present study was conducted to determine the possibility of the employed of jute (Corchorus olitorius L.), a natural fiber used as geotextile cover material, under saline soilless culture conditions, which constitute of the biggest problems in agriculture. The effect of the jute material employed in Mexican marigold (Tagetes erecta) cultivation under saline soilless conditions was determined. For this purpose, the Mexican marigold cultivation was tested in a pot experiment under greenhouse conditions for two growth periods in İzmir, Turkey. The salinity levels in the experiment were compared based on whether the treatment included the jute material (G1) or did not include jute material (G0). The measured salinity level of Hoagland nutrient solution employed the irrigation and fertilization of plants was one of the studied factors and was considered as control (S1). The measured EC (electrical conductivity) level of Hoagland nutrient solution was increased by 1 dS m (S2) and 2 dS m (S3) with stock NaCl solution, and 3 salinity factors were tested. According to the results of the present study the highest salinity was observed in the G0 group without jute cover. It was demonstrated that the jute cover on the pot media reduced the mean salinity concentration in growth medium by 22.56%. This finding was associated with the ability of the jute material to hold the salt in the nutrient solution, preventing spread of the salinity around the root zone.
FIRST-ORDER INFINITESIMAL MECHANISMS

This paper discusses the analytical conditions under which a pin-jointed assembly. which has s independent states of self-stress and m independent mechanisms. tightens up when its mechanisms are excited. A matrix algorithm is set up to distinguish between first-order infinitesimal mechanisms (which are associated with second-order changes of bar length) and higher-order inlinitesimal or tinite mechanisms. It is shown that. in general. this analysis requires thecomputation of . v quadratic forms in w variables. which can be eaily computed from the states of self-stress and mechanisms of the assembly. If any linear combination of these quadratic forms is sign definite. then the mechanisms are lirst-order infinitesimal. An eflicient and general algorithm to investigate these quadratic forms is given. The calculations required are illustrated for some simple examples. Many ;~sscmblies of pr;tknl relevance admit ;I single state of self-stress 0 = I). and in this cme the algorithm proposed is straightforward to implement. This work is rclcvant to the analysis and destgn of pre-stressed mechanisms. such BS cable systems and tcnscgrity fr;tmcworks.
Expert advice problem with noisy low rank loss

We consider the expert advice problem with a low rank but noisy loss sequence, where a loss vector l t ∈ [ − 1 , 1] N in each round t is of the form l t = U v t + ϵ t for some ﬁxed but unknown N × d matrix U called the kernel, some d -dimensional seed vector v t ∈ R d , and some additional noisy term ϵ t ∈ R N whose norm is bounded by ϵ . This is a generalization of the works of Hazan et al. and Barman et al., where the former only treats noiseless loss and the latter assumes that the kernel is known in advance. In this paper, we propose an algorithm, where we re-construct the kernel under the assumptions, that the low rank loss is noised and there is no prior information about kernel. In this algorithm, we approximate the kernel by choosing a set of loss vectors with a high degree of independence from each other, and we give a regret bound of O ( d √ T + d 4 = 3 ( Nϵ ) 1 = 3 √ T ). Moreover, even if in experiment, the proposed algorithm performs better than Hazan’s algorithm and Hedge algorithm.
TEMPORAL DROUGHT ASSESSMENT USING VARIOUS INDICES OF THE SEYHAN AND CEYHAN BASINS, TURKEY

Drought is a natural phenomenon with various social, environmental, and economic effects. Adverse impacts of drought at a regional and global scale have become more evident with climate change. The Seyhan and Ceyhan basins were chosen as study areas in Turkey. Drought analyses were performed using data from 14 meteorological stations gathered between January 1989 and July 2020 in Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), based on 3-4-6-12 monthly scales. SPI and SPEI are common indices used for drought tracking. The coefficient of determination (R) was found to be 0.954 and used to analyze the regression between the annual SPI and SPEI. According to SPI and SPEI annual analyses results of study area, the driest year was found to be 2014. SPEI determined more arid periods than SPI for the same time scales. Analyses reveal that the dry period in basins has increased in the last decade and the region is facing a risk of drought. Analyses were made to predict and evaluate risks and to help the efficient use of agricultural lands and water resources in the study area during dry periods.
Novel Intrusion Detection System for 5G

The telecommunication industry is majorly transforming towards 5G networks. It needs to satisfy the needs of the new and existing users. The users and the customers need much better quality of the corresponding services and they need the corresponding security in order to secure the transmitting data and other services. Therefore, the mobile leading networks must provide much better quality of an experience and security, and they must improve the performance for the services they provide. Novel services envisioned by 5G, new networking, service deployment, the new processing technologies and the storage is required. The mentioned technologies will involve the new security problems for the 5G systems. The world scientists are seriously working on analyzing the 5G security. The researchers have identified the existing problems of 5G systems. Our analysis illustrates the basics reasons of security problems in 5G. The researchers have found the vulnerabilities in 5G, which give the attackers opportunity to integrate the malicious code and to run it. MiTM, MNmap and Battery drain attacks can be successfully implemented on 5G. Our paper analyzes an existing security problems of 5G. As the result, we offer the new Intrusion Detection System using machine-learning approaches. The paper offers an integration of this intrusion detection systems into an existing 5G architecture. We offer the methodology and a pseudo code of this system.
Pan-cancer detection and typing by mining patterns in large genome-wide cell-free DNA sequencing datasets

Background: Cell-free DNA (cfDNA) analysis holds great promise for non-invasive cancer screening, diagnosis and monitoring. We hypothesized that mining the patterns of big datasets of shallow whole genome sequencing cfDNA from cancer patients could improve cancer detection. Methods: By applying unsupervised clustering and supervised machine learning on large shallow whole-genome sequencing cfDNA datasets from healthy individuals (n=367), patients with different hematological (n=238) and solid malignancies (n=320), we identify cfDNA signatures that enable cancer detection and typing. Results: Unsupervised clustering revealed cancer-type-specific sub-grouping. Classification using supervised machine learning model yielded an overall accuracy of 81.62% in discriminating malignant from control samples. The accuracy of disease type prediction was 85% and 70% for the hematological and solid cancers, respectively. We demonstrate the clinical utility of our approach by classifying benign from invasive and borderline adnexal masses with an AUC of 0.8656 and 0.7388, respectively. Conclusions: This approach provides a generic and cost-effective strategy for non-invasive pan-cancer detection.
MARKET-SENSING CAPABILITIES, PROFITABILITY

From the perspective of market competition, this paper investigates the impact of market-sensing capability on a company, and sources profits within a slow growth industry. As an example, our results are applied to demonstrate how effective customer value propositions can be formulated. Among others, it is shown that market-sensing capability empowers a company to categorize customers, lower its operational costs while increase expected profits; that when an industry experiences slow growth, its member company’s profit can grow only through a combination of raising unit price and lowering unit cost; and how market knowledge and innovative understanding of market invitations potentially lead to effective CVPs. Instead of employing either a statistics-based approach or an anecdotal analysis, this work relies on the rigor of game theory so that all results established are generally true unless given conditions are violated. Managerial recommendations for decision makers and potential questions for future research are provided.
ADVANCES IN EYE BLINK DETECTION: EVALUATING CONVOLUTIONAL NEURAL NETWORK BASED BLINK DETECTION ON REAL WORLD DATA

: Subjects with varying races, wearing glasses, hair in the way of their eyes and periodically moving faces makes it difficult for convolutional neural networks (CNN) to detect eye blinks in scenarios such as detecting driver fatigue. However, if the accuracy of the CNN is too low, then using it for eye blink detection in life like scenarios would not be advisable because the CNN would be incorrectly classifying to many times. To increase the accuracy of the CNN researchers have tried varying race of subjects and situational profiles. The research goal in this paper is to show a similar method to increase the accuracy of a premade convolutional neural network that detects eye blink by varying the training data with different races and situational profiles. The races tested included Caucasian, Hispanic and Asian. Situational profiles included eyeglasses, multiple people in frame, and hair being in the way of the eye. The training data also incorporated frames of video data; this meant the subject periodically moved their face in different directions. Being able to detect eye blink is an important task by itself; therefore, with an accurate CNN we could further the field in eye blink detection. Additionally, I have shown that training with a variety of races, wearing glasses, hair in the way of their eyes and periodically moving faces increased the accuracy (Figures 5,8). However, even though I found an increase of accuracy, many mysteries still remain.