Reviewerly/exHarmony huggingface dataset:
Assessing Scrum Project Management and Teamwork in Electrical and Computer Engineering Courses

Abstract Assessing teamwork and scrum project management in ECE courses Teamwork and project management are essential skills for engineering students, as recognized in the proposed new ABET topic area 7. Our team of instructors exposes students to project management techniques at multiple levels within our undergraduate ECE program. By learning project management early and practicing it often, students improve their teamwork efficacy in projects, courses, and in their future careers. Scrum is a cyclical project management technique commonly used in high-tech industries. Scrum provides a framework that facilitates teamwork through an adaptable and incremental process. Our variant of scrum is tailored to students working on engineering projects in a higher-education environment. We intend to better understand student learning of project management and teamwork so that we can improve our curriculum. We use scrum “artifacts” (schedules, user stories, kanban boards) as mechanisms for assessing team project management and use a rubric for evaluating the boards. Our initial observations of first-year students show that they need close guidance and supervision, such as through the use of templates and weekly kanban reviews. These interventions have resulted in marked improvement of student project management performance. One significant obstacle to implementation is the lack of assistants familiar with scrum. We intend to start a program that prepares upper-level undergraduate and graduate students to serve as team supervisors or scrum masters. We will report student self-assessment of the usefulness of scrum in their projects. To assess the effectiveness of student teamwork, we use a CATME Peer Evaluation survey, which garners information about team member contributions and experiences through self and peer evaluation. The tool measures team member contributions within five areas using a behaviorally-anchored rating scale: contributing to the team’s work; interacting with teammates; keeping the team on track; expecting quality; and, having related knowledge, skills and abilities. These areas align with our objectives for teaching project management within undergraduate ECE courses. Not surprisingly, initial results from CATME assessment indicate that first-year teams demonstrate less cohesion and do not perform as well as senior teams. By administering a CATME assessment mid-way through the term, malfunctioning teams are identified so that the instructor can intervene. We will present data and our evaluation in the full paper. We will also report on the scrum-related curricula that we use in our classes. In our experience, modern tools paired with careful planning of student activities help develop effective student teams and successful implementation of scrum-based project management. Our preference is for regular presentation.
Noise-added selection method for location-based service using differential privacy in Internet of Things

With the development of Internet of Things, many applications need to use people’s location information, resulting in a large amount of data need to be processed, called big data. In recent years, people propose many methods to protect privacy in the location-based service aspect. However, existing technologies have poor performance in big data area. For instance, sensor equipments such as smart phones with location record function may submit location information anytime and anywhere which may lead to privacy disclosure. Attackers can leverage huge data to achieve useful information. In this article, we propose noise-added selection algorithm, a location privacy protection method that satisfies differential privacy to prevent the data from privacy disclosure by attacker with arbitrary background knowledge. In view of Internet of Things, we maximize the availability of data and algorithm when protecting the information. In detail, we filter real-time location distribution information, use our selection mechanism for comparison and analysis to determine privacy-protected regions, and then perform differential privacy on them. As shown in the theoretical analysis and the experimental results, the proposed method can achieve significant improvements in security, privacy, and complete a perfect balance between privacy protection level and data availability.
Size Independent Neural Transfer for RDDL Planning

Neural planners for RDDL MDPs produce deep reactive policies in an offline fashion. These scale well with large domains, but are sample inefficient and time-consuming to train from scratch for each new problem. To mitigate this, recent work has studied neural transfer learning, so that a generic planner trained on other problems of the same domain can rapidly transfer to a new problem. However, this approach only transfers across problems of the same size. We present the first method for neural transfer of RDDL MDPs that can transfer across problems of different sizes. Our architecture has two key innovations to achieve size independence: (1) a state encoder, which outputs a fixed length state embedding by max pooling over varying number of object embeddings, (2) a single parameter-tied action decoder that projects object embeddings into action probabilities for the final policy. On the three challenging RDDL domains of SysAdmin, Game Of Life and Academic Advising, our approach powerfully transfers across problem sizes and has superior learning curves over training from scratch.
Applying GMDH-type neural network for modeling and prediction of turbidity and free residual aluminium in drinking water

Water has a fundamental role in human life.The proper quality and quantity of water is therefore an important issue.Coagulation and flocculation are essential processes for turbidity removal from drinking water.Metals such as aluminium have been implicated in the pathogenesis of Alzheimer's disease.In this study, group method of data handling (GMDH)-type neural networks have been used for modeling and prediction of turbidity and free residual aluminium in drinking water.To validate the proposed model, a case study was carried out based on the data sets obtained from Guilan WTP.For modeling, the experimental data were divided into train and test sections (70% for training and 30% for testing).Eventually, the results of modeling were compared with experimental data and demonstrated good data compliance, with the coefficient of determination (R 2 ) in GMDH-type network was 0.8239 and 0.9138 for residual turbidity and residual aluminium, respectively.Moreover, the results of error analysis showed good performance of the proposed models, in this regard can be referred to the mean square error results which obtained 0.0248 for residual turbidity and 0.00000438 for residual aluminium.
Combining RSS-SVM with genetic algorithm for Arabic opinions analysis

Due to the large-scale users of the Arabic language, researchers are drawn to the Arabic sentiment analysis and precisely the classification areas. Thus, the most accurate classification technique used in this area is the support vector machine (SVM) classifier. This last, is able to increase the rates in opinion mining but with use of very small number of features. Hence, reducing feature's vector can alternate the system performance by deleting some pertinent ones. To overcome these two constraints, our idea is to use random sub space (RSS) algorithm to generate several features vectors with limited size; and to replace the decision tree base classifier of RSS with SVM. Later, another proposition was implemented in order to enhance the previous algorithm by using the genetic algorithm as subset features generator based on correlation criteria to eliminate the random choice used by RSS and to prevent the use of incoherent features subsets.
Diagonal Sums of Doubly Substochastic Matrices

Let $\Omega_n$ denote the convex polytope of all $n\times n$ doubly stochastic matrices, and $\omega_{n}$ denote the convex polytope of all $n\times n$ doubly substochastic matrices. For a matrix $A\in\omega_n$, define the sub-defect of $A$ to be the smallest integer $k$ such that there exists an $(n+k)\times(n+k)$ doubly stochastic matrix containing $A$ as a submatrix. Let $\omega_{n,k}$ denote the subset of $\omega_n$ which contains all doubly substochastic matrices with sub-defect $k$. For $\pi$ a permutation of symmetric group of degree $n$, the sequence of elements $a_{1\pi(1)},a_{2\pi(2)}, \ldots, a_{n\pi(n)}$ is called the diagonal of $A$ corresponding to $\pi$. Let $h(A)$ and $l(A)$ denote the maximum and minimum diagonal sums of $A\in \omega_{n,k}$, respectively. In this paper, existing results of $h$ and $l$ functions are extended from $\Omega_n$ to $\omega_{n,k}.$ In addition, an analogue of Sylvesters law of the $h$ function on $\omega_{n,k}$ is proved.
User Experience Design in Software Product Lines

User experience design is an important part of software product development, and yet software product line engineering has largely ignored this topic.This paper presents a set of industry best practices for user experience design in software product lines.We conducted multiple-case case study research using two different product lines within the multinational company Siemens AG: in a healthcare software division and in an industrial automation software division.We performed a preliminary exploratory study that will serve as a baseline for future research in the design, implementation, and management of user experience design in the context of software product lines.Practitioners can use our findings and the resulting best practices to improve their user experience design, particularly within healthcare and industrial automation software product lines.
Peningkatan Kemampuan Mengenal Huruf Pada Anak Usia 5-6 Tahun Melalui Metode Bermain Dengan Media Kotak Pintar

This study aims to improve the ability to recognize letters through the method of playing with the smart box media for children aged 5-6 years in Sion Blora Kindergarten. The activities to identify letters using the smart box media is able to motivate children's interest to learn. This classroom action research was conducted in two cycles with three meetings in each cycle. The subjects of the study were 14 children aged 5-6 years. The data collection was done with interviews, observation, and documentation. The research instrument referred to Permendikbud (Regulation by the Minister of Education and Culture) Number 146 Year 2014. The observation was done with the assessment sheets, documentation, and interviews with the classroom teachers to determine the conditions and problems of children in the classroom, analyzed with a descriptive quantitative technique. The success indicator of this research is 90% for the average value. The results showed an increase in the average score from the pre-cycle of 14,29% to 60,71% in cycle I; up to 92.86% in cycle II. Thus, it can be said that the percentage of success reached ≥ 90%.
Combination of domain knowledge and deep learning for sentiment analysis of short and informal messages on social media

Sentiment analysis has been emerging recently as one of major natural language processing (NLP) tasks, with the increasing significance of social media channels for brands to observe user opinions about their products. In the previous work, we proposed to combine the typical deep learning techniques with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. However, there is a high volume of short and informal messages posted by users which makes the existing works suffer from many difficulties. In this work, we further improve our architecture, aiming to handle those problems, by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements help us to enjoy significant improvement in performance once experimenting on real datasets.
Application of Combinatorial Techniques to the Ghanaian Board Game Zaminamina Draft

Games happen to be a part of our contemporary culture and way of life. Often mathematical models of conflict and cooperation between intelligent rational decision-makers are studied in these games. Example is the African board game â€™Zaminamina draftâ€™ which is often guided by combinatorial strategies and techniques for winning. In this paper we deduce an intelligent mathematical technique for playing a winning game. Two different starting strategies were formulated; center starting and edge or vertex starting. The results were distorted into a 3x3 matrix and elementary row operations were performed to establish all possible wins. MatLab was used to distort the matrix to determine the diagonal wins. A program was written using python in artificial intelligence (AI) to help in playing optimally
Multilayer Perceptron (MLP) and Autoregressive Integrated Moving Average (ARIMA) Models in Multivariate Input Time Series Data: Solar Irradiance Forecasting

Solar irradiance needs to estimate power consumptions for requiring of saving energy. The demand accomplished with providing facilities to predict. Time series data is a dataset that has complex problems. Using multilayer perceptron (MLP) and autoregressive integrated moving average (ARIMA) with multivariate input to solve the problem of predicting solar irradiance. The dataset is collected from solar irradiance sensor by an online monitoring station with 10 minutes data interval for 18 months. Prediction experimented with t, t-2, and t-6 data inputs that represent t as the day to get the predictive model (t+1). In ARIMA model, optimization was obtained in the input parameter (t-6) and ARIMA(1,1,2) with minimum RMSE is 43.91 W/m2, whereas MLP model used single layer, 10 neurons and using relu activation function to predict with minimum RMSE is 8.68 W/m2 using (t) input parameter. The deep learning model is better than the statistical model in this experiment. RMSE, MSE, MAE, MAPE, and R2, are used as an evaluation for model performance.
non-transitive relevant implication corresponding to classical logic consequence

In this paper we first develop a logic independent account of relevant implication. We propose a stipulative denition of what it means for a multiset of premises to relevantly L-imply a multiset of conclusions, where L is a Tarskian consequence relation: the premises relevantly imply the conclusions iff there is an abstraction of the pair &lt;premises, conclusions&gt; such that the abstracted premises L-imply the abstracted conclusions and none of the abstracted premises or the abstracted conclusions can be omitted while still maintaining valid L-consequence. Subsequently we apply this denition to the classical logic (CL) consequence relation to obtain NTR-consequence, i.e. the relevant CL-consequence relation in our sense, and develop a sequent calculus that is sound and complete with regard to relevant CL-consequence. We present a sound and complete sequent calculus for NTR. In a next step we add rules for an object language relevant implication to thesequent calculus. The object language implication reflects exactly the NTR-consequence relation. One can see the resulting logic NTR-&gt; as a relevant logic in the traditional sense of the word. By means of a translation to the relevant logic R, we show that the presented logic NTR is very close to relevance logics in the Anderson-Belnap-Dunn-Routley-Meyer tradition. However, unlike usual relevant logics, NTR is decidable for the full language, Disjunctive Syllogism (A and ~AvB relevantly imply B) and Adjunction (A and B relevantly imply A&amp;B) are valid, and neither Modus Ponens nor the Cut rule are admissible.
Sci-tech libraries’ routes to open access (IATUL-2018)

This is brief review of the 39-th IATUL conference in Norway, June 18-21 2018. Open access issues are analyzed for several countries - Norway, Germany, Australia, and Canada. According to the Google Scholar data up to 2/3 of research articles in Norway are open access. It is the highest global level. Nevertheless the oaDOI data gives pretty lower part of open access publications, ca 1/3. The difference is explained by determination of open access preconditions, for the oaDOI there are the most rigid ones. One of the important factors to proliferation of open access is negative attitude of some part of the academic community. Researchers prefer traditional formats of publications taking into account the prestige consideration and evaluations by peers. They believe that academic freedom gives them right to choose which kind of publication is appropriate to them. The national approach of German scientists to the licensing on the ground of Publish&amp;Read principle has been implemented in the DEAL project. Australian librarians shared experience on the openness development for research communications. Methods of information literacy learning are described. There are outlined examples of the library makerspace activity. Director of the National library of Norway discusses some problems of the library development.
Adaptive Bayesian label fusion using kernel-based similarity metrics in hippocampus segmentation

The effectiveness of brain magnetic resonance imaging (MRI) as a useful evaluation tool strongly depends on the performed segmentation of associated tissues or anatomical structures. We introduce an enhanced brain segmentation approach of Bayesian label fusion that includes the construction of adaptive target-specific probabilistic priors using atlases ranked by kernel-based similarity metrics to deal with the anatomical variability of collected MRI data. In particular, the developed segmentation approach appraises patch-based voxel representation to enhance the voxel embedding in spaces with increased tissue discrimination, as well as the construction of a neighborhood-dependent model that addresses the label assignment of each region with a different patch complexity. To measure the similarity between the target and training atlases, we propose a tensor-based kernel metric that also includes the training labeling set. We evaluate the proposed approach, adaptive Bayesian label fusion using kernel-based similarity metrics, in the specific case of hippocampus segmentation of five benchmark MRI collections, including ADNI dataset, resulting in an increased performance (assessed through the Dice index) as compared to other recent works.
Estimating 2D Multi-hand Poses from Single Depth Images

We present a novel framework based on Pictorial Structure (PS) models to estimate 2D multi-hand poses from depth images. Most existing single-hand pose estimation algorithms are either subject to strong assumptions or depend on a weak detector to detect the human hand. We utilize Mask R-CNN to avoid both aforementioned constraints. The proposed framework allows detection of multi-hand instances and localization of hand joints simultaneously. Our experiments show that our method is superior to existing methods.
Image contrast enhancement method based on display and human visual system characteristics

At the present time, there are many image contrast enhancement methods where the main considerations are detail enhancement, noise suppression, and high contrast suppression. Traditional methods ignore the characteristics of the display or merely consider the display as a whole. However, due to the limited dynamic range of most display devices on the market, the difference between two adjacent grayscales of the display is often below the just noticeable difference of the human visual systems, which causes many image details to be invisible on the display. To solve this problem, we present a preprocessing method for image contrast enhancement. The method combines the characteristics of the human eye and the display to enhance the image by examining the local histogram. When displaying the processed image, the algorithm maintains as much image information as possible, and image details will not be lost due to the limits of the display device. Moreover, this algorithm performs well for noise suppression and high contrast suppression. The algorithm is an image enhancement method and can also be a correction method for images enhanced by other methods when prepared for display.
Nonlinear Markov Random Fields Learned via Backpropagation

Although convolutional neural networks (CNNs) currently dominate competitions on image segmentation, for neuroimaging analysis tasks, more classical generative approaches based on mixture models are still used in practice to parcellate brains. To bridge the gap between the two, in this paper we propose a marriage between a probabilistic generative model, which has been shown to be robust to variability among magnetic resonance (MR) images acquired via different imaging protocols, and a CNN. The link is in the prior distribution over the unknown tissue classes, which are classically modelled using a Markov random field. In this work we model the interactions among neighbouring pixels by a type of recurrent CNN, which can encode more complex spatial interactions. We validate our proposed model on publicly available MR data, from different centres, and show that it generalises across imaging protocols. This result demonstrates a successful and principled inclusion of a CNN in a generative model, which in turn could be adapted by any probabilistic generative approach for image segmentation.
Intelligent Support Development of Educational Programs Based on the Neural Language Models Taking into Account of the Labor Market Requirements

The active development of the digital economy today imposes high requirements on the adaptability, practical orientation and quality of educational content. Existing approaches to the intelligent decision support of the formation of educational programs based on ontological models, expert systems and heuristic algorithms do not allow effectively taking into account and tracking changes both in the labor market and in the open educational content space in the Massive Open Online Courses (MOOC). Instead, it is proposed to use approaches to the semantic analysis based on the well-known neural network language model word2vec, which is trained without supervision on large text corpora. The complexity of semantic analysis is to move the definition of semantic similarity measures for short texts of the extracted entities (course topics, learning outcomes, job requirements, etc.) to matching of large structured documents, such as professional standard, an educational program. To take into account the interrelations of entities, a graph model is introduced for representing the educational and professional domain. The paper proposes an artificial intelligent method of forming recommendations for the actualization of the learning outcomes and content of educational programs. At the first stage, the actual requirements of the labor market are determined based on a semantic matching of job requirements with the content of professional standards. The second stage includes a semantic matching of the content of academic disciplines with the requirements of the labor market. At the third stage, a semantic search of relevant educational content is carried out among the programs of disciplines of leading universities and massive open online courses (MOOC). During the fourth stage, final recommendations on updating the educational program are formed. The experiment demonstrated the possibility of applying the method for matching learning outcomes and content of disciplines with the requirements of professional standards and evaluation using the example of the educational program (bachelor degree) of computer science and engineering.