Reviewerly/exHarmony huggingface dataset:
The Hard Task of Soft Skills: Project Management for the Materials Review Process

This article summarizes a presentation given by Kurt Blythe and Jennifer Solomon at the 28th annual North Carolina Serials Conference in 2019. Blythe and Solomon were brought on to lead a project to review resources in anticipation of budget constraints in 2018 and 2019. The presentation sought to answer why libraries should apply project management principles in such a situation and what the presenters learned from their experience and offered tips to any audience members considering applying project management practices to their own work.
Transfer Learning is a Crucial Capability of Intelligent Systems Self-Integrating at Runtime

The claim of this position paper is that Self-Improving and Self-Integrating Systems (SISSY) should be designed in view of facilitating Transfer Learning (TL) in order to leverage the given opportunity of collaborative knowledge acquisition and sharing. We corroborate our claim by briefly introducing the field of TL and bridging the gap to the recent research initiative of SISSY. Furthermore, an exemplifying scenario that allows such intelligently acting systems interweaving at runtime to be recognized as an obvious candidate with a strong demand for incorporating TL techniques is outlined.
Single Scan Polynomial Algorithms for Frequent Itemset Mining in Big Databases

This paper considers frequent itemset mining in big transactional databases. It first introduces a novel approach (Bio-SS) that combines the bio-inspired algorithms with the single scan algorithm (SSFIM). The proposed approach addresses the limitations of SSFIM by utilizing the bio-inspired operators in the generation process. This reduces the time complexity of SSFIM from exponential to polynomial, while taking advantage of the capacity to derive the frequent itemsets by performing a single database scan, independently from the minimum support value. This allows to considerably accelerate the scan procedure compared to existing approaches, especially when dealing with large scale databases. The numerical results show that the designed Bio-SS outperforms both accurate and metaheuristics baseline FIM approaches when dealing with big databases.
MULTI-CLASS TEXT SUPERVISED CLASSIFICATION ON ROMANIAN FINANCIAL BANKING REVIEWS

Nowadays, with the proliferation of social networks, forum discussions, blogs, and other forms of expression, more and more users have engaged in financial banking communities to gather information about financial banking products or services, to share experiences or to interact with other users.Reviews expressed by users on social media and Internet search activity are a valuable source of information.Text Classification is a growing research area used in social media analysis.The usefulness of Text Classification is manifested in different areas for various needs.For instance, the ability to automatically classify a review into its right financial banking product or service category would be appreciated by users when looking for a specific product or service as well as financial banking institutions to analyze the customers' feedback.Most of the research papers are focusing on exploring Text Classification methods for English-based text.Further, supervised machine learning methods for Text Classification require a huge data volume.In this paper, we focus on implementing supervised machine learning methods for multi-label text classification of Romanian financial banking reviews.We investigate the efficiency of using various supervised machine learning algorithms such as Random Forest, Support Vector Machine, Multinomial Naïve Bayes and Logistic Regression to classify text applied to a limited amount of Romanian financial banking reviews.We analyze the corpus, features and the results data.The baseline features are unigrams and bigrams weighted by TF-IDF.The results illustrate that the SVM classifier outperforms in classification of text into 30 categories of financial banking products and services from Romanian reviews.
Stacked Mixed-Scale Networks for Human Pose Estimation

Human pose estimation is an important problem in computer vision, which has been dominated by deep learning techniques in recent years. In this paper, we propose a novel model, named Mixed-Scale Dense Block, that exploits dilation convolution layers and dense concatenation connections to maximise the information flow through the block. Consequently, it captures the feature representation in different scales more effectively and efficiently. Comparing with the baseline method, Hourglass models, our model employs fewer learning parameters. Nevertheless, experiments demonstrate that the proposed model produces more accurate predictions. Meanwhile, our method achieves the comparable accuracy to state-of-the-art techniques. Especially in some indicators, our approach has better performance. In addition, this model is easy to implement and could be improved by most existing techniques that are adopted to promote the hourglass models.
MINING ASSOCIATION RULES FOR EDUCATIONAL DATA

Educational data mining is a fast developing field of computer science and technology, which are helpful to enable end users for decision making process. One of the most important data mining applications is that of mining association rules. With the widespread use of medical information systems that include databases which consist of students records, course grade and name of subjects. The purpose of this paper is to develop a educational management system that can provide the possible decision and prediction information for students without the aids of knowledge expert. Finding frequent patterns plays an essential role in mining associations, correlations and many other interesting relationships among data. In this paper we analyzed the frequent pattern in education courses learning behaviour and discover the rules for association of these by using Apriori Algorithm.
Geostatistical Analysis of Soil Pb and Cd Distribution Using Environmental Indexes in the Southwest of Isfahan in 2017

Geostatistical Analysis of Soil Pb and Cd Distribution Using Environmental Indexes in the Southwest of Isfahan in 2017
CONDITIONS FOR DEVELOPING SUBJECTIVE POSITION IN STUDENTS OF ENGINEERING TRAINING PROGRAMME

Problem and purpose. In contemporary publications the issue of the urgency of developing young peoples’ intellectual, spiritual and professional qualities is particularly stressed. The changes in the requirements to university students make the task of forming such individual characteristics as proactivity, competitiveness, ability to make decisions and work in quickly changing circumstances are quite topical. However, the urgency of these qualities development does not exclude the necessity of possessing fundamental knowledge. We find a confirmation to this in the demand put forward by employers, the state and the world community. All these factors make actual the problem of developing a university student’s subjective position. Engineering training programme students are not an exception in this respect. In view of all this there appears a necessity to create special conditions that would allow students to develop their individual images of future after which they would aspire. These conditions would provide the basis for making this aspiration realistic in the process of studying at the University. The aim of the research is to give a theoretical and practical foundation for the hypothesis concerning the development of students’ subjective platforms in the process of studying. The methodology of the research is based on the legal-normative documents for the sphere of higher education, international standards, scientific research by domestic and foreign scholars recognized by scientific community. The results. On the ground of theoretical guidelines the psychological structure of subjectivity as a personal quality of an individual, a hypothesis was formulated about the development of the subjective standpoint of a student in the conditions specially created for him or her in the process of education. The materials suggested contain theoretical substantiation of the problem. The characteristic features of students’ subjective positions were singled out, the course of the subjective position development was described, diagnostic and formative procedures were worked out. The results of the research showed that the number of students obviously demonstrating the signs of the subjective standpoint development grew by 46%, thanks to the conditions specially introduced into the process of education. Conclusion. The authors conception suggested in the article for developing the subjective position in students can become the basis for further research and elaboration in other universities. The logic of the research performed presupposes including students in open environments within the framework of the in-class learning and extra-curricular activities. It also presupposes choosing independently the direction of projecting within the framework of the future professional activity.
WeldVUI: Establishing Speech-Based Interfaces in Industrial Applications

Voice User Interfaces (VUIs) and speech-based applications have recently gained increasing popularity. During the past years, they have been included in a wide range of mass-market devices (smart phones or technology installed in common car cockpits) and are thus available for many everyday interaction scenarios (e.g., making phone calls or switching the lights on and off). This popularity also led to a number of guidelines for VUI design, software libraries and devices for speech recognition becoming available for interface designers and developers. Although generally helpful, these resources are often broad and do not fully satisfy the specific requirements of certain industrial applications. First, grammar and vocabulary in such settings usually differ drastically from everyday scenarios. Second, common software libraries and devices are often not able to comply with the conditions in industrial environments (e.g. involving high levels of noise). This paper describes the iterative, user-centered design process for VUIs and functional speech-based interaction prototypes for the domain of industrial welding, including a two-stage Wizard of Oz procedure, rapid prototyping, speech recognition improvement and thorough user involvement. Our experiences throughout this process generalize to other industrial applications and so-called “niche applications” where grammar and vocabulary usually have to be established from scratch. They are intended to guide other researchers setting up a similar process for designing and prototyping domain-specific VUIs.
Study sedimentology and origin of fluvial modern sediment by using integrated methods, case study of Zagros foreland Basin

Zagros foreland Basin places along with Himalyas- Alp orogeny that it is created by collision Eurasia with Arabic plane and closing Neotethys. Two rivers Karkhe and Dez are the most important Iranian rivers in which this study has examined sedimentology and their sediment origin in Zagros foreland Basin in Khuzestan province (Shush region). The most important used data in this study include Granometry data and thin section from 30 samples from two rivers and Geochemical data XRD from 2 samples and 14 samples are main elements as well as 12 samples were studied from two rivers in order to analyzing heavy minerals. The most frequent seeds of our sample are in sand and gravel that they were sort from bad to very bad. Regarding to faces and architectural elements, two meander rivers model except that Dez river has less twist. Main minerals constituent gravel sediments (carbonate gravel with more frequency and Chert gravel with less frequency) and quartz. Constituent elements and also geochemistry analysis results in analytical graphs show the origin of collision orogeny and rock recycle and weathering dry to wet weather for origin rock. Regarding to the total results, it is considered more carbonate gravel from Mishan formations, more quartz seeds from Aghajari formations and older debris formations and more heavy minerals is originated from zone of Sanandaj-Sirjan.
Outsourcing Computing of Large Matrix Jordan Decomposition

The Jordan decomposition of matrix is a typical scientific and engineering computational task, but such computation involves enormous computing resources for large matrices, which is burdensome for the resource-limited clients. Cloud computing enables computational resource-limited clients to economically outsource such problems to the cloud server. However, outsourcing Jordan decomposition of large-scale matrix to the cloud brings great security concerns and challenges since the matrices usually contain sensitive information. In this paper, we present a secure, verifiable, efficient, and privacy preserving algorithm for outsourcing Jordan decomposition of large-scale matrix. Security analysis shows that our algorithm is practically secure. Efficient verification algorithm is used to verify the results returned from the cloud.
WHATSAPP UTILISATION AT AN INITIAL TEACHER PREPARATION PROGRAMME AT A UNIVERSITY OF TECHNOLOGY IN SOUTH AFRICA

All universities in South Africa are encouraged to use a Learning Management System such as Blackboard to facilitate blended learning.Despite, the availability of Blackboard at one university of technology in the country, some lecturers and students prefer utilising WhatsApp for teaching and learning.The purpose of this study is to investigate students and lecturers' perspectives about learning and teaching using WhatsApp at a university in South Africa.The study was done using a qualitative case study within an intepretivist paradigm.It was guided by Garrison, Anderson and Archer's Community of Inquiry as a theoretical framework.Sixteen students and two lecturers who heavily use WhatsApp were purposively selected to participate in semi-structured interviews and focus group discussions.The study found that lecturers use WhatsApp not only for communication purposes, but to foster collaborative learning among students.Although students noted some limitations which they experience when using WhatsApp, they preferred the social media to blackboard.The study concludes that although WhatsApp is response to students' needs, it does not adequately prepare students to graduate with digital literacy skills expected by the Department of Education in the country.
Learning a Gaussian Process Model on the Riemannian Manifold of Non-decreasing Distribution Functions

In this work, we consider the problem of learning regression models from a finite set of functional objects. In particular, we introduce a novel framework to learn a Gaussian process model on the space of Strictly Non-decreasing Distribution Functions (SNDF). Gaussian processes (GPs) are commonly known to provide powerful tools for non-parametric regression and uncertainty estimation on vector spaces. On top of that, we define a Riemannian structure of the SNDF space and we learn a GP model indexed by SNDF. Such formulation enables to define an appropriate covariance function, extending the Matérn family of covariance functions. We also show how the full Gaussian process methodology, namely covariance parameter estimation and prediction, can be put into action on the SNDF space. The proposed method is tested using multiple simulations and validated on real-world data.
Response to Katzhendler and Weinshall: Initial visual degradation during development may be adaptive

We thank Katzhendler and Weinshall for their thought-provoking comment (1) on our paper (2). They argue that the computational simulations are insufficient to suggest that initially poor acuity may be an adaptive feature of visual development.The logic of their argument is this: Our evaluation of DNN performance is based not on classification of high-resolution images exclusively, but a range of image resolutions. Katzhendler and Weinshall (1) argue that low-resolution images either are not ecologically relevant and should therefore be neglected or, if relevant, then are likely available at all developmental time points, enabling training with a mix of low- and high-resolution images. Using such a mix, they indicate, … [↵][1]1To whom correspondence may be addressed. Email: psinha{at}mit.edu. [1]: #xref-corresp-1-1
Research on Global Path Planning Method of Mobile Robot Based on BAS

In this paper, a path planning algorithm based on the beetle antennae search (BAS) is presented, which can effectively improve the speed, obstacle avoidance and environmental adaptability of the robot path planning. The distance between the beetle and the target point is the fitness function, and the changing step size is to avoid the local optimization. According to compare random coordinates of the beetle's left and right antennae, the direction of walking is determined to plan the better path in the static environment. The obstacle avoidance is judged by whether the beetle's position and walking trail are in the effective area. Multiple beetles are set at each path point, and the optimal direction which approaches the theoretical optimal path, is chosen after comparing with every beetle's trail. A large number of results of the experiments based on RRT and BAS algorithm indicate that BAS algorithm is more capable of searching its path and avoiding obstacles. Even in a complex environment, compared with other algorithms, BAS is more likely to rapidly plan an optimal path which can successfully avoid obstruction.
FORMATION OF AN IMPROVED RC6 (IRC6) CRYPTOGRAPHIC ALGORITHM

IRC6 cryptosystem is an improvement on RC6 which was developed in the course of this research to guard against crypto-analytical attack. This is achieved by doubling its security at little or no computational cost. RC6 is an improvement on RC5, and RC5 was an improvement on RC4. IRC6 is designed to meet the requirements of increased security and better performance. Object-oriented analysis and design methodology (OOADM) together with Java programming and Development technologies were used to implement both Normal RC6 algorithm and improved RC6 (IRC6) cryptographic algorithm in this research in order to achieve the research goal which is to show the significant improvement of IRC6 over normal RC6 by carrying out performance evaluations of the two algorithms. The developed IRC6 was also evaluated to have transparent cipher-text and thereby found to survive any crypto-analytical attack.
Implicit Rating Methods Based on Interest Preferences of Categories for Micro-Video Recommendation

Collaborative filtering (CF) without explicit information is one of the most challenging research directions in the field of video recommendation, as the effectiveness of traditional CF methods strongly depend on the ratings of videos for users. However, in the actual online video platforms, explicit ratings are very rare or even completely unavailable in most cases. This makes the effects of traditional recommendation algorithms are not satisfactory. In addition, micro-videos have attracted wide attention, while they have not be considered differently by the traditional recommendation algorithms. It is meaningful to study the recommendation methods for micro-videos. Considering that micro-videos have categories, two implicit rating methods based on interest preferences of categories are proposed to improve the performance of recommendation for micro-videos under implicit feedback. Its core idea is to construct a rating matrix based on implicit information by mining users’ implicit interest preference information for different categories of micro-videos, and use it as the basis of recommendation algorithms. The proposed rating methods are validated on a large online video content provider, and they are correct and can effectively mine users’ preferences without explicit ratings according to the experimental results. They can bring better results than some existing algorithms, and can be better applied to the video recommendation system.
PROOF OF CONCEPT FOR A BPMN-DRIVEN SEMANTIC ORCHESTRATION OF WEB APIs

Nowadays, the speed of information flows inside a company has become a vital factor in determining the outcome of a business project.In order to remain competitive at the highest rate, companies need to be able to easily access necessary data for supporting business decisions.Moreover, information must be delivered in a very efficient and effective way to the persons that have leading roles in each business.In a hedge fund business, the speed of data flows is the most important factor, making a difference between success and failure.These business goals can be achieved by automating steps in every business process with the help of web services by using APIs and semantic orchestration.In this work, we show the way in which a series of APIs can be orchestrated together with business process modelling techniques and data management tools in order to achieve speed and data management goals inside a company.The proposal of this paper is an orchestration method that combines the practice of BPMN modelling (typically associated to design-time) with run-time orchestration based on a semantic bridge enabled by the possibility of exporting diagrammatic models into RDF knowledge graphs.The work follows the Design Science methodology starting from identified requirements and following a solution engineering cycle that overall can inspire a new kind of software engineering method.
Oblique incidence dynamic phase-shifting interferometer based on inclination angle deflection

An oblique incidence dynamic phase-shifting interferometer based on inclination angle deflection is proposed to quickly obtain the surface distribution of optical surface with flatness of micron dimension. A 2×2 point source array is introduced into a Michelson interference system, and the incidence angle of each point source on the interferometer cavity is adjusted precisely to induce equal phase shift. Spatial separation is realized in combination with a lens array. The four phase-shifting interferograms are captured simultaneously on a single CCD, thereby realizing dynamic measurement. The flatness of a 35 mm aperture silicon wafer is measured at oblique incidence angle of 68°, the root mean square (RMS) is 1.631 μm and peak-to-valley (PV) is 9.082 μm. The experimental results indicate that the proposed interferometer overcomes the disturbance of vibration environment and extends the measurement range of interferometer with high precision by introducing the simultaneous phase-shifting interferometry based on inclination angle deflection into the oblique incidence interference system.
Priority-based parameter propagation for distributed deep neural network training

Data parallel training is commonly used for scaling distributed Deep Neural Network ( DNN ) training. However, the performance benefits are often limited by the communication-heavy parameter synchronization step. In this work, we take advantage of the domain specific knowledge of DNN training and overlap parameter synchronization with computation in order to improve the training performance. We make two key observations: (1) the optimal data representation granularity for the communication may differ from that used by the underlying DNN model implementation and (2) different parameters can afford different synchronization delays. Based on these observations, we propose a new synchronization mechanism called Priority-based Parameter Propagation (P3). P3 synchronizes parameters at a finer granularity and schedules data transmission in such a way that the training process incurs minimal communication delay. We show that P3 can improve the training throughput of ResNet-50, Sockeye and VGG-19 by as much as 25%, 38% and 66% respectively on clusters with realistic network bandwidth.