Reviewerly/exHarmony huggingface dataset:
Reflection on AI: The Cognitive Difference Between Libraries and Scientists

The aim is to make appropriate reflections on artificial intelligence AI through the differences in cognitive systems between intelligent digital libraries and well-trained experts, especially scientists. The method is: first, compare the difference and connection between data structure and knowledge reserve, then compare the difference and connection between query, retrieval or search algorithm path, finally, compare the difference and connection between cognitive computing and cognitive ability. Its characteristics are: data query or search algorithm and logical reasoning or cognitive computing, knowledge graph and cognitive graph are all conducive to the construction of intelligent digital library. The result is: it is found that whether it is based on rules or statistics or a combination of the two cognitive systems, there are limitations in cognitive computing and cognitive ability or intelligence. Its significance lies in: from this we can at least reflect on the theoretical limits of AI and some worthy questions, such as the calculability problem and Turing test problem and the Chinese Room Argument of the intelligent digital library. The problem of isomorphism with the information processing mode of the intelligent digital computer, and why can’t the intelligent digital library become a well-trained expert or even a scientist, and so on are worthy of the philosophy of logic and mind with Cognitive Science.
Accelerating GPU-based Machine Learning in Python using MPI Library: A Case Study with MVAPICH2-GDR

The growth of big data applications during the last decade has led to a surge in the deployment and popularity of machine learning (ML) libraries. On the other hand, the high performance offered by GPUs makes them well suited for ML problems. To take advantage of GPU performance for ML, NVIDIA has recently developed the cuML library. cuML is the GPU counterpart of Scikit-learn, and provides similar Pythonic interfaces to Scikit-learn while hiding the complexities of writing GPU compute kernels directly using CUDA. To support execution of ML workloads on Multi-Node Multi- GPU (MNMG) systems, the cuML library exploits the NVIDIA Collective Communications Library (NCCL) as a backend for collective communications between processes. On the other hand, MPI is a de facto standard for communication in HPC systems. Among various MPI libraries, MVAPICH2-GDR is the pioneer in optimizing GPU communication.This paper explores various aspects and challenges of providing MPI-based communication support for GPU-accelerated cuML applications. More specifically, it proposes a Python API to take advantage of MPI-based communications for cuML applications. It also gives an in-depth analysis, characterization, and benchmarking of the cuML algorithms such as K-Means, Nearest Neighbors, Random Forest, and tSVD. Moreover, it provides a comprehensive performance evaluation and profiling study for MPI-based versus NCCL-based communication for these algorithms. The evaluation results show that the proposed MPI-based communication approach achieves up to 1.6x, 1.25x, 1.25x, and 1.36x speedup for K-Means, Nearest Neighbors, Linear Regression, and tSVD, respectively on up to 32 GPUs.
A Method for Classification of Heavy Mineral Based on Machine Learning

With the development of machine learning, this technology is more and more used in people's daily life, such as object classification, portrait recognition, text analysis and so on. In recent years, researchers have tried to apply machine learning to Earth Sciences, such as image recognition of geological ores. In this study, we apply the method of machine learning to the classification and recognition mineral based on chemical composition, and propose a classification algorithm based on feature selection. we obtain the key attribute of classification, namely feature selection, through accurate data, and add a classification algorithm on this basis, so that the category can be obtained quickly when new samples are encountered. At the same time, our experiments are based on two real types of data in geosciences: EMPA data and EDS data. Our algorithm is verified by using real experimental data, and a large number of test results show that our algorithm has high accuracy and can quickly identify the categories of mineral through EDS composition data, which also shows that our algorithm is effective.
The Comparison of Energy Usage of Modular Housing using Sefaira®

Abstract As the fourth largest population in the world with 52% urban inhabitant, housing as the most primer need, Indonesia has a significant impact on urban energy consumption. The backlog of housing in the urban area is known as much as 8%. The government tries to fulfill those needs by supplying an affordable house that requires not more than 36m2 and less than IDR 121 million The need for affordable housing occupies the largest ratio in urban areas. This is related to the middle-class family’s income structure that 21% of the total population of Indonesia. The complexity in modular affordable house design that derives from the building material, construction cost, as well as the energy consumption, later becomes the urgency of this study. This paper aims to compare simulating several modular houses with 36m2 total ground floor area, namely: 6m x 6m, 4m x 9m, and 3m x 12m module houses using metal wall cladding and Glassfibre Reinforced Cement (GRC). From these comparisons will be found modular house designs and material that have optimal energy efficiency. This paper presents modular house design. The modular house design will recommend which modules and materials will best optimize the building’s performance, thus optimizing energy efficiency based on the simulation results. Based on each material’s U-Value, a house with a GRC wall has lower energy consumption than a house with metal wall cladding because GRC has a lower U-Value.
The Application of Support Vector Regression in Particle Filtering

In this paper, we propose a resampling method for particle filtering (PF) based on the support vector regression (SVR). The SVR is introduced to fit the posteriori probability density function of the current state in the procedure of the filtering. Particles was resetting in the high-probability region, and the weights of these particles are calculated according the fit function. Simulation shows that the proposed resampling method is effective for non-linear filter problem.
Research on Sina Weibo Altmetrics Index about Sentiment Analysis

[Purpose/significance] In order to explore the social public's emotional tendency towards academic achievements on social platforms and whether academic information will cause certain social risks to scholars when facing the public with great differences. [Method/process] Links to original texts of all academic information related to Sina Weibo provided by Altmetric company from October 2011 to October 2017 have been selected. On this basis, python was used to crawl Sina Weibo comment data, and a total of 53826 valid comment data were obtained, which constituted the basic data set of this study for emotional analysis. [Result/conclusion] The research finds that different scholars and the public have a greater acceptance of networked academic information, and the emotional tendency of Sina Weibo data comments is generally positive. However, if the original Sina Weibo has ambiguous or extreme comments, the public may have negative emotions due to misunderstanding of the original text and attacks on these extreme comments. This paper also tries to introduce The Schwartz value Scale from the micro perspective and put forward the idea of emotional evaluation of Sina Weibo academic information review based on values, in order to provide more detailed thinking of emotion dimension for the study of Altmetrics index from the perspective of emotion analysis.
Sigma: a Scalable High Performance Big Data Architecture

In this paper, a new architecture, named Sigma (as Sigma uppercase usually denote a sum and this architecture is a summarization of Lambda and Kappa), is proposed to provide a solution for build a complete Big Data System, interactive and scalable, using a variety of tools and techniques. The architecture have been tested in a real life scenario and the early results obtained are quite encouraging both on scalability and high performance.
Developing A GIS-Based Approach for Enhancing Revenue Management of Fish Cold-Room Businesses in Oyo East Territory, Oyo State, Nigeria

Cold-room or Refrigerating Chamber is a post-harvest warehouse in which a specific temperature is artificially generated, maintained and purposely designed for storing products in an environment below the outside temperature. An ideal fish cold-room is a category of cold storage system usually design for frozen fish or kinds of seafood since the spoilage of post-harvest fish due to protein denaturation, fat changes and dehydration are experimentally slowed down by reducing the storage temperature. Fish is one of easily perishable protein foods that need careful handling but most traditional fish farmer in the study area always prefer to sell their fish in bulk at either profitable or unprofitable price during harvest time due to lack of adequate post-harvest fish preservative and processing techniques. Hence, the study aimed at developing GIS based approach for the available post-harvest fish cold-rooms in the study area to ensure effective control and management of revenue from this informal business. The objectives include: to determine the available number of fish storage cold-rooms and identify likely problems associated with cold-rooms storage businesses in the study area, to identify necessary practical measures for solving the so identified problems. Also, the methodology adopted includes database design, data acquisition using GPS and Scanner respectively and as well as creation of spatial and attribute database for the acquired Cold-rooms Storage Facilities using ArcGIS 10.2.2. Various results were generated that pointed in the direction of uneven distribution of the fish storage facilities. It is therefore recommended that it is necessary for various concerned Local Government Authorities to align with “Sustainable Development Goal’s Fourteen (SDGs14th) [1] on Food Security from Life below Water” by encouraging the establishment of adequate Modern Post-Harvest Cold-rooms Storage Facilities within their territory and equally ensure that they are always in good condition for public use.
Comment Based Product Recommendation System using Association Rule Approach and Naive Bayes Algorithm

Recommendation System have emerged rapidly in the past decade. Recommendation systems on E-commerce websites helps consumer to find products. A recommendation systems learns consumer behavior in order to suggest products to those consumers. Recommendation system allow consumer to have new experience discovering new products rather than needing to search for them. When making purchase decisions consumer often use the comments left by previous buyers to help them. We are going to research on Recommendation system that can help the use to find the better product from large data set.
Formation of creative potential of students-philologists

In the article the authors highlight the results of work with students-philologists when teaching disciplines of pedagogical orientation (methods of teaching Russian at school, methods of speech development, etc.). The novelty of the research is seen in the application of the methodology for the development of students' creative abilities in the classroom at the university by organizing the educational process based on the problem-based teaching method. It is reported that the combination of methods of logical and creative solution of the assigned tasks contributes to the formation of a creative approach in the educational system at the university. The classification of methods of logical and creative mastering of the knowledge system is carried out. It is emphasized that the teacher must create conditions for the development of the student's creative potential by introducing methods of creative cognition into the learning process (the method of brainstorming, the method of constructing definitions of concepts, the method of experiment, the method of modeling, the method of design, the method of conversation / discussion, the method of consultation, the method of reflection). The article presents examples of tasks of creative orientation according to the methodology of teaching Russian language, a methodological commentary on the tasks is given. The authors of the article argue that creative work not only contributes to deepening and expanding the knowledge and capabilities of students, but also has a positive effect on their personal growth, increases cognitive interest in learning in general and helps modern students in socialization and interaction with the team. It is concluded that creative methods of working with students described in the article can be used in teaching university disciplines.
Findings and Core Practices in the Domain of Agile Methodologies

This chapter analyzes the outcome of the research done on distributed agile teams working on digital platforms and software products. This chapter also aims to explore the gaps surrounding distributed agile and team performance which has been mentioned in the literature; hence, this was rarely co-related, and therefore, its probable impact was never explored in depth. In addition to this, the chapter highlights some key observations for the two selected maturity models and highlights its strength and weakness which seems to have been ignored by the organization while using the models. For this research, a large financial service company was examined as a case study. The chapter then concludes by drawing core practices to organizations having similar distributed agile software development model. In addition to this, the chapter also leaves open discussion points for the readers to think upon and question themselves on the way agile is being used within their organizations and how it contributes to working on digital systems and platforms.
Extensive Huffman-tree-based Neural Network for the Imbalanced Dataset and Its Application in Accent Recognition

To classify the data-set featured with a large number of heavily imbalanced classes, this paper proposed an Extensive Huffman-Tree Neural Network (EHTNN), which fabricates multiple component neural network-enabled classifiers (e.g., CNN or SVM) using an extensive Huffman tree. Any given node in EHTNN can have arbitrary number of children. Compared with the Binary Huffman-Tree Neural Network (BHTNN), EHTNN may have smaller tree height, involve fewer neural networks, and demonstrate more flexibility on handling data imbalance. Using a 16-class exponentially imbalanced audio data-set as the benchmark, the proposed EHTNN was strictly assessed based on the comparisons with alternative methods such as BHTNN and single-layer CNN. The experimental results demonstrated promising results about EHTNN in terms of Gini index, Entropy value, and the accuracy derived from hierarchical multiclass confusion matrix.
Job-aware Communication Scheduling for DML Training in Shared Cluster

Distributed machine learning (DML) systems equipped with multiple computing nodes have been widely adopted to accelerate large model training in the industry. To maximize resource utilization, a critical problem is how to schedule the communication of DML jobs efficiently. However, previous approaches work well only when a job can use the network resources exclusively. Training multiple jobs in shared cluster without scheduling will bring significant performance degradation since network contention. In this paper, we propose JCS, a job-aware communication scheduler to overcome the above problems. JCS profiles the priority with a novel metric among jobs and schedule communication of jobs according to both computation and communication information. To demonstrate the effectiveness of our algorithm, we perform extensive simulations with DML job traces. The simulation results show that our algorithm can reduce average job completion time by 19%, 39% and 46% over RRSP, SCF and LCoF.
Shoulder Joint Rehabilitation Training System Based on Virtual Reality Technology

To guide patients to perform rehabilitation training of motor function in virtual environment and improve the effect of joint training of interest and visual and auditory stimulation, this paper analyzes the limitations of traditional rehabilitation training methods, and puts forward a design scheme of shoulder joint rehabilitation training system based on virtual reality technology. The system collects the motion information of human body through Kinect sensor, calculates the human skeleton vector, and combines with DTW algorithm to match the actual actions of patients with standard actions. The corresponding software system is specially developed which is used for real-time data communication, upper limb posture information calculation and real-time communication with virtual reality software. The experimental results show that the virtual rehabilitation interactive system can achieve the desired effect, and 3D scene training can also improve the enthusiasm of patients, which effectively improves the quality of life of patients with upper limb motor dysfunction.
Development of Learning Media Applications for Android Smartphone Based Augmented Reality (Ar) on Antenna and Propagation Subjects

Abstract. The application technology based on the Android smartphone as an AR learning media begins with the analysis and design stage which results in the initial product of the Augmented Reality learning media application. This study aims to determine: (1) The stages of developing an Augmented Reality (AR) application for learning media based on an Android smartphone. and (2) produce a valid, practical, and efficient Augmented Reality (AR) learning media application based on Android smartphones. The testing procedure in this study went through the expert testing stage and implementation in the form of small group trials and field trials. The subjects in this study were experts and students. Data collection techniques used in this study were observation, questionnaires, and interviews. While the data analysis technique used is descriptive data analysis. The results of the research at the development stage based on the results of the instrument validation analysis, it can be concluded that the Android Smartphone-based Augmented Reality (AR) media assessment instrument that has been compiled is an RPS instrument with an average of 3.74, a Media instrument with an average of 3.70, instrument material with an average of 3.57, instrument implementation of the media with an average of 3.65, instruments of teaching materials with an average of 3.78, instrument of student response with an average of 3.75, instrument of student activity with an average 3.67, and the lecturer activity instrument with an average of 3.67. Fulfills the criteria for validity in the Very Valid category based on the assessment of the expert appointed as the validator. Based on the results of the assessment obtained in small group trials, this learning media application is feasible to go to the next level of testing, namely field trials. The overall value of the average percentage of the ignition rate is 89.30% with the very valid category. Thus the results of the study indicate that the Development of Learning Media Applications for Android Smartphone Based Augmented Reality (AR) in the Antenna and Propagation Subjects developed is declared very valid, practical, and efficient. Keywords: Learning media, augmented reality (ar), smartphone, android
Clustering High-Dimensional Data using AE-Hubness

In high dimensional data, the clustering accuracy of traditional clustering algorithms is not high. However, the hub-based clustering algorithm can only solve high dimensional data, but the redundant data and noise features in the data cannot be solved effectively. To solve this problem, AE-Hubness clustering method using auto-encoder for dimensionality reduction processing is proposed. The AE-Hubness clustering method utilizes the relationship between skewness of anti-nearest-neighborhood's number and intrinsic dimension. According to the rate of change of the skewness, it can improve the clustering effect under the condition that it will not lose too much important available data after dimensionality reduction. The experimental analysis on the UCI data sets shows that the performance of the AE-Hubness clustering method is stable and the Silhouette Index is improved.
El DESARROLLO DE LA COMPRENSIÓN Y APLICACIÓN DE TECNOLOGÍAS EN EL ÁREA DE EDUCACIÓN PARA EL TRABAJO DE LA SECUNDARIA: DESARROLLO DE LA COMPRENSIÓN Y APLICACIÓN DE TECNOLOGÍAS EN EDUCACIÓN

The article presents results of an investigation applied projective with qualitative approach that she studied the employment of multimedia presentations in the teaching-learning process for the development of the understanding and application of technologies in the area of Education for the Work of the Secondary. In the investigation one worked with an educational deliberate sample of seven of the area and hundred twenty-five students. They were used as instruments the surveys and records of documental analysis. The results evidence that the educational of Education for the Work use strategies and traditional means, not very effective for the development of the understanding and application of technologies the students are shown however cheerful and motivated when presentations are used in the development of the learning sessions and in their majority, they would like use of these didactic materials to be made.
Handwritten Arabic Character Recognition: Comparison of Conventional Machine Learning and Deep Learning Approaches

Over the last decades, automatic handwriting recognition has received a lot of attention, as it is a crucial component for many applications in various fields. Research for this issue has focused on handwriting recognition in Latin languages and fewer studies have been dedicated to the Arabic language. In this paper, we propose and compare two approaches to classifying Arabic characters. The first is based on conventional machine learning using the SVM classifier by comparing different sets of features, most commonly used in the pattern recognition field. The second is based on deep learning by testing different CNN (convolutional neural networks) architectures, which brings a self-characterization of Arabic features. In this context, a new fast and simplified CNN architecture is proposed. We also test different transfer learning strategies on two versions of the OIHACDB dataset and the AIA9K dataset proposed in the literature. In the experimental section, we show that the proposed CNN model achieves accuracies of 94.7%, 98.3%, and 95.2% on the test set of the three databases OIHACDB-28, OIHACDB-40, and AIA9K respectively. Our experiments enrich the tests already carried out on these datasets and show good results in comparison with the literature.
A Chromium-Based Memento-Aware Web Browser

Current web browsers do not differentiate between the live Web and the past Web. If a user loads an archived webpage, or memento, they have to rely on elements in the page itself to be aware that they are not viewing a live webpage. Another problem is that browser bookmarks only save the URL of a webpage. But if the webpage is deleted or changed, the user may have lost access to the very information that they had wanted to save. We present a proof-of-concept Memento-aware Browser, built on Google's Chromium open-source web browser. We add native memento detection that displays an icon when the user is visiting an archived webpage and a "bookmark as archive" feature that allows users to request archiving of individual webpages when they are bookmarked.