Reviewerly/exHarmony huggingface dataset:
Comprehensive Evaluation of E-Service Quality Determinants. "Studying the Impact of these Key Quality Determinants On The Satisfaction and Loyalty of Online Jordanian Customers"

The wide use of Internet and the advancement in information and communication technologies have significantly changed the way business is conducted.Over the last decade or so online shopping tendency has increased all over the world.Hence, the purpose of this study was to investigate the impact of customer service quality, system quality, information quality, security &privacy quality, website quality, convenience quality, economic value quality and empathy quality on customer's satisfaction and loyalty "the case of online Jordanian customers".The study is based on a set of null hypotheses that hypnotizes no significance impact of these qualities on customers' satisfaction and customers' loyalty.A quantitative approach is used and relevant data was collected through structured questionnaire distributed to residents of Jordan.The relevant responses of subjects for the study consisted of 680 people.SPSS version 16 was used to for data analysis.Descriptive and inferential statistics were used for data analysis.Statistical tools were aligned with objective of the research.For this purpose, frequency tables, percentages, means and standard deviations were computed and then substantively interpreted.Inferential statistics like Pearson product moment correlation coefficient (r) and linear regression were used to determine if there is a significant positive relationship existed between the independent variables and the dependent variables.The findings indicate that customer service quality, system quality, information quality, security &privacy quality, website quality, convenience quality, economic value quality and empathy quality were found to be positively correlated and claimed statistically significant relationship with customers' satisfaction and customers' loyalty.Analysis and interpretations made at 0.05 level of significance.The study concluded that the impact of these variables is differentials as per customer's perceptions.Furthermore, service providers should concentrate on all e-service qualities to satisfy customers and earn their loyalty.Strict adherence well enable e-service providers to gain online competitive advantage and sustain their businesses in the virtual environment.
Extended Query Model for MOOC Education Resource Metadata Based on Big data

In traditional education resource metadata extended query methods, data recall is poor. In order to solve this problem, an extended query model for MOOC education resource metadata based on big data information fusion clustering scheduling is proposed. The semantic ontology model is adopted to analyse the storage structure of MOOC education resource metadata and extract binary semantic feature quantity of MOOC education resource metadata to construct a binary semantic decision model for extended query of MOOC education resource metadata; the integrated information processing technology of big data is adopted for extended query and adaptive scheduling of MOOC education resource metadata to improve the ability to retrieve and identify metadata. The simulation results show that the proposed method can provide an average precision ratio of 0.976 in extended query of MOOC education resource metadata and good data recall performance.
Pelatihan Competitive Programming Tingkat SMA untuk Siswa Kelas XII SMA Kolese De Britto

nowadays contained in various aspects of education, starting in elementary school up to high school course. There are many programming contests for students, including FindIT! Competition that held by Universitas Gadjah Mada. This programming competition using Competitive Programming format, which is the type of programming to solve a certain problem using the most effiective and efficient algorithm. Kolese De Britto's students want to join that competition, but their teacher didn't have capabilites to teach programming, so they asked Universitas Kristen Duta Wacana for a help.
Computational architecture of a visual model for biological motions segregation

This paper outlines a neural model inspired by the dorsal stream of the visual system for motion recognition. Two areas are considered: the primary visual area (V1) and the middle temporal area (MT). In model area, V1 neurons are organized to detect eight local motion directions. MT is modelled using classical receptive field (CRF), where the cells respond to wide-field motion. The biological motion can be identified through spatial motion dynamics for the limbs and body. In this article, we propose spatio-temporal sampling detectors, where a set of circular masks over motion scenario are utilized to detect the motion dynamics. Two alternative mechanisms, Max-pooling and Sum-pooling, are used to extracting spatio-temporal descriptors from motion energy occupied by the circular masks. To improve the classification results, centroid kinematics is added to the feature vectors, where this feature contributes substantially to characterizing the motion pattern of an action. We evaluate our model by using two challenging datasets: the Weizmann biological action dataset and the KTH biological motion dataset. Our results reflect the potential of spatio-temporal sampling detectors in describing the biological motion of body and limbs using only short video frames (snippets). In addition, the centroid kinematic feature improves the recognition rate and refines the action classification.
New observations of Tin Mineralization Potential Vis-à-Vis Ore Petrographic, Alteration and Geochemistry in the Southeastern part of Bastar Craton, Central India

The Tin mineralizations occur around the Katekalyan area, hosted in the acid magmatic rocks. The evolution differentiating granitic magma shows residual melt enrichment where end products intruded as pegmatites into the rocks. The different kind of pegmatite occur as simple unzoned, recrystallized (granitic pegmatite), and metasomatic greisenised and albitised pegmatites which emplaced within the pre-existing rocks of metabasic intrusive, granite (KG), granite gneiss (KGG). Sometimes it also found in metasediments as mineralised and non-mineralised characters along the fractures and foliation planes trending N-S, E-W and more frequently are observed NNW-SSE trends. Cassiterite is most important Tin-ore mineral and associated with pegmatites. Some cassiterite samples exhibit colourless to brown shades zoning which indicate multi stage growth. The cassiterite samples contain significant amounts of Sn, Nb, Ta with minor W.&#x0D; The partial melting model shows that the variation 5 to 50% partial melting of bulk continental crust for KG as well as KGG rocks but bulk distribution coefficient for Sr (DSr) shows low i.e. &lt;&lt;10. The upper limit of partial melting of bulk crust estimates ~50 % for KG and KGG rocks are consistent with required rheological, critical melt percentage to leave the source region has decreased granite melt which were capable to mineralised tin ore elements. It is interesting to note that the SnF4 and SnCl4 probably not stable in presence of water under geologically reasonable conditions.
Multiple Point Statistics with Pyramids Application on the Multi-scale Multi-structure Training Images

Summary Multiple Point Statistics (MPS) is now a well-known geostatistical method. In practice, one of the first operational need is to reproduce the heterogeneity with its small and large scales features, which are often present in natural phenomena. To respond to this need, multiple grid or flexible size template can be used. But unfortunately, even using these options, the large scale structure is not correctly reproduced or not reproduced at all. The human eye may make an abstraction from small scale texture and capture the large scale feature. Could the MPS approach be inspired by this idea to "see" large scale organization? A possible solution is to use a pyramidal representation of the Training Image similar to a Google Earth satellite image storage. This idea was implemented on the basis of the Multiple Point Direct Sampling algorithm (MPDS-pyramid) and this work presents its application to one synthetic and one real cases.
Markov Chain Monte Carlo Methods for High-dimensional Mixture Distributions

Summary We present a Markov chain Monte Carlo method for the computation of the posterior distribution of discrete and continuous properties in geophysical inverse problems. Mixture distributions, Gaussian or non-parametric, have been proposed to model the multimodal behaviour or subsurface properties. However, due to the spatial correlation of subsurface properties, the number of modes of the mixture distribution increases exponentially with the number of samples in the data vector. In this work, we propose a new Markov chain Monte Carlo method based on two steps. First, we update the configuration of the discrete property (for example, facies or rock types), then we update the configuration of the continuous properties (for example, elastic or petrophysical properties). The first step can be performed according to a jump move, where a new configuration is proposed, or a local move, where the configuration of the previous iteration is preserved. The second step is performed by sampling the new configuration of continuous properties either from the analytical expression of the Gaussian distribution of the continuous properties conditioned by the facies configuration in the Gaussian-linear case, or by numerically sampling from the non-parametric conditional distribution in the non-Gaussian and non-linear case. The methodology is demonstrated through the application to synthetic and real datasets.
Adversarial Feature Distillation for Facial Expression Recognition

Human face image contains abundant information including expression, age and gender, etc. Therefore, extracting discriminative feature for certain attribute while expelling others is critical for single facial attribute analysis. In this paper, we propose an adversarial facial expression recognition system, named expression distilling and dispelling learning (ED $$^2$$ L), to extract discriminative expression feature from a given face image. The proposed ED $$^{2}$$ L framework composed of two branches, i.e. expression distilling branch ED $$^{2}$$ L-t and expression dispelling branch ED $$^{2}$$ L-p. The ED $$^{2}$$ L-t branch aims to extract the expression-related feature, while the ED $$^{2}$$ L-p branch extracts the non-related feature. The disentangled features jointly serve as a complete representation of the face. Extensive experiments on several benchmark databases, i.e. the CK+, MMI, BU-3DFE and Oulu-CASIA, demonstrate the effectiveness of the proposed ED $$^{2}$$ L framework.
На пути к социальной теории интернета

Lovink, G. (2019). Critical theory of Internet. Moscow: Ad Marginem Press, Garage Museum of Modern Art. (in Russian).
A Four-Position Calibration Method of Inconsistent Angles between Units of TRUSINS

Calibration is very important to improve accuracy and reliability of inertial navigation system (INS). Especially for a novel triple rotary unit strap down inertial navigation system (TRUSINS), the inconsistent angles between three units can bring a cross-coupling error related to the measured angular velocity and the specific force. Therefore, it is necessary to precisely calibrate and compensate the inconsistent angles in a rapid and efficient way. This paper proposed a four-position calibration method based on a single-axis turntable. Making use of double sets of measurements along all three axes of body frame, this paper established an error model for the inconsistent angles in this novel TRUSINS. A four-position calibration experiment was designed and carried out. The results showed that the calibration accuracy of inconsistent angles between units meets the navigation requirements of TRUSINS.
Problems, Causes and Countermeasures of Chinese Writing of Senior Students in Primary School

In primary school Chinese teaching, composition is an important part of teaching, and it is the practice of comprehensive ability of primary school students’ understanding level and language expression. The level of students’ essays not only reflects the students’ ability to use the language, but also marks the language literacy. Therefore, the teaching of the primary school language is crucial to the improvement of primary school composition. However, in the language teaching of primary schools, the cultivation of writing ability has always been the focus and difficulty. The upper primary school is an important link between primary and secondary schools. The cultivation of writing ability is more important and should not be underestimated. In short, in the process of Chinese language teaching in the upper primary school, teachers should pay attention to cultivating students’ writing ability and improve students’ writing ability as an important task in Chinese teaching. This paper mainly explains the existing problems and causes of the writing of senior students in primary school through theory, and proposes corresponding countermeasures for the high school students’ writing ability.
Retrospect: October 19 – November 7, 2003: Halloween Solar Storms

NOAAHazardous weather on Earth can take many forms—such as tornadoes, hurricanes, blizzards, and floods. All these phenomena are the result of what can be called terrestrial weather—weather that oc...
Comparison of Deep Generative Models for the Generation of Handwritten Character Images

In this study, we compare deep learning methods for generating images of handwritten characters. This problem can be thought of as a restricted Turing test: A human draws a character from any desired alphabet and the system synthesizes images with similar appearances. The intention here is not to merely duplicate the input image but to add random perturbations to give the impression of being human-produced. For this purpose, the images produced by two different generative models (Generative Adversarial Network and Variational Autoencoder) and the related training method (Reptile) are examined with respect to their visual quality in a subjective manner. Also, the capability of transferring the knowledge that is obtained by the model is challenged by using different datasets for the training and test processes. Using the proposed model and meta-learning method, it is possible to produce not only images similar to the ones in the training set but also novel images that belong to a class which is seen for the first time.
Общество и культура: концепция культуры Ю. А. Жданова

In the article were reconstructed the main milestones of Yu.A. Zhdanov’s theory of culture. In the framework of understanding the problems of society and culture by Yu.A. Zhdanov, in this article several plot lines are highlighted: Yu.A. Zhdanov’s culture theory, which is based on his joint work with V.E. Davidovich activity concept of culture; questions of cultural history and, first of all, cultural history of the Caucasus nation, culturological Caucasian studies; the idea of Yu.A. Zhdanov’s cultural man. It is concluded that in national philosophy and culturology Yu.A. Zhdanov’s concept of culture hasn’t lost its value and is one of the authoritative modern culturological theories.
Measurement methodology to characterize acoustic parameters of Thai fiddle (Sx $\hat{\mathrm{d}}\text{wng}$)

The acoustic parameters and sound characteristics of an acoustic instrument are essential for a general use into the musical field. With the information, the musical instrument maker can use for developing in the design so that the quality of the musical instrument will be improved. The study in this area also provides knowledge for virtual acoustic parameters and recording techniques. This research aims to develop an effective and economic measuring methodology to characterize the acoustic parameters such as sound directivity of Thai string instruments (Sx $\hat{\mathrm{d}}\text{wng}$ ) in a real acoustic environment.
Breast Cancer Computer-Aided Diagnosis System Using k-NN Algorithm Based on Hausdorff Distance

World statistics indicate that the breast cancer is the most common worldwide type of cancer among women. The development of computer-aided diagnosis techniques may contribute to a more effective therapy against this type of cancer. In this work, we present preliminary research regarding cell nuclei classification based on the Hausdorff distance. The obtained results indicate that using only Hausdorff distance to the classification of individual cell nuclei allows us to achieve 75% accuracy. Moreover, the speed of calculations and the possibility of using additional features describing cell nuclei open new paths to computer-aided diagnosis support systems development.
Report from the Digital Partnership Production

In the past 12 months we have refined the Digital Partnership Production (DPP) Technology Strategy and pushed ahead with four key pillars of work: versioning, automation, delivery, and security.
An investigation of humanoid robots and their implications for Australian public libraries: research report

What is the impact of humanoid robots on Australian public libraries? This report presents the key findings of a project that responds to the question. Titled “An investigation of humanoid robots and their implications for Australian libraries”, the purpose of the project was to provide a holistic picture of the role that humanoid robots play in Australian public libraries. METHOD The project adopted a qualitative research approach. To capture authentic insights, the researcher sought the views of 10 librarians in public libraries in Queensland, South Australia and Victoria. The librarians had experience as they were directly involved in humanoid robots in their libraries. Data collection involved individual in-depth interviews with all 10 librarians. Interviews were audio recorded and transcribed. The researcher also directly observed five events in five public libraries, in which the audience interacted with humanoid robots. Field notes were taken and used as a supplemental source of data for the research. A thematic analysis approach was used to analyse both sources of data. KEY FINDINGS Data analysis revealed four themes that described the way humanoid robots impacts Australian public libraries. Humanoid robots are considered as: A community builder This role was represented via:•Appealing to people•Entertaining people•Connecting and empowering people A teacher This role was represented via:•Educating people•Stimulating desire to learn•Motivating people•Awaking potential An assistant This role was represented via:•Performing basic tasks•Lacking flexibility in behaviours•Playing supportive roles A challenger This role was represented via:•Creating new challenges•Requiring more expertise•Requiring resilience IT IS RECOMMENDED THATPublic libraries (in collaboration with their parent organisations, i.e. city councils), devise a strategic plan for new and emerging technologies such as humanoid robots. •Public libraries seek to expand their collaboration with other stakeholders such as universities and robotics centres where there are roboticists. •Public libraries and city councils strengthen their relationships with robot vendors in order to take advantage of direct support. •Library management boards and their librarians keep a good line of communication, especially issues related to humanoid robots.•The community and other stakeholders consider humanoid robots in libraries as a work-in-progress. CONCLUSION This research has presented fresh insights about the impact of humanoid robots on Australian public libraries. The research findings were drawn from the lived experiences of librarians who directly plan and deploy humanoid robot programs and from direct observations of interactions between the robots and their audience. The findings could inform the development of such robotic programs in libraries to enhance the experience of library user communities.
USING NON-DIALOGUE SHORT ANIMATED STORIES AS A TEACHING MEDIA TO IMPROVE NARRATIVE TEXT WRITING SKILL OF THE TENTH GRDERS OF SMA EL SHADAI MAGELANG IN THE SCHOOL YEAR 2017/2018

Most students get difficulties in writing English especially narrative text. There are internal and external factors which influence students’ skill. One of them is the teaching media that is used by the teacher, so the writer conducts this research to overcome the problem by applying the Non-Dialogue Short Animated Stories media. The main objectives of this research; the first is to know whether the Non-Dialogue Short Animated Stories media can increase the student’s learning motivation of the narrative text writing skill and the second is to know how great the improvement of the students’ narrative text writing skill, taught by the Non-Dialogue Short Animated Stories media. The writer conducted a classroom action research which was divided into Pre-cycle, Cycle I, and Cycle II. The writer took 22 students of the tenth grade in SMA El Shadai Magelang as the subject of the research. The writer used non-test and test to collect the data. The result shows that the use of the Non-Dialogue Short Animated Stories media can increase the students’ learning motivation. They were more active and had higher motivation to study. The result of the test, the students’ average score of all cycles; Pre-Cycle (62.95), Cycle I (71.36), Cycle II (82.50). The improvement from Pre-Cycle to Cycle I is 8.41 points. The improvement from Cycle I to Cycle II is 11.14 points. The total of the improvement from Pre-Cycle to Cycle II is 19.55 points.
On Conditioning GANs to Hierarchical Ontologies

The recent success of Generative Adversarial Networks (GAN) is a result of their ability to generate high quality images given samples from a latent space. One of the applications of GANs is to generate images from a text description, where the text is first encoded and further used for the conditioning in the generative model. In addition to text, conditional generative models often use label information for conditioning. Hence, the structure of the meta-data and the ontology of the labels is important for such models. In this paper, we propose Ontology Generative Adversarial Networks (O-GANs) to handle the complexities of the data with label ontology. We evaluate our model on a dataset of fashion images with hierarchical label structure. Our results suggest that the incorporation of the ontology, leads to better image quality as measured by Frechet Inception Distance and Inception Score. Additionally, we show that the O-GAN better matches the generated images to their conditioning text, compared to models that do not incorporate the label ontology.
Adaptive Self-Sufficient Itemset Miner for Transactional Data Streams

Most studies on pattern mining consider itemsets that have a high frequency of occurrence as useful, often determined by the support of the itemsets. However, current research has shown that we need to move beyond a pure “support-confidence” framework for pattern mining. Recently, there is an interest on finding statistically significant patterns and one of the most popular type of patterns is self-sufficient itemsets. One limitation is that these works do not consider concept drifts and cannot be used in a data stream. Learning in the online environment requires us to develop efficient and effective mechanisms to address the online characteristics of non-static data and non-stationary data distributions. In our research we will concentrate on detecting self-sufficient itemsets from data streams. These patterns have a frequency that is significantly different from the frequency of their subsets and supersets. We present a comprehensive framework for mining self-sufficient itemsets from data streams along with a drift detector. This supports mining self-sufficient itemsets in an online environment and provides the ability to adapt to changes in the stream. Our experimental evaluations show that our framework can mine self-sufficient itemsets faster in an online environment and with better precision and recall.