Reviewerly/exHarmony huggingface dataset:
Learning Continuous User and Item Representations for Neural Collaborative Filtering

Collaborative filtering (CF) is one of the most successful approach commonly used by many recommender systems. Recently, deep neural networks (DNNs) have performed very well in collaborative filtering for recommendation. Conventional DNNs based CF methods directly model the interaction between user and item features by transforming users and items into binarized sparse vectors with one-hot encoding, then map them into a low dimensional space with randomly initialized embedding layers and automatically learn the representations in training process. We argue that randomly initialized embedding layers can not capture the contextual relations of user interactions. We propose an approach that uses the optimized representations of user and item generated by doc2vec algorithm to initialize embedding layers. Items with similar contexts (i.e., their surrounding click) are mapped to vectors that are nearby in the embedding space. Our experiments on three industry datasets show significant improvements, especially in high-sparsity recommendation scenarios.
Educational Resource Information Sharing Algorithm Based On Big Data Association Mining and Quasi-Linear Regression Analysis

In order to solve the problem that the information sharing level of educational resources is not high in the cloud computing environment, it is required to improve the design of educational resource information sharing algorithm in cloud computing environment. Therefore, an educational resource information sharing algorithm based on big data association mining and quasi-linear regression analysis is proposed. In this algorithm, information fusion and comprehensive measurement index system reconstruction are performed to resource distribution information of the educational resource database based on attribute type and semantic ontology characteristics. The simulation results show that the algorithm performs well in convergence during resource scheduling in educational resource information sharing and can provide average sharing level up to 0.9247, so this algorithm can be adopted to allocate resources in a balance way.
[PDF] NCERT Solutions for Class 4 English Unit-4 Alice in wonderland

NCERT Solutions for Class 4 English Unit-4 Alice in wonderland, are part of  NCERT Solutions for Class 4 English. Here we have given NCERT Solutions for ...
Speculation and the price of virtual currency

What drives the volatility of Bitcoin? This column explains a theoretical framework to link exchange rates to currency creation, speculative behaviour, and real growth in goods and services transactions. It suggests that the exchange rate will be less sensitive to speculators' beliefs when a virtual currency becomes more established as a means of payment.
SISTEM INFORMASI STOK BARANG PADA CV. SONOMA BERBASIS WEB

CV. SONOMA is a form of business in the field of distribution with sales and purchase of convection items. Various types of convection items are available here. CV. SONOMA is engaged in the stock and distribution of convection goods. CV, which was established in the village of Tedunan Wedung Demak in 2005, sent convection goods which included ready-made clothing, team shirts, school uniforms and convection materials. After the products produced from the factory, the product is sent to the distributor by a supplier, the distributor then sells goods or products to the retailer or customer.The system needed to help manage data at SONOMA CV is a web-based system. The advantage of a web-based system is that it is more flexible, can be accessed in various places that have an internet connection, and can also be accessed using a smartphone.
Entropy Estimation Using Bootstrap and Jackknife Methods and its Application in Testing Normality

In this paper, the Bootstrap and Jackknife methods are stated and using these methods, entropy is estimated. Then the estimators based on Bootstrap and Jackknife are investigated in terms of bias and RMSE using simulation. The proposed estimators are compared with other entropy estimators by Monte Carlo simulation. Results show that the entropy estimators based on Bootstrap and Jackknife have a good performance as compared to the other estimators. Next, some tests of normality based on the proposed estimators are introduced and the power of these tests are compared with other tests.
Application of Clustering Algorithms to Group Medical Documents

Medical documents contain valuable information about medication and symptoms, which help in improving health care.Recently, large volumes of medical documents are generated by electronic health record systems.These medical documents are unstructured or semi-structured from which extraction of useful information is a difficult task.Application of document clustering techniques is an efficient way for navigation and summarization of documents and very important for many natural language technologies [1].Various partitional and agglomerative clustering techniques are applied in order to cluster the medical documents for grouping them into meaningful clusters.
A Framework for Efficient Implementation and Effective Visualization of Dempster-Shafer Belief Theoretic Computations for Reasoning Under Uncertainty

The convenience, intuitiveness, and versatility of Dempster-Shafer (DS) theoretic models of data uncertainty make DS evidence theory an ideal framework for reasoning under uncertainty and decision making in artificial intelligence (AI) applications. However, a major criticism cast towards DS theoretic (DST) evidential reasoning is the heavy computational burden it entails. If the advantages offered by DS theory are to be fully realized, it is essential that one explores efficient data structures and algorithms that can be used for DST operations and computations. We present a novel general computational framework for exactly this purpose. We develop three representations — DS-Vector, DS-Matrix, and DS-Tree — which allow DST computations to be performed with significantly less time and space complexity. We provide the algorithms to carry out DST operations as well as to work with dynamic frames. These three representations can also be utilized as tools for visualizing DST models and computations. A new strategy, which we refer to as REGAP, which allows REcursive Generation of and Access to Propositions, is introduced and harnessed in the development of this framework and computational algorithms. As data streams expand and the associated DST computations become more complex, devising tools to visualize these complex computations is essential to augment decision-making and reasoning. We introduce two new visualization tools, DS-LASIC and DS-TRISEV, and propose a novel family of geometrical diagrams, which we refer to as nested layered diagrams, for the flexible representation of DST quantities. Based on the DS-Tree from our computational framework and the DS-LASIC tool, we generate a new class of graphical diagrams in the family of nested layered diagrams. These new diagrams, which we refer to as concentric layered diagrams, satisfy certain symmetric and concentric properties, and address the limitations of illustrating higher order DST computations. Also introduced are variations of the proposed diagrams, which can be employed to design new computational models and efficient algorithms. As in Bayesian probability theory, the conditional operation plays a pivotal role in DST strategies for evidence updating and fusion. Again, a major limitation in applying DST techniques for reasoning under uncertainty is the absence of a feasible computational framework to overcome the prohibitive computational burden this conditional operation imposes, which is a non-deterministic polynomial-time hard (NP-hard) problem. We address this critical challenge via two novel generalized conditional computational models — DS-Conditional-One and DS-Conditional-All — which allow significantly less computational time and space complexity when computing conditional mass and belief. They provide deeper insight into the DST conditional itself and so act as a valuable visualization tool during conditional computation. We also provide two highly…
Computational Detection of Medieval References in Metal

The digital humanities offer many new methods to scholars interested in metal studies. This chapter demonstrates two such methods, that is, stylometric clustering and topic modelling, by interpreting the collected lyrics of over 8,000 metal bands. This allowed the authors to identify general trends that would be hard to derive by personally reading lyrics. This analysis showed several recurring medieval topics in metal lyrics, for example, a Fight for Glory expressed with words like ‘die’, ‘glory’, ‘warriors’, and ‘victory’. The authors were also able to distinguish metal bands with medieval references from those without – a line of research that could help in categorising (new) bands – and track the stylistic development of bands over time. Such statistical methods might help scholars not only by allowing for large-scale studies, yet also by providing inter-subjective feedback for theories.
Multiple Knowledge Transfer for Cross-Domain Recommendation

Collaborative filtering based recommendation systems rely on underlying similarities among users and items across multiple dataset and hence requires sufficiently large amount of ratings data to achieve accurate and reliable results. However, newly established businesses do not have sufficient ratings data and hence this requirement is rarely met. In this research, we propose Multiple Latent Clusters (MultLC) transfer to exploit the correlations among multiple datasets that do not necessarily have an identical dimension of information. In particular, we transfer different aspects of knowledge across different data sources where while transferring each aspect from a source to the target, we only soft-transfer common latent clusters while preserving unique (domain-specific) latent clusters of the target. By soft-transfer, we mean that we minimize the difference among the shared clusters (while not making them identical). Comprehensive experiments on real-world datasets demonstrate the effectiveness of our proposed MultLC over other widely utilized cross-domain recommendation algorithms. The performance improvements demonstrate the benefits of transferring knowledge from multiple sources while preserving the unique information of the target-domain for cross-domain recommendations.
Workshop on Wearables and Machine Learning

We describe a workshop on applications of Artificial Intelligence on Textile Technology through the usage of Machine Learning. The participants are given an overview of the state-of-the-art methods and technologies and are introduced to them through practical exercises. The exercises involve simple hands-on group work with a basic pre-built electrical circuit, as well as the possibility to train small models of Machine Learning given a user-friendly tool, whereas after the workshop, the participants can take home their own self-made electronic accessories
STUDI LAYANAN INFORMASI BERBASIS STIFIn UNTUK SISWA KELAS IX DI SMP IT ROBBANI BANJARBARU

The teenage phase is a fairly critical phase so that every teenager needs a suitable environment so that later they are able to achieve achievements in the family, school and community. Based on this, an environment that is designed in the family, school and community is needed so that each individual can develop according to his potential. Although basically schools are not the only institution that forges students but schools have a considerable impact on the formation of an individual. Therefore, researchers are interested in implementing STIFIn-based Information Services with the aim of optimizing the potential of students. The method used in this study is qualitative. The subjects in this study were students of class IX Rabbani Banjarbaru IT Middle School. The sample in this study were 1 teacher, 2 students, and 2 studaents.The implementation stages of STIFIn-based Information Services for grade IX students at Rabbani Banjarbaru IT Middle School start from the process of exploring the needs of students after the explanation service of the results of the STIFIn-Based Personality Test, to adjust the needs of students then the information service content provided is STIFIn-based Learning Information Services.The results showed that not all information content could be understood, because there was the use of foreign languages and scientific terms that were not familiar to students. In addition, the results and explanation of STIFI-based personality tests are not fully recognized by students, this is due to the environmental factors that influence students in carrying out their developmental tasks.
Black-Box Policy Search Algorithms for Reinforcement Learning for Robots

The IEEE Artificial Intelligence Challenge requires a high-level of decision making within robots, as they must decide how to behave autonomously to win matches versus other universities. Reinforcement Learning with a Gaussian Processes based modeler and a CMA-ES optimizer are currently the state-of-the-art for real-world robots, where Deep Learning simulations often fail.
NEGes 2019 task: Negation in Spanish

This paper presents the 2019 edition of the NEGES task, Negation in Spanish, held on September 24 as part of the evaluation forum IberLEF in the 35th International Conference of the Spanish Society for Natural Language Processing. In this edition, two sub-tasks were proposed: Sub-task A: “Negation cues detection” and Sub-task B: “Role of negation in sentiment analysis”. The dataset used for both sub-tasks was the SFU ReviewSP-NEG corpus. About 13 teams showed interest in the task and 5 teams finally submitted results.
Blockchain Finance – Massive Disruption or Status Quo?

We evaluate the potential disruptive power of the emergence of Block Chain Technology. The central question is whether this technology is simply a new tool to be used by an existing infrastructure or if this new tool will in fact lead to the emergence of a new way of performing the critical role of finance in society. Although the likely outcome may not the development of a new financial services paradigm, it is clear that the likely outcome will be a quantum shift in exactly how the world of financial services operates.
The Analysis of Factors that Affect Students Anxiety in Speaking for Presentation Class (A Study of the Fourth Semester Students at the English Education Study Program of University of Bengkulu in Academic Year 2018/2019)

This research attemted to find out the factors that affect students speaking anxiety in speaking for presentation class. This research was carried out by employing descriptive quantitative design. The population of this research was the fourth semester students of English Education Study Program of University of Bengkulu in academic year 2018/2019. The samples of this research were taken by using random sampling tehcnique, the total of the sample was 57 students. The data were collected by questionnaire consisted of 29 questions related to the anxiety. The results of this study showed that the students mostly had the mildly anxiety. There were three factors that causing the anxiety of the students in speaking; Fear of Negative Evaluation (Mildly Affected; Mean Score: 3.12), Communication Apprehension (Mildly Affected; Mean Score: 3.09), and Test Anxiety (Mildly Affected; Mean Score: 2.89). In conclusion, there were three factors that students speaking anxiety in speaking for presentation class and the most dominant factors is the Fear of Negative Evaluation
Geostatistical and multi-fractal modeling of geological and geophysical characteristics in Ghalandar Skarn-Porphyry Cu Deposit, Iran

This work aims at figuring out the spatial relationships between the geophysical and geological models in a case study pertaining to copper-sulfide mineralization through an integrated 3D analysis of favorable target. The Ghalandar Skarn-Porphyry Cu Deposit, which is located in NW Iran, is selected for this research work. Three geophysical surveys of direct current electrical resistivity and induced polarization tomography along with magnetometry are performed to construct the physical properties of electrical resistivity, chargeability, and magnetic susceptibility, respectively. Inverse modeling and geostatistical interpolation are utilized to generate the physical 3D models. A 3D model of Cu grade is generated using ordinary kriging; however, the indicator kriging method is run to design a 3D model of rock types through incorporating the drilling results. Block models of geophysical and geological characteristics are cast in a similar 3D mesh to investigate their relationships in copper mineralization. A concentration-volume multi-fractal method is utilized to divide each model into its sub-sets, where the most productive portions in association with Cu-bearing mineralization are distinguished. Note that sub-sets of geophysical models are spatially matched with geological models of Cu grade and rock types. The zones with low electrical resistivity, high chargeability, and low magnetic susceptibility correspond to the main source of Cu mineralization in a dominated skarn rock type setting.
USING MACHINE LEARNING FOR FEATURE DETECTION IN TRANSMISSION ELECTRON MICROSCOPY

In situ testing performed in a transmission electron microscope (TEM) represents an im- portant technique for materials analysis. However, it produces a large amount of data in the form of images and video, which cannot typically be analyzed using traditional image- analysis algorithms. Therefore, a machine-learning approach is proposed to detect the shape of a body by recognizing and locating the border of the material. This supervised-learning al- gorithm is applied and a convolutional neural network is built to rapidly label all pixels. This network explores the relationship between small sub-images cropped from original images and their corresponding labels, and then it predicts the label when given new sub-images, thus generating a segmented image. In this project, the performance was assessed based on specificity, sensitivity, and accuracy of results. The overall accuracy of the present model is over 90%; however, the precision and recall rate are low due to high false-positive detection. This research suggests key factors for improving future machine-learning algorithms for TEM image analysis.
Helpfulness-Aware Matrix Factorization for Cross-Category Service Recommendations

Matrix factorization is a popular method for building recommendation models. On e-commerce platforms, this method makes predictions of product ratings for goods which have not been rated. Similarly, in service computing, service rating platforms have been proposed to help users to select services. The idea is constantly evolving and the proposed models are often only validated by synthetic data. Existing recommendation systems rarely consider the fact that while customer feedbacks are usually valuable, some are questionable. Hence, how objective the given ratings are is an important factor. By considering the contribution of each rating according to its helpfulness and its objectivity, this paper proposes a platform that can make precise and cross-category recommendations. We exploit the parallelism between service and product recommendations to validate our proposed model by real-world data.
AnalisaTingkat Kepuasan Mahasiswa Terhadap Layanan Pembelajaran Menggunakan K-Means dan Algoritma Genetika

The level of student satisfaction with learning services in higher education is one factor in the quality of college learning. To determine the level of student satisfaction with learning services in higher education, it is necessary to analyze the level of student satisfaction with learning services. K-Means method is a technique of grouping data based on the level of similarity of each member. K-Means can be used to classify the student satisfaction index on learning services. The K-Means method can also be optimized with genetic algorithms to determine the best centroid value. K-means optimization with Genetic Algorithms can be used as a technique to determine the level of student satisfaction with learning services. Obtained by Davies Bouldien Index from the K-Means and Genetics method is 1.593 with cluster number 5
DeepCleanse: Input Sanitization Framework Against Trojan Attacks on Deep Neural Network Systems.

We propose Februus; a new idea to neutralize highly potent and insidious Trojan attacks on Deep Neural Network (DNN) systems at run-time. In Trojan attacks, an adversary activates a backdoor crafted in a deep neural network model using a secret trigger, a Trojan, applied to any input to alter the model's decision to a target prediction---a target determined by and only known to the attacker. Februus sanitizes the incoming input by surgically removing the potential trigger artifacts and restoring the input for the classification task. Februus enables effective Trojan mitigation by sanitizing inputs with no loss of performance for sanitized inputs, Trojaned or benign. Our extensive evaluations on multiple infected models based on four popular datasets across three contrasting vision applications and trigger types demonstrate the high efficacy of Februus. We dramatically reduced attack success rates from 100% to near 0% for all cases (achieving 0% on multiple cases) and evaluated the generalizability of Februus to defend against complex adaptive attacks; notably, we realized the first defense against the advanced partial Trojan attack. To the best of our knowledge, Februus is the first backdoor defense method for operation at run-time capable of sanitizing Trojaned inputs without requiring anomaly detection methods, model retraining or costly labeled data.
Dimensional Situation Analytics: An Introduction and Its Application Prospects

Dimensional situation analytics provides a formal framework to analyze situations from Data Information Knowl-edge Wisdom (DIKW) point of view. To date, the advent of big data driven applications opens up many frontiers in artificial intelligence and computer science, and yet it also raises a series of challenges due to theirs empirical and experimental nature. In particular, to systematically and analytically understand the logical connection through which intelligence and knowledge is derived from data and information deterministically is one of the far-reaching future objectives in computer science. Starting from an earlier result on Dimensional Situation Analytics (DSA), where the initial efforts targeted at the integration of situations and DIKW ontology, this paper brings the prospect of real world applications of DSA into perspective. A good example is UAV path planning for efficient radiation detection and monitoring, which links the pervious result, i.e., the DSA formal framework, with real world experimentation and thereof further explores on the effectiveness and future work for the DSA.
The Effect of Using Frontloading Strategy in Teaching Reading Comprehension at The Seventh Grade Students of SMP N 10 Bengkulu In The Academic Year of 2018/2019

This study was conducted to find out the effect of using frontloading strategy in teaching reading comprehension at the seventh grade students of SMPN 10 Bengkulu. This research was a quasi experimental research that is one short experiment which used post-test only. The population of this research were the seventh grade students of SMPN 10 Bengkulu there are 180 students. The sample of this research were 57 students from two classes; the experimental class and control class. The experimental class was treated by using frontloading strategy, while the control class was taught by conventional strategy. The two classes have similar ability in English based on the scores of mid-term examination. In experimental class there was a significant positive effect towards the students reading comprehension (it was found that the t-count 2.047 while t-table was 1.676). T-count was bigger than t-table (2.047&lt;1.676). Based on those results, it can be assumed frontloading strategy was effective in increasing students’ reading comprehension.