Reviewerly/exHarmony huggingface dataset:
Movie recommender systems using hybrid model based on graphs with co-rated, genre, and closed caption features

A movie recommendation is a long-standing challenge. Figuring out the viewer’s interest in movies is still a problem since a huge number of movies are released in no time. In the meantime, people cannot enjoy all available new releases or unseen movies due to their limited time. They also still need to choose which movies to watch when they have spare time. This situation is not good for the movie business too. In order to satisfy people in choosing what movies to watch and to boost movie sales, a system that can recommend suitable movies is required, either unseen in the past or new releases. This paper focuses on the hybrid approach, a combination of content-based and collaborative filtering, using a graph-based model. This hybrid approach is proposed to overcome the drawbacks of combination in the content-based and collaborative filtering. The graph database, Neo4j is used to store the collaborative features, such as movies with its genres, and ratings. Since the movie’s closed caption is rarely considered to be used in a recommendation, the proposed method evaluates the impact of using this syntactic feature. From the early test, the combination of collaborative filtering and content-based using closed caption gives a slightly better result than without closed caption, especially in finding similar movies such as sequel or prequel.
Tracking of Elliptical Extended Object with Unknown but Fixed Lengths of Axes

This paper studies tracking of an elliptical extended object with unknown but fixed lengths of major and minor axes. In most practical applications (e.g., tracking vehicles or aircraft carriers), the size of the extended object is time invariant, while the orientation and kinematics may change over time. In order to describe this problem accurately and improve tracking performance, we handle the problem by modeling the kinematics and orientation information as a state vector, and estimate it in a Bayesian framework. We model the unknown but fixed lengths of axes of the object as non-random parameters, and estimate them using maximum likelihood estimation (MLE). To evaluate the proposed approach, simulation results of an extended target tracking scenario are presented, which illustrate that the proposed modeling and estimation is effective.
TimeTracker App: Facilitating Migrants’ Engagement in Their Second Language Learning

With globally increased migration and mobility across countries, it has become critical for many people to acquire a language of the host country. This study focuses on the case of adult migrant workers who are i) enrolled in second language education and ii) faced to a challenge of balancing between their professional and private lives and their studies of the target second language, needed for their successful integration into the host society. This explorative case study aims to support migrant learners in their second language acquisition across learning settings. It introduces and evaluates the use of specifically designed tool, TimeTracker, aimed at helping learners to keep track on how much time they spend on studying the target language influences their engagement and motivation to continue to study the host language. Eight participants from Sweden and Norway used the tool during the period of one to two weeks. Overall, the results show that using TimeTracker to log and keep track of the time spent on studying a host language inspired the learners to i) allocate more time on their second language learning, and ii) become more engaged in it through raising awareness and monitoring of their learning activities.
Automatic Docking System of Fuel Filler with CAD model-based tracking and Visual Servoing Control

In this paper, a high-precision docking method is proposed with CAD model-based tracking and position-based visual servoing controller for the automatic docking system of fuel filler. The deformable template matching algorithm is used to calculate the initial frame image pose. CAD-based tracking with Moving-Edge method in virtual visual servoing framework is utilized to estimate the current pose in real time. An adaptive gain is used to reduce the convergence time of the visual servoing control law. Experiments of a 6-DOF manipulator verify the robustness and effectiveness of the algorithm.
Trust-Region Variational Inference with Gaussian Mixture Models

Many methods for machine learning rely on approximate inference from intractable probability distributions. Variational inference approximates such distributions by tractable models that can be subsequently used for approximate inference. Learning sufficiently accurate approximations requires a rich model family and careful exploration of the relevant modes of the target distribution. We propose a method for learning accurate GMM approximations of intractable probability distributions based on insights from policy search by using information-geometric trust regions for principled exploration. For efficient improvement of the GMM approximation, we derive a lower bound on the corresponding optimization objective enabling us to update the components independently. Our use of the lower bound ensures convergence to a stationary point of the original objective. The number of components is adapted online by adding new components in promising regions and by deleting components with negligible weight. We demonstrate on several domains that we can learn approximations of complex, multimodal distributions with a quality that is unmet by previous variational inference methods, and that the GMM approximation can be used for drawing samples that are on par with samples created by state-of-theart MCMC samplers while requiring up to three orders of magnitude less computational resources.
Analysis and Simulation of Multimedia English Auxiliary Handle Based on Decision Tree Algorithm

In this paper, through the improved decision tree algorithm, the handles in multimedia English assistance are parsed and simulated. In order to better perceive the sense of language in English composition and improve the rationality of intelligent evaluation, an N element based on association analysis is proposed. Sense value quantification calculates its support in the corpus by obtaining N-tuples of the composition. If the degree of support is lower than the threshold, the part where the language sense problem occurs is analyzed, and the type of language sense problem is judged for the students to provide assistance in modifying the composition. In addition, this paper also extracts word features, sentence features, and text structure features in the composition to fit the English handles analytical score. By testing the test set, the experiment shows that, by extracting the language sense features of the candidate’s English composition, it can not only judge whether there is a problem with the language sense of the candidate, but also provide a basis for the overall evaluation of the composition.
Differentially Private Consensus for Multi-Agent Systems

This paper considers the distributed consensus problems for first-order multi-agent systems subject to differentially private requirements, where we protect the privacy of the agents' initial states against an adversary that has access to all the messages. Compared with the existing results, we propose a stochastic approximation-type protocol to deal with the injected noises. Furthermore, the ε-differential privacy and convergence property of the proposed algorithm are established. The privacy of each agent's initial states is protected by the designed protocol. Meanwhile, the agent's state converges in mean square to a common random variable, whose expectation is right the average of the initial states of the whole system. Finally, the effectiveness of the proposed method is demonstrated by numerical simulations.
Open Access as a new Paradigm. An inevitable Evolution?, Open Access come nuovo paradigma. Un'evoluzione inevitabile?

After almost 20 years from the first Berlin Declaration, the end of 2018 saw a new impulse in open access to publications (and not only) with the launch of Plan S by the Coalition S and with the announcement of the launch of “European Open Science Cloud”. The two events are part of a new scenario that sees, in particular, publishers strongly urged by academic and government institutions to take the definitive step towards a true Open Science. After the transition from paper to electronic, everyone is called to a new challenge: to find one (or more) economically sustainable models that are no longer based on the subscription model but on that of publications. In this highly unstable phase, we speak generically of “transformative agreements”. Some characteristics and problems are here analyzed.
3D SAFE ROUTING NAVIGATION APPLICATION FOR PEDESTRIANS AND CYCLISTS BASED ON OPEN SOURCE TOOLS

Abstract. The recent advancement in Information &amp;amp; Communication Technology (ICT) is seen as a critical enabler to design intelligent smart cities targeting different domains. One such domain is modes of transport in a city. Currently, various cities around the world are envisioning innovative ways to reduce emissions in the cities by increasing physically active mobility. However, there is still limited information about the safety of cyclists and pedestrians within city limits. To address this, we develop a 3D web-based safe routing tool called Vision Zero. Our concept prototype used Augsburg city, Germany, as a case study. The implementation is based on open-source tools. In the back-end, the OGC 3D Portrayal Service standard helps to deliver and integrate various 2D and 3D geospatial contents on a web-based client using CesiumJS. The OGC SensorThings API (STAPI) standard is used to manage historical and real-time open road-incident data from the Federal Statistical Office of Germany. The navigation system is built up based on the routing engine pgRouting, which calculates the safest route based on the mentioned STAPI server and the road-network dataset from OpenStreetMap.
Publisher Correction to: Deep learning-based image analysis methods for brightfield-acquired multiplex immunohistochemistry images

An amendment to this paper has been published and can be accessed via the original article.
PENERAPAN ALGORITMA K-NEAREST NEIGHBOR DALAM KLASIFIKASI DATA HASIL PRODUKSI KELAPA SAWIT PADA KUD TIRTA KENCANA

KUD Tirta Kencana is located in Air Emas Village, Singingi Subdistrict, Kuantan Singingi Regency, Riau, assisting the processing of oil palm plantations owned by its citizens for the development of the economy in the Air Emas Village. KUD Tirta Kencana consists of 17 Farmers Groups.The data processing system in KUD TirtaKencana still uses conventional recording where the palm oil production data of each farmer group is recapitulated into Ms. Excel, so the data entered into Ms. Excel is increasingly piling up and irregular, which makes data difficult to understand and the lack of information that can be used for the development of KUD TirtaKencana in the field of palm oil production in the future.With this, it is necessary to analyze and process the data of palm oil production in KUD Tirta Kencana using data mining calculation techniques, one of the algorithms contained in the data mining technique is the k-Nearest Neighbor (k-NN) algorithm. This algorithm is a method that uses a supervised learning algorithm, where the results of the new test sample are classified based on the majority of the categories in k-NN.From this research, it is known that the similarity of production yields among farmer groups, thus it can be predicted that the yield of palm oil production in the future, revolves around the relationship between the similarity of production between farmer groups based on their respective clusters. The test results using the values ​​of K = 2, K = 3, and K = 4, have an accuracy value of K = 2 and K = 3 that is 85.71%, for K = 4 produces an accuracy value of 71.43%.
Investigating an Approach for Low Resource Language Dataset Creation, Curation and Classification: Setswana and Sepedi

The recent advances in Natural Language Processing have only been a boon for well represented languages, negating research in lesser known global languages. This is in part due to the availability of curated data and research resources. One of the current challenges concerning low-resourced languages are clear guidelines on the collection, curation and preparation of datasets for different use-cases. In this work, we take on the task of creating two datasets that are focused on news headlines (i.e short text) for Setswana and Sepedi and the creation of a news topic classification task from these datasets. In this study, we document our work, propose baselines for classification, and investigate an approach on data augmentation better suited to low-resourced languages in order to improve the performance of the classifiers.
Text Categorization for Conflict Event Annotation

We cast the problem of event annotation as one of text categorization, and compare state of the art text categorization techniques on event data produced within the Uppsala Conflict Data Program (UCDP). Annotating a single text involves assigning the labels pertaining to at least 17 distinct categorization tasks, e.g., who were the attacking organization, who was attacked, and where did the event take place. The text categorization techniques under scrutiny are a classical Bag-of-Words approach; character-based contextualized embeddings produced by ELMo; embeddings produced by the BERT base model, and a version of BERT base fine-tuned on UCDP data; and a pre-trained and fine-tuned classifier based on ULMFiT. The categorization tasks are very diverse in terms of the number of classes to predict as well as the skeweness of the distribution of classes. The categorization results exhibit a large variability across tasks, ranging from 30.3% to 99.8% F-score.
Соціально-психологічний клімат педагогічного колективу: ocoбливocтi cтилiв керівництва

The article covers the topic of the management and facts, that in some cases,  manager’s style in their work is an extremely rare thing. Attention is focused on the most common and universal leadership styles: authoritarian, democratic and liberal ones. The attention is focused on one of the most important types of growth in the field of economic activity and the identification of the human factor in the development of civic activities – the sphere of moral and psychological management’s optimization in the collective. It is noted that the moral and psychological climate is influenced by such activity motives as: needs, interests, beliefs, aspirations, ideals, attitudes, emotions. It is known that, depending on the nature of the moral and psychological climate, its influence on a person will be different: stimulate to activity, good mood, vigor and self-confidence or vice versa, act depressingly: reduce activity and energy, lead to production and moral losses.
Developing a release management tool to support global software development

Global Software Development (GSD) has been a trend as the software industry is experiencing increasing commercial globalization. On the other hand, working with distributed teams also face new difficulties and challenges, resulting from geographic separation such as time zone, culture, and activities synchronism. Sidia is a R&D Institute, located in Manaus-Brazil, that develops innovative software solutions on Android Platform in all Latin America. The institute works collaborating with Samsung Mobile Division, located in South Korea, and external stakeholders provided by Mobile Network Operators (MNO) from Latin America countries (e.g., Brazil, Mexico, Chile, Peru). For this reason, to meet the demands of MNOs, Sidia works in GSD environment. In this context, the project management process becomes difficult due to the coordination of many different stakeholders in a distributed environment, such as tracking requirements, wrong releases, tracking issues. To minimize these difficulties, we developed a tool to support our project management process, called Release Manager (RM). This paper describes the introduction of the RM Tool to improve management in distributed projects for the Android Platform Update.
Industrial use cases at the cusp of the IoT and blockchain paradigms

There are fair amount of reasons and requirements for both the IoT and blockchain concepts to cooperate closely to solve bigger problems at hand. This deadly combination is to result in scores of fresh and fabulous opportunities and possibilities for the total human society. The Internet of Things (IoT) paradigm has made it possible to have digitized and connected things in plenty in our everyday environments. That is, all kinds of physical, mechanical, electrical, and electronics systems are systematically being digitized and connected through proven and potential edge and connectivity technologies. The leading market analysts and researchers have come out with forecasts that there will be billions of connected devices and trillions of digitized entities in the years ahead. The noteworthy point here is that all these empowered entities, on purposefully collaborating and correlating with one another, can generate massive amounts of multistructured data. The challenge is how to secure IoT devices and data. The arrival of the blockchain technology is being celebrated as the best thing toward convincingly meeting up the IoT security requirements. This chapter is to explore and expound how the cool linkage between IoT and blockchain is to substantially enhance the security and privacy needs of IoT devices and data.
Aspect Based Sentiment Analysis: A Systematic Literature Review

Aspect based sentiments can provide more detailed information about the sentiment (positive, negative, and neutral) based on an aspect in a review. It can provide better recommendations to users in decision making process. A number of previous studies have been conducted on aspect-based sentiment analysis indicating that survey is needed to provide an overview of the method available in aspect-based sentiment analysis. The survey method has been implemented since the last 5 years to obtain novelty from existing methods. The Systematic Literature Review (SLR) method is used to review a collection of 34 papers from various academic databases which focus on the aspect of extraction, sentiment analysis, and aspect aggregation. The papers will be sorted based on the focus of the method used. For each analysis, a detailed analysis is described on the contribution of the method to the aspect-based sentiment analysis alongside a comparison with other methods as well as advantages and disadvantages. The last section discusses the method commonly used in this study as well as future challenges in the study focusing on aspect-based sentiment analysis.
APLIKASI ABSENSI GURU PADA SEKOLAH BERBASIS ANDROID DENGAN KEAMANAN QR CODE (STUDI KASUS : SMP NEGERI 4 BATANG GANSAL)

Attendance is a part of the important role in every educational institution. Where attendance is one of the main supporting that can support and motivate every activity done. As in the absence of attendance at the 4 Batang Gansal Junior High School that performs signatures on paper and writes the entry time at the teaching hour in which case this can result in fraud due to the uncontrollable level of discipline. The administration also difficult to make reports and convey attendance information to each teacher regarding the recording of the present time as well as the time recording of the work because the report is still created using Microsoft Excel, in this way can be mistyped data and the deletion of data. The purpose of this research is to design and build an Android application that makes it easy for teachers to perform attendance at school, an application that utilizes Qr Code as the main component in assisting attendance process every teacher. Based on the above statement, the application of Qr Code technology in attendance system in junior high school teacher 4 Batang Gansal with Android device can accelerate the attendance process, in the results of the system test has been completed and successfully created, the system will be implemented immediately on the Android smartphone. Based on the research result of the creation of attendance application in Android based schools with the security of this Qr Code then it can be concluded that the research resulted in the teacher attendance application in Android based schools with Qr Code security which can be able to process attendance. In the test results of the system that has been completed and successfully created, the system will be implemented immediately on the Android smartphone and this application can also provide information about the attendance in the form of charts.
Design of Multi-Level Intelligent Control Systems for Complex Technical Objects on the Basis of Theoretical-Information Approach

The problem of designing multi-level intelligent control systems for complex technical objects on the basis of the theoretical-information approach is discussed.The proposed approach allows us to make well-grounded project decisions when designing control algorithms for advanced control systems.The problem statement of applying the proposed approach to the design of intelligent control systems for aviation gas-turbine engines is considered.
Semantics Based Clustering through Cover-Kmeans with OntoVsm for Information Retrieval

Document clustering plays a significant task in the retrieval of the information, which seeks to divide documents into groups automatically, depending on their content similarity. The cluster consists of related documents within the group (having high intra-cluster similarity) and dissimilar to other group documents (having low inter-cluster similarity). Clustering documents should be considered an unsupervised process that aims to classify documents by identifying underlying structures, i.e. the learning process is unsupervised. So there is no need to determine the correct output for an input. Previous clustering methods do not know the semantic associations between words such that the context of documents cannot be correctly interpreted. In order to address this problem, the advent of semantic ontology information such as WordNet was widely used to enhance text clustering consistency. This paper initially proposes an OntoVSM model to reduce the dimension of the document efficiently. The cover K-means clustering algorithm is proposed for semantic document clustering. The proposed algorithm is a hybrid version of K-Means and covers coefficient-based clustering methodology (C3M) that is improved semantically using WordNet ontology. The dimensionality reduction based on semantic knowledge of each term preserves the information without loss. The performance of the proposed work is analysed through experimental results. This shows that the proposed work gives improved results compared to other standard methods.
Improving 3D Medical Image Compression Efficiency Using Spatiotemporal Coherence

Advanced methodologies for transmitting compressed images, within acceptable ranges of transmission rate and loss of information, make it possible to transmit a medical image through a communication channel. Most prior works on 3D medical image compression consider volumetric images as a whole but fail to account for the spatial and temporal coherence of adjacent slices. In this paper, we set out to develop a 3D medical image compression method that extends the 3D wavelet difference reduction algorithm by computing the similarity of the pixels in adjacent slices and progressively compress only the similar slices. The proposed method achieves high-efficiency performance on publicly available datasets of MRI scans by achieving compression down to one bit per voxel with PSNR and SSIM up to 52:3 dB and 0:7578, respectively.