Reviewerly/exHarmony huggingface dataset:
Advanced Topic: Knowledge Graph Completion

With the possible exception of good data collection and ontology design, information extraction and entity resolution are the two most important data-driven steps in a domain-specific knowledge graph construction pipeline. Yet, it is very rarely the case that the story ends there. Once constructed, the knowledge graph is so noisy that additional knowledge graph completion steps often have to be applied to refine the initial KG further. These steps entail procedures like knowledge graph embeddings, which tend to rely on neural techniques, but also graphical models like probabilistic soft logic. After completion, the KG also has to be stored and indexed so that it can be queried in an application framework. The Semantic Web has produced a great deal of research in this realm, along with NoSQL methodologies that have emerged from the mainstream database and knowledge discovery communities. In this chapter, we briefly survey some of these topics. While covering any one of these topics in depth is out of scope, we provide pointers to additional material, in each of these topical areas, for the interested reader.
Decreasing diagrams for confluence and commutation

Like termination, confluence is a central property of rewrite systems. Unlike for termination, however, there exists no known complexity hierarchy for confluence. In this paper we investigate whether the decreasing diagrams technique can be used to obtain such a hierarchy. The decreasing diagrams technique is one of the strongest and most versatile methods for proving confluence of abstract rewrite systems. It is complete for countable systems, and it has many well-known confluence criteria as corollaries. So what makes decreasing diagrams so powerful? In contrast to other confluence techniques, decreasing diagrams employ a labelling of the steps with labels from a wellfounded order in order to conclude confluence of the underlying unlabelled relation. Hence it is natural to ask how the size of the label set influences the strength of the technique. In particular, what class of abstract rewrite systems can be proven confluent using decreasing diagrams restricted to 1 label, 2 labels, 3 labels, and so on? Surprisingly, we find that two labels suffice for proving confluence for every abstract rewrite system having the cofinality property, thus in particular for every confluent, countable system. Secondly, we show that this result stands in sharp contrast to the situation for commutation of rewrite relations, where the hierarchy does not collapse. Thirdly, investigating the possibility of a confluence hierarchy, we determine the first-order (non-)definability of the notion of confluence and related properties, using techniques from finite model theory. We find that in particular Hanf ’s theorem is fruitful for elegant proofs of undefinability of properties of abstract rewrite systems.
Education and socialization: A study of social importance of education in the development of society

Education as a social institution has a great social importance, especially in the modern, complex societies. Education everywhere has the function of the formation of the social personalities while society is the system of social organism, education is social process of development of all those capacities like physical, mental, social and moral etc in an individual which enable him to adjust in the society, control and improve the societal environment. The main focus of this study was analytical examination of (a)how education helps the society's to be worth living and helps to reduce social inequalities in a society(b) and shows the importance of education towards the development and progress of society. The main objective of this study is to describe the importance of education on the development of societies and their progress and to explain the relationships of education with society. The main objective of this study is to describe the importance of education on the development of societies and their progress and to explain the relationships of education with society. This work was carried out to study how education enhances personal development and self-fulfillment, how education encourages the individual to develop his spiritual, emotional, mental and physical talents. This study helps us to understand how education prepares the people to adjust with all the factors of society
Conclusion – The future of printcrime: intellectual property, innovation law, and 3D printing

In a 2006 short story, ‘Printcrime’, Cory Doctorow imagined a dystopian future of contraband 3D printers. In the work, police try to shut down a bootleg operation engaged in the 3D printing of intellectual property. In his 2009 novel Makers, Doctorow explored the rise of the maker community, and its do-it-yourself ethic.
A Machine Learning Approach for Predicting Student Performance

An University’s reputation and its standard are weighted by its students performance and their part in the future economic prosperity of the nation, hence a novel method of predicting the student’s upcoming academic performance is really essential to provide a pre-requisite information upon their performances. A machine learning model can be developed to predict the student’s upcoming scores or their entire performance depending upon their previous academic performances.
Deep learning for UAV autonomous landing based on self-built image dataset

An end-to-end deep learning (DL) control model is proposed to solve autonomous landing problem of the quadrotor in way of supervised learning. Traditional methods mainly focus on getting the relative position of the quadrotor through GPS signal which is not always reliable or position-based vision servo (PBVS) methods. In this paper, we have constructed a deep neural network based on convolutional neural network(CNN) whose input is raw image. A monocular camera is used as only sensor to capture down-looking image which contains landing area. To train our deep neural network, we have used our self-built image dataset. After training phase, the well-trained control model is tested and the results perform well. Light changes and background interferences have little influence on the model`s performance, which shows the robustness and adaptation of our deep learning model.
Reasoning about Changes of Observational Power in Logics of Knowledge and Time

We study dynamic changes of agents' observational power in logics of knowledge and time. We consider CTL*K, the extension of CTL* with knowledge operators, and enrich it with a new operator that models a change in an agent's way of observing the system. We extend the classic semantics of knowledge for agents with perfect recall to account for changes of observational power, and we show that this new operator increases the expressivity of CTL*K. We reduce the model-checking problem for our logic to that for CTL*K, which is known to be decidable. This provides a solution to the model-checking problem for our logic, but it is not optimal, and we provide a direct model-checking procedure with better complexity.
Review on Latest Trending Topic Detection in Twitter With Stream Processing (Using Fission Pattern)

Twitter is a famous microblogging and interpersonal interaction which benefits from more than 100 million clients. Clients make short messages relating to a wide assortment of themes. Certain subjects are featured by Twitter as the most mainstream and are known as “drifting points.” In this article, the authors will plot strategies of distinguishing and recognizing slanting themes from spilling information utilizing a fission pattern. Information from Twitter's fission-spilling API will be gathered and put into reports of equivalent span. Information accumulation strategies will take into consideration investigation over numerous timespans, including those not as of now connected with Twitter-distinguished inclining themes. Term recurrence converse archive recurrence investigation and relative standardized term recurrence examination are performed on the reports to distinguish the slanting themes.
Learning Ensembles in the Presence of Imbalanced Classes

Class imbalance occurs when data classes are not equally represented. Generally, it occurs when some classes represent rare events, while the other classes represent the counterpart of these events. Rare events, especially those that may have a negative impact, often require informed decision-making in a timely manner. However, class imbalance is known to induce a learning bias towards majority classes which implies a poor detection of minority classes. Thus, we propose a new ensemble method to handle class imbalance explicitly at training time. In contrast to existing ensemble methods for class imbalance that use either data driven or randomized approaches for their constructions, our method exploits both directions. On the one hand, ensemble members are built from randomized subsets of training data. On the other hand, we construct different scenarios of class imbalance for the unknown test data. An ensemble is built for each resulting scenario by combining random sampling with the estimation of the relative importance of specific loss functions. Final predictions are generated by a weighted average of each ensemble prediction. As opposed to existing methods, our approach does not try to fix imbalanced data sets. Instead, we show how imbalanced data sets can make classification easier, due to a limited range of true class frequencies. Our procedure promotes diversity among the ensemble members and is not sensitive to specific parameter settings. An experimental demonstration shows, that our new method outperforms or is on par with state-of-the-art ensembles and class imbalance techniques.
Malignant microcalcification clusters detection using unsupervised deep autoencoders

Detection and localization of microcalcification (MC) clusters are very important in mammography diagnosis. Supervised MC detectors require learning from extracted individual MCs and MC clusters. However, they are limited by number of datasets given that MC images are hard to obtain. In this work, we propose a method to detect malignant microcalcification (MC) clusters using unsupervised, one-class, deep convolutional autoencoder. Specifically, we designed a deep autoencoder model where only patches extracted from normal cases’ mammograms are used during training. We then applied our trained model on patches extracted from testing images. Our training dataset contains 408 normal subjects, including 1961 full-field digital mammography images. Our testing datasets contains 276 subjects. Specifically, 106 of them were patients diagnosed with Ductal Carcinoma <i>In-Situ</i> (DCIS); 70 of them were diagnosed with Invasive Ductal Carcinoma (IDC); the rest 100 are normal cases containing 484 negative screening mammograms. Patches extracted from DCIS and IDC cases (positive patches) contain MC clusters, whereas patches extracted from normal cases (negative patches) don’t. As the model is trained only on negative images that do not contain MCs, it cannot reconstruct MCs well, and thus, the reconstruction error will be larger on positive patches than negative patches. Our detection algorithm’s decision is made based on Max-Squared Error between autoencoder’s input and output patches. To confirm the results were not simply due to blurring, we then compared our designed detector with unsharp mask with Gaussian blur results. The results using the unsupervised autoencoder on testing patches with size 64&times;64 achieves an AUC result of 0.93. The best performance on testing patches using Gaussian blur with kernel size equal to 11has an overall AUC of 0.82.
Comprehensive evaluation of key management hierarchies for outsourced data

Key management is an essential component of a cryptographic access control system with a large number of resources. It manages the secret keys assigned to the system entities in such a way that only authorized users can access a resource. Read access control allows read access of a resource by the authorized users and disallows others. An important objective of a key management is to reduce the secret key storage with each authorized user. To this end, there exist two prominent types of key management hierarchy with single key storage per user used for read access control in data outsourcing scenario: user-based and resource-based. In this work, we analyze the two types of hierarchy with respect to static hierarchy characteristics and dynamic operations such as adding or revoking user authorization. Our analysis shows that the resource-based hierarchies can be a better candidate which is not given equal emphasis in the literature. A new heuristic for minimizing the key management hierarchy is introduced that makes it practical in use even for a large number of users and resources. The performance evaluation of dynamic operations such as adding or revoking a user’s read subscription is shown experimentally to support our analytical results.
Two-layer Residual Feature Fusion for Object Detection

Recently, a lot of single stage detectors using multi-scale features have been actively proposed. They are much faster than two stage detectors that use region proposal networks (RPN) without much degradation in the detection performances. However, the feature maps in the lower layers close to the input which are responsible for detecting small objects in a single stage detector have a problem of insufficient representation power because they are too shallow. There is also a structural contradiction that the feature maps not only have to deliver low-level information to next layers but also have to contain high-level abstraction for prediction. In this paper, we propose a method to enrich the representation power of feature maps using a new feature fusion method which makes use of the information from the consecutive layer. It also adopts a unified prediction module which has an enhanced generalization performance. The proposed method enables more precise prediction, which achieved higher or compatible score than other competitors such as SSD and DSSD on PASCAL VOC and MS COCO. In addition, it maintains the advantage of fast computation of a single stage detector, which requires much less computation than other detectors with similar performance.
Automatic segmentation of the prostate in transrectal ultrasound images using deep learning for application in MRI-TRUS fusion

Introduction & Objectives: In recent years, prostate biopsy increasingly involves targeting magnetic resonance imaging (MRI)-suspicious lesions after fusion with real-time transrectal ultrasound (TRUS). Such fusion currently requires (semi)manual prostate delineation, burdening clinicians with this lengthy procedure. A reliable automatic prostate segmentation on TRUS is still an unsolved challenge; therefore, here we propose a real-time prostate segmentation algorithm through deep-learning that readily translates between different scanners and user settings.
Proposal of Methodology of a Bipedal Humanoid Gait Generation Based on Cognitive Algorithm

This article proposes a methodology of generating a bipedal humanoid gait pattern for the knee and hip leg joints in the sagittal plane. The multi-functional gait movement is divided into three different movement patterns with a specific function, which can be modulated individually and superposed to compose a gait pattern. This strategy is based on the potential field navigation algorithm concept (proposed by Khatib, 1986) and the architecture for its application is based the AuRA cognitive architecture (proposed by Arkin, 1990). Finally, this methodology was validated using a MATLAB-SimulinkTM implementation of a simplified kinematic model of the legs movements. The results show movement patterns close to the biomechanical ones at the joints. When applied to the kinematic model, those joint movements generate feet trajectories that are compatible to the gait both in shape and amplitude. The strategy was also embedded in a microcontroller prototype, revealing low computational demands as well as another view for the generated movement.
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models

Bayesian inference for Factorial Hidden Markov Models is challenging due to the exponentially sized latent variable space. Standard Monte Carlo samplers can have difficulties effectively exploring the posterior landscape and are often restricted to exploration around localised regions that depend on initialisation. We introduce a general purpose ensemble Markov Chain Monte Carlo (MCMC) technique to improve on existing poorly mixing samplers. This is achieved by combining parallel tempering and an auxiliary variable scheme to exchange information between the chains in an efficient way. The latter exploits a genetic algorithm within an augmented Gibbs sampler. We compare our technique with various existing samplers in a simulation study as well as in a cancer genomics application, demonstrating the improvements obtained by our augmented ensemble approach.
A convolutional neural network based 3D ball tracking by detection in soccer videos

Tracking of ball in sports videos is one of the most challenging tasks in computer vision and video processing domain. Recent ball tracking approaches fail to handle tracking of a small size and fast moving ball. Inaccurate 2D ball detection leads to further deterioration of 3D ball tracking results. This paper presents a soccer ball tracking by detection approach using a pre-trained Convolutional Neural Network (CNN). The proposed algorithm used CNN for identifying ball from background and other moving objects like players and referees. The 2D ball detection results are fine-tuned for identifying true ball positions. True ball positions, from cameras shooting the scene from different angle, are further mapped on ground plane. The actual ball movement is tracked in 3D from top-view. Experiments show that the proposed algorithm can tackle challenges like small ball size, shape changes, occlusion and tracking high-speed balls.
Cuckoo Search based Ensemble Classifier for Predictive Analysis of Malaria Infection Scope on thin Blood Smears

Classification of machine learning models had an ultimate achievement by means of supervised learning, but the “state of the art models” have not yet extensively applied the “biological image data”. To categorize the erythrocytes as malaria infested or not, we categorize erythrocytes thru ensemble classification method. With this regard, the script strived to prolong our former “decision tree based binary classifier” to accomplish the ensemble classification. Training data which is given clustered into diverse groups that are based on the variety perceived in all probable features projection. Every cluster is engaged to a greater extent to train single classifier. In this esteem, morphological features and entropy-based features are included. From legitimate pathology laboratories, the investigation carried on the real time inputs and combination of benchmark datasets composed as anonymous data. Unlike the existing methods, proposal of this document executed experiments on voluminous data that proliferate in size when compared to present benchmark datasets. With the statistical assessment the execution of proposal is evaluated by comparative analysis amid the two existing models “Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear (Hybrid Classification Approach)” and “Scale to Estimate Premature Malaria Parasites Scope (SEMPS)”and the proposed model “Cuckoo Search Based Ensemble Classifier (CSEC)”. As depicted by the experimental study the suggested model is surpassing the two existing models.
A Research Preview on TAICOS – Tailoring Stakeholder Interests to Task-Oriented Functional Requirements

[Context and Motivation] Without a concrete functional context, non-functional requirements can be approached only as cross-cutting concerns and treated uniformly across the feature set of an application. This neglects, however, the heterogeneity of non-functional requirements that arises from stakeholder interests and the distinct functional scopes of software systems. [Question/problem] Earlier studies have shown that the different types and pursued objectives of non-functional requirements result in either vague or unbalanced specification of non-functional requirements. [Principal ideas/results] We propose a task analytic approach for eliciting and modeling user tasks with the software product. Stakeholder interests are structurally related to these user tasks and refined individually as a constraint in the context of each concrete user task. This individual refinement provides DevOps teams with important guidance on how the respective constraint can be satisfied in the software lifecycle and thus how the interest of the stakeholder can be satisfied sufficiently. [Contribution] We provide a structured approach, intertwining task-centered functional requirements with non-functional stakeholder interests to specify constraints on the level of user tasks. The results of a preliminary interview study with domain experts reveal that our task-constraint tailoring method increases the comprehensibility of requirements, clarity and quality of specifications.
Orom Sasadu: Hakikat dan Maknanya Bagi Masyarakat Suku Sahu Di Halmahera Barat, Maluku Utara

Indonesia has a variety of dining customs. Traditions that can be mentioned, among others Makan Patita in Ambon, Makan Basamo in Padang, Megibung in Bali, Bancakan in Bandung, and Orom Sasadu in Sahu, West Halmahera. This paper aimes to raise the meaning in the traditional dining custom (Orom Sasadu) organized by the Sahu tribe community in West Halmahera, North Maluku. This paper uses a qualitative approach that describes Sasadu's cultural background in Sahu including Orom Sasadu tradition and explores the meaning contained in it. This paper will begin with a general description of the area of West Halmahera where the Sahu tribe lives, be followed by Sasadu's historical background as a traditional house which is the characteristic of the Sahu culture, Sasadu’s architecture, and put forward a few point of views on dining together in a cultural perspective to enrich the paper, and be ended by presenting the Orom Sasadu tradition or traditional dining custom held in Sasadu and its meaning for the Sahu tribe community. This paper concludes that the Sasadu Orom tradition has at least 5 (five) meanings, such as: becoming a venue for enforcing rules that culminate in peace; reinforcing social relations of brotherhood; strengthening the protecting social structure; becoming moral education for the community; and reinforcing the pattern of human relations, especially the Sahu tribe community with its natural environment.
A Soft Computing Model of Soft Biometric Traits for Gender and Ethnicity Classification

There is paucity of information on the possibility of ethnicity identification through fingerprint biometric characteristics and so, this work is set to combine two soft biometric traits (Gender and Ethnicity) in order to ascertain if individual of different ethnicity and gender bias can be identified through their fingerprint.Live scan mechanism was used in order to minimize human errors and as well speed up the rate of fingerprint acquisition which unequivocally ensure good quality capturing of the fingerprint image.In this work, fingerprints of over a thousand people from three different ethnic groups of both male and female gender in Nigeria were captured and subjected to training, testing and classification using Gabor filter and K-NN respectively.Histogram equalization was used for image enhancement and the system performance was evaluated on the basis of some selected metrics such as Recognition Accuracy, Average Recognition Time, Specificity and Sensitivity.Result of this work indicated over 96% accuracy in predicting person's ethnicity and gender with an average recognition time of less than 2secs.