Reviewerly/exHarmony huggingface dataset:
Continuous Learning of the Structure of Bayesian Networks: A Mapping Study

Bayesian networks can be built based on knowledge, data, or both. Independent of the source of information used to build the model, inaccuracies might occur or the application domain might change. Therefore, there is a need to continuously improve the model during its usage. As new data are collected, algorithms to continuously incorporate the updated knowledge can play an essential role in this process. In regard to the continuous learning of the Bayesian network’s structure, the current solutions are based on its structural refinement or adaptation. Recent researchers aim to reduce complexity and memory usage, allowing to solve complex and large-scale practical problems. This study aims to identify and evaluate solutions for the continuous learning of the Bayesian network’s structures, as well as to outline related future research directions. Our attention remains on the structures because the accurate parameters are completely useless if the structure is not representative.
The BSAVA Library Access for your practice

Abstract For individuals and practices alike, the BSAVA Library is proving to be a daily reference resource. Ian Mellor, BSAVA Publications Manager, explains the practice access options available.
The Digital Turn in Higher Education: “Digital Natives” Mythbusted

Digital technology has recently become a ubiquitous feature of the modern era posing problems to higher education institutions. Digitization of the academic life has brought forth the claims that there is an evident disparity between the digital naturals and digital immigrants and that the myth of the former is “undemystifiable”. The claim that the new digitized generation has its own distinguished learning preferences, skills of learning and beliefs about how learning in such an era should make higher education authorities revisit their curricula and “reshuffle the academic cards” so that the digital immigrants could catch up with the fast-running pace of the digitized train and provide the digital natives with what they need for a successful academic life. The overall aim of the present work therefore is to investigate the extent to which the so-called digital natives really have control of the use of educational technology either as part of their self-directed learning practices or as part of a formal tertiary level teaching, the type of technologies they prefer to use, whether they possess the required digital skills that are important for their future careers, and how vital the digital skills are in boosting their employability. In line with the major findings of the present study, it could be concluded that the media change under the created discourse of “moral panic” has unveiled the singularity of this generation and has forced academic authorities to reconsider learning, teaching as well as both skills and employability of a such a generation for the betterment of a healthy academic higher education system.
An Automatic Medical Image Registration Approach Based on Common Sub-regions of Interest

Accurate and efficient image registration, based on interested common sub-regions is still a challenging task in medical image analysis. This paper presents an automatic features based approach for the rigid and deformable registration of medical images using interested common sub-regions. In the proposed approach, interested common sub-regions in two images (target image and source image) are automatically detected and locally registered. The final global registration is performed, using the transformation parameters obtained from the local registration. Registration using interested common sub-regions is always required in image guided surgery (IGS) and other medical procedures because it considers only the desired objects in medical images instead of the whole image contents. The proposed interested common sub-regions based registration is compared with the two states-of-the-art methods on MR images of human brain. In the experiments of rigid and deformable registrations, we show that our approach outperforms in terms of both the accuracy and time efficiency. The results reveal that interested common sub-region based registration can achieve good performance, regarding both the accuracy as well as the the time efficiency in monomodal brain image registration. In addition, the proposed approach also indicates the potential for multimodal images of different human organs.
A Block Lanczos Method for the Extended Trust-Region Subproblem

We present a block Lanczos method for solving the large-scale extended trust-region (ETR) subproblem. During the algorithm, the original ETR subproblem is projected to a small-scale one, and then the active-set method is employed to solve this small-scale ETR subproblem to get a solution that can be used to derive an approximate solution of the original ETR subproblem. Theoretical analysis of error bounds for the optimal value, the optimal solution, and the multipliers is also proposed. We compare the block Lanczos method with the TOMLAB solver. Numerical experiments demonstrate that the block Lanczos method is effective and can achieve high accuracy for large-scale ETR subproblems.
Test-Retrieval Framework: Performance Profiling and Testing Web Search Engine on Non Factoid Queries

Performance profiling and testing is one of the interesting topics in the big data management and Cloud Computing. In testing, we use test cases composed to different type of queries to evaluate the performance aspects of the information retrieval system for large scale information collection. This test scenarioperforms the evaluation ofretrieval accuracy for all kind of ambiguity and non factoid queries with result set as Training data. This stands difficult to evaluate the retrieval method in order to schedule or optimize the Recommendation and prediction technique of the IR method to the Real time queries. The Queries is considered as requirement specification which has to supply to search engine or web information provider applications for information or web page retrieval. In this paper, we propose a novel technique named as “Test Retrieval Framework“a performance profiling and testing of the web search engines on the information retrieved towards non factoid queries. In this technique, we apply expectation maximization algorithm as an iterative method to find maximum likelihood estimate.We discuss on the important aspects in this work based on Recommendation models integrating domain and web usage, Query optimization for navigational and Transactional queries, Query Result records.The Experimental results demonstrates the proposed technique outperforms of state of arts approaches in terms of set based measures like Precision, Recall and F measure and rank based measures like Mean Average Precision and Cumulative Gain.
Analysis of Ordinal Logistic Regression Model on Breast Cancer Diagnosis by Birads Mammography

The right diagnosis is needed for appropriate therapy. The diagnosis of breast cancer is quite ambiguous and requires high accuracy. Mammography is a method of diagnosing breast cancer using BIRADS (Breast Imaging-Reporting and Data System) assessment. This study aimed to assess the accuracy of BIRADS classification in the diagnosis of breast cancer and predictors that influence it through a logistic regression model test. The research method was cross sectional study by collecting data from the results of mammography examinations obtained from Medical Record documents, SIRS (Hospital Information Systems), and the radiologist's expertise of mammography. The data came from 47 hospital breast cancer patients that contained information on potential predictors of breast cancer namely tumor location, metastases, age, weight, and education. Logistic regression model analysis was performed to find the best statistical test model for breast cancer diagnosis classification based on BIRADS assessment. The diagnosis classification of BIRADS was consisting of normal, benign, and malignant grades. For this reason, hypothesis testing was conducted with G test for simultaneous model testing. Then, a development of an appropriate logit model by using a partial test. Followed by conducting a suitability and feasibility test model with the Goodness of Fit using the Hosmer-Lemeshow Test. The results of the analysis revealed that the ordinal logistic regression was the best model of BIRADS classification diagnosis with an accuracy value of 52.5%. The result of ordinal logistic regression model for malignant breast cancer:
An improved spatio-temporal context tracking algorithm based on scale correlation filter

Spatio-temporal context algorithm is commonly used in geology and has been introduced into the field of target tracking in recent years. The algorithm improved the robustness of visual tracking through dense contextual information around the target and thus achieved great tracking results. However, updating errors may occur in spatio-temporal context algorithm when the target has rapid changes in scale and appearance, resulting in the algorithm cannot extract the target area accurately and completely. In order to overcome this problem, this article proposes an improved spatio-temporal context algorithm based on scale correlation filter. First of all, the algorithm extracts samples of different scales around the target after the target is settled by spatio-temporal context algorithm and then forms the pyramid of scale characteristics through samples extracted by histogram of oriented gradients operator. Second, the best scale parameter will be achieved by means of scale correlation filter to update the scale model. Finally, the results show that the algorithm has good tracking effect and robustness through two experiments which are contrast experiment before and after the improvement of algorithm and comparison experiment with other advanced algorithms.
Multi-Label Network Classification via Weighted Personalized Factorizations

Multi-label network classification is a well-known task that is being used in a wide variety of web-based and non-web-based domains. It can be formalized as a multi-relational learning task for predicting nodes labels based on their relations within the network. In sparse networks, this prediction task can be very challenging when only implicit feedback information is available such as in predicting user interests in social networks. Current approaches rely on learning per-node latent representations by utilizing the network structure, however, implicit feedback relations are naturally sparse and contain only positive observed feedbacks which mean that these approaches will treat all observed relations as equally important. This is not necessarily the case in real-world scenarios as implicit relations might have semantic weights which reflect the strength of those relations. If those weights can be approximated, the models can be trained to differentiate between strong and weak relations. In this paper, we propose a weighted personalized two-stage multi-relational matrix factorization model with Bayesian personalized ranking loss for network classification that utilizes basic transitive node similarity function for weighting implicit feedback relations. Experiments show that the proposed model significantly outperforms the state-of-art models on three different real-world web-based datasets and a biology-based dataset.
PENGARUH MEDIA PEMBELAJARAN TERHADAP MOTIVASI BELAJAR SISWA KELAS X PROGRAM KEAHLIAN TATA KECANTIKAN RAMBUT DI SMKN 7 PADANG

This research is motivated by the learning process. Quantitative research methods with correlational research types.The population is 58 students,technique uses total sampling. Quantitative analysis techniques. The results showed the level of use of instructional media by class X students of hairstyling at Padang State Vocational High School amounted to 75.81% good category which was viewed from visual media indicators as much as 73.95% good category and audio visual media by 75.81% good category. The level of student motivation is 78.74% in the good category which is viewed from the indicators of desire and desire of success of 79.23% in the good category, the encouragement and learning needs of 78.16% in the good category, the hopes and aspirations of the future amount to 78.10% good category, the award in learning amounted to 77.70% in the good category, the presence of interesting desires in learning by 50.23% in the fairly good category and the existence of a conducive learning environment of 82.41% in the very good category. The results of the analysis and hypothesis testing have a significant effect with a low level of influence of 6.9%.
03:27 PM Abstract No. 289 Registration of 3D holographic models of patient imaging onto a CT grid: assessment of manual and automatic techniques using HoloLens

To develop and compare registration times using manual and automatic registration methods for aligning 3D holographic models of patient imaging onto a commonly used CT grid in CT-guided IR procedures A 3D holographic model of an abdominal CT scan with a CT grid (Beekley Medical Grid 117) was segmented using ITK-SNAP and projected using Microsoft HoloLens. Custom code was developed in Unity 2017.1 and C# for model manipulation using one of three interactive methods: 1) hand gestures and voice commands, 2) a wireless Xbox controller, and 3) computer vision with automated image detection of the CT grid through Vuforia. Registration times for aligning the model to the CT grid were measured for each method. The endpoint of registration was determined when the user was visually satisfied with the alignment and verified with errors < 1 cm in the x-, y-, and z-planes. Six users (3 attendings and 3 residents) attempted each registration method using HoloLens three times. Comparisons between the methods were made using paired t-tests. Mean manual registration times were 146 sec and 55 sec using hand gestures/voice commands and Xbox controller, respectively. Mean automatic registration time was 7 sec using automated image detection. Manual registration using a Xbox controller reduced registration time on average by 61% (p < 0.001) compared to hand gestures/voice commands, which are native interactions on the HoloLens. Automated image detection of a CT grid using HoloLens was possible and automatic registration by computer vision drastically reduced registration time on average by 95% (p < 0.001). Automatic registration through automated image detection of an existing CT grid is possible and significantly reduces registration times from minutes to few seconds compared to manual registration methods. Accurate and near instantaneous registration of 3D holographic models onto patients will facilitate the use of augmented reality for real-time IR procedure guidance and combined virtual/actual 3D navigation.
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization

Pre-conditioning is a well-known concept that can significantly improve the convergence of optimization algorithms. For noise-free problems, where good pre-conditioners are not known a priori, iterative linear algebra methods offer one way to efficiently construct them. For the stochastic optimization problems that dominate contemporary machine learning, however, this approach is not readily available. We propose an iterative algorithm inspired by classic iterative linear solvers that uses a probabilistic model to actively infer a pre-conditioner in situations where Hessian-projections can only be constructed with strong Gaussian noise. The algorithm is empirically demonstrated to efficiently construct effective pre-conditioners for stochastic gradient descent and its variants. Experiments on problems of comparably low dimensionality show improved convergence. In very high-dimensional problems, such as those encountered in deep learning, the pre-conditioner effectively becomes an automatic learning-rate adaptation scheme, which we also show to empirically work well.
Relational Database to Resource Description Framework and Its Schema

A relational database is a digital collection of data constructed on data stored in relations. RDF (Resource Description Framework) is a standard exemplification for data substitution on the Web RDF Schema is a semantic addition of RDF. It renders the technique for narrating groups of allied resources. The direct mapping of RDF interprets the description of data in a relational database. It uses input data, schema and result in RDF graph, termed as direct graph. In this paper, online automatic conversion tool of RDB to RDF and RDFS is discussed. RDB to RDF and its schema is used to get relationships between tables using foreign keys and generate direct graph using data of RDB as input source. RDFS is a general-purpose language for demonstrating RDF. In RDFS, Object Oriented programming concepts are used to find Classes, Sub-Classes, Properties and Sub-Properties etc. to define relationships between the resources automatically.
Volumetric texture modeling using dominant and discriminative binary patterns

Volumetric texture analysis is an import task in medical imaging domain and is widely used for characterizing tissues and tumors in medical volumes. Local binary pattern (LBP) based texture descriptors are quite successful for characterizing texture information in 2D images. Unfortunately, the number of binary patterns grows exponentially with number of bits in LBP. Hence its straightforward extension to 3D domain results in extremely large number of bit patterns that may not be relevant for subsequent tasks like classification. In this work we present an efficient extension of LBP for 3D data using decision tree. The leaves of this tree represent texture words whose binary patterns are encoded using the path being followed from the root to reach the leaf. Once trained, this tree is used to create histogram in bag-of-words fashion that can be used as texture descriptor for whole volumetric image. For training, each voxel is converted into a 3D LBP pattern and is assigned the label of it’s corresponding volumetric image. These patterns are used in supervised fashion to construct decision tree. The leaves of the corresponding tree are used as texture descriptor for downstream learning tasks. The proposed texture descriptor achieved state of the art classification results on RFAI database 1. We further showed its efficacy on MR knee protocol classification task where we obtained near perfect results. The proposed algorithm is extremely efficient, computing texture descriptor of typical MRI image in less than 100 milliseconds.
“Soft Law” Governance of Artificial Intelligence

Author(s): Marchant, Gary | Abstract: On November 26, 2017, Elon Musk tweeted: “Got to regulate AI/robotics like we do food, drugs, aircraft a cars. Public risks require public oversight. Getting rid of the FAA wdn’t [sic] make flying safer. They’re there for good reason.”In this and other recent pronouncements, Musk is calling for artificial intelligence (AI) to be regulated by traditional regulation, just as we regulate foods, drugs, aircraft and cars. Putting aside the quibble that food, drugs, aircraft and cars are each regulated very differently, these calls for regulation seem to envision one or more federal regulatory agencies adopting binding regulations to ensure the safety of AI. Musk is not alone in calling for “regulation” of AI, and some serious AI scholars and policymakers have likewise called for regulation of AI using traditional governmental regulatory approaches .
Application of SIFT feature vector matching algorithm in binocular stereo vision system

Stereo vision matching is to search the corresponding relation of one spatial object observed from different angles in the projected image, and obtain the parallax image depending on the deviation (parallax is the geometric distance between different points projected from the same spatial point on different image. In a parallel binocular stereo vision system, two cameras have the same focal length and parallel imaging planes, so there is no rotation and scale conversion for images obtained by two cameras. SIFT binocular stereo vision system does not need multi-scale conversion and coordinate axis rotation, therefore the algorithm is simplified and the fault tolerance for SIFT target matching is maintained.
Word-Based POMDP Dialog Management via Hybrid Learning

Dialog management plays an important role in the task-oriented dialog system. Most of the previous works divide dialog management into state tracker and action selector. The two parts are modeled separately and implemented in a pipelined way, which suffers from the problem of error accumulation, and the feedback signal from action selector cannot be propagated to state tracker and natural language understanding module. This paper proposes a word-based partially observable Markov decision processes' dialog management that integrates natural language understanding, state tracker, and action selector into an end-to-end architecture. Our proposed dialog management takes the words from user utterances as inputs and then produces optimal action as well as slot values of natural language understanding which are necessary for response generation. To this end, we propose a hybrid learning method, which integrates reinforcement learning and supervised learning, to optimize the action selector and slot filler jointly. In addition, we develop a high-return prioritized experience replay to speed up the convergence of the training process. The experimental results show that the proposed dialog management outperforms four strong baselines in a series of different dialog tasks. A human user's evaluation also shows the same results. The high-return prioritized experience replay accelerates the convergence effectively, especially in the scenario in which the proposed dialog management works on more complex tasks.
Toward Evaluating Trustworthiness of Social Networking Site Users: Reputation-Based Method

As social networking sites (SNSs) have risen in popularity, attackers have been using social engineering traps and tactics to trick SNS users into obeying them, accepting threats, and falling victim to various crimes and attacks, such as phishing, sexual abuse, financial abuse, identity theft, impersonation, physical crime, and many other forms. Recent research on SNS security shows that most of the attackers rely mainly on fake identities. However, one of the key challenges that has faced researchers recently is how to distinguish between legitimate users and attackers. In this paper, we propose a simple yet effective method of evaluating the trustworthiness of an SNS user. The proposed method relies on a user’s reputation, which can be evaluated from the user’s friendship history. As such, this method contributes to reducing the risks associated with the lack of identity authentication in SNSs, as well as the failure to filter fake profiles when receiving friendship invitations, looking for people on search engines, and dealing with spam messages.
Automatic equivalence checking for assembly implementations of cryptography libraries

This paper presents an approach and a tool, CASM-Verify, to automatically check the equivalence of highly optimized assembly implementations of cryptographic algorithms. The key idea of this paper is to decompose the equivalence checking problem into several small sub-problems using a combination of concrete and symbolic evaluation. Given a reference and an optimized implementation, CASM-Verify concretely executes the two implementations on randomly generated inputs and identifies likely equivalent variables. Subsequently, it uses symbolic verification using an SMT solver to determine whether the identified variables are indeed equivalent. Further, it decomposes the original query into small sub-queries using a collection of optimizations for memory accesses. These techniques enable CASM-Verify to verify the equivalence of assembly implementations (e.g., x86 and SSE) of various algorithms such as SHA-256, ChaCha20, and AES-128 for a message block.