Reviewerly/exHarmony huggingface dataset:
Partitioning based incremental marginalization algorithm for anonymizing missing data streams

The IoT and its applications are the inseparable part of modern world. IoT is expanding into every corner of the world where internet is available. IoT data streams are utilized by many organizations for research and business. To benefit from these data streams, the data handling party must secure the individuals' privacy. The most common privacy preservation approach is data anonymization. However, IoT data provides missing data streams due to the varying device pool and preferences of individuals and unpredicted devices' malfunctions of IoT. Minimization of missingess and information loss is very important for anonymizing of missing data streams. To achieve this, we introduce IncrementalPBM (Incremental Partitioning Based Marginalization) for anonymizing missig data streams. IncrementalPBM utilizes time based sliding window for missing data stream anonymization, and it aims to control the number of QIDs for anonymization while increasing the number of tuples for anonymization. Our experiment on real dataset showed IncrementalPBM is effective and efficient for anonymizing missing data streams compared to existing missing data stream anonymization algorithm. IncrementalPBM showed significant improvement; 5% to 9% less information loss, 4500 to 6000 more number of re-use anonymization while showing comparable clustering, suppression and runtime.
3D Visibility Graph based Motion Planning and Control

This paper proposed a motion planning and control approach for real-time collision avoidance of high degree-of-freedom (DoF) robots. It constructs a 3D visibility graph by representing external obstacles with discrete polyhedrons and searches for the shortest path on it. To consider the complex internal structure of high DoF robots and take full advantage of redundancy, inverse kinematics is solved as an optimization problem which allows for flexible cost functions and constraints. The distance between the robot and external obstacles is treated as an extra constraint in the optimization which works together with visibility graph for collision avoidance. At the end, a pick-place task was successfully performed among obstacles by a simulated dual-arm quadrupedal robot considering the whole-body kinematics and stability, which demonstrated the feasibility of the proposed method.
Infrared Line Following and Ultrasonic Navigating Robot with ATMEGA328 Pro

This paper presents a low-cost scheme for developing an Arduino robotic car with varying functions, including line following, ultrasonic navigation, and automatic parking. In terms of line following, an array of IR reflective sensors, distinguishing the binary colors, can be used for tracking the lines, in addition, the speed of the car can be adjusted by a closed-loop PID controller. The speed of both wheels can be measured by Hall elements or raster. Turning to ultrasonic navigation, an ultrasonic sender can lead the car with two corresponding receivers to the location. The variance between the ultrasonic receivers can determine the moving direction. For automatic parking process, a combination of ultrasonic sensors and IR reflective sensors, respectively used for detecting the distance and parking line, can detect the final parking area. This multifunctional robot can be used for human assistance purposes, indoor deliver services, etc.
Several two-component mixture distributions for count data

Finite mixture model is a flexible approach for modeling multimodal data. Multimodality can be present in the data when the data constitute several subpopulations. In this study, several two-component mixture distributions for count data are proposed and described to cater for bimodality issue. The distributions considered in developing mixture distributions are Poisson (P), Poisson Lindley (PL), negative binomial (NB) as well as negative binomial Lindley (NBL), and altogether, a total of ten two-component mixture distributions are obtained. The maximum likelihood estimators for each mixture distribution are obtained by employing the L-BFGS-B method. A comparison study based on graphical approach is conducted to investigate the effect of mixing proportion on the resulting mixture distribution which are found based on different shapes of probability curve and positions of the mode. A simulation study is conducted to investigate the performance of each mixture distribution in fitting data that come from two subpopulations with different mean and dispersion values. Three mixture models which are P-NB, PL-NB and NB-NB, are the most commonly identified as adequate in describing the simulated data with various different types of mixing properties. These three distributions are considered to be the most flexible and thus, suggested for real data applications.
Exploratory Data Analysis Applied in Mapping Multi‐element Soil Geochemical Anomalies for Drill Target Definition: A Case Study from the Unpha Layered Non‐magmatic Hydrothermal Pb‐Zn Deposit, DPR Korea

Abstract A factor analysis was applied to soil geochemical data to define anomalies related to buried Pb‐Zn mineralization. A favorable main factor with a strong association of the elements Zn, Cu and Pb, related to mineralization, was selected for interpretation. The median + 2MAD (median absolute deviation) method of exploratory data analysis (EDA) and C‐A (concentration‐area) fractal modeling were then applied to the Mahalanobis distance, as defined by Zn, Cu and Pb from the factor analysis to set the thresholds for defining multi‐element anomalies. As a result, the median + 2MAD method more successfully identified the Pb‐Zn mineralization than the C‐A fractal model. The soil anomaly identified by the median + 2MAD method on the Mahalanobis distances defined by three principal elements (Zn, Cu and Pb) rather than thirteen elements (Co, Zn, Cu, V, Mo, Ni, Cr, Mn, Pb, Ba, Sr, Zr and Ti) was the more favorable reflection of the ore body. The identified soil geochemical anomalies were compared with the in situ economic Pb‐Zn ore bodies for validation. The results showed that the median + 2MAD approach is capable of mapping both strong and weak geochemical anomalies related to buried Pb‐Zn mineralization, which is therefore useful at the reconnaissance drilling stage.
What Is Deep Learning?

Deep learning is a subset of machine learning which is itself a subset of artificial intelligence and statistics. Artificial intelligence research began shortly after World War II [24]. Early work was based on the knowledge of the structure of the brain, propositional logic, and Turing's theory of computation. Warren McCulloch and Walter Pitts created a mathematical formulation for neural networks based on threshold logic. This allowed neural network research to split into two approaches: one centered on biological processes in the brain and the other on the application of neural networks to artificial intelligence. It was demonstrated that any function could be implemented through a set of such neurons and that a neural net could learn. In 1948, Norbert Wiener's book, Cybernetics, was published which described concepts in control, communications, and statistical signal processing. The next major step in neural networks was Donald Hebb's book in 1949, The Organization of Behavior, connecting connectivity with learning in the brain. His book became a source of learning and adaptive systems. Marvin Minsky and Dean Edmonds built the first neural computer at Harvard in 1950.
Augmented Reality Application in Laboratories and Learning Procedures

It is commonly said that Augmented Reality opens new perspectives for training. Today, it is unlikely that the audit objectives will be met. One of the major criticisms of traditional computing is that the digital world of the computer is too disconnected from the real world in which users evolve. Mixed Reality (RM) focuses on interactive systems in which real objects and computer data are coherently merged. However, integrating these new tools into the existing learning processes remains complex, due to the technological aspects and the data continuum to be implemented, through the identification of use cases and the associated gains and by the diversity actors and experts to involve in this process: the expert of the main field, the designer and the IT developer. In this paper we aim to develop an augmented reality application by following a developing process that will led to the planned results. The proposed application will be dictated for learners in higher education context, especially newbies that will practice their first experiment in the laboratory e.g. "biology, chemistry" by giving them an interactive environment to understand the safety procedure followed during experiments in laboratories.
vGuard: A Spatiotemporal Efficiency Supervision Method For Vaccine Production Based On Double-level Blockchain

A vaccine is a biological production that is related to people's lives. Currently, vaccine production supervision is very rough. The vaccine production records are completely controlled by the enterprise. Enterprises only submit production records to review agency for review when they need to sell vaccines. Production records are easy to forge and modify. In order to solve the shortcomings of traditional centralized management. We propose a supervision method for vaccine production based on double-level blockchain. We have designed a double-level blockchain structure. The first level is private data of vaccine production enterprise, including production records and corresponding hash. The next level is public data, including production records hash and vaccine information. In this way, we make vaccine enterprise to submit production records in a timely manner without fear of privacy leaks. We avoid enterprise tampering or falsification of production records through the non-tampering features and time stamps of the blockchain. Through these methods, we have realized efficiency supervision of vaccine production.
An Empirical Investigation of Instance-Specific Causal Bayesian Network Learning

Significant progress has been made in developing algorithms for learning graphical causal models from data. Most of these algorithms learn a causal structure that is shared by all the instances (e.g., patients) in the training dataset. However, different instances may not all share the same causal structure. We introduced an instance-specific method called IGES [15] that learns a causal model for each instance T by using the features of T and the instances in the training dataset. In the current paper, we study the empirical performance of the IGES method on several biomedical datasets. The results provide support that instance-specific structure exists and is important to model in these real domains.
A classification model for prediction of clinical severity level using qSOFA medical score

Purpose The purpose of this study is to develop an efficient prediction model using vital signs and standard medical score systems, which predicts the clinical severity level of the patient in advance based on the quick sequential organ failure assessment (qSOFA) medical score method. Design/methodology/approach To predict the clinical severity level of the patient in advance, the authors have formulated a training dataset that is constructed based on the qSOFA medical score method. Further, along with the multiple vital signs, different standard medical scores and their correlation features are used to build and improve the accuracy of the prediction model. It is made sure that the constructed training set is suitable for the severity level prediction because the formulated dataset has different clusters each corresponding to different severity levels according to qSOFA score. Findings From the experimental result, it is found that the inclusion of the standard medical scores and their correlation along with multiple vital signs improves the accuracy of the clinical severity level prediction model. In addition, the authors showed that the training dataset formulated from the temporal data (which includes vital signs and medical scores) based on the qSOFA medical scoring system has the clusters which correspond to each severity level in qSOFA score. Finally, it is found that RAndom k-labELsets multi-label classification performs better prediction of severity level compared to neural network-based multi-label classification. Originality/value This paper helps in identifying patient' clinical status.
Designing Mamdani Fuzzy Inference Systems for Decision Support Systems

Decision support systems with the fuzzy inference system method, especially Mamdani, have been widely applied in various fields of science. Fuzzy inference systems are able to map input output variables in the form of I/O surfaces. Characteristics of I/O surfaces in decision support systems can produce surfaces where the movement between values is continuous. In this study conducted an experiment on several scenarios and observed changes in the shape of the I/O surface. It turns out that the results obtained by I/O surface behavior are influenced by the number of linguistics in one variable, the number of input variables and the rules made.
Acceleration of the Pair-HMM forward algorithm on FPGA with cloud integration for GATK

The Pair-HMM forward-algorithm is an essential algorithm found in many genomic related analyses.The high number of floating point operations in the algorithm makes it one of the main contributors to the compute time of analysis pipelines.To speed-up computations we propose an FPGA based hardware accelerator for the Amazon AWS F1 Cloud platform.The accelerator is open source and has been tested within the popular Genomic Analysis Toolkit (GATK) pipeline.The accelerator achieved up to 15× speed-up against the software implementation when used in-pipeline.The accelerator has also been tested in the experimental Spark (distributed) version of the GATK HaplotypeCaller tool.An in-depth analysis of the compute time contributions allowed to point out the main bottlenecks for accelerators in the GATK pipeline, resulting in a hybrid CPU-FPGA solution to best exploit both resources.
Exploring in-Service Teachers' Acceptance of Augmented Reality

Augmented reality (AR) is an emerging technology that has been shown to have positive impact on the cognitive and affective learning outcomes for students of all grades. However, the use of AR has been limited to self-study space in India and not percolated into school classrooms. A precondition for this to happen is teacher acceptance of AR as a teaching resource. In this paper, we report a pilot study done with six secondary school teachers to test their acceptance of AR through semi-structured interviews. These interviews explored teachers' AR acceptance, based on the three core constructs of the Technology Acceptance Model (TAM2) - perceived usefulness, perceived ease of use and behavioral intention to use. Thematic analysis of interviews show teachers have positive acceptance for AR. The analysis also throws light on two additional contextual factors that govern teachers' intention to use and the type of teacher support needed for effective integration of AR in school education in India. The findings of this paper will be of interest to AR researchers and learning solutions companies.
A knowledge-driven layered inverse reinforcement learning approach for recognizing human intents

There is a rising trend in exploring the capability of inverse reinforcement learning (IRL) in high dimensional demonstrations. Our aim is to recognise human intents from video data within an IRL framework. For this, we present a two-layered maximum likelihood IRL model. The usefulness of knowledge representation (KR) schemes and availability of advisors at different layers is exploited through this model. Two main aspects are addressed: a. the importance of having abstract high-level information to the IRL framework in terms of semantic object affordance and b. deductively exploring the utility of a state at different temporal abstractions. The effectiveness of the proposed model has been evaluated with the help of standard Cornell Activity Dataset (CAD-120).
EXtracting product lines from vAriaNTs (EXPLANT)

The project EXtracting Product Lines from vAriaNTs (EXPLANT) funded by the German Research Foundation (DFG) is currently in its second phase. In this project, we are concerned with the stepwise migration of cloned variants into a software product line (i.e., the extractive approach of adopting systematic software reuse). While the extractive approach is the most common one in practice, many of its characteristics (e.g., processes, costs, best practices) are still unclear and tool support is limited (e.g., for feature location, refactoring, quality assurance). Within this extended abstract, we report on a selection of results we achieved in EXPLANT so far, and highlight our goals as well as opportunities for research collaborations in the second phase.
A New Hybrid-Enhanced Recommender System for Mitigating Cold Start Issues

With the significant expanding flows of data circulating on the Internet, users are overwhelmed by an era of information explosion. Hence, Recommender Systems have become essential tools in helping each user find items that meet her needs and interests and filter out uninteresting ones. However, recommender systems suffer from a significant impediment that limits their performance which is known as the cold-start problem. This hurdle refers to the situation where the system is lacking valuable data to infer accurate neighbors for new users or items. By leveraging demographic, semantic, and collaborative recommendation techniques we propose, in this paper, a new hybrid recommender system to tackle cold-start challenges. A comprehensive set of experiments on the MovieLens dataset, using a broadly applied performance metric, verify that our proposed system outperforms state-of-the-art methods, in terms of recommendation accuracy, and cold-start mitigation.
Harmony across Music, Visuals and Movement in a New Audio-visual Gestural Performance

This paper describes the technology, concepts and development of Computer Storm, a live audio-visual piece created for a gestural instrument, the 'AirSticks'. The AirSticks allow the composition, performance and improvisation of live electronic music and graphics using movements captured by handheld motion controllers. In this piece, the AirSticks are combined with commodity depth sensors, and a custom visualisation system 'Confluence' which generates graphics from music and motion in real-time. The hardware used to display the images within the performance space is also described, as well as an overview of the resulting performance, which explores harmony across music, visuals and movement, and investigates our complex relationship with technology.
Covariance Intersection Fusion Estimator for Nonlinear System with correlated noise

In this paper, for the nonlinear system with correlated noise, the decorrelation method is applied to obtain the noise independent nonlinear system. Furthermore, the Unscented Kalman Filter method is applied to obtain the nonlinear estimator. In order to improve the filtering accuracy, this paper uses the covariance intersection fusion algorithm. The algorithm avoids solving the covariance matrix, and the algorithm is simple and easy to implement. A simulation example verifies the correctness and effectiveness of the algorithm.
Mining effect of Famous Chinese Medicine Doctors on Lung-cancer based on Association rules

Traditional Chinese medicine is a treasure house of the Chinese nation. Using data mining method to study traditional Chinese medicine can summarize the experience of traditional Chinese medicine and promote the development of traditional Chinese medicine. The dose-effect relationship of traditional Chinese medicine mainly refers to that the effect of drugs changes with the dose of drugs in a certain range, and there is a great relationship between the effect produced by different combinations of drugs and the dose of drugs. With the development of data mining technology, it is widely used to study the dose-response relationship between drugs with the data of famous old traditional Chinese medicine doctor's medical record. In this paper, based on the characteristics of traditional Chinese doctor's medicine data, the Apriori algorithm is improved from the construction of the data model, so that the transaction database does not need to be converted into Boolean database in the mining, which can improve the reseracher's friendliness. The results of the excavation not only included the common drugs used by famous tra-dional Chinese medicine doctors to treat lung cancer, but also analyzed the proportional relationship between the dosage of different drugs. To provide a reference for clinical researchers in the actual use of drugs between the proportion of the relationship.
A Closeness- and Priority-Based Logical Study of Social Network Creation

Abstract This paper is part of an on-going programme on the study of the logical aspects of social network formation. It recalls the so-called social network model, discussing the properties of a notion of closeness between agents (in terms of the number of traits they have in common); then introduces an extended social network model in which different agents might assign different values to different traits, discussing the properties of the notion of weighted closeness that arises. These notions are used to define social network creation operations by means of a threshold strategy. The paper studies the properties of the social networks the updates create, providing sound and complete axiom systems for formal languages describing these updates’ effects.