Reviewerly/exHarmony huggingface dataset:
Adaptive importance sampling supported by a variational auto-encoder

Obtaining good samples from high dimensional target distributions by adaptive importance sampling (AIS) methods to approximate target distributions is usually very hard, especially when not much is known about the target distribution. The main difficulty is in adapting the proposal distribution to become closer to the target distribution. The Variational Auto-Encoder (VAE) is a probabilistic deep computational network that can capture lower dimensional coding of high dimensional data. This property gives it a potential to capture the structure of high dimensional target distributions. In this paper, we propose an AIS method that exploits variational autoencoding to construct better proposal distributions. We demonstrate that the proposed method has better performance than the standard AIS in terms of convergence speed and goodness-of-fit when sampling from high dimensional and highly structured target distributions.
Scalable and Adaptive KNN for Regression Over Graphs

The family of k-nearest neighbor ( k NN) schemes comprises simple and effective algorithms that can be used for general machine learning tasks such as classification and regression. The goal of linear k NN regression is to predict the value of an unseen datum as a linear combination of its k neighbors on a graph, which could be found via certain distance metric. Despite the simplicity and effectiveness of kNN regression, a general problem facing this approach is the proper choice of k. Most of the conventional k NN algorithms simply apply the same k for all data samples, meaning that the data samples are assumed to lie on a regular graph wherein each data sample is connected with k neighbors. However, a constant choice may not be optimal for data lying in a heterogeneous feature space. On the other hand, existing algorithms for adaptively choosing k usually incur high computational complexity, especially for large training datasets. In order to cope with this challenge, this paper introduces a novel algorithm which can adaptively choose k for each data sample; meanwhile it is capable of greatly reducing the training complexity by actively choosing training samples. Real data tests corroborate the efficiency and effectiveness of the novel algorithm.
Incorporating New Concepts Into the Scientific Variables Ontology

We present a preliminary methodology, currently in development, for automated generation of domain-specific, machine-readable representations of qualitative and quantitative scientific variable concepts. The method presented is based on the top level universal categories and modular design patterns declared within the Scientific Variables Ontology (v 1.0.0) blueprint. These scientific variable representations can be used to annotate electronic resources, such as data and models and, along with reasoning algorithms, can be used to provide explainable automated resource alignment capabilities in the assembly of scientific workflows.
Early Sepsis Prediction in ICU Trauma Patients with Using An Improved Cascade Deep Forest Model

In the intensive care unit (ICU), Trauma is one of the leading causes of death worldwide, about 40\% of death occurred during hospitalization and 22\% are caused by sepsis. Sepsis is a complication disease that is easily caused by the ICU patient, especially in trauma patients. Sepsis is a systemic inflammatory response that is very dangerous to patient's lives. So early prediction of sepsis for trauma patients can help doctors intervene and diagnose in advance to improve treatment and patients outcomes. This work developed an early sepsis prediction diagnostic model by using machine learning method-a improved cascade deep forest model. We extracted the patient' s Electronic medical record (EMR) data with the first 24 hours of ICU admission to classify patients as sepsis by using the new definition of Sepsis-3. From MIMIC-III dataset, 3125 patients were included(sepsis patients =1187, non sepsis =1938). Compare to different machine learning methods, the improved cascade deep forest model is superior or comparable predictive performance. Our model achieved AUROC of 0. S0, Sensitivity is 0.79 and specificity is 0.64. Besides, related risk factors of sepsis and important predictors variables were scored.
The Importance of Non-technical Skills in Agile Project Teams

Agile project management grew up around technical practice, with roots planted in software development. Agile project management has emerged from application of principles and values expressed in Agile manifesto. Every software development organization today seems to practice the agile software development methodology, or a version of it. It goes without saying that technical skills are essential for any software developer, but non-technical skills matter to, and can be just as important. The main objective of this research was to determine how agile project team members, in software development, perceive the importance of various non-technical, soft skills and how they self-assess their level of possession of different non-technical skills. We empirically addressed this question, on a sample of 190 respondents, agile team members, in one software development company from the Republic of Serbia.
Designing augmented reality-based teaching resource of three dimensional geometry

Abstract Indonesia, like many other countries, sees Information and Communication Technology (ICT) as a potential tool for enhancing education and the country is keen to integrate this digital technology in the classroom classrooms. Therefore, since 2004 Indonesian Ministry Education and Culture has stipulated a regulation on the implementing the technology in teaching and learning process. Furthermore, since 2013 Indonesian government implemented a new curriculum emphasizing the use of ICT in the classroom. Recently, Augmented Reality (AR) is one of digital technologies that has received increasing attention for teaching and learning of mathematics. However, lack of research has been devoted to design and develop Augmented Reality-Based teaching resources for secondary school mathematics. This study aimed at designing and developing Augmented Reality - based teaching resources (e.g., lesson plans, student worksheets and Augmented Reality Applets) for teaching Geometry in senior secondary schools. In order to achieve the aim, in this study, we employed a design research model proposed by Plomp and Nieveen. This model consists of three phases that are preliminary phase, prototyping phase and assessment phase. It is important to note that this paper reports results from preliminary. In the preliminary phase we distributed questionnaires to teachers and students which were then followed by interviews. The findings revealed that teachers and students were keen to integrate Augmented Reality technology in teaching and learning mathematics. Furthermore, the teachers expected to be equipped with ICT-based learning resources that support discovery and project based teaching approach. Based on the findings, we designed and developed Augmented Reality applet, lesson plan and student worksheet for teaching three dimensional geometry in senior secondary schools with a project based-teaching approach.
Active Domain Transfer on Network Embedding

Recent works show that end-to-end, (semi-) supervised network embedding models can generate satisfactory vectors to represent network topology, and are even applicable to unseen graphs by inductive learning. However, domain mismatch between training and testing network for inductive learning, as well as lack of labeled data often compromises the outcome of such methods. To make matters worse, while transfer learning and active learning techniques, being able to solve such problems correspondingly, have been well studied on regular i.i.d data, relatively few attention has been paid on networks. Consequently, we propose in this paper a method for active transfer learning on networks named active-transfer network embedding, abbreviated ATNE. In ATNE we jointly consider the influence of each node on the network from the perspectives of transfer and active learning, and hence design novel and effective influence scores combining both aspects in the training process to facilitate node selection. We demonstrate that ATNE is efficient and decoupled from the actual model used. Further extensive experiments show that ATNE outperforms state-of-the-art active node selection methods and shows versatility in different situations.
An Improved Artificial Neural Network Design for Face Recognition utilizing Harmony Search Algorithm

Abstract Face recognition has become an interesting field for researchers where it is used in many applications. One of the most common methods of soft computing is named the artificial neural network (ANN) has been suggested to achieve the face recognition process. Nonetheless, the performance of ANN depends on the number of neurons in the hidden layers and the value of the learning rate. These variables are usually defined based on the trial and error method which is time-consuming. Furthermore, in many cases, it is very difficult to find the optimum value for these variables. Hence, to improve the performance of the ANN for the face recognition process, the optimization algorithm is needed to get promising outcomes. Therefore, this paper introduces an improved ANN design for face recognition using a meta-heuristic optimization algorithm. The ANN represents a distributed processing system consists of neurons which are simply connected elements. One of the most popular techniques for pattern recognition called back propagation algorithm (BP) is used to train the ANN (BP-ANN) to achieve the face recognition process. To enhance the face recognition system performance, the ANN has been hybridized with the well-known meta-heuristic optimization algorithm namely harmony search algorithm (HSA). The HSA based on the principle work of musicians to find the best harmonies. This technique is Implemented based on the results of the fitness function evaluation. In this research, the mean squared error (MSE) has been used as a fitness function. The HSA optimizes the ANN such that the face recognition system provides the lowest MSE and thus enhances the performance of the face recognition system. The accuracy of the optimum hybrid system (HSA-ANN) is investigated using the MATLAB environment conducted for 10 persons. The results revealed that the proposed system (HSA-ANN) achieved lower MSE compare with the ANN. Furthermore, the HSA-ANN gives a better face recognition rate than traditional ANN.
Navigation of Mini Unmanned Aerial Vehicle in Unknown Environment

Abstract In this paper, two algorithms are proposed to navigate the mini unmanned aerial vehicle (MUAV) in unknown environment. The first algorithm is path planning algorithm, it proposed to force the MUAV to track a specific trajectory. The second algorithm is obstacle avoidance algorithm, it proposed to avoid the collision between the MUAV and borrower when the MUAV is maneuvering in unknown environment. The pure pursuit algorithm (PPA) is used to force the MUAV to follow the desired trajectory depends on the given waypoints. While the vector field histogram algorithm (VFHA) is used to avoid collision of the MUAV with obstacles block in the desired path. To keep the flight of MUAV without collision, the 3D laser sensors are used to collect the information from the surrounding environment. The GPS sensor is used to keep the MUAV on the specific path. The collected information from the two sensors are analysed by the controller of the MUAV to improve its performance. The Matlab program is used to implement the proposed algorithms and plot the trajectories of MUAV in the unknown algorithm. The Matlab program is connected with the gazebo program via robotic operating system (ROS) to simulate the visual movement of the MUAV in any unknown environment. The obtained results show the effectiveness of the proposed algorithm to flight the MUAV in different environments.
An efficient hand gestures recognition system

Abstract Talking about gestures make us return to the historical beginning of human communication because there is no language completely free of gestures. People cannot communicate without gestures. Any action or movement without gestures is free of real feelings and cannot express the thoughts. The purpose of any hand gesture recognition system is to recognize the hand gesture and used it to transfer a certain meaning or for computer control or/and a device. This paper introduced an efficient system to recognize hand gestures in real-time. Generally, the system is divided into five phases, first to image acquisition, second to pre-processing the image, third for detection and segmentation of the hand region, fourth to features extraction and fifth to count the numbers of fingers for gesture recognition. The system has been coded by Python language, PyAutoGUI library, OS Module of Python and the Open CV library.
Classification and Detection of Breast Cancer Using Machine Learning

One of the major second largest issues found in the world is breast cancer. For increasing in long term, the continuance of women can be achieved by early and accurate diagnosis. Early diagnosis is the only remedy to prevent breast cancer. Since detection of this disease is a critical issue, this research work focuses on various machine learning methods which can assist doctors in giving promising results in a correct diagnosis of cancer. Thirteen machine learning models are employed and compared on the various measures. Wisconsin Breast Cancer Database (WBCD) dataset is employed in performing the experimentation, which is extract from UCI repository. AdaBoost, logistic regression and 1-NN machine learning models give promising accuracy of 98% in performing the experiment among all the models.
Sampling Query Variations for Learning to Rank to Improve Automatic Boolean Query Generation in Systematic Reviews

Searching medical literature for synthesis in a systematic review is a complex and labour intensive task. In this context, expert searchers construct lengthy Boolean queries. The universe of possible query variations can be massive: a single query can be composed of hundreds of field-restricted search terms/phrases or ontological concepts, each grouped by a logical operator nested to depths of sometimes five or more levels deep. With the many choices about how to construct a query, it is difficult to both formulate and recognise effective queries. To address this challenge, automatic methods have recently been explored for generating and selecting effective Boolean query variations for systematic reviews. The limiting factor of these methods is that it is computationally infeasible to process all query variations for training the methods. To overcome this, we propose novel query variation sampling methods for training Learning to Rank models to rank queries. Our results show that query sampling methods do directly impact the ability of a Learning to Rank model to effectively identify good query variations. Thus, selecting appropriate query sampling methods is a key problem for the automatic reformulation of effective Boolean queries for systematic review literature search. We find that the best sampling strategies are those which balance the diversity of queries with the quantity of queries.
Client Insourcing: Bringing Ops In-House for Seamless Re-engineering of Full-Stack JavaScript Applications

Modern web applications are distributed across a browser-based client and a cloud-based server. Distribution provides access to remote resources, accessed over the web and shared by clients. Much of the complexity of inspecting and evolving web applications lies in their distributed nature. Also, the majority of mature program analysis and transformation tools works only with centralized software. Inspired by business process re-engineering, in which remote operations can be insourced back in house to restructure and outsource anew, we bring an analogous approach to the re-engineering of web applications. Our target domain are full-stack JavaScript applications that implement both the client and server code in this language. Our approach is enabled by Client Insourcing, a novel automatic refactoring that creates a semantically equivalent centralized version of a distributed application. This centralized version is then inspected, modified, and redistributed to meet new requirements. After describing the design and implementation of Client Insourcing, we demonstrate its utility and value in addressing changes in security, reliability, and performance requirements. By reducing the complexity of the non-trivial program inspection and evolution tasks performed to meet these requirements, our approach can become a helpful aid in the re-engineering of web applications in this domain.
Online Probability Model Estimation for Video Compression

Modern video codec uses arithmetic coding for entropy coding. The arithmetic coding asymptotically achieves the entropy bound provided the true probability distribution. Hence the compression efficiency heavily relies on the ability to capture the time-variant probability model in video signals. Variants of first-order linear probability model update schemes have been used in recent generation video codecs. Built on top of those, a multimodal estimation scheme that forms a higher order probability model update has been proposed in this work. We experimentally demonstrate its coding efficiency.
Voice-based Reformulation of Community Answers

Community Question Answering (CQA) websites, such as Stack Exchange1 or Quora2, allow users to freely ask questions and obtain answers from other users, i.e., the community. Personal assistants, such as Amazon Alexa or Google Home, can also exploit CQA data to answer a broader range of questions and increase customers’ engagement. However, the voice-based interaction poses new challenges to the Question Answering scenario. Even assuming that we are able to retrieve a previously asked question that perfectly matches the user’s query, we cannot simply read its answer to the user. A major limitation is the answer length. Reading these answers to the user is cumbersome and boring. Furthermore, many answers contain non-voice-friendly parts, such as images, or URLs.
Frozen Binomials on the Web: Word Ordering and Language Conventions in Online Text

There is inherent information captured in the order in which we write words in a list. The orderings of binomials — lists of two words separated by ‘and’ or ‘or’ — has been studied for more than a century. These binomials are common across many areas of speech, in both formal and informal text. In the last century, numerous explanations have been given to describe what order people use for these binomials, from differences in semantics to differences in phonology. These rules describe primarily ‘frozen’ binomials that exist in exactly one ordering and have lacked large-scale trials to determine efficacy.
The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance

Search advertising is one of the most commonly-used methods of advertising. Past work has shown that search advertising can be employed to improve health by eliciting positive behavioral change. However, writing effective advertisements requires expertise and (possible expensive) experimentation, both of which may not be available to public health authorities wishing to elicit such behavioral changes, especially when dealing with a public health crises such as epidemic outbreaks.
Efficient and Low-Cost Arduino based Solar Tracking System

Abstract Sun energy plays an important role as a primary source of energy which can be harvested successfully using solar cells. The solar cell efficiency depends on the location of sun and its light intensity. Hence, solar tracking can be exploited to maximize the efficiency of solar panel. In this work, we developed a novel system of an inexpensive automatic microcontroller-based scaled down solar tracker using Arduino platform. Servo-motor is controlled by an Arduino Mega unit in order to re-orient the solar panel according to the position of sun with the help of light dependent resistors (LDR) and servomotors. A dual-axis solar tracker prototype is designed to maximize the harvested solar energy. Experimental results show that the proposed system performs 25% better than a fixed solar panel under the same conditions.
Metadata Extraction Using DeepLab V3 and Probabilistic Latent Semantic Analysis for Intelligent Visual Surveillance Systems

Recently, surveillance cameras are ubiquitous for both real-time monitoring and recording important moments. Temporarily seamless surveillance using multiple cameras requires increasing amount of human efforts and enormous size of storage. The use of dynamic cameras further requires advanced computer vision algorithms, and is another challenge for intelligent visual surveillance. To solve those problems, we present an enhanced metadata extraction method for robust object search and a person re-identification. More specifically, the proposed method accurately extracts an object region using a modified DeepLab version 3, and then extracts metadata including representative color, size, aspect ratio, and moving trajectory of the object. The proposed metadata extraction method can be applied to a wide range of surveillance systems such as search for missing children in a large public space and crowd monitoring system.
Two Efficient Algorithms for Mining High Utility Sequential Patterns

High utility sequential pattern mining (HUSPM) is an emerging topic in data mining. Compared with the previous topics (sequential pattern mining and high utility itemset mining), HUSPM can provide more applicable knowledge, for it comprehensively considers utility indicating the business value and sequential indicating the causality of different items. However, the combination of utility and sequential brings the dramatic challenges and makes HUSPM more difficult than the previous problems. In this paper, we propose an two efficient algorithms, HUS-UT and HUS-Par, for HUSPM. The proposed HUS-UT algorithm adopts a novel data structure named Utility-Table to facilitate the utility calculation, so it can find the desired patterns quickly. The HUS-Par algorithm is a parallel version of HUS-UT based on the thread model, which also exploits two balance strategies to improve efficiency. We also conduct substantially experiments to evaluate the performance of our algorithms. The experimental results show that our algorithms are much faster than the state-of-the-art algorithms.
A Decidable Multi-agent Logic for Reasoning About Actions, Instruments, and Norms

We formally introduce a novel, yet ubiquitous, category of norms: norms of instrumentality. Norms of this category describe which actions are obligatory, or prohibited, as instruments for certain purposes. We propose the Logic of Agency and Norms ($$\mathsf {LAN}$$) that enables reasoning about actions, instrumentality, and normative principles in a multi-agent setting. Leveraging $$\mathsf {LAN}$$, we formalize norms of instrumentality and compare them to two prevalent norm categories: norms to be and norms to do. Last, we pose principles relating the three categories and evaluate their validity vis-à-vis notions of deliberative acting. On a technical note, the logic will be shown decidable via the finite model property.