Reviewerly/exHarmony huggingface dataset:
Challenges of designing hand recognition for a manual assembly assistance system

Manual assembly remains an important task in production with changing requirements for the worker due to i.e. mass personalized products. To keep up with these requirement changes, modern assistance systems are suitable to support the workers. The goals of these assistance systems are to detect errors in the process, guide the worker through new processes and document the process. To achieve this, the assistance system needs to follow each step of the worker reliably. This can be realized with a visual scene analysis based on machine learning. In the presented work, one 3D-sensor (active stereo vision with an additional RGB camera) is used for the scene analysis. The presented work evaluates existing methods for hand localization and hand pose recognition for the application in manual assembly assistance systems. A new procedure for hand localization based on current machine learning techniques is developed specifically for the scenario of recognizing hand joints (also called hand pose) in a manual assembly scenario. Additionally, different methods for hand pose recognition are compared in the application scenario. Based on the results of the developed hand localization method, current hand pose detection methods were evaluated (Dense-Regression and DeepPrior++). While the methods worked well in a scenario comparable to the dataset, they did not perform very well in a manual assembly scenario. Improvements can be made using pixel-wise segmentation or using specific datasets for training containing data from manual assembly scenarios.
Rock Burst Classification Prediction Method Based on Weight Inverse Analysis Cloud Model

Abstract A cloud model comprehensive evaluation method based on weight inverse analysis is proposed and applied to the prediction of rock burst grade. At first, the objective function with weight as independent variable is derived and established. The objective weight of each evaluation factor is obtained by genetic algorithm. Then the concrete steps of the coupling of weight inverse analysis and cloud model are given. After that, σ θ /σ c σ c /σ t and Wet are selected as the evaluation factors. σ θ /σ c is the ratio of the maximum tangential stress in the cavern to the compressive strength of rock. σ c /σ t is the ratio of tensile strength to the compressive strength of rock. Wet is the elastic energy index. According to the rock burst engineering example, the inverse analysis and calculation of the factor weights are carried out. Finally, this method is applied to the rock burst grade prediction of Jiangbian Hydropower Station and Maluping Mine. Compared with the cloud model prediction results of other weighting methods, its feasibility and effectiveness has been verified. The research shows that the cloud model for rock burst based on weighted inverse analysis is less subjective in the weighting process and the prediction effect is better.
Understanding User Query Intent and Target Terms in Legal Domain

Lexis Advance is a legal research service provided by LexisNexis that can respond to natural language queries. It includes a module called Lexis Answers which implements advanced Natural Language Processing (NLP) capabilities to improve understanding of the intent of the user's queries. Lexis Answers can respond to natural language questions concerning legal question types such as statute of limitations, elements of a claim, definition of legal terms, and others. Herein, we report on the successful use of advanced NLP approaches for detecting not only named entities, but entire legal phrases, a skill previously requiring domain knowledge and human expertise. We have utilized the Conditional Random Fields (CRFs) approach that employs hand-engineered features combined with word2vec embeddings trained on legal corpus. Furthermore, to reduce our dependency on hand-engineered features, we have also implemented deep learning architecture comprising of bidirectional Long Short-Term Memory (BiLSTM) and linear chain CRF. Both approaches were benchmarked against a rule-based approach for different types of legal questions. We find that both CRF and BiLSTM-CRF can identify query intents and legal concepts with comparable precision but much higher recall and F-score than the baseline. The resulting models have been employed in Lexis Answers as critical improvement in our natural language query understanding.
A model of owner estimate cost for software development project in Indonesia

Abstract The purpose of this study is to develop a model of owner estimate cost of software development projects. The model consists of three main components, namely, personnel direct costs, nonpersonnel direct costs, and taxes. Personnel direct costs are obtained by using the use case points (UCP) method that has been advanced. Initially, UCP was utilized to calculate the effort required to complete the software project. In this study, UCP was institutionalized to obtain personnel direct costs by distributing the acquired effort into each activity in a project. Further, the effort per activity is multiplied by the pay rate for each activity. Personnel direct costs are resulted by adding up the cost of all activities. The experiment has been conducted on nine software development projects in which the model is applied to get the estimated cost calculation.
Reflections on the Professional Practice of Knowledge Capture

Abstract The practice of capturing cognitive performance and expertise has grown into a profession over the past several decades. This chapter offers reflections from a professional knowledge capturer. The chapter describes the scope of, uses for, and origins of knowledge capture. It then moves to a description of the praxis of knowledge capture, including a discussion of protocols and methods for knowledge elicitation, focusing on the components common to all capture methods—structure and probing questions. The issues of cognitive burden sharing across the capturer and holder, how purpose guides execution, and how constraints can shape practical developments in approaches to knowledge capture are also discussed. Throughout, the chapter offers illustrative stories of knowledge capture from the field. The chapter concludes by looking at future directions for the profession.
Cluster CV2: a Computer Vision Approach to Spatial Identification of Data Clusters

This work shows a novel application based on techniques of Computer Vision and Machine Learning to identify k clusters into a data set with overlapping issue. Used in area of unsupervised data clustering, where separation between groups is tricky. Through pair-to-pair distance calculations upon original data, is gotten a Distances Matrix as representative information of data. This matrix contains visual information, then using morphological operators extract relevant features for individual identification of groups in data set. Next, matrix decomposition performed to covariance matrix, being calculation of data elements for each cluster in order to project data into a new linear space. So, overlapping and separation distances among clusters are corrected without loss information. Results present correct identification of k clusters, without loss information, and eliminating data overlap. Clustering validation metrics such as Silhouette and Precision was used to test the methodology.
Analytic Emitter Geolocation and Filtering via Time Difference of Arrival

Kalman filters have routinely been applied to geolocation problems using Time Difference of Arrival (TDOA) measurements due to the difficulty of obtaining analytic solutions to the hyperbolic isochrons. Extensive testing has been performed with Extended and Unscented Kalman Filters (EKFs and UKFs) for typical TDOA problems, and to a lesser extent Cubature Kalman Filters (CKFs). This paper expands that testing through further simulation to test the limits of the CKFs and UKFs in TDOA applications focusing on Time of Arrival (TOA) measurement noise and reduced dimensions for constraining the problem when a limited number of receivers are available. Analytic solutions to TDOA measurements offer potential performance increases, especially when coupled with Kalman Filters. A recent paper detailing an analytic solution to ellipsoid intersections for multistatic radar is applied to this problem of passive TDOA geolocation of a emitter. This paper develops the analytic solution for passive TDOA geolocation of an emitter and applies this solution through simulations, coupled with a Kalman filter. Limitations of this solution are analyzed, both for dimensionality and robustness with respect to noise in the TOA measurements. Further, performance evaluations for computational time versus accuracy for this technique compared to direct use of Kalman filters on TDOA measurements are detailed. Together, the techniques evaluated in this paper provide data to aid in choosing the appropriate Kalman Filtering method for geolocation through TDOA measurements.
Prototype-Based Classifiers in the Presence of Concept Drift: A Modelling Framework

We present a modelling framework for the investigation of prototype-based classifiers in non-stationary environments. Specifically, we study Learning Vector Quantization (LVQ) systems trained from a stream of high-dimensional, clustered data. We consider standard winner-takes-all updates known as LVQ1. Statistical properties of the input data change on the time scale defined by the training process. We apply analytical methods borrowed from statistical physics which have been used earlier for the exact description of learning in stationary environments. The suggested framework facilitates the computation of learning curves in the presence of virtual and real concept drift. Here we focus on time-dependent class bias in the training data. First results demonstrate that, while basic LVQ algorithms are suitable for the training in non-stationary environments, weight decay as an explicit mechanism of forgetting does not improve the performance under the considered drift processes.
Tutorial: How to Create an Ecosystem and Framework for Digital Public Sector Service Delivery

This half-day tutorial, to be delivered in English, provides a practical answer to the question: How do governments ensure a minimum level of usability in public service offers online?1 Two elements are of particular interest here: first, the tutorial focuses on usability guidelines and standards, which have emerged, in the last few years, as a directive tool with the aim of assisting governments in its implementation; second, the tutorial will streamline the governance model conceived to ensure compliance with the usability criteria as outlined in the usability guidelines. [1]-[4].
Introduction to Game Theory via an Interactive Gameplay Experience

This paper mentions a work-in-progress about an immersive gameplay experience that was designed to give players an introduction to the formal field of Game Theory in a memorable and interactive fashion. Game Theory is a field that has achieved a high level of pervasiveness in many domains, but it is generally misunderstood by those who have not been exposed to it formally. This gameplay experience, through narrative and interactivity, was designed to be informative and memorable. First, the content focus was narrowed down to Prisoner's Dilemma game. Then, low-fidelity card-based prototyping was completed and system design was laid out. After that, a high-fidelity prototype was implemented in Adobe XD software. Informal testing was conducted with 10 individuals. Users enjoyed using the interactive prototype more, as compared to the text-based traditional teaching material. There were no difference in the quiz scores.
Consistency in Multi-device Environments: A Case Study

The development of interactive environments has led to the creation of useful tools for controlling them. However, developers should be careful when considering the control level left to users, since the applications may become unusable. Whether the system or user decides the distribution of the available devices, Graphical User Interface (GUI) consistency must always be maintained. Consistency not only provides users with a stable framework in similar contexts but is an essential learning element and a lever to ensure the GUI efficient usage. Nevertheless, maintaining GUI consistency in an interactive environment is an open issue. Therefore, this paper proposes a set of consistency guidelines that serve as a tool for the construction of multi-device applications. As a case study, Spotify was evaluated by experts who identified consistency violations and assessed their severity.
A problem decomposition approach for large-scale global optimization problems

Abstract In fact, many modern real-world optimization problems have the great number of variables (more than 1000), which values should be optimized. These problems have been titled as large-scale global optimization (LSGO) problems. Typical LSGO problems can be formulated as the global optimization of a continuous objective function presented by a computational model of «Black-Box» (BB) type. For the BB optimization problem one can request only input and output values. LSGO problems are the challenge for the majority of evolutionary and metaheuristic algorithms. In this paper, we have described details on a new DECC-RAG algorithm based on a random adaptive grouping (RAG) algorithm for the cooperative coevolution framework and the well-known SaNSDE algorithm. We have tuned the number of subcomponents for RAG algorithm and have demonstrated that the proposed DECC-RAG algorithm outperforms some state-of-the-art algorithms with benchmark problems taken from the IEEE CEC’2010 and CEC’2013 competitions on LSGO.
Attribute Grammar Applied to Human Activities Recognition in Intelligent Environments

Researches about context awareness have been growing in the past decades. The development of services that considers the context of users are getting popular and are gaining more functionalities, making them smarter. One of the most common features is the monitoring of activities through a diversity of sensors. Yet, this is still superficial monitoring where the devices lack information sharing. Intelligent environments aim the exchanging of information with the purpose of creating models that represent real-world situations. This paper describes the use of an attribute grammar in order to create a formal specification of situations in such domains. The problem of representation of human activities is tackled through a case study to demonstrate how attribute grammar can help the improvement of this process.
Investigation Methods of Parameters Random Variations in Control Systems

Most publications on the theory and practice of creating control systems are devoted to deterministic problems, when the input quantities and output characteristics have deterministic values or functions. In the article, the authors draw attention to the fact that all input and output signals, as well as the internal parameters of control systems, always have random variations. Technological random deviations have parameters for construction materials and electronic components and devices. In addition, the ambient temperature randomly influences the control systems, mechanical effects from the installation object, as well as temporary aging, wear and other factors. The authors draw attention to the insufficient number of publications devoted to the system discussion of probabilistic methods for studying control systems. In this article, the authors not only systematized the methods for studying random variations in the parameters of control systems, but also set forth their unified approach to solving the most frequently encountered problems of studying accuracy, stability, serial seriality and parametric reliability in the case of gradual failure of control systems. The necessary mathematical apparatus is given.
Application of decision support system using the K-Nearest Neighbor and Weighted Product method for determining the recipients of low-income family scholarship (GAKIN) (case study: Poltekkes Kemenkes Semarang)

Abstract Poltekkes Kemenkes Semarang is a university of health that has a scholarship facility for underprivileged students or often referred as Beasiswa Keluarga Miskin (Gakin) . At this time the determination of Gakin scholarship recipients is still in the form of manual selection. Decision-Making is a systematic approach to a problem with the gathering of facts, a mature determination of the alternatives to be faced, and the taking of actions that are calculated to be the most appropriate action. To simplify the process of determining a decision, present an application of decision support system with input and output required. This research makes an application of decision support system with K-Nearest Neighbor method to recommend the decision in the system. It aims to shorten the time and provide convenience to policymakers (decision makers) in Poltekkes Kemenkes Semarang in selecting recipients of Poor Family Scholarships ( GAKIN ). Based on existing data, KNN method can handle all new data from potential recipients with a value of neighbor is 5 to produce an accuracy of 99.61%, precision 99.85% and recall 99.42% with data of scholarship recipients last five years.
Generation and Evaluation of Personalised Push-Notifications

A shared challenge in the domain of User Modeling, Adaptation and Personalisation is proposed for the 2019 EvalUMAP workshop whereby the evaluation of user models generating personalised push-notifications is to be explored. As such, this paper presents a description of the evaluation process, a solution to the first proposed challenge, a discussion of results obtained from the Gym-Push evaluation environment and a number of benchmarks which can be used as a baseline for future work.
Blind Source Camera Clustering of Criminal Case Data

This work focuses on the examination of a real word criminal case data set, consisting of still images found on a suspect's computer during a sexual abuse case investigation. Various source camera clustering algorithms, all based on the photo-response non-uniformity (PRNU), are employed to organise the images according to their source camera. The investigated data set poses many challenges to the algorithms due to the unknown origin of its images. The clustering result's quality is examined using different external and internal cluster validity indices (CVIs). Before attempting to cluster the criminal case data, the clustering algorithms and CVIs have been examined on a different data set with known ground truth, which revealed that some algorithms and CVIs are not appropriate for this scenario.Finally, we give some recommendations on which clustering algorithms and CVIs can be used in this scenario and discuss the problems and challenges we faced while investigating the data set.
Entrepreneurship in Nepali Higher Education: An Interpretive Inquiry

The purpose of this study was to explore how academic leaders of Nepali universities are experiencing entrepreneurship, changing contexts of higher education, and entrepreneurial activities to cope up with those changing contexts in order to lead universities. This inquiry is based on interpretive paradigm, which includes a multi-method approach. Five universities of Nepal were the researcher's living territories, where he interacted with nine participants in order to generate their narrative stories and experiences. The researcher explored and derived meanings from their stories and experiences, and in doing so, the researcher went through the process of coding, synthesizing, developing themes, analyzing, and interpreting meanings merging signature literature and theories of leadership and entrepreneurship. Through this enquiry, the researcher learned that entrepreneurship has multilayered meanings rooted in academic culture and society which could be understood being a part of the process. The researcher also learned that entrepreneurship is a process of dreaming and tracking a big picture, advocacy of change, elaboration of cognition, crossing the point of no return, a journey of togetherness, and tactful management of conflicts. The researcher also learned that changes in higher education in Nepal could be grouped under demand and supply market change. Within demand market, access, policy and awareness of parents and students are observed to have changed whereas within supply market, institutional providers, technology, pedagogy, curriculum and evaluation are found to have changed in higher education in Nepal. Apart from these, the researcher also learned that academic leaders of Nepali higher education have performed entrepreneurial activities like value added new programs, research and technological activities, fundraising activities, plan giving activities, retail sales and services, and real estate activities creating different values in order to cope up with those changing contexts in higher education. When the researcher examined the experiences and stories of the participants from leadership standpoint, he concludes that a vignette of entrepreneurship, changing context in higher education, and performance of entrepreneurial activities, have a nexus of dynamism in higher education.
Improved Automatic Speed Control for 3D Navigation

As technology progresses, it is possible to increase the size and complexity of 3D virtual environments. Thus, we need deal with multiscale virtual environments today. Ideally, the user should be able to navigate such environments efficiently and robustly, which requires control of the user speed during navigation. Manual speed control across multiple scales of magnitude suffers from issues such as overshooting behaviors and introduces additional complexity. Most previously presented methods to automatically control the speed of navigation do not generalize well to environments with varying scales. We present an improved method to automatically control the speed of the user in 3D virtual environment navigation. The main benefit of our approach is that it automatically adapts the navigation speed in a manner that enables efficient navigation with maximum freedom, while still avoiding collisions. The results of a usability test show a significant reduction in completion time for a multi-scale navigation task.
Genuine Counterfactual Communication with a Nanophotonic Processor

In counterfactual communication particles and information can travel in opposite directions. With our high-fidelity programmable nanophotonic processor we implement the first trace-free counterfactual protocol without post-selection with a counterfactual violation as low as 2.4%.
Classification of Arteriovenous Fistula Stenosis Using Shunt Murmur Analysis and Random Forest

Although patients undergoing hemodialysis generally have shunts implanted within a body part, problems such as blood vessel stenosis can occur. Patients undergoing hemodialysis can conveniently check their own shunt function by listening to shunt murmurs. However, manually judging the shunt function is difficult and requires experience. In this study, we propose a method to classify shunt stenoses using Random Forest (RF). The resistance index (RI) obtained from the ultrasound system is used as a class label. The normalized cross-correlation coefficient, the ratio of frequency power to mel frequency cepstrum coefficient (MFCC), was used as a feature to train in an RF classifier. As a result, the classification accuracy of RI by RF was found to be higher than that achieved by a support vector machine (SVM).