Reviewerly/exHarmony huggingface dataset:
A multi-labelled sequent calculus for Topo-Logic

Abstract We present a labelled sequent calculus for a trimodal epistemic logic exhibitied in Baltag et al. (2017, Logic, Rationality, and Interaction, pp. 330–346), an extension of the so called ‘Topo-Logic’. To the best of our knowledge, our calculus is the first proof-calculus for this logic. This calculus is obtained via an adaptation of the label technique by internalizing a semantics over topological spaces. This internalization leads to the generation of two kinds of labels in our calculus and the labelling of formulae by pairs of labels. These novelties give tools to provide a simple calculus that is intuitively connected to the semantics. We prove that this calculus enjoys many structural properties such as admissibility of cut, admissibility of contraction and invertibility of its rules. Finally, we exhibit a proof search strategy for our calculus that allows us to prove completeness in a direct way by the extraction of a countermodel from a failure of proof. To define this strategy, we design a tool for controlling the generation of labels in the construction of a search tree, although the termination of this strategy is still open.
A New Approach for Agile Product Line Engineering

Software Product Line (SPL) and Agile Software Development (ASD) are well known approaches used in software engineering (SE). Although these approaches have been built using different methodologies which belong to different school of thoughts but still they have many common objectives and goals. These common goals and challenges associated with each of them is the main factor that motivates the researchers to find a way to integrate ASD and SPL. This results in Agile Product Line Engineering (APLE) which integrates the long term strategic efforts in SPL with short term agility in ASD. The aim of APLE is to maximize the benefits of both ASD and SPL while minimizing their drawbacks. A number of approaches have been proposed for practical implementation of APLE but they suffer from different limitations as discussed in literature. In this paper we have proposed a new APLE methodology that not only fulfil all the requirements of APLE but also addresses the limitations of existing methodologies. We have adopted the survey technique for the evaluation of our proposed method and a survey in two medium sized organizations has also been performed conducted for this purpose. Evaluation results shows that our proposed approach is useful and suitable for application in software engineering domain.
Study on the Learning Algorithms of Artificial Intelligence

Abstract This article delves into artificial intelligence algorithms. From a functional and formal point of view, the artificial intelligence algorithms are divided into four categories which named: statistical based algorithms, tree-based algorithms, neural network-based algorithms, and comprehensive algorithms. On this basis, the common algorithm model and its principle in the field of artificial intelligence are analyzed in this paper. In order to further study the semantic logic in artificial intelligence field, based on the tools of semantic vector space model, this paper abstract and analyze the process of understanding “Knowledge Set” on the empirical level and “Control Knowledge Set” on the thinking level. This paper lays a theoretical foundation for the research of artificial intelligence and robots.
3D Pose Estimation and Tracking of an Electricity Pylon

We demonstrate an algorithm for estimating the 6 degree-of-freedom pose of a textureless object such as a power pylon. The method is designed to be part of the inspection process for a transmission line. The approach involves three steps, namely predicting the vertices of the pylon with a neural network, establishing the correspondence between vertices with hashing techniques, and incrementally tracking the movement of the camera. We built a pylon model for the experiments and used it to generate our dataset.
CEN: Understanding Rook vs Minor Piece Endgames

This is a response to 'Understanding Rook vs Minor Piece Endgames' by Mueller and Konoval, the third volume in their 'Understanding ...' series. It provides a selection of positions for comment and instruction. The supplementary material provides the full set of maxDTC(onversion) positions which are in the URME domain, together with DTC-optimal play from those positions.
Deep Correlation Filter based Real-Time Tracker

Visual tracking is a challenging task in computer vision. Recently correlation filter based trackers have gained much attention due to their high efficiency and impressive performance. Several methods have been developed to utilize deep features extracted from Convolutional Neural Networks (CNNs) for correlation filter tracking. Despite their success, most of these approaches suffer from low tracking speed due to high computation burden of the deep models. In this paper, we propose a CNN based real-time deep tracker for robust visual tracking. We first exploit hierarchical deep features as target representation, which can better distinguish the target from the backgrounds in the presence of complex situations. Before applied to learn correlation filters, the channel number of hierarchical features is reduced by the principal component analysis (PCA). This method can decrease both feature redundancy and computation. Then we construct a reliable map with features from the last convolutional layer as they encode the most semantic information. The reliable map is used to constraint the searching area where the target most likely exist. To further handle long-term tracking, we improve the model discrimination capability by introducing the original template into the current correlation filter model. As the target movement is usually small between two adjacent frames, we reuse the deep features from location step to update current model. The features are reused by making a circular shift with position displacement of the target, which increases the tracking speed significantly. Extensive experimental results on large benchmarks show that our proposed tracker can perform at real-time and achieves the state-of-the-art performance.
Automated Partial Credit for STEM classes

This paper introduces a system that allows the award of partial credit for exams and tests consisting of multiple choice and true/false problems. Traditional multiple-choice exams have a number of issues. For one, they result in overall lower grades than exams using the same problems in an open-ended format. It is well known, that students see multiple-choice and true/false problems as not completely fair because they do not take into account partially correct answers. Non-major courses such that Math and Statistics for Engineering and Computer Science majors create additional challenges because they require simultaneous content knowledge in more than one subject. A reliable automated method to assign partial credit for multiple-choice and true/false problems can greatly improve both quality and perception of fairness of the assessment. There are two major approaches to address the issue with multiple-choice problems, see e.g. [1], [2]. In this work we suggest a combined and generalized approach for assigning partial credit for non-open-ended problems by using Concept Inventory among other testing approaches. One of the main benefits of our suggested approach is content independence and applicability to both major and non-major STEM subjects.
Comparative study of classifier models for Crime Mitigation and Risk Modelling

Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets) for sentiment extraction. It automatically identifies whether a tweet expresses positive, negative or neutral opinion about and individual or an entity. The aim of this paper is to give detail explanation about the process of Crime mitigation by analyzing sentiment over the data generated by a social platform twitter using machine learning. This study focuses on a comparison done between, two classifier models, for data streamed from twitter. The paper then uses the comparative study for crime mitigation. Classification of sentiment can be done at various level. In our paper, the focus is on Feature level classification. [2]Risk analysis and profiling is then done on the predicted classes.
Outlier Accommodation in Sensor Rich Environments: Moving Horizon Risk-Averse Performance-Specified State Estimation

This paper considers state estimation for nonlinear systems when the measurements may be corrupted by outliers. The traditional approach to outlier accommodation utilizes the extended Kalman filter over a single time epoch only for those residuals that pass a Neyman-Pearson (NP) threshold test. This approach may utilize risky measurements that are unneeded to achieve a user-defined performance level, yielding unnecessary risk. Risk-Averse Performance-Specified (RAPS) state estimation is an optimization based approach that chooses a subset of measurements with minimum risk that satisfies an user-defined performance specification.This article extends the RAPS approach to moving horizon (MH) state estimation for nonlinear systems. The moving horizon approach allows past outlier decisions within the time window to be reevaluated in the light of new measurements. This paper includes experimental results for vehicle state estimation using Global Navigation Satellite Systems (GNSS) aiding an Inertial Navigation System (INS). The MH-RAPS performance is compared with MH-NP state estimation.
Frontiers in Precision Medicine IV: Artificial Intelligence, Assembling Large Cohorts, and the Population Data Revolution

Large cohort studies and more recently electronic medical records (EMR) are being used to collect massive amounts of genetic information. Implementation of artificial intelligence has become increasingly necessary to interpret this data with the goal of augmenting patient care. While it is impossible to predict what the future holds, policy makers are challenged to create guiding principles and responsibly roll out these new technologies. On March 22, 2019, the University of Utah hosted its fourth annual Precision Medicine Symposium focusing on artificial intelligence, assembling large cohorts, and the population data revolution. The symposium brought together experts in medicine, science, law and ethics to discuss and debate these emerging issues.
Roos' Matrix Permanent Approximation Bounds for Data Association Probabilities (Poster)

Matrix permanent plays a key role in data association probability calculations. Exact algorithms (such as Ryser&#x0027;s) scale exponentially with matrix size. Fully polynomial time randomized approximation schemes exist but are quite complex. This paper introduces to the tracking community a simple approximation algorithm with error bounds, recently developed by Bero Roos, and illustrates its potential use for estimating probabilities of data association hypotheses.
DoSSAD - Leveraging Domain Specific Semantics in Aspect Detection for Product Design

Accurately identifying customer requirements is an essential activity in product design and development. Particularly, the development lifecycle of mechanical products is long-term, and enhancements or upgrades are challenging to execute. The growth of online product reviews paves way for a data-driven approach to capture customer experiences with existing products and their sentiments towards specific features of products, thereby enabling faster product development cycles. Aspect Detection is becoming an increasingly popular way to identify the feature of a subject expressed by a segment of text. Through this work, we propose to study Aspect Detection as an essential step towards the qualitative analysis of customer reviews. Product design and development is often a focused domain, where one cannot set generic product guidelines. Therefore, our study focuses specifically on eco-friendly products as an example of a product category. Existing research in aspect detection has often leveraged huge volumes of text data with a wide vocabulary to provide generic aspect detection. Through this paper, we study the impact of domain-specific semantics on aspect detection, with a focus on product development and design as an application. We present an extensive evaluation of aspect detection models trained using datasets with varying amounts of domain-specific data (i.e., product-category-specific data) during the training phase.
A Method of Security Emergency Plan Decision Making by Cloud Model Computing

Abstract This paper designed a method of security emergency plan decision making by cloud model computing. This method is made in order to accurately determine security emergency plan in time when safety accident occurred. First, built a index system which is suitable for comprehensive transportation projects. Second, ran a cycle improvement rate according to cloud thickness to get the evaluation indexes and cloud weight, and then put them into the comprehensive formula to get the reference cloud. At last, drew reference cloud and evaluation clouds on a 3D picture by Matlab and compare each evaluation cloud with the reference cloud, the plan which had the finest similarity is the best one. This method can realize visual comparison for safety emergency plans.
Information Retrieval in Kannada using Ontology

As Internet technology has become a part of the lifestyle of the common man, research efforts are extensively made in the fields of Natural Language Processing (NLP) and Information Retrieval. Studying regional languages for developing the system to store, retrieve, extract the information from the database has gained lots of prominence nowadays. Case studies show that Ontological Information Retrieval has many advantages over keyword-based approach. In this paper we have focused on the general architecture of ontology-based Information Retrieval used for Kannada.
The impact of translated <i>Harry Potter</i> on children’s fantasy literature system in South Korea

Abstract This study examined the impact of translated Harry Potter series on Korea’s literature system, adopting Even-Zohar’s polysystem theory. Literature review shows that (1) discourses on children’s fantasy literature increased; (2) the number of the country’s original children’s fantasy books apparently inspired by Harry Potter rose; (3) the country’s discourses about children’s fantasy literature began to focus more on its own originals and their distinctive features. This indicates that translated Harry Potter moved to the centre in Korea’s children’s literary system. And this is mainly attributed to the weakness of the country’s fantasy literature system. This research is based on Even-Zohar’s polysystem theory which is considered an effective framework to describe the dynamic and competitive interplay between systems, and it is significant as the first attempt to explain the rise of translated fantasy children’s books in Korea’s children’s literature system in the wake of the sweeping popularity of translated Harry Potter .
Analyzing Data Mining and Its Application to Smart Business

This paper presents the main concepts of smart business (SB) and the application of smart business to provide solutions to the problems faced by a corporation. In this context, the main emphasis is on the approach of data mining including one of the tools used by SB that uses Decision Trees and Artificial Intelligence techniques along with other techniques. Moreover, algorithms such as Genetic Algorithms system and the Neural Networks are considered in its implementation. The concept of Smart Business (SB) has clarified these problems and consists of broad category technologies and application programs used to extract, store, analyze and transform high dimensional data. The approach of Smart Business is presented according to the concepts of Data Warehouse, Data Mart and Data Mining.
Chaotic Maps: Applications to Cryptography and Network Generation for the Graph Laplacian Quantum States

In this article, we proposed a new chaotic map and is compared with existing chaotic maps such as Logistic map and Tent map. The value of maximal Lyapunov exponent of the proposed chaotic map goes beyond 1 and shows more chaotic behaviour than existing one-dimensional chaotic maps. This shows that proposed chaotic maps are more effective for cryptographic applications. Further, we are using one-dimensional chaotic maps to generate random time series data and define a method to create a network. Lyapunov exponent and entropy of the data are considered to measure the randomness or chaotic behaviour of the time series data. We study the relationship between concurrence (for the two-qubit quantum states) and Lyapunov exponent with respect to initial condition and parameter of the logistic map which is showing how chaos can lead to concurrence based on such Lyapunov exponents.
Automatic Individual Information Aggregation using Publicly Available Social Media Data

In this paper, we make use of freely available data on public social media sites in two ways. First, we develop a search application capable of aggregating information about an individual, using only their name as input. Second, we investigate the feasibility of mining public data and linking this information across multiple social media sites in an attempt to produce an information profile for an individual. The inspiration of our work is to allow a person to see firsthand how much information about them exists online, and how this information could potentially compromise their privacy whereas the objective of our research is to analyze the feasibility of building a profile by gathering and linking information of an individual across different social media sites. This is done in the hope of perhaps inspiring an individual pay additional heed to the privacy settings on social media accounts, and to be more vigilant about what information they choose to share online.
Response Obligation Estimation That Considers Users' Repetitive Utterances using Knowledge-Guided Random Forest

Response obligation is whether a spoken dialogue system should react to an input sound. This paper focuses on the false negative errors in response obligation estimation (ROE) that are displayed as the system's neglect of its users. When the users repeat after the system ignores their speech, ROE will likely fail again because the repeated input is similar to the previous input. Therefore, we propose an improved ROE method that considers users' repetitions. First, we show that a simple concatenation of ROE and repetition features is better than two other integration architectures. Then, we propose a modified random forest algorithm that incorporates human domain knowledge. The effectiveness is demonstrated with simulated repetitions as a 7.6-point gain from the baseline.
Assessing a Refugee Camp in Mayukwayukwa Zambia

Observations based on mixed mode assessment data concerning electricity supply of the Mayukwayukwa refugee settlement of the Western Province of Zambia are presented. The mixed mode data include a survey that represents perhaps 500 residents of the 15000 settlement and interviews of a random sample of 15 residents. The data is the result of a study that was authorized by UN and Zambian authorities. The settlement is one the oldest UN initiated camps, dating back to 1966. The camp has spawned a large number of businesses and the only bank branch office in a 90 km radius. No date set for the national electric grid to extend to the camp. The camp was once partially powered by a 24 kW water turbine located on the Luena River that borders the camp. This micro hydro installation operated well below rating for some months a year from 1999 - 2012 but has been out of service since. Perhaps 10% of settlement has electricity for a few hours a day. This electricity is primarily from individual diesel generators. There are some small solar panels, mainly for charging cell phones. Various phased-in plans for providing the settlement with power are herein discussed and evaluated.
Collecting High-Quality Dialogue User Satisfaction Ratings with Third-Party Annotators

The design, evaluation and adaptation of conversational information systems are typically guided by ratings from third-party, i.e. non-user, annotators. Interfaces used in gathering such ratings are designed in an ad-hoc fashion as it has not yet been investigated which design yields high-quality ratings. This work describes how to design user interfaces for gathering high-quality ratings with third-party annotators. In a user study, we compare a base interface that consolidates best practices from literature, an interface with clear definitions and an interface in which tasks are separated visually. We find that these interfaces yield annotations of high quality and separation of tasks. We find no significant improvements in quality between UIs. This work can serve as a starting point for researchers and practitioners interested in collecting high-quality dialogue user satisfaction ratings using third-party annotators.
Particle Filter Track-before-detect Algorithm with Discontinuous Signals in Passive Sensor Systems

This paper considers the tracking problem of a moving emitter, which discontinuously emits certain signals, using a passive sensor system. There are two main difficulties, i.e., the detection uncertainty by classic thresholding process and the uncertainty of signal existence due to the discontinuous emission. The aim is to estimate both the kinematic state of the emitter and the unknown discontinuous property of the signal emission. This problem becomes more challenging when the signal-to-noise ratio (SNR) of the emitter is low. To this end, we first formulate it in the Bayesian framework and construct a joint posterior probability density function (pdf) incorporating the kinematic state with the discontinuous property. To calculate the goal pdf with signal emission uncertainty, we propose a varying period estimation (VPE) approach. First, a likelihood ratio test (LRT) is designed to estimate the discontinuous property, and determine the update time for Bayesian filtering. Then, the varying period Bayesian filtering for the kinematic state is derived based on the updated time. Furthermore, to avoid the performance degradation by detection uncertainty, a particle filter based track-before-detect (VPE-PF-TBD) with unthresholded measurements is designed. Simulation results demonstrate the effectiveness of the proposed algorithm.
Comparison of Binary Rain Prediction on HIMAWARI using MPI and CUDA

Predictions of rainfall events are very important to determine the preventive steps needed to avoid adverse effects with climate change that slowly changing the impact of rain events in Indonesia's area. One way to calculate the prediction of rain events is to use the Lookup Table and Probability of Rain method. To perform that, parallel programming is conducted to help solve large and complex computational problems. In this study, CPU (Central Processing Unit) programming and GPU (Graphical Processing Unit) programming are done to see each performance where computation design and Lookup Table design are implemented on both programming. The results show that CPU programming is more effective for the given case and data rather than GPU programming. Meanwhile, non-ranged Lookup Table design in CPU programming gives more accurate predictions even though it takes more time in computation.