Reviewerly/exHarmony huggingface dataset:
Propositional Modal Logic

Modal logicsConnectives/operatorsof modal logic address uncertainty about truth values of statements by introducing and discussing the notion of possibility of truth in addition to the necessity of truth. In this, modal logics trespass the boundary between the realm of dichotomy true-false into the less transparent realm of certainly true-possibly true. As the latter is less rigorous about truth and the notions of necessity and possibility are open to various interpretations, the result is the existence of many variants of modal logics. We will follow in this chapter some line of more and more complex interpretations of necessity and possibility as well as their mutual relations.
Research on Single Observation Station Target Tracking Based on UKF Algorithm

Aiming at the problem of single observation station target tracking, this paper studies the single observation station target tracking algorithm based on Unscented Kalman filter (UKF). Firstly, this paper analyzes the UKF algorithm, and focuses on the implementation of UKF algorithm. On this basis, the modeling principle of single observation station target tracking is given, and the single observation station target tracking algorithm based on UKF algorithm is proposed. Finally, the simulation experiment is carried out by MATLAB. The simulation results show that, compared with the extended Kalman filter algorithm, the target tracking algorithm based on UKF has higher tracking accuracy, but takes longer time.
COVID-19 tracking apps

Dr. Elissa M. Redmiles shares her experience researching accurate contract tracing apps that respect the privacy concerns of users.
Knowledge and Trust Nexus Support the Link Among Six-sigma and Project Success: A Case of Developing Country

The fundamental purpose of this study is to check the impact of six sigma on project success by introducing the knowledge and trust nexus for engineering projects. The sample size of the study is 235 respondents and data is collected from the engineering industries through convenience sampling technique. The data collected is then analyzed by applying the structural equation modeling (SEM) technique in Smart-PLS in order to examine the relationships among observed variables. The outcome confirms the sharing of knowledge within the employees is directly linked to the project success, which align with the previous studies. Moreover, sharing of knowledge will help the employee to more learning and enhance their performance to achieve the project goals and objectives which leads to the success of the project. The outcomes also demonstrate the moderating role of trust between the six sigma and knowledge sharing. The study is unique in its scope and implications as the focus is upon empirical investigation of the impact of application of six sigma on project success in the context of developing economy like Pakistan.
Computational Curation and the Application of Large-Scale Vocabularies

Paper presents an exploratory case study comparing stemming and lemmatization results for the automatic application of large-scale controlled vocabularies processed against archival encyclopedia entries. The results report relative recall and precision evaluations across both results. Research shows that while stemming has a higher relative recall, lemmatization results in a higher relevance score and eliminates the over-stemming challenges. Results provide insight into improving automatic curation workflows for archival resources.
Speech, Mockery, and Sincere Concern: An Account of Trolling

This paper offers an account of a phenomenon that seems increasingly common in public discourse: trolling. The term “troll” is colloquial, and no formal synonym exists in English. But the informality of the term should not mislead us into thinking that the underlying concept is so unimportant as to be unworthy of philosophical attention or so ill-behaved as to be resistant to philosophical analysis. This paper presents such an analysis.
A Labeling Support System for Participants’ Situated Assessments by Multi-modal Sensing

This research aims to build a system that estimates assessment expressions to others by machine learning, collects them in real time, and feeds them back to the participants, and this study proposes two techniques that are necessary for this purpose. First, we propose a method to estimate the evaluative behavior to others, which is simplified to positive, negative, and neutral, from the facial images and head acceleration of participants in the conversation. The supervised machine learning algorithm requires ground-truth labeling. To analyze the expression of a large number of people, such as in a classroom, ground-truth labeling becomes a challenge in analysis and system construction. Second, to ease the labeling process we propose and implement a method to recommend the estimated labels on the labeling software. The accuracy of the proposed method is approximately 0.45 in F-value. Considering the time-series information, the proposed method recognizes major changes in positive evaluation behaviors. Presenting recommended labels on our labeling support system increase with time for the ground-truth labeling. However, the accuracy of labeling positive assessments improve in five out of six subjects.
Mining Cross-Domain Apps for Software Evolution: A Feature-based Approach

The skyrocketing growth of mobile apps and mobile devices has significantly fueled the competition among app developers. They have leveraged the app store capabilities to analyse app data and identify app improvement opportunities. Existing research has shown that app developers mostly rely on in-domain (i.e., same domain or same app) data to improve their apps. However, relying on in-domain data results in low diversity and lacks novelty in recommended features. In this work, we present an approach that automatically identifies, classifies and ranks relevant popular features from cross-domain apps for recommendation to any given target app. It includes the following three steps: 1) identify cross-domain apps that are relevant to the target app in terms of their features; 2) filter and group semantically the features of the relevant cross-domain apps that are complementary to the target app; 3) rank and prioritize the complementary cross-domain features (in terms of their domain, app, feature and popularity characteristics) for adoption by the target app’s developers. We have run extensive experiments on 100 target apps from 10 categories over 15,200 cross-domain apps from 31 categories. The experimental results have shown that our approach to identifying, grouping and ranking complementary cross-domain features for recommendation has achieved an accuracy level of over 89%. Our semantic feature grouping technique has also significantly outperformed two existing baseline techniques. The empirical evaluation validates the efficacy of our approach in providing personalised feature recommendation and enhancing app’s user serendipity.
Application of Multi-Element Soil Geochemistry and Particulate Gold Exploration in the Kambele Plain, Batouri Gold District, Southeast Cameroon

Soil geochemistry remains an essential tool in mineral exploration, especially in areas where the scarcity of outcrops and a thick lateritic overburden undermine the understanding of subsurface ore deposits. Thirty-five soil samples collected from six profiles within a 40 km2 block around the Batouri gold district, southeast Cameroon were analyzed for major elements (Fe, K) and trace elements (Au, As, Ba, Hg, Sb, W, Cd, Cu, Mo, Mn, Ni, Pb, Zn, Ga, Ge, Se, Ta, Cs, Ti, Bi) using both inductively coupled plasma mass spectrometry (ICP-MS) and instrumental neutron activation analysis (INAA). Furthermore, a heavy mineral concentrate from the sampling point with the highest Au concentration (64 mg Kg−1 Au) was examined for the presence of particulate gold. The element concentrations obtained were plotted on element distribution maps and contoured to show the distribution patterns of the various elements in the soil. The results show that anomalous gold concentrations are found in the northeastern part of the study area. The data were analyzed using multivariate statistics and results from the principal component analysis (PCA) shows six components with components 2 (Au-As-Sb-W) and 3 (Cu-Ni-Zn-S) pointing to hydrothermal sulphide-related mineralization in the underlying rocks. As, Sb, W and S are identified in this study as potential pathfinder elements for gold mineralization in the Batouri gold district. Gold grains were recovered from the concentrate and this point to gold dispersal in the solid-state within the weathering cycle.
Identifying Social Media Content Supporting Proud Boys

While most conversations on social media are inspiring and uplifting, a fraction of the users do engage in sharing content that supports extremist, radical philosophies and organizations. Such rhetoric, if left unchecked, can propagate virally on these platforms, ultimately escalating to turbulence and violence in the physical, offline spaces. Identifying such content from the volumes of social media feeds is therefore necessary to prevent the damage that it may cause, yet it is infeasible to undertake manually, calling for an automated approach. This paper demonstrates the potential of machine learning to separate social media feeds that are sympathetic to radical extremism using the example of Proud Boys, a contemporary right-wing group. From the tweets collected after Proud Boys protests in August 2020; linguistic, social and auxiliary features are extracted. Significance tests are used to select a subset of these features that contribute towards separating between extremist and normal content. Several machine learning models are trained based on a combination of these features. These models can identify tweets that support Proud Boys with excellent performance metrics. Artificial Neural Networks offer the best accuracy, precision, recall and F1-score. Feature importance, assessed using the Random Forest model indicates that users rely both on the expressive power of the language and other metadata features such as punctuations, mentions and URLs to voice and spread hateful ideology on online platforms.
Social Issues

Higher Education AbstractsVolume 56, Issue 4 p. 360-360 SOCIAL ISSUESToken Access Social Issues First published: 08 January 2022 https://doi.org/10.1111/hea.35_12178Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume56, Issue4October 2021Pages 360-360 RelatedInformation
Load Balancing and Predictive Analysis Model Implementation in Public Cloud

The data centre is fundamental to cloud computing. Data centers are currently being strained by the rising demand for cloud computing services. Cloud computing practices are very important in terms of device performance and schedule that can make it easier for users to distribute the workload among network resources. Any data-center services can eventually become overloaded/ under loaded, resulting in increased energy usage, as well as decreased functionality and resource waste.As a result, this paper uses a contextual with multiple metrics to adopt optimization algorithms that are implemented by load balancing. Load balancing with system integration strengthens resource utilization but can increase Performance of System (Latency) metrics. This research aims to incorporate a new system for congestion control and server expansion including migration latency, device threshold, QoS, and energy consumption.
Condition-Invariant Physical Adversarial Attacks via Pixel-Wise Adversarial Learning

Research has validated that deep neural networks are vulnerable to a series of methods named adversarial attacks. Such methods greatly impair performance of target networks by manipulating data with perturbations imperceptible to human. However, digital adversarial examples are vulnerable to various physical disturbances, while existing physical attack methods are strictly condition-variant and cannot generate adversarial data maintaining effectiveness with unforeseen disturbances. In this paper, we propose a physical adversarial attack method named Pixel-wise Adversarial Learning in Physical Attacks (P-ALPhA) to generate effective condition-invariant adversarial examples. Each adversarial example is decoupled into two separate clusters of pixels. Pixels in the first cluster are perturbed for adversarial purposes, while pixels in the second cluster are manipulated with a competitive optimization criterion against the adversarial aim. ALPhA enhances aggressiveness of the first cluster of pixels through intra-competition. Moreover, the original benign optimizations on the second cluster of pixels guide stochastic environmental disturbances to have adversarial impacts on the data. Experimental results show that P-ALPhA greatly enhances effectiveness of digital adversarial examples in establishments or simulations of multiple physical conditions. Success of P-ALPhA increases hazardness of adversarial attacks in physical applications of neural networks.
Visual Analytics of Autonomous Drone Trajectories with Dimensionality Folding

The design model of machine learning consists of multiple parameters in the reward function. However, different parameters affect the reward in numerous training epochs. Researchers are hard to analyze and evaluate the reward function in a learning model. Therefore, this study proposes a set of analysis tools for visual analytics of the flight trajectories where the drone learns to pass through obstacles, testify the reinforcement model's correctness and effectiveness and evaluate the parameter changes in the reward function design.
Determinantal Reinforcement Learning with Techniques to Avoid Poor Local Optima

Reinforcement learning for controlling multiple collaborative agents is an important task for many real-world applications, such as those involving robots and IoT devices. For most applications, the action of each agent should not only be relevant but also have diversity according to the role of the agent in the target task. In our prior work, we have proposed the use of the determinant of a positive semidefinite matrix to approximate the action-value function in reinforcement learning. We have designed an algorithm to efficiently learn the matrix such that it represents both the relevance and diversity of the actions. The algorithm is parameterized by the effective rank of the matrix that can be significantly lower than the full one, hence resulting in efficient learning. Based on theoretical derivation of our proposed approach, here we show how our algorithm can avoid the poor local optima that often hinder reinforcement learning approaches.
Re-examining Aristotle’s Categories as a Knowledge Organization System

In his Categories, Aristotle details the kinds of being that exist, along with what can be understood and predicated of existing things. Most notably within this work, Aristotle advances a set of ten, top-level categories that can be used to classify all kinds of being. Even today, the influence of the Categories is felt in many domains, particularly in knowledge organization (KO). Here, Aristotle’s Categories bear deep, long-standing connections with works examining categorization, subject analysis, and theory of classification. Though its relation to ontology might seem obvious, connections to KO perspectives on knowledge organization systems (KOSs) and ontological modeling are curiously lacking. The aim of this work is to offer a re-examination of the Categories as a KOS, particularly through the lens of the KO field’s understandings of ontology. Utilizing Zeng’s classification of KOSs as a theoretical framework, this study draws parallels between the first two sections of the Categories and the defining features of ontologies and offers an initial ontological model of this work. The results of this re-examination stand to offer a new view of a fundamental work in the KO canon, draw further connections between past and present perspectives in KO, and further contribute to the theoretical grounding of contemporary KOS research and practice.
A Research on the Application of Adaptive Rotation Deep Forest on Low Dimensional Datasets

In recent years, the research on deep learning is based on neural networks and mainly trains multilayer nonlinear parameters through a back propagation algorithm. Recent research on deep forest represented by gcForest opens a door to explore non-neural network depth models. This model is a deep learning model based on random forest, a training process of which does not depend on the backpropagation algorithm. Compared with the general ensemble learning method, it has better classification accuracy, especially on small-scale training sets. Low-dimensional data is also very common in the digital age. Because of the small number of features available for learning, it is very challenging for both neural networks and general ensemble learning models. This paper proposes a rotated adaptive deep forest (RADF) model based on improved RotBoost. The model has two main characteristics: Firstly, RotBoost is introduced to solve the problem that data cannot be scanned with multi-granularity due to a lack of spatial correlation to improve the diversity of low-dimensional data. Secondly, for better adapting to low-dimensional data, we use dense connections to optimize the cascade structure to provide more learning features for the model. Experiments show that the performance of RADF on low-dimensional data sets is better than that of deep forest and some mainstream ensemble learning models.
Investigation of Super-Resolution Methods for Processing a Sequence of Aerial Photographs

Today, one of the main problems in the field of small object detection is the low resolution of the image because in most cases an object can only occupy a small area of the frame (areas of 15 × 15 pixels or 30 × 30). Until recently, the main way to improve the quality of images was to upgrade the technical characteristics of the equipment installed on board the aircraft. However, the limitations created by the element base increase the cost of such equipment. With a dramatic leap in the performance of computing resources, it has become possible to apply digital image processing algorithms. The challenges posed by the constraints imposed on the installed hardware on board the aircraft are being overcome by machine learning techniques. With the advent of the possibility of training neural networks, variational methods and classical interpolation methods have faded into the background and are practically not used due to their shortcomings. Algorithms that are used to improve the visual quality of an image are called super-resolution methods. The task of the methods is to search in a space of higher dimension for such an instance, which, upon de-citation, is transformed into an original image of low quality. This article compares some super-resolution methods in terms of quality / speed. Examples of the best algorithms among the considered ones are given. Based on the analysis of the results obtained, a conclusion is made about the advisability of using the investigated methods aboard. Conclusions are given on the advisability of using neural network approaches, at best based on compensation between personnel movements. The main advantages and disadvantages of existing approaches are noted.
Research on Intelligent Recommendation System Based on Knowledge Management

With the rapid development of the information age, the traditional knowledge management system has many problems such as large amount of information and complex content, which leads to the low efficiency of users to obtain the correct information and affects the efficiency of knowledge transfer and knowledge sharing. In this paper, the intelligent recommendation system based on knowledge management is introduced to solve the problem of information overload, so as to effectively improve the efficiency of knowledge transmission and sharing and maximize the value of knowledge.
Sample Size Determination for Credibility Estimation

The credibility estimator has been successfully and widely applied by practitioners in the insurance industry for decades. However, an important issue remains unresolved: What sample size should the actuary choose when he or she applies the credibility estimator? In particular, is the size of a given claims dataset large enough for the credibility estimator to be accurate? This article aims to address this issue by first suggesting a sample size criterion for the credibility estimator and then proposing a sample size based on that. The proposed sample size is easy to apply, and it guarantees provable accuracy for credibility estimation.
PAR(1) model analysis: a web-based shiny application for analysing periodic autoregressive models

In this paper, a web-based shiny application called the ‘PAR(1) Model Analysis’— that allows the modelling, estimation and prediction of a periodic autoregressive time series with scale mixtures of skew-normal innovations, a general and quite flexible class of error distributions—is presented. The class of scale mixtures of skew-normal distributions is often used for statistical procedures of analysing symmetrical and asymmetrical data. The formulation of the scale of a mixture of skew-normal periodic autoregressive models and the estimating methods, which will be applied in the web application, is briefly explained. The embedded tools in the web application and their applications on data analysis are fully described, and finally, a real data example is completely analysed by using this app to illustrate its handling.
A Context-Aware Model with Flow Mechanism for Conversational Question Answering

Conversational Question Answering (ConvQA) requires a deep understanding of conversation history to answer the current question. However, most existing works ignore the sequential dependencies among history turns and treat all history indiscriminately. We propose a Flow based Context-Aware Question Answering model to alleviate the above problems. In specific, we first use a hierarchical history selector to filter out irrelated history turns according to the features of word level, utterance level and dialogue level. Then we introduce a FlowRNN to model the sequential dependencies between history turns along dialog direction. Finally we incorporate these hidden dependencies to BERT for answer predictions. Experiments on a large-scale conversational question answering dataset QuAC show that our proposed method can use conversation history effectively and outperforms most of the recent ConvQA models.