Reviewerly/exHarmony huggingface dataset:
Value of Information in Probabilistic Logic Programs.

In medical decision making, we have to choose among several expensive diagnostic tests such that the certainty about a patient's health is maximized while remaining within the bounds of resources like time and money. The expected increase in certainty in the patient's condition due to performing a test is called the value of information (VoI) for that test. In general, VoI relates to acquiring additional information to improve decision-making based on probabilistic reasoning in an uncertain system. This paper presents a framework for acquiring information based on VoI in uncertain systems modeled as Probabilistic Logic Programs (PLPs). Optimal decision-making in uncertain systems modeled as PLPs have already been studied before. But, acquiring additional information to further improve the results of making the optimal decision has remained open in this context. We model decision-making in an uncertain system with a PLP and a set of top-level queries, with a set of utility measures over the distributions of these queries. The PLP is annotated with a set of atoms labeled as observable; in the medical diagnosis example, the observable atoms will be results of diagnostic tests. Each observable atom has an associated cost. This setting of optimally selecting observations based on VoI is more general than that considered by any prior work. Given a limited budget, optimally choosing observable atoms based on VoI is intractable in general. We give a greedy algorithm for constructing a conditional plan of observations: a schedule where the selection of what atom to observe next depends on earlier observations. We show that, preempting the algorithm anytime before completion provides a usable result, the result improves over time, and, in the absence of a well-defined budget, converges to the optimal solution.
Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic Modeling Based Approach

The 4th International Workshop on Health Recommender Systems (HealthRecSys 2019), Copenhagen, Denmark, 20 September 2019
Geometry Verification of Arbitrary Shaped Conductive Cavities by Means of Eigenmodes classified with Artificial Neuronal Networks

In this paper a method for fault geometry verification and fault detection based on geometric feature extraction and neural network classification is proposed. The geometric feature extraction is done by means of broadband s-parameter measurements followed by eigenmode extraction for geometry verification of arbitrary shaped conductive cavities. The target is a classifier that is fault detection of fabricated arbitrary shaped conductive cavities with the help of a simulation based database only. For a proof of concept a simplified geometry model with four variable dimensions was defined. With the help of CST Studio Suite a large database consisting of 625 different simulations was created. In a second step, the simulation data is reduced to eigenmodes that are further investigated with respect to the frequency change per geometry deviation. A classifier has been developed from the simulated eigenmodes of the scattering transmission parameter |S21| with the aid of a multilayer feed forward neuronal network. Considering five selected eigenmodes, the classifier can detect the geometry parameters of six reference channels with absolute geometric deviations of 1 mm or less.
2019 National Marine Educators Association Conference: Save the Date!

Current is the peer-reviewed journal of the National Marine Educators Association. It features lively, in-depth articles and activities relating to all aspects of the world of water. Articles explore the latest in marine and aquatic education research, ocean literacy, classroom and free-choice learning activities, marine science, art, maritime history and literature.
Tagging a Norwegian Dialect Corpus

This paper describes an evaluation of five data-driven part-of-speech (PoS) taggers for spoken Norwegian. The taggers all rely on different machine learning mechanisms: decision trees, hidden Markov models (HMMs), conditional random fields (CRFs), long-short term memory networks (LSTMs), and convolutional neural networks (CNNs). We go into some of the challenges posed by the task of tagging spoken, as opposed to written, language, and in particular a wide range of dialects as is found in the recordings of the LIA (Language Infrastructure made Accessible) project. The results show that the taggers based on either conditional random fields or neural networks perform much better than the rest, with the LSTM tagger getting the highest score.
Multi-scalar Shifts and Drifts

This chapter unpacks the approaches to creative measuring that work across many scales and dimensions of action, signalling the challenges and offerings that a creative project that surveys multi-scalar relationships entails. The chapter explores the mediation of technological systems that connect individuals to both local and global scales of movement. An emphasis on consumer technologies such as GPS enabled devices, online mapping software, and fitness trackers enable individuals to “know” their location and position in the world through various mobile, technological, and biological techniques. Examples are drawn from the authors’ creative artworks, experiments, and participatory activities that provide a wide range of approaches, sensibilities, and sites of intervention to and locate one’s position in relation to, and across, multiple scales.
P2P: From Packets to Pepole, Leverage Wireless Artifical Intelligence for Bridging Network Performance Healthiness to Quality of Experience (QoE) in 4G Networks

In the network world, successful delivery of binary 0 and 1 encapsulated in a network packet does not necessarily indicate a dazzling smile from end user. Good network performance may give false comfort to mobile operators since it is easy to measured and control but does not lead to the best user perception based Quality of Experience (QoE). We propose a wireless Artificial Intelligence (AI) based system to bridge the gap between network performance and user perception with an ultimate goal to maximize user QoE in 4G LTE networks through Deep Learning based Convolutional Neural Networks (CNN) and Sigmoid Activation Functions, both of which are customized for 4G LTE networks. We also implement a Geo-bin solution to enable network performance quantified by Network Performance Healthiness (NPH) Score to better approximate users’ true QoE as close as possible. With NPH and Geo-Bin Solution, the ultimate goal of network optimization is shifted from network performance driven to user QoE oriented in a new P2P (from packets to people) paradigm.
사이버대학교 성인학습자의 학습경험 탐색

This study is to explore the learning experiences of adult learners at cyber universities. The study explores some questions such as what it means to have learning experience in higher education as an adult learner. This is to understand their own subjective perceptions and meaning of life, what they mean to them and how the experience leads to meaningful changes in their lives. To this end, five cyber-university adult learners were interviewed in depth and its content was analyzed in terms of synchronous and emotional dimensions and post-adaptation changes. The result showed that, on the motivational level, the motivation for adult learners of cyber universities was closely related to their life experiences. On the emotional level, they experience a variety of negative emotions such as fear and blindness in their learning situations, which were having a lot of influence on their academic adaptation process. The change after adaptation indicated that the learning experience at cyber universities was leading to individual self-realization and desires to achieve. Through this process, it has been confirmed that the learning of adult learners makes qualitative changes and growth in their lives after when they have overcome negative sentiments, with it going beyond the compensation for study opportunities once they could not have. This study is meaningful in that it understood adult learners' motivations and adaptation process at the lifelong education level and explored the meaning of their learning in depth through their experiences.
Aggregating Probabilistic Judgments.

In this paper we explore the application of methods for classical judgment aggregation in pooling probabilistic opinions on logically related issues. For this reason, we first modify the Boolean judgment aggregation framework in the way that allows handling probabilistic judgments and then define probabilistic aggregation functions obtained by generalization of the classical ones. In addition, we discuss essential desirable properties for the aggregation functions and explore impossibility results.
Learning Node Embeddings with Exponential Family Distributions

Representing networks in a low dimensional latent space is a crucial task with many interesting application in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks with the traditional Skip-Gram approach, modeling center-context node relationships. In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic exponential family graph embedding (EFGE) model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. Our experimental evaluation demonstrates that the proposed technique outperforms well-known baseline methods in two downstream machine learning tasks.
Decoding via Sampling

We propose a novel decoding algorithm called “sampling decoding”, which is constructed using a Markov Chain Monte Carlo (MCMC) method and implements Maximum a Posteriori Probability decoding in an approximate manner. It is also shown that sampling decoding can be easily extended to universal coding or to be applicable for Markov sources. In simulation experiments comparing the proposed algorithm with the sum-product decoding algorithm, sampling decoding is shown to perform better as sample size increases, although decoding time becomes proportionally longer. The mixing time, which measures how large a sample size is needed for the MCMC process to converge to the limiting distribution, is evaluated for a simple coding matrix construction.
Data and Combine

In the last chapter, we covered a lot of UI elements and had a look at how they work. We shall explore more UI elements in the chapters as we go forward; however, there were some interesting things, namely, the @State annotation that we used to store values. In this chapter, we shall explore this and the related information on how it all works and makes this possible.
Conversational Agents to Address Abusive Online Behaviors

Abusive online behaviors occur at a large scale on all social media, and have dire consequences for their victims. Although the problem is largely acknowledged, technological solutions remain limited to detecting and hiding abusive comments. More can be done to address abusers themselves. We propose to investigate the potential of conversational technologies to dialogue with abusers. In this problem description paper, we outline directions for studying the effectiveness dialogue strategies, e.g., to educate or deter abusers, or keep them busy with chatbots thus limiting the time they spend perpetuating abuses.
A Knowledge Selection Model in Pointer-Generator Dialogue Systems

Conversation generation is one of the core problems in natural language processing. In this paper, we present a new model for knowledge grounded conversations. Our model is enhanced by copying mechanism, which can produce a response with tokens either copied from conversation history or be generated from states. Besides, to utilize related knowledge precisely, we add attention mechanism to select the most related knowledge for each decoding step. Furthermore, we use beam search to reduce the generation of meaningless responses. Evaluations show that our model achieves better performance than other baselines.
Corona Discharge Detection System Based on Ultraviolet Sensor and Optical Lens

Ultraviolet radiation produced by corona discharge can be used for discharge diagnosis, but the ultraviolet imager is expensive and the ultraviolet signal is susceptible to external interference during the day, so it can not accurately identify the occurrence of corona discharge. In this paper, an optical lens is designed to collect ultraviolet signal. In this paper, a corona detection method based on ultraviolet sensor and optical lens was proposed. The design of optical lens to concentrate the ultraviolet signal so as to suppress external interference was presented. By carrying out insulator corona discharge experiments, the feasibility of the proposed method was studied. Finally, a comparison and verification was made with the combination of corona discharge images collected by UV imager. Research results by this paper indicate that, when the detection distance is smaller than 5 m, and the power supply is about 700 V, the proposed corona detection method can effectively collect the ultraviolet rays generated by corona discharge, thus realizing the feasible recognition of corona discharge. Moreover, this method can feasibly reflect the changing of UV intensity with applied voltage and detection distance. The research results can provide important reference for the development and calibration of ultraviolet detection equipment.
An Error analysis of using present tense by the tenth grade students of senior high school in writing descriptitive text at MAN 2 Model Medan

Simple Present Tense is part of grammar that stated in tenses. It uses to express daily activities. Writer must use simple present when they will write about their activities. The data analysis was six steps through: errors detection, locating error, describing error, classifying error, counting error, and putting the result into table.it was conducted in MAN 2 Model Medan, especially Tenth Grade. The data by students Tenth Grade to write descriptive text by using present tense then their works was collected and analyzed to get the data as objective as possible, so they can be better in writing descriptive text by using present tense. After analysis all text, the researcher found some finding of the research. The types of errors made by the students on writing descriptive text are based on surface strategy taxonomy which are classified into omission, addition, misformation, and misordering. After counting the errors, the researcher found 121 total of errors consisting of 54 errors of omission, 30 errors of addition, 28 errors of misformation and 9 errors of misordering. From the data above, it can be seen that omission error was the highest number of errors made by the students. types of errors found in descriptive text in students writing of the tenth grade of MAN 2 Model Medan, the researcher can find some causes of errors. They are interlingual and intralingual transfer. Interlingual transfer is errors that are influenced by the interference of users’ mother tongue.The users still apply Indonesian pattern into English when they translate the sentences. Then Intralingual transfer occurs when students get difficulty learn the second language. They made errors is because of their mother tongue interference and logical thinking and not doing practice English well.
MANAGING PERFORMANCE OF HEALTH PROGRAM USING LOGICAL FRAMEWORK APPROACH: BEST PRACTICE

Performance management constitutes of several major processes, namely measurement of the performance, implementation, monitoring and evaluation of improvements. Developing a framework is recommended and as for that, the use of the Logical Framework Approach (LFA) is suggested in developing a framework for managing the performance of any programs. The LFA is a powerful analytical tool for planning and managing objective-oriented projects. This tool could be utilised during the designing process and initial planning. It could also functions as a management tool during the program implementation, monitoring and evaluation. As a standardised tool for many projects at one go due to its comprehensiveness component, the LFA framework could be planned by the program manager and is easily monitored by the program coordinator or supervisor. Therefore, the main objective of this article is to discuss the application of the LFA in managing program performance. Keywords: program performance, Logical Framework Approach, planning, monitoring, evaluation
ИМИТАЦИОННАЯ МОДЕЛЬ ФУНКЦИИ РАСПОЗНАВАНИЯ И ПОНИМАНИЯ СТАТИЧЕСКИХ ОБЪЕКТОВ САМООБУЧАЮЩИМСЯ РОБОТОМ НА ОСНОВЕ МУЛЬТИАГЕНТНЫХ НЕЙРОКОГНИТИВНЫХ АРХИТЕКТУР

The purpose of this study is to develop a simulation model based on multi-agent neurocogni-tive systems, which will create an effective tool for solving the problems of recognition and under-standing by the robot of unstructured input data flows. The task is to develop a simulation model of the recognition function of static objects shown on the camera. The presented simulation model of recognition and understanding of static objects by a self-learning robot on the basis of multi-agent neurocognitive architectures allows to create concepts and categories using multimodal flows of input information and interaction with users to expand the system of these concepts and categories and clarify the relationship between them. On this basis, the problems of formalization of the semantics of natural language for linguistic complexes limited by the subject area can be solved, which will allow the use of a dialogue system to solve the problem of setting complex mis-sions to robots. Formalization of semantics of common sense on the basis of self-organization of multi-agent neurocognitive architectures will allow to develop simulation models of understanding of natural language statements. This, in turn, will remove the restriction on the creation of a dia-logue system that allows operators using a natural language description to form the formulation of the problem and the statement of its essential conditions.
Fostering Learner Autonomy: Self-evaluation in Enhancing English Writing Skills

The article is dedicated to the problem of teaching learners to be responsible for their academic achievements. An independent and autonomous student is a peer partner of a teacher in the teaching and learning processes. The author suggests a self-evaluating technique which enables the learner to develop an attitude and gain experience with autonomy and investigates the role of autonomy in enhancing the English writing skills of students in Ukrainian secondary schools. The self-evaluating technique consists of three steps, with the first involving self-assessment of formal writing skills by the students themselves. The second step presupposes self-evaluating activity on enhancing English writing skills. The final step involves the self-evaluation of enhanced writing skills. According to the purpose and the outcomes of the research, the assessment-and-evaluation tasks gave students the opportunity to develop their learners’ autonomy and independence. The participants of the research were 4 teachers and 40 ninth-grade students from Ukrainian secondary schools.
THE EFFECT OF STANDARDS OF ETHICAL CONDUCTION FOR MANAGEMENT ACCOUNTANTS ON FINANCIAL REPORTING QUALITY

This study aims to investigate the impact of the standards and the ethics of professional conduct of the managerial accountant on the quality of the financial reports. These standards embodied in competence, confidentiality, integrity, and credibility. To achieve the objective of the study a questionnaire was built and distributed to the sample of the study where the study population comprised Extractive and Mining industries enlisted in Amman Stock Exchange (ASE). In order to test the hypothesis, Stepwise Multiple Regression was used. The results have demonstrated that each of competence, confidentiality, and credibility has an impact on the financial reporting quality whereas there is no significant impact of the standard of integrity in the financial reporting quality. In light of the results of the study, the researchers recommend the need to emphasize the role of the standards of ethical conduct of the managerial accountant in achieving the quality of accounting information included in the internal financial reports. As well as the compatibility between the freedom of the managerial accountant to adhere to binding accounting standards and his commitment to the standards of ethical conduct while establishing an organizational mechanism to guide and monitor the compliance with these standards.
SIDE CHANNEL ANALYSIS WITH MIMO ESTIMATION TECHNIQUES

In summary, the work done in this thesis presents an innovative idea for performing a cryptanalytic attack. Specific methods and steps of implementation are given, including comparisons of results. This work considerably reduces attack complexity and is straight-forward to be implemented in real systems.
Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning.

We introduce the problem of Dynamic Real-time Multimodal Routing (DREAMR), which requires planning and executing routes under uncertainty for an autonomous agent. The agent can use multiple modes of transportation in a dynamic transit vehicle network. For instance, a drone can either fly or ride on terrain vehicles for segments of their routes. DREAMR is a difficult problem of sequential decision making under uncertainty with both discrete and continuous variables. We design a novel hierarchical hybrid planning framework to solve the DREAMR problem that exploits its structural decomposability. Our framework consists of a global open-loop planning layer that invokes and monitors a local closed-loop execution layer. Additional abstractions allow efficient and seamless interleaving of planning and execution. We create a large-scale simulation for DREAMR problems, with each scenario having hundreds of transportation routes and thousands of connection points. Our algorithmic framework significantly outperforms a receding horizon control baseline, in terms of elapsed time to reach the destination and energy expended by the agent.
LibGuides: Adopt, Adapt, Improve: Presentation Materials

Adopt, Adapt, & Improve: An Instruction Librarian Development Program | April 4, 2019 | IL Summit
Heavy metals level in water, sediments and health risks assessment of IkoliCreek, Bayelsa State, Nigeria

Water and sediments sample from Ikoli Creek, Bayelsa state were analyzed for levels of Heavy Metals (Cd, Cr, Pb Zn and Fe) using atomic absorption spectrometer (GBC Avanta PM AAS, SNA6600) under standard operating conditions and was compared with permissible limits set by World Health Organization (WHO). The mean concentration of different metals in water followed this order: Fe>Cd>Cr>Pb>Zn. Sediments Fe>Zn>Cd>CD>Pb. The health risk assessment of the various heavy metals were calculated. The values for Chronic Daily Intake (CDI) for heavy metals (Cd, Cr, Pb and Zn) were found not detectable except for Iron (Fe) having a high value which was ascribed to the soil composition of the area. Hazard Indices were below 1 for the heavy metals in drinking water samples and the Life Cancer Risk (LCR) indicated no health risk when compared with USEPA recommended standard limits. This study recommended that continuous monitoring of (Cd, Cr, Pb Zn and Fe) in water and sediments of Ikoli Creek should be done to assess the risk of these metals in this creek.