Reviewerly/exHarmony huggingface dataset:
Approximately Supermodular Scheduling Subject to Matroid Constraints

Control scheduling refers to the problem of assigning agents or actuators to act upon a dynamical system at specific times so as to minimize a quadratic control cost, such as the objective of the Linear-quadratic-Gaussian (LQG) or the Linear Quadratic Regulator (LQR). When budget or operational constraints are imposed on the schedule, this problem is in general NP-hard and its solution can therefore only be approximated even for moderately sized systems. The quality of this approximation depends on the structure of both the constraints and the objective. This work shows that greedy scheduling is near-optimal when the constraints can be represented as an intersection of matroids, algebraic structures that encode requirements such as limits on the number of agents deployed per time slot, total number of actuator uses, and duty cycle restrictions. To do so, it proves that the LQG cost function is alpha-supermodular and provides a new alpha/(alpha + P)-optimality certificates for the greedy minimization of such functions over an intersections of P matroids. These certificates are shown to approach the 1/(1+P) guarantee of supermodular functions in relevant settings. These results support the use of greedy algorithms in non-supermodular quadratic control problems as opposed to typical heuristics such as convex relaxations and surrogate figures of merit, e.g., the logdet of the controllability Gramian.
LibGuides: Learning & Working Remotely Guide: About this Guide

This guide lists virtual resources, services and support for faculty, staff, students, residents, and fellows supported by the UNLV School of Medicine Library.
Performance of K-Means Clustering Algorithm in enriching a new concept of Amenities into Dwipa Ontology III within the Indonesia Tourism Domain

Responding to the development of Tourism in Indonesia, Ontology DWIPA III was built to represent the tourism domain in Indonesia, especially in the Regency of Bali that consists of Class/subclass Accommodation, Attraction, Regency and Event. Tourism consist of 3 main elements, namely Attraction, Accessibility and Amenity. Related to this, Ontology DWIPA III still has shortcomings in the Amenity concept that represent the tourism domain. This research uses the Ontology Enrichment methodology to add new concepts to the existing DWIPA ontology. The sources of the data are scraped from TripAdvisor web. Amenity data that has been collected is given a feature to differentiate the amenities, and the features is determined by its weighted using the Terms Frequency Inverse Document Frequency method. The process continues by Classifying the data using K-Means Algorithm. The final results of this study indicate that DWIPA Ontology was successfully enriched by having 4 main Classes, 29 subclasses and 319 instances.
Software Effective Evaluating Technology: SWEET

In open source community, there are a large number of software resources existing. Such software resources distribute in different societies or storehouses, which require different software characteristic. This phenomenon results in difficult to evaluate software quality using traditional methods. In this case, a novel open-source software sorting algorithm may be an effective solution. Considering both subjective and objective levels, we propose a new method on software sorting and retrieving. In the subjective level, metrics are selected from the corresponding collaborative development community based on software topic. In the objective level, metrics are obtained from the group emotional evaluation value of the knowledge sharing community. We have proved the effectiveness of the method through comparison experiments. Combining with the Solrcloud tool, this method has been integrated into the OSSEAN platform.
Translating tourism brochure and website content of Malang city

This report is written based on internship program at the Department of Cultural and Tourism of Malang City from 21 January – 21 February 2019. The objectives ofthis report are to descibe about internship activities,  to explain the obstacles experienced when translating and to describe the strategies to overcome the problems during the internship program.This report shows the internship activities done to translate Malang Menyapa website content and tourism brochure of Malang. There are three steps done in order to to make my translation readable in target language, they are : analyzing the whole text, understanding the source text, and transferring into target language. I also faced several problems during internship program such as selecting diction, describing technical terms, and translating spesific cultural terms. The strategies to overcome the problems are: 1) finding the parallel texts in google, and 2) searching the meaning of spesific words in online dictionary such as oxford dictionary and cambridge dictionary
Describing Video with Multiple Descriptions

Automatically describing video content with text description is challenging but important task, which has been attracting a lot of attention in computer vision community. Previous works mainly strive for the accuracy of the generated sentences, while ignoring the sentences diversity, which is inconsistent with human behavior. In this paper, we aim to caption each video with multiple descriptions and propose a novel framework. Concretely, for a given video, the intermediate latent variables of conventional encode-decode process are utilized as input to the conditional generative adversarial network (CGAN) with the purpose of generating diverse sentences. We adopt different CNNs as our generator that produces descriptions conditioned on latent variables and discriminator that assesses the quality of generated sentences. Simultaneously, a novel DCE metric is designed to assess the diverse captions. We evaluate our method on the benchmark datasets, where it demonstrates its ability to generate diverse descriptions and achieves superior results against other state-of-the-art methods.
Obravnava besedil: merila za razlikovanje med besedilno vrsto in besedilnim tipom

Texts that display common prototypical characteristics are often grouped into text types or genres. Genres are the result of habitual language and behavioural activities within the communicative field of a specific discursive community. The definition of a genre derives from the complementary model of text definitions. Unlike genres, text types are formed based on language infrastructure; they are not part of systematic text grouping (classification and typologisation). Knowledge of genres is crucial to participating in social processes.
LibGuides: HLC Library Materials Move: Home

This guide serves as a communication tool to keep the ACC community up to date on the relocation of library materials.
Polyphonic Voicing Optimization for Automatic Music Completion

In this paper, we present a new algorithm for automatic music completion. We have proposed automatic music completion as the class of music composition assistance problems of generating a complete piece of music given fragments of musical ideas input by a user. These fragments include partial melodies in multiple voices or parts of the underlying harmony progression. Therefore, it is a generalization of common problems such as melody harmonization or harmony constrained melody generation, but also includes problems with constraints in multiple domains, i.e. multiple voices and harmony. We present a new polyphonic voicing model for automatically completing four-part chorales. It is based on a hidden Markov model and what we call correction factors. These factors are trainable functions that efficiently capture the context of a voicing in order to account for a multitude of music theoretical rules without having to resort to a rule-based system. We observed improvement over our old model with regards to metrics that are derived from music theory for polyphonic voicing, and also invite the reader to try our algorithm themselves at http://160.16.202.131/music_completion_apsipa.
On Fusion of Partial Estimates Under Implicit Partial Knowledge of Correlation

Covariance Intersection fusion is bound-optimal under unknown correlations. Partial knowledge can improve the fusion. An implicit constraint on correlation has been introduced in the literature for full-vector estimates. This paper considers fusion of partial estimates. Weak and strong counterparts of the full-vector assumption are proposed. An analysis of admissible ideal fusions reveals that the considered implicit partial knowledge cannot improve the Covariance Intersection fusion of partial estimates. An exception is found for the strong assumption and fusion of one partial and one full-vector estimate. For this case, an improved fusion rule is presented.
Knowledge Creation Model for Emotion Based Response Generation for AI

The traditional view on the nature of rationality has proposed that emotions and reasons do not influence decision making. Hence, socially assistive robot (SAR) and chatbots are designed to act rationally and do not take emotions into account throughout the reasoning process. Therefore, the current conversation systems frequently use the user’s linguistic message in response generation. Such systems take into account factual and behavior related conversations, where these systems are unable to grasp the dynamic situations such as the user’s emotional state when generating a response. Studies by psychologists have provided contrary to popular belief where memories, both semantic, or learned knowledge, and episodic, or personal experiences, play an important role in an individual’s decision making. In this paper, authors present a system to support new findings which can enhance response generation regarding conversation flow while considering the actual emotional state of the user by analyzing the user’s current emotional state and emotional memories.
WEIGHT ENUMERATORS OF TWO CLASSES OF LINEAR CODES

Recently, linear codes constructed from defining sets have been studied widely and determined their complete weight enumerators and weight...
District Heating System Load Prediction Using Machine Learning Method

Accurate prediction of heating load can help improve operational efficiency of district heating systems (DHSs). The selection of feature variables is of great significance to prediction performance. Most existing methods only use the meteorological data and historical thermal demand data. In this study, correlation analysis method is employed to analyze predominant variables affecting prediction accuracy. The correlation of supply/return temperature, outdoor temperature, and historical load data were examined. The obtained results were used to select minimal input variables subset so as to avoid multiple input variables. The extreme learning machine (ELM) was used to predict the energy consumption of the next 6, 12, and 24 h. The approach was adopted to predict heating load of a DHS in Changchun, China. Historical heating load data were proved to be the most essential prediction inputs. The results show that the root-mean-square error predicted by the ELM model can reach 4.1%.
Review of codes of conduct, voluntary guidelines and principles relevant for farm data sharing (Working Paper)

Codes of conduct, voluntary guidelines, sets of principles on how to transparently govern farm data are a recent thing. While laws and regulations that govern personal data are becoming more and more common, legislation still does not cover data flows in many industries where different actors in the value chain need to share data and at the same time protect all involved from the risks of data sharing. Data in these value chains is currently governed through private data contracts or licensing agreements, which are normally very complex and on which data producers have very little negotiating power. Codes of conduct have started to emerge to fill the legislative void and to set common standards for data sharing contracts: codes provide principles that the signatories/subscribers/members agree to apply in their contracts.
A Comparative Study to Enhance the Performance of Web-Page Recommendation System

Efficient web browsing demands effective aid in searching anticipated content. Recommender systems are prodigious source of providing assistance to the internet users. Diverse algorithms are in practice to give recommendations. All recommender systems work on policy of scrutinizing behaviors of users. They find neighbors of each user to give final recommendations. Similarity Matrix computations outcomes in developing the recommendations for each user. However, accuracy of recommendations depends upon the correctly made predictions. Hence, web page recommendation system demands improvements in the traditional practices. This paper verdict the differences in results for user based collaborative filtering recommendation strategies and clustering users before implementing recommendation algorithm for web page recommendation system. Results are compared through computation of RMSE. Differences in results suggest that accuracy of recommendations is improved if clustering-based approach is applied for recommendation of web pages. This study gauges the results on the web log data set to recommend users the required content on the internet by evaluating the browsing behavior with PCC similarity measure.
Using Video Technology in Music Theory Assignments

In this chapter, I propose incorporating a performed component, recorded using a smartphone or computer, into weekly music theory assignments, showing how video-capture technology empowers instructors to forge a closer connection between page and sound. I present a sample assignment, outlining some of its practicalities and advantages. I also consider the potential drawbacks of this method, and suggest tweaks for those who wish to avoid keyboard skills and/or incorporate four-part chorale writing. I conclude with some thoughts about the relationship between this methodology and my philosophy of music theory curriculum building as a whole.
Explainable and Advisable Learning for Self-driving Vehicles

Author(s): Kim, Jinkyu | Advisor(s): Canny, John | Abstract: Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers, etc., can understand what triggered a particular behavior. Explanations may be triggered by neural controller, namely introspective explanations, or informed by neural controller's output, namely rationalizations. Our work has focused on challenge of generating introspective explanations of deep models for self-driving vehicles. In Chapter 3, we begin by exploring use of visual explanations. These explanations take form of real-time highlighted regions of an image that causally influence network's output (steering control). In first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence output. This produces more succinct visual explanations and more accurately exposes network's behavior. In Chapter 4, we add an attention-based video-to-text model to produce textual explanations of model actions, e.g. the car slows down because road is wet. The attention maps of controller and explanation model are aligned so that explanations are grounded in parts of scene that mattered to controller. We explore two approaches to attention alignment, strong- and weak-alignment. These explainable systems represent an externalization of tacit knowledge. The network's opaque reasoning is simplified to a situation-specific dependence on a visible object in image. This makes them brittle and potentially unsafe in situations that do not match training data. In Chapter 5, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present first step toward advice-giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts way it attends to scene (visual attention) and control (steering and speed). Further, in Chapter 6, we propose a new approach that learns vehicle control with help of long-term (global) human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. see a pedestrian crossing, so I stop), and predict controls, accordingly.
Influence of Internet of Things Technology on Intelligent Training Research under the Background of Healthy China

The increasing influence of Internet of Things in the intelligent training research under the background of the healthy china is presented in this paper. In this regard, we firstly optimize the traditional IoT system. In the process of construction, various networks have formed many heterogeneous IoT due to the different hardware devices, software modules and even programming methods. We give the 2 core major modifications. (1) Re-design the function call and load mode for the cache feature of the function page. The register relative addressing mode is used instead of the direct addressing mode. By centrally managing the function address in the function page, the read and write operations caused by the cache function page can be effectively reduced. (2) The algorithm based on function page between transfer relations establishment mapping structure drawing, and analyzes each function page using the markov chain in the preliminary stage the possibility which renews, may enhance effectively promotes continuously the preliminary stage buffer hit probability. Then, we apply the idea with the healthy china construction. The intelligent system for the health monitoring is presented. The simulation results prove that the proposed model has the satisfactory performance.
PENDIDIKAN ISLAM DAN KECERDASAN MAJEMUK (MULTIPLE INTELLIGENCE)

Education is guidance or help given by educators to the development of students to reach maturity with the aim that children are capable enough to carry out their own life tasks not with the help of others. The purpose of education is to create a person of high quality and character so that he has a broad view of the future to achieve the goals that are expected and able to adapt quickly and precisely in various environments. The educational objectives will be achieved if done with the teaching and learning process both in the school environment or elsewhere. Learning is one of the activities or human endeavors that is very important and must be carried out throughout life, because through the effort of learning we can make changes or improvements in various matters that concern our self-interest. Learning is done intentionally or not with the teacher, with the help of others, or without anyone's help. The measurement of success, to the potential possessed, to the process of change always departs from experience whether it is research or theories that have been prepared previously. As is the case with human success there are those who see it from the intelligence of their brains (intelligence) or also their work skills. Intelligence is often interpreted as the ability to understand things and the ability to think. Intelligence in this sense is usually measured by the ability to answer standardized test questions in the classroom (IQ test). However the glorification of IQ in determining success still dominates learning in schools and one of them appears in the use of traditional learning methods, such as lectures and stories that are more in line with linguistic intelligence and rational approaches to mathematical logic that are more in line with logical mathematical intelligence. Even Islamic Religious Education  (PAI) learning is still mostly filled with memorization, ritual worship practices, religious dogmas and the like, to the point of unattractive, boring and less meaningful for students whose linguistic and mathematical intelligence is less prominent. Students can only study well if the material is delivered using methods that are in accordance with their most prominent intelligence. The types of intelligence incorporated in 9 multiple intelligences or Multiple Intelligence are: Linguistic Intelligence, Mathematical Logic Intelligence, Spatial Intelligence, Body Kinesthetic Intelligence, Musical Intelligence, Interpersonal Intelligence, Intrersonal Intelligence, Natural Intelligence, and Existential Intelligence. Compound intelligence in the view of Islam is explained in the ESQ book from Ari Ginanjar Brain Gym or Brain Gym can be done to improve the work performance of the brain. Keywords: Education, Learning, Intelligence and Multiple Intelligence, Brain Gym.
해방/패전 직후 한국과 일본의 민족주의 교육 비교연구

The purpose of this study is to examine the United States’ occupation policies under the occupation period influence on nationalism in history education of Korea and Japan. This covers non-militarization, democratization, and nationalism issues in history education. This study also analyzes history education under the GHQ/SCAP and USAMGIK, or nationalism issues in textbooks. What happened to nationalism components that were included in militarism education which was done not only in Japan during the wartime but also in colonized Korea? In this context, nationalism issue is associated with ethnocentrism, ultranationalism, and totalitarianism. There is another nationalism issue, which is associated with agent issue in the context of construction/reconstruction. In other words, it analyzes the issues with national pride during the occupation period.
Evaluating Students’ attitudes and usage e of mobile in educational activities at Paramedical Sciences School

Introduction: Mobile learning is a new way of modern teaching method and a subset of e-learning that refers to a change in thinking about the design and planning of learning goals and environments. The purpose of this study was to investigate the attitude of students of the Medical Sciences School toward the mobile phone in educational activities and its usage.&#x0D; Methods: 150 students of Mashhad School of Paramedical Sciences were investigated in this cross-sectional study in 2019-20, using a researcher-made questionnaire including three sections on student demographic information, mobile applications in education, and student attitude toward mobile phone use in educational activities. The questionnaire`s validity was obtained according to 7 experts' opinions in the field of education, and its reliability was calculated using Cronbach's alpha of 0.794. Data were analyzed by descriptive and analytical statistics using SPSS 16 software.&#x0D; Result: From the investigation of 150 students of Mashhad Paramedical Sciences opinions in six educational groups indicated that students of health information technology, laboratory science, physiotherapy, ophthalmology, radiology, speech therapy, medical records, mostly used mobile phones for their educational activities respectively. The most common use of students' mobile phones at university was the use of the Internet and social networks, taking photos and videos, and the most common use in teaching activities was reading files, viewing scores, recording audio and selecting units. From the students' perspective, using mobile phones in educational activities leads to getting higher scores, doing activities faster, make better communication between teacher and student, and speeded up the learning feedback.&#x0D; Conclusion: According to the results, we suggest encouraging teachers to use mobile phones in academic activities and running workshops to increase the awareness of mobile capabilities. Also educational planners, and mobile based learning programs should design educational software to promote the correct culture of mobile use in educational activities.