Reviewerly/exHarmony huggingface dataset:
Penerapan Strategi Promosi Perpustakaan

The library will be only a space or place that provides various dusty collections, due to the lack of users who use it.Libraries and users are like two sides of a coin that cannot be separated.Both influence each other.The library service will not run if there is no user.Users are also like information services that are physical or real will be difficult to obtain if there is no library.The presence of advanced technology in the hands of the user makes it easy for him to get information, this has an impact on the emergence of a sense of laziness to come to the library which in fact the results obtained between using technology and going directly to the library are relatively the same.This phenomenon becomes a whip for librarians to always innovate in increasing the number of library visitors by continuing to carry out library promotions.Responding to this, strategies and efforts are needed to promote or communicate libraries to the public so that they become loyal, namely loyal, visitors in droves.This research is a research by taking descriptive qualitative research method through literature study approach and case study.This study aims to find out how the implementation of promotional strategies carried out by the library in increasing visitor visits.The results of this study indicate that library promotion strategies can be carried out by: (1) Carrying out library promotion activities using: (a) Print media, for example newspapers, magazines, and brochures (b) Electronic media such as television media, websites, social media which is currently popular with users and radio.(c) Non-mass media such as banners and billboards.(d) Meeting media such as exhibitions, seminars, talk shows and counseling.(2) The main obstacle in carrying out library promotion activities is that it collides with budget problems and inadequate human resources.
Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model

The problem of minimal cost path search is especially difficult when no useful heuristics are available. A common solution is roll-out-based search like Monte Carlo Tree Search (MCTS). However, MCTS is mostly used in stochastic or adversarial environments, with the goal to identify an agent's best next move. For this reason, even though single player versions of MCTS exist, most algorithms, including UCT, are not directly tailored to classical minimal cost path search. We present Plackett-Luce MCTS (PL-MCTS), a path search algorithm based on a probabilistic model over the qualities of successor nodes. We empirically show that PL-MCTS is competitive and often superior to the state of the art.
PENERAPAN METODE PER CONNECTION CLASSIFIER (PCC) PADA PERANCANGAN LOAD BALANCING DENGAN ROUTER MIKROTIK

The NOC (Network Operation Center) Division is highly dependent on the availability of internet access in carrying out its duties to monitor and maintain the network remotely. Therefore, the absence of adequate internet access will determine the performance of the work performed by the NOC division. However, problems often occur on computer networks or the internet that can hinder the work done by the NOC division, these problems include, such as the process of uploading or downloading files from the server feeling slow, unable to send emails smoothly, connections often experiencing time-out, There is a long network downtime, as well as traffic congestion on the network which can reduce network reliability. To solve this problem, one solution that can be done is to implement load balancing and failover mechanisms on the network using a MikroTik router. This study aims to design a load balancing scheme using the per connection classifier (PCC) method to overcome traffic congestion problems on the network. The research was conducted using field research methods using observation and interview techniques which were conducted from August to December 2020. The results showed that the application of the load balance technique on the xMikrotik router with the PCC (Per Connection Classifier) method can separate the internet connection through the two available ISP lines so that it can solve the problem of traffic buildup on one of the ISP links to reduce latency and improve existing network performance.
KOMPARATIF HASIL BELAJAR MAHASISWA REGULER DENGAN MANDIRI PENDIDIKAN TEKNIK INFORMATIKA FT-UNP

Padang State University is a state university that was established in West Sumatra with various faculties including engineering faculties in charge of various departments, one of which is electronics engineering majors consisting of electronics engineering study programs, electronic engineering education programs and informatics and computer engineering education courses . Various assumptions emerged with student admissions from both the regular and independent lane. To answer these assumptions, a study was conducted aimed at looking at a comparison of learning outcomes between regular entry students and regular independent entry pathways through a cumulative grade point average (GPA) which was carried out through hypothesis testing and research prerequisites for data sources obtained from descriptive data on students of educational study programs informatics and computer engineering so that it is known between semester credit (X) with the Grade Point Average (GPA) of significance value 0.409 for regular students and a significance value of 0.258 for independent regular students. It was concluded that there was a Positive and Significant Relationship between Regular students and Independent Selection students Informatics and Computer Engineering Education in 2014-2017 and there were no significant differences in learning outcomes in the January-June 2017 semester, in the form of GPA.Keywords: Student, learning outcomes, comparison, population and sample, hypothesis testing.
Task Uncertainty Loss Reduce Negative Transfer in Asymmetric Multi-task Feature Learning (Student Abstract)

Multi-task learning (MTL) is frequently used in settings where a target task has to be learnt based on limited training data, but knowledge can be leveraged from related auxiliary tasks. While MTL can improve task performance overall relative to single-task learning (STL), these improvements can hide negative transfer (NT), where STL may deliver better performance for many individual tasks. Asymmetric multi-task feature learning (AMTFL) is an approach that tries to address this by allowing tasks with higher loss values to have smaller influence on feature representations for learning other tasks. Task loss values do not necessarily indicate reliability of models for a specific task. We present examples of NT in two orthogonal datasets (image recognition and pharmacogenomics) and tackle this challenge by using aleatoric homoscedastic uncertainty to capture the relative confidence between tasks, and set weights for task loss. Our results show that this approach reduces NT providing a new approach to enable robust MTL.
Enforcing Consistency in Weakly Supervised Semantic Parsing

The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons.Prior work uses elaborate search strategies to mitigate the prevalence of spurious programs; however, they typically consider only one input at a time.In this work we explore the use of consistency between the output programs for related inputs to reduce the impact of spurious programs.We bias the program search (and thus the model's training signal) towards programs that map the same phrase in related inputs to the same sub-parts in their respective programs.Additionally, we study the importance of designing logical formalisms that facilitate this kind of consistencybased training.We find that a more consistent formalism leads to improved model performance even without consistency-based training.When combined together, these two insights lead to a 10% absolute improvement over the best prior result on the Natural Language Visual Reasoning dataset.
On the Softmax Bottleneck of Recurrent Language Models

Recent research has pointed to a limitation of word-level neural language models with softmax outputs. This limitation, known as the softmax bottleneck refers to the inability of these models to produce high-rank log probability (log P) matrices. Various solutions have been proposed to break this bottleneck, including Mixture of Softmaxes, SigSoftmax, and Linear Monotonic Softmax with Piecewise Linear Increasing Functions. They were reported to offer better performance in terms of perplexity on test data. A natural perception from these results is a strong positive correlation between the rank of the log P matrix and the model's performance. In this work, we show via an extensive empirical study that such a correlation is fairly weak and that the high-rank of the log P matrix is neither necessary nor sufficient for better test perplexity. Although our results are empirical, they are established in part via the construction of a rich family of models, which we call Generalized SigSoftmax. They are able to create diverse ranks for the log P matrices. We also present an investigation as to why the proposed solutions achieve better performance.
Artificial Intelligence and Machine Learning for Autonomous Agents that Learn to Plan and Operate in Unpredictable Dynamic Environments

My research activity focuses on the integration of acting, learning and planning. The main objective is to build a system that is capable to learn how to plan and act in an unknown, dynamic and complex environment. The only knowledge the agent has about the environment is provided by a set of sensor observations which returns continuous measures on the environment. On the learning side, I’m interested in developing algorithms that allow an artificial agent to learn an abstract model of the dynamics of the environment (either an explicit model like a deterministic finite state machine or a model described in a language to express planning domains). The type of abstract model is specified by means of discrete state variables rather than continuous variables representing agent observations. In addition to learning the abstract model, I’m interested in learning probabilistic (generative) models that connects the abstract model with the perceptions of the agents. On the acting and planning side, the artificial agent does not rely on a prior set of execution traces, it rather decides online how to act by means of state-of-art planners. With its own experience, it enriches the planner knowledge, as well as the learned model of the environment. On the learning part, the agent applies techniques for dynamic probabilistic clustering of perceptions in a set of abstract states, neural network for learning transition models, and inductive reasoning for learning action model descriptions. Notice that this is different from Reinforcement Learning where the focus is to learn a policy for achieving a goal, we are interested in learning an abstract model of the environment. We do not have a reward function that encodes the goal to be reached. Indeed in this work an agent does not necessarily need to reach a goal.
Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement (Student Abstract)

Low light conditions in aerial images adversely affect the performance of several vision based applications. There is a need for methods that can efficiently remove the low light attributes and assist in the performance of key vision tasks. In this work, we propose a new method that is capable of enhancing the low light image in a self-supervised fashion, and sequentially apply detection and segmentation tasks in an end-to-end manner. The proposed method occupies a very small overhead in terms of memory and computational power over the original algorithm and delivers superior results. Additionally, we propose the generation of a new low light aerial dataset using GANs, which can be used to evaluate vision based networks for similar adverse conditions.
Minimizing the Carbon Footprint by Using Rammed Earth Technique

The construction sector holds a large of share in carbon dioxide emissions. in pursuit of minimizing the carbon footprint, it started applying new building techniques and employing environmentally friendly materials One of those applications is rammed earth wall which was originally a traditional construction method that uses soil as the main material. The purpose of this study is to propose the rammed earth technique as an alternative approach for minimizing carbon footprint. It is also discussed its thermal behaviour and required energy to construct. The advantages and disadvantages are stated accordingly. This paper help architects to rethink the traditional techniques which use soil as the main material and how they can reuse it in their design of more sustainable buildings. As a result, minimizing the carbon footprint in the built environment.
Privacy-Preserving Data Mining Metrics for Social Networking Services

This study aims to determine whether privacypreserving data mining method can be effectively applied in data mining for a social networking service (SNS).By implementing privacy-preserving data mining on personal information collected by a SNS, it becomes possible to provide secure personalized services to SNS users.In this study, we consider using privacy-preserving data mining by the anonymization approach.By this approach, all input information is anonymized while performing data mining.We examine whether the anonymization approach can be applied to data that can be partially anonymized, such as the SNS data, and how many users can be identified by the anonymization approach.In the experiments conducted in this study, we anonymized some dataset attributes and then counted whether we can identify the data of the anonymized attributes from the data of the non-anonymized attributes.The ratio of anonymization to all data is defined as the security level of an anonymization approach.In this study, it became clear that the anonymization approach can be applied to data, which can be partially anonymized, such as a SNS data.In addition, it became clear that it is necessary to consider removing a small number of attributes as identifiers because they can be narrowed down even if they are anonymized as quasiidentifiers.We also propose clutering using randomization as a privacy-preserving data mining algorithm for SNS.
Nonparametric Causal Structure Learning in High Dimensions

The PC and FCI algorithms are popular constraint-based methods for learning the structure of directed acyclic graphs (DAGs) in the absence and presence of latent and selection variables, respectively. These algorithms (and their order-independent variants, PC-stable and FCI-stable) have been shown to be consistent for learning sparse high-dimensional DAGs based on partial correlations. However, inferring conditional independences from partial correlations is valid if the data are jointly Gaussian or generated from a linear structural equation model -- an assumption that may be violated in many applications. To broaden the scope of high-dimensional causal structure learning, we propose nonparametric variants of the PC-stable and FCI-stable algorithms that employ the conditional distance covariance (CdCov) to test for conditional independence relationships. As the key theoretical contribution, we prove that the high-dimensional consistency of the PC-stable and FCI-stable algorithms carry over to general distributions over DAGs when we implement CdCov-based nonparametric tests for conditional independence. Numerical studies demonstrate that our proposed algorithms perform nearly as good as the PC-stable and FCI-stable for Gaussian distributions, and offer advantages in non-Gaussian graphical models.
Scaffolding Language Development and Learning in Teacher-Student Interactions

This paper is a review of empirical research on scaffolded teacher-student interactions in English as a Second Language (ESL) and English as a Foreign language (EFL) classrooms that covers two decades, from 2000 to 2019. Research articles were selected through different databases from Science Direct, Google Scholar, Scopus and Emerald. The review focuses on five key features of the articles, namely temporal distribution, location, research design, learning type scaffolded and level of education in which the studies were conducted. Findings of the review reveal that studies on teacher-student interactions in the ESL and EFL classrooms continue to be relevant. It was also found that scaffolding oral skills development was the most frequent pedagogic target of teacher-student interactions in the ESL and EFL classrooms. The review also revealed that the dominant research design used in these studies was the mixed mode method, with most of the studies conducted at the upper secondary and university levels. The review suggests that teacher-student scaffolded interactions in the ESL/EFL classrooms remain a potential means of helping students develop their English language communicative competence.
Long term (1901-2016) temperature based potential evapotranspiration and aridity index analysis for lower eastern region of Kenya

Climate change has caused unreliable precipitation (P) and temperature (T) changes influencing global, regional and local water availability, agricultural production hence food insecurity.Precipitation and temperature are key climatic parameters among others in estimating potential evapotranspiration (PET).The main goal of this paper was to estimate long term potential evapotranspiration time series using seven temperature-and two temperature and terrestrial radiation-based methods and compare the values estimates within four counties of Kenya.Data was sought from the World Bank database constructed based on CRU TS (Climatic Research Unit Timeseries).The estimates were computed and Thornthwaite method was used as a reference in comparison with the other methods.The study established that Hamon III and Thornthwaite equations gave lower annual PET estimates than the other methods.For instance, in Makueni county, the two methods gave potential evapotranspiration estimates ranging from 899±49.6 mm/year to 1131±49.6 mm/year and 918±76.3mm/year to 1274±76.3 mm/year respectively for the whole time period.Jensen-Haise and McGuinness-Borden PET estimates were the highest ranging from 2782±119.9mm/year to 3343±120.0mm/year and 2040±70.7 mm/year to 2371±70.7 mm/year in Makueni, respectively.Thornthwaite method depicted an increasing linear trend in all stations.Further, the Aridity Index (in our case P/PET based on the Thornthwaite method) varied from one climatic period (30 years) to the other with a large range of 0.3 to 1.2 in Makueni county.The study also established that Kitui county had the highest estimate values of PET from all methods used.The study concludes that different temperature-and temperature and terrestrial radiation-based methods perform differently and give different PET estimation based on the parameter specific requirement.The study is suitable in selection and comparisons of best temperature-based, temperature and terrestrial radiation methods in PET estimation in the tropical, data scarce regions in Kenya.
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources

Machine Reading Comprehension (MRC) aims to extract answers to questions given a passage, which has been widely studied recently especially in open domains.However, few efforts have been made on closeddomain MRC, mainly due to the lack of largescale training data.In this paper, we introduce a multi-target MRC task for the medical domain, whose goal is to predict answers to medical questions and the corresponding support sentences from medical information sources simultaneously, in order to ensure the high reliability of medical knowledge serving.A high-quality dataset (more than 18k samples) is manually constructed for the purpose, named Multi-task Chinese Medical MRC dataset (CMedMRC), with detailed analysis conducted.We further propose a Chinese medical BERT model for the task (CMedBERT), which fuses medical knowledge into pre-trained language models by the dynamic fusion mechanism of heterogeneous features and the multi-task learning strategy.Experiments show that CMedBERT consistently outperforms strong baselines by fusing context-aware and knowledge-aware token representations.
Carrot and Stick approaches revisited when managing Technical Debt in an educational context

Technical Debt management is an important aspect in the training of Software Engineering students. In this paper we study the effect of two assessment strategies in an educational context: One based on penalisation, the other based on rewards. Both are applied to assignments where the students develop a project focusing on keeping a low technical debt level, and obtaining a high quality code. We describe the design, tools and context of the strategies applied. SonarQube, a tool commonly used in production environments, is used for measuring the metrics. The penalisation strategy is based on a SonarQube quality gate. The reward strategy is based on a contest, where an automatic judge tool is devised to provide an online leaderboard with a classification based on the SonarQube metrics. An empirical study is conducted to determine which of the strategies works better to help the students/trainees keep the Technical Debt low. Statistically significant results are obtained in 5 of the 8 analysed metrics, showing that the reward strategy works much better. The effect size of the executed statistical tests is analysed, resulting in medium and large effect size in the majority of the analysed metrics.
New binary self-dual codes of lengths 80, 84 and 96 from composite matrices

Abstract In this work, we apply the idea of composite matrices arising from group rings to derive a number of different techniques for constructing self-dual codes over finite commutative Frobenius rings. By applying these techniques over different alphabets, we construct best known singly-even binary self-dual codes of lengths 80, 84 and 96 as well as doubly-even binary self-dual codes of length 96 that were not known in the literature before.
A fast and calibrated computer model emulator: an empirical Bayes approach

Mathematical models implemented on a computer have become the driving force behind the acceleration of the cycle of scientific processes. This is because computer models are typically much faster and economical to run than physical experiments. In this work, we develop an empirical Bayes approach to predictions of physical quantities using a computer model, where we assume that the computer model under consideration needs to be calibrated and is computationally expensive. We propose a Gaussian process emulator and a Gaussian process model for the systematic discrepancy between the computer model and the underlying physical process. This allows for closed-form and easy-to-compute predictions given by a conditional distribution induced by the Gaussian processes. We provide a rigorous theoretical justification of the proposed approach by establishing posterior consistency of the estimated physical process. The computational efficiency of the methods is demonstrated in an extensive simulation study and a real data example. The newly established approach makes enhanced use of computer models both from practical and theoretical standpoints.