Reviewerly/exHarmony huggingface dataset:
Towards a Resource Grammar for Runyankore and Rukiga

Currently, there is a lack of computational grammar resources for many under-resourced languages which limits the ability to develop Natural Language Processing (NLP) tools and applications such as Multilingual Document Authoring, Computer-Assisted Language Learning (CALL) and Low-Coverage Machine Translation (MT) for these languages. In this paper, we present our attempt to formalise the grammar of two such languages: Runyankore and Rukiga. For this formalisation we use the Grammatical Framework (GF) and its Resource Grammar Library (GF-RGL).
Solving K-MDPs

Markov Decision Processes (MDPs) are employed to model sequential decision-making problems under uncertainty. Traditionally, algorithms to solve MDPs have focused on solving large state or action spaces. With increasing applications of MDPs to human-operated domains such as conservation of biodiversity and health, developing easy-to-interpret solutions is of paramount importance to increase uptake of MDP policies. Here, we define the problem of solving K-MDPs, i.e., given an original MDP and a constraint on the number of states (K), generate a reduced state space MDP that minimizes the difference between the original optimal MDP value function and the reduced optimal K-MDP value function. Building on existing non-transitive and transitive approximate state abstraction functions, we propose a family of three algorithms based on binary search with sub-optimality bounded polynomially in a precision parameter: ϕQ*eK-MDP-ILP, ϕQ*dK-MDP and ϕa*dK-MDP. We compare these algorithms to a greedy algorithm (ϕQ*e Greedy K-MDP) and clustering approach (k-means++ K-MDP). On randomly generated MDPs and two computational sustainability MDPs, ϕa*dK-MDP outperformed all algorithms when it could find a feasible solution. While numerous state abstraction problems have been proposed in the literature, this is the first time that the general problem of solving K-MDPs is suggested. We hope that our work will generate future research aiming at increasing the interpretability of MDP policies in human-operated domains.
Analysis of Computer Network and Communication System

The network communication is realized and developed based on the gradual improvement of the computer network. Although the communication system itself is not equivalent to the computer network, but there are many integration and connection in current communication system and the computer network, which has formed a complementary and mutual development of the situation. With the emergence and application of new systems and computer network technologies, such integration and development will be more in-depth and closer. This article is focus on the computer network and the communication system, and does a brief analysis of the status quo, so that it is easy to understand by the majority of readers in the shallow level.
A Hybrid Feature Selection Algorithm for Big Data Dimensionality Reduction

Data dimensionality causes problems like large storage requirements due to data redundancy, noisy data visualisation and high computational cost in analytical processes. Hence, reduction of high data dimension data set is the vital task in big data analytics. Feature selection is a dimensionality reduction approach that targets to reduce the high data dimensions into l subset by attaining a set of uncorrelated principal variables based on key features from the data source by eliminating redundant, noisy features and irrelevant. In the proposed work, ant colony optimisation (ACO) and quick branch and bound (QBB) based hybrid algorithm for feature selection is presented to improve the efficiency of feature selection in big data analytics. ACO was implemented for feature selection by observing real ants in their search of their food resources. QBB algorithm is used to partition the large data set into two partitions initially. These two algorithms are combined and implemented to reduce the data dimensionality based on feature selection approach. This algorithm was simulated and compared against existing feature selection algorithms in terms of a set of performance metrics such as precision, recall, F-score, classification accuracy, size of selected feature and proved the efficacy of proposed hybrid algorithm.
IoT Capable Mechanism for Crowd Analysis

This paper describes a crowd analysis of different activities using surveillance videos is an important topic for communal security. This paper also describes the detection of dangerous crowds if the weapon is present in the crowd. In our study, we are using raspberry pi 3 board for the development of a system that consists of ARMv8 CPU that detects the human heads and provides a count of humans in the region using Open CV-Python. The direction of the movement of the person can be achieved by human tracking. Generally, there are three different stages algorithm for computer-based crowd analysis, (1) people counting, (2) people tracking, and (3) crowd behavior analysis. This project is made for security purposes where there is a possibility of a dangerous crowd, for example, mall, railway station, shopping center. In our method, we are used CNN to trained dangerous weapons and DNN used for human detection. This method not only detects the direction of the crowd but also detects if the crowd is dangerous or not. In this method, also count the total number of human and it also gives confidence score that means, in how many percents it is related to original people. In this way, we could have prevented many deaths and injuries.
“Aku Istimewa, Aku Bisa”: Membaca Permulaan Bahasa Inggris melalui Metode Fonik bagi Anak Speech Delay

Speech delay becomes a phenomenon for children such as in English pre-reading instruction for non-native English speaker, thus it is needed a proper method for them. This study aims to identify the implementation of phonics method that used in an English pre-reading instruction for a child with speech delay. This study uses a case study which involves one teacher and one child with speech delay. The data obtained by interviewing a teacher and parents, observing the child, and identifying lesson plans. Thematic analysis is used to determine relevant themes of the topic. The results show each instruction has one objective, focuses on letters’ sound and spelling spelling two up to four sounds of a word, and a child could utter words clearly. Additionally, interactions with peers encourage a child to be competitive and enthusiastic. Furthermore, phonics method might be an alternative method for children with speech delay to acquire English pre-reading skills, especially in Early Childhood Education (ECE).
Monitoring and Automation

Both monitoring and automation are crucial to a resilient system. Effective monitoring ensures timely detection and alerting of what failed, when it failed, and why. Automating various manual and prescriptive actions helps to reduce the amount of human error and minimize RTO. There are several factors that decide the degree and amount of monitoring and automation. Here are a few of them.
Mobile Service Composition

Service composition supports and realizes complex business logics through combining multiple single services. However, the mobile environment brings great challenges to the reliability of service composition due to the uncertain services quality and usability. In this chapter, a mobile service provisioning architecture is proposed to tackle the composition in mobile communities. Additionally, a dependable composition model is constructed to reduce the risk of the mobility of both service providers and requesters. Finally, a differential evolutionary for constraint-driven service composition algorithm is designed to ensure the successful composition.
Keefektifan Model Teams Games Tournament Terhadap Minat Belajar

This study aims to determine the effectiveness of the Teams Games Tournament learning model on the interest in learning in fifth grade students of SDN 01 Ngeling, Jepara Regency. This type of research is a quantitative study with the design of Pre Experimental Design in the form of One-Group Pretest Posttest Design. Data in the study were obtained through (1) observation, (2) questionnaire sheets, and (3) documentation. For data analysis in this study using the normality test and t test (hypothesis test). The results of the analysis of student learning interest questionnaire sheets before and after treatment using the Teams Games Tournament learning model have increased, as seen from the results of the t test that t count&gt; t table is 9,305&gt; 2,110. So the conclusion is the Teams Games Tournament model is effective against students' learning interests
THE RESPONSIBILITY OF PUBLIC INSTITUTIONS IN GENETICS EDUCATION: INTERNATIONAL EXPERIENCE

The article is devoted to the issue of genetics education considered as a narrowly focused education. The goals of the article are: (a) to build an understanding of the necessity of education in the fi eld of genetics; and (b) to examine laws and regulations related to genetics research in diff erent legal systems from the perspective of comparative law. The article presents the analysis of genetics education models based on the cases of the USA, France and Russia. It is also focused on requirements for and essential components of genetics education; modern examples of genetics education and genetics legislation. The article explains the necessity for the development of a web-portal titled ‘Genetics and Law’. The discussion of the necessity for genetics education is based on the concept of liability law.
Hidden Independence in Unstructured Probabilistic Models.

We describe a novel way to represent the probability distribution of a random binary string as a mixture having a maximally weighted component associated with independent (though not necessarily identically distributed) Bernoulli characters. We refer to this as the latent independent weight of the probabilistic source producing the string, and derive a combinatorial algorithm to compute it. The decomposition we propose may serve as an alternative to the Boolean paradigm of hypothesis testing, or to assess the fraction of uncorrupted samples originating from a source with independent marginals. In this sense, the latent independent weight quantifies the maximal amount of independence contained within a probabilistic source, which, properly speaking, may not have independent marginals.
Algorithm Engineering for High-Dimensional Similarity Search Problems (Invited Talk)

Similarity search problems in high-dimensional data arise in many areas of computer science such as data bases, image analysis, machine learning, and natural language processing. One of the most prominent problems is finding the k nearest neighbors of a data point q ∈ ℝ^d in a large set of data points S ⊂ ℝ^d, under same distance measure such as Euclidean distance. In contrast to lower dimensional settings, we do not know of worst-case efficient data structures for such search problems in high-dimensional data, i.e., data structures that are faster than a linear scan through the data set. However, there is a rich body of (often heuristic) approaches that solve nearest neighbor search problems much faster than such a scan on many real-world data sets. As a necessity, the term solve means that these approaches give approximate results that are close to the true k-nearest neighbors. In this talk, we survey recent approaches to nearest neighbor search and related problems.The talk consists of three parts: (1) What makes nearest neighbor search difficult? (2) How do current state-of-the-art algorithms work? (3) What are recent advances regarding similarity search on GPUs, in distributed settings, or in external memory?
On-Premises to On-Premises Using ASR

In this chapter we are going to focus on DR planning and configuration points we must consider while we face customer requirements to design and build a DR solution between the primary on-premises datacenter and a physical secondary datacenter in another location.
Large-scale video semantic recognition based on consistency of segment-level and video-level predictions

Segment-level video semantic recognition, which known to be an important task in video analysis, attempts to identify the semantic concepts in short video clips.Labeling video segments is difficult because there is an extremely large number of segments and there are no network tags; consequently, only a portion of the video segments are labeled. Determining how to improve the accuracy of semantic recognition of fragmented videos with limited semantic labels is a key challenge in video semantic recognition.This paper proposes a video semantic recognition algorithm based on the consistency of video- and segment-level predictions. The proposed algorithm introduces the constraint of consistency between complete video semantics and fragmentary video semantics. The proposed algorithm can be applied to filter the video segment semantic results to improve recognition accuracy. The proposed algorithm achieved 82.62% mean average precision on the video segment semantic recognition task using the large-scale video dataset YouTube-8M and ranked second in the third YouTube-8M competition.
Extracting Keyphrases from News Articles Using Crowdsourcing

Keyphrase extraction is a very important task in text mining. However, keyphrase extraction of news articles cannot be addressed by existing machine-based approaches effectively because of various reasons. This paper employs crowdsourcing for keyphrase extraction of news articles. We first design a proper crowdsourcing mechanism to extract keyphrases from news articles and then adapt three truth inference algorithms (namely IMLK, IMLK-I, and IMLK-ED) for integrating multiple lists of keyphrases provided by workers. The experiments show that crowdsourcing can significantly improve the performance of the machine-based approach (i.e., KeyRank).
Pipeline Image Dehazing Algorithm Based on Atmospheric Scattering Model and Multi-Scale Retinex Strategy

At present, the detection video of the drainage pipeline is often affected by the water mist in the pipeline. As a result, a large number of pipeline detection videos are highly blurred, which seriously affects the detection effect. In this paper, we proposed a algorithm based on atmospheric scattering model and Multi-Scale Retinex strategy. Firstly, the threshold is used to select the atmospheric light in the fuzzy image. The algorithm then uses the estimated depth information to calculate the transmission map, which is substituted into the atmospheric scattering model. To overcome the issue of low image contrast, we improved the calculation of transmission by adding loss compensation factor. In addition, we use Multi-Scale Retinex strategy as color restoration algorithm to enhance the improved dark primary color method to further improve the image clarity. The experimental results show that the algorithm has good effect on the image of underground pipeline with large reflection area. In the future, the algorithm will be applied to pipeline robots to ensure the normal operation of underground pipelines.
Construction and Alignment of Multilingual Entailment Graphs for Semantic Inference

This paper presents ongoing work on the construction and alignment of predicate entailment graphs in English and German. We extract predicate-argument pairs from large corpora of monolingual English and German news text and construct monolingual paraphrase clusters and entailment graphs. We use an aligned subset of entities to derive the bilingual alignment of entities and relations, and achieve better than baseline results on a translated subset of a predicate entailment data set (Levy and Dagan, 2016) and the German portion of XNLI (Conneau et al., 2018).
Visual Salience

A “searchlight” model of visual attention is introduced to describe the way eye movements are used to sweep for information. The bulk of the chapter is taken up with a description of the massively parallel processes whereby the visual image is broken into elements of color, form, and motion. Preattentive processing theory is applied to critical issues of making one data object distinct from another. Methods for coding data so that it can be perceptually integrated or separated are discussed. The applications discussed include the following: display for rapid comprehension, information coding, the use of texture for data coding, the design of symbology, and multidimensional discrete data display.
Networked Expertise for Research Data

December 2019 presentation at the Coalition for Networked Information Fall Membership Meeting in Washington, D.C.
k-Approximate Quasiperiodicity under Hamming and Edit Distance.

Quasiperiodicity in strings was introduced almost 30 years ago as an extension of string periodicity. The basic notions of quasiperiodicity are cover and seed. A cover of a text T is a string whose occurrences in T cover all positions of T. A seed of text T is a cover of a superstring of T. In various applications exact quasiperiodicity is still not sufficient due to the presence of errors. We consider approximate notions of quasiperiodicity, for which we allow approximate occurrences in T with a small Hamming, Levenshtein or weighted edit distance. In previous work Sim et al. (J Korea Inf Sci Soc 29(1):16–21, 2002) and Christodoulakis et al. (J Autom Lang Comb 10(5/6), 609–626, 2005) showed that computing approximate covers and seeds, respectively, under weighted edit distance is NP-hard. They, therefore, considered restricted approximate covers and seeds which need to be factors of the original string T and presented polynomial-time algorithms for computing them. Further algorithms, considering approximate occurrences with Hamming distance bounded by k, were given in several contributions by Guth et al. They also studied relaxed approximate quasiperiods. We present more efficient algorithms for computing restricted approximate covers and seeds. In particular, we improve upon the complexities of many of the aforementioned algorithms, also for relaxed quasiperiods. Our solutions are especially efficient if the number (or total cost) of allowed errors is small. We also show conditional lower bounds for computing restricted approximate covers and prove NP-hardness of computing non-restricted approximate covers and seeds under the Hamming distance.
Hidden Words Statistics for Large Patterns.

We study here the so called subsequence pattern matching also known as hidden pattern matching in which one searches for a given pattern $w$ of length $m$ as a subsequence in a random text of length $n$. The quantity of interest is the number of occurrences of $w$ as a subsequence (i.e., occurring in not necessarily consecutive text locations). This problem finds many applications from intrusion detection, to trace reconstruction, to deletion channel, and to DNA-based storage systems. In all of these applications, the pattern $w$ is of variable length. To the best of our knowledge this problem was only tackled for a fixed length $m=O(1)$ [Flajolet, Szpankowski and Vallee, 2006]. In our main result we prove that for $m=o(n^{1/3})$ the number of subsequence occurrences is normally distributed. In addition, we show that under some constraints on the structure of $w$ the asymptotic normality can be extended to $m=o(\sqrt{n})$. For a special pattern $w$ consisting of the same symbol, we indicate that for $m=o(n)$ the distribution of number of subsequences is either asymptotically normal or asymptotically log normal. We conjecture that this dichotomy is true for all patterns. We use Hoeffding's projection method for $U$-statistics to prove our findings.
A Rule for Gradient Estimator Selection, with an Application to Variational Inference.

Stochastic gradient descent (SGD) is the workhorse of modern machine learning. Sometimes, there are many different potential gradient estimators that can be used. When so, choosing the one with the best tradeoff between cost and variance is important. This paper analyzes the convergence rates of SGD as a function of time, rather than iterations. This results in a simple rule to select the estimator that leads to the best optimization convergence guarantee. This choice is the same for different variants of SGD, and with different assumptions about the objective (e.g. convexity or smoothness). Inspired by this principle, we propose a technique to automatically select an estimator when a finite pool of estimators is given. Then, we extend to infinite pools of estimators, where each one is indexed by control variate weights. This is enabled by a reduction to a mixed-integer quadratic program. Empirically, automatically choosing an estimator performs comparably to the best estimator chosen with hindsight.
D. Peningkatan Hasil Belajar Matematika Pada Siswa Kelas III Melalui Model Pembelajaran Kooperatif Tipe NHT (Numbered Heads Together) Di SDN 2 Banyuurip Ujungpangkah Gresik Tahun Pelajaran 2018/2019

Abstract This research is motivated by the low problem of achievement of Mathematics values ??because students who are still not paying attention to the lessons when learning takes place, the low understanding of students about the measurement material (weight, length, and time), the lack of creativity of the teacher in delivering. So that student learning outcomes tend to be low. This is what makes researchers want to apply the cooperative learning model type NHT (Numbered Heads Together).The steps of learning by applying the cooperative learning model type NHT (Numbered Heads Together) are, 1) Numbering 2) Submission of questions 3) Think together 4) Giving answers. Based on the results of this study it can be concluded that through the cooperative learning model type NHT (Numbered Heads Together) can improve student learning outcomes from 57% with an average of 72 increasing to 87% with an average of 83. Keywords: Mathematics Learning Outcomes, Cooperative Learning Model NHT Type (Numbered Heads Together)
Adaptive Reticulum

Neural Networks and Decision Trees: two popular techniques for supervised learning that are seemingly disconnected in their formulation and optimization method, have recently been combined in a single construct. The connection pivots on assembling an artificial Neural Network with nodes that allow for a gate-like function to mimic a tree split, optimized using the standard approach of recursively applying the chain rule to update its parameters. Yet two main challenges have impeded wide use of this hybrid approach: (a) the inability of global gradient ascent techniques to optimize hierarchical parameters (as introduced by the gate function); and (b) the construction of the tree structure, which has relied on standard decision tree algorithms to learn the network topology or incrementally (and heuristically) searching the space at random. Here we propose a probabilistic construct that exploits the idea of a node's unexplained potential (the total error channeled through the node) in order to decide where to expand further, mimicking the standard tree construction in a Neural Network setting, alongside a modified gradient ascent that first locally optimizes an expanded node before a global optimization. The probabilistic approach allows us to evaluate each new split as a ratio of likelihoods that balances the statistical improvement in explaining the evidence against the additional model complexity --- thus providing a natural stopping condition. The result is a novel classification and regression technique that leverages the strength of both: a tree-structure that grows naturally and is simple to interpret with the plasticity of Neural Networks that allow for soft margins and slanted boundaries.