Reviewerly/exHarmony huggingface dataset:
New Derivation for Gaussian Mixture Model Parameter Estimation: MM Based Approach

In this letter, we revisit the problem of maximum likelihood estimation (MLE) of parameters of Gaussian Mixture Model (GMM) and show a new derivation for its parameters. The new derivation, unlike the classical approach employing the technique of expectation-maximization (EM), is straightforward and doesn't invoke any hidden or latent variables and calculation of the conditional density function. The new derivation is based on the approach of minorization-maximization and involves finding a tighter lower bound of the log-likelihood criterion. The update steps of the parameters, obtained via the new derivation, are same as the update steps obtained via the classical EM algorithm.
Encoding geometric sources with unknown statistics

Universal encoding method of an arbitrary set of sources without memory generating letters of an infinite alphabet is proposed. The probabilities of the input alphabet letter appearance are a geometric progression. The proposed method is weakly universal for the set of all geometric sources. If the denominator of the geometric progression exceeds δ, δ &gt; 0, the proposed encoding is universal. Redundancy estimates are obtained for an arbitrary subset of geometric sources.
Feasibility test of activity index summary metric in human hand activity recognition

Activity monitoring is a technique for assessing the physical activity that a person undertakes over some time. Activity Index (AI) is a metric that summarizes the raw measurements from tri-axial accelerometers, often used for measuring physical activity. Our research compared the Activity Index for different activity groups and hand usage [1]. We also tested this metric as a classification feature, and how different data acquisition and segmentation parameter configurations influence classification accuracy. Data acquisition was done with a previously developed system that includes a smartwatch on each wrist and a smartphone placed in the subject?s pocket; raw data from smartwatch accelerometers was used for the analysis. We calculated the Activity Index for labeled data segments and used ANOVA1 statistical test with Bonferroni correction. Significant differences were found between cases of hand usage (left, right, none, both). In the next analysis phase, the Activity Index was used as the classification feature with three supervised machine learning algorithms-Support Vector Machine, k-Nearest Neighbors, and Random Forest. The best accuracy (measured by F1 score) of classifying hand usage was achieved by using the Random Forest algorithm, 50 Hz sampling frequency, and a window of 10 s without overlap for AI calculation, and it was 97%. On the other hand, the classification of activity groups had a low accuracy, which indicated that a specific activity group can?t be identified by using only one simple feature.
Evaluating the Effectiveness of Margin Parameter when Learning Knowledge Embedding Representation for Domain-specific Multi-relational Categorized Data

Learning knowledge representation is an increasingly important technology that supports a variety of machine learning related applications. However, the choice of hyperparameters is seldom justified and usually relies on exhaustive search. Understanding the effect of hyperparameter combinations on embedding quality is crucial to avoid the inefficient process and enhance practicality of vector representation methods. We evaluate the effects of distinct values for the margin parameter focused on translational embedding representation models for multi-relational categorized data. We assess the margin influence regarding the quality of embedding models by contrasting traditional link prediction task accuracy against a classification task. The findings provide evidence that lower values of margin are not rigorous enough to help with the learning process, whereas larger values produce much noise pushing the entities beyond to the surface of the hyperspace, thus requiring constant regularization. Finally, the correlation between link prediction and classification accuracy shows traditional validation protocol for embedding models is a weak metric to represent the quality of embedding representation.
Nonparametric Universal Copula Modeling

To handle the ubiquitous problem of "dependence learning," copulas are quickly becoming a pervasive tool across a wide range of data-driven disciplines encompassing neuroscience, finance, econometrics, genomics, social science, machine learning, healthcare and many more. Copula (or connection) functions were invented in 1959 by Abe Sklar in response to a query of Maurice Frechet. After 60 years, where do we stand now? This article provides a history of the key developments and offers a unified perspective.
Optimizing Rank-based Metrics with Blackbox Differentiation

Rank-based metrics are some of the most widely used criteria for performance evaluation of computer vision models. Despite years of effort, direct optimization for these metrics remains a challenge due to their non-differentiable and non-decomposable nature. We present an efficient, theoretically sound, and general method for differentiating rank-based metrics with mini-batch gradient descent. In addition, we address optimization instability and sparsity of the supervision signal that both arise from using rank-based metrics as optimization targets. Resulting losses based on recall and Average Precision are applied to image retrieval and object detection tasks. We obtain performance that is competitive with state-of-the-art on standard image retrieval datasets and consistently improve performance of near state-of-the-art object detectors. The code is available at https://github.com/martius-lab/blackbox-backprop
More (or Less) Economic Limits of the Blockchain

This paper extends the blockchain sustainability framework of Budish (2018) to consider proof of stake (in addition to proof of work) consensus mechanisms and permissioned (where the number of nodes are fixed) networks. It is demonstrated that an economically sustainable network will involve the same cost regardless of whether it is proof of work or proof of stake although in the later the cost will take the form of illiquid financial resources. In addition, it is shown that regulating the number of nodes (as in a permissioned network) does not lead to additional cost savings that cannot otherwise be achieved via a setting of block rewards in a permissionless (i.e., free entry) network. This suggests that permissioned networks will not be able to economize on costs relative to permissionless networks.
Ask to Learn: A Study on Curiosity-driven Question Generation

We propose a novel text generation task, namely Curiosity-driven Question Generation. We start from the observation that the Question Generation task has traditionally been considered as the dual problem of Question Answering, hence tackling the problem of generating a question given the text that contains its answer. Such questions can be used to evaluate machine reading comprehension. However, in real life, and especially in conversational settings, humans tend to ask questions with the goal of enriching their knowledge and/or clarifying aspects of previously gathered information. We refer to these inquisitive questions as Curiosity-driven: these questions are generated with the goal of obtaining new information (the answer) which is not present in the input text. In this work, we experiment on this new task using a conversational Question Answering (QA) dataset; further, since the majority of QA dataset are not built in a conversational manner, we describe a methodology to derive data for this novel task from non-conversational QA data. We investigate several automated metrics to measure the different properties of Curious Questions, and experiment different approaches on the Curiosity-driven Question Generation task, including model pre-training and reinforcement learning. Finally, we report a qualitative evaluation of the generated outputs.
Dembowski-Ostrom polynomials and Dickson polynomials

&lt;p style='text-indent:20px;'&gt;We give a complete classification of Dembowski-Ostrom polynomials from the composition of Dickson polynomials of arbitrary kind and monomials over finite fields. Moreover, by using a variant of the Weil bound for the number of points of affine algebraic curves over finite fields, we discuss the planarity of the obtained Dembowski-Ostrom polynomials.&lt;/p&gt;
My View is the Best View: Procedure Learning from Egocentric Videos

Procedure learning involves identifying the key-steps and determining their logical order to perform a task. Existing approaches commonly use third-person videos for learning the procedure, making the manipulated object small in appearance and often occluded by the actor, leading to significant errors. In contrast, we observe that videos obtained from first-person (egocentric) wearable cameras provide an unobstructed and clear view of the action. However, procedure learning from egocentric videos is challenging because (a) the camera view undergoes extreme changes due to the wearer's head motion, and (b) the presence of unrelated frames due to the unconstrained nature of the videos. Due to this, current state-of-the-art methods' assumptions that the actions occur at approximately the same time and are of the same duration, do not hold. Instead, we propose to use the signal provided by the temporal correspondences between key-steps across videos. To this end, we present a novel self-supervised Correspond and Cut (CnC) framework for procedure learning. CnC identifies and utilizes the temporal correspondences between the key-steps across multiple videos to learn the procedure. Our experiments show that CnC outperforms the state-of-the-art on the benchmark ProceL and CrossTask datasets by 5.2% and 6.3%, respectively. Furthermore, for procedure learning using egocentric videos, we propose the EgoProceL dataset consisting of 62 hours of videos captured by 130 subjects performing 16 tasks. The source code and the dataset are available on the project page https://sid2697.github.io/egoprocel/.
Strategising template-guided needle placement for MR-targeted prostate biopsy

Clinically significant prostate cancer has a better chance to be sampled during ultrasound-guided biopsy procedures, if suspected lesions found in pre-operative magnetic resonance (MR) images are used as targets. However, the diagnostic accuracy of the biopsy procedure is limited by the operator-dependent skills and experience in sampling the targets, a sequential decision making process that involves navigating an ultrasound probe and placing a series of sampling needles for potentially multiple targets. This work aims to learn a reinforcement learning (RL) policy that optimises the actions of continuous positioning of 2D ultrasound views and biopsy needles with respect to a guiding template, such that the MR targets can be sampled efficiently and sufficiently. We first formulate the task as a Markov decision process (MDP) and construct an environment that allows the targeting actions to be performed virtually for individual patients, based on their anatomy and lesions derived from MR images. A patient-specific policy can thus be optimised, before each biopsy procedure, by rewarding positive sampling in the MDP environment. Experiment results from fifty four prostate cancer patients show that the proposed RL-learned policies obtained a mean hit rate of 93% and an average cancer core length of 11 mm, which compared favourably to two alternative baseline strategies designed by humans, without hand-engineered rewards that directly maximise these clinically relevant metrics. Perhaps more interestingly, it is found that the RL agents learned strategies that were adaptive to the lesion size, where spread of the needles was prioritised for smaller lesions. Such a strategy has not been previously reported or commonly adopted in clinical practice, but led to an overall superior targeting performance when compared with intuitively designed strategies.
Student Information Searching for Library and Information Science Study Program Padang State University: Ellis Model Analysis

This study aims to describe the information-seeking behavior of students of the Library and Information Science Study Program in searching for information. This research is quantitative research with a descriptive method. The population in this study were library and information science students totaling 288 people with a sample of 75 people. Sampling was determined based on the simple random sampling technique which is a random sampling technique without regard to the existing strata in the population. The data of this study were obtained through the distribution of questionnaires. Data analysis was carried out descriptively with the formula for calculating relative frequency and percentage. Based on the results of the study, it can be concluded that the information-seeking behavior of students of the Library and Information Science Study Program in searching for information as a whole is positive. These results are known from the average score obtained which is 2.92, this score is on an interval scale of 2.52 - 3.27.
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning

Practising and honing skills forms a fundamental component of how humans learn, yet artificial agents are rarely specifically trained to perform them. Instead, they are usually trained end-to-end, with the hope being that useful skills will be implicitly learned in order to maximise discounted return of some extrinsic reward function. In this paper, we investigate how skills can be incorporated into the training of reinforcement learning (RL) agents in complex environments with large state-action spaces and sparse rewards. To this end, we created SkillHack, a benchmark of tasks and associated skills based on the game of NetHack. We evaluate a number of baselines on this benchmark, as well as our own novel skill-based method Hierarchical Kickstarting (HKS), which is shown to outperform all other evaluated methods. Our experiments show that learning with a prior knowledge of useful skills can significantly improve the performance of agents on complex problems. We ultimately argue that utilising predefined skills provides a useful inductive bias for RL problems, especially those with large state-action spaces and sparse rewards.
Swift and Secure Medical Data Transaction

At present, healthcare faces several challenges in executing, sustaining, updating Electronic Healthcare Records (EHCR) in a digital era. As third party agencies are involved while sharing the records, they possess threats like lack of trust, unauthorized access, less secured systems which can be exploited easily. Furthermore, mostly the records are stored in a centralized model. It leads to data loss if there is no proper data recovery mechanism. It also leads to a security hole. In order to address these issues, we propose a model for Swift and Secure Medical Data Transaction (SSMDT). The proposed system incorporated Interplanetary File System (IPFS) and a block chain technology for providing increased security in transferring Healthcare Reports (HCR) with reduced latency. It is a decentralized and a peer to peer model. It stores the healthcare report compiled by various health monitoring sensors on IPFS using ethereum-based Decentralized App (DApp). Furthermore, encrypted data is stored in IPFS using cryptographic techniques such as Elliptic Curve Cryptography (ECC) instead of storing plain text for providing improved security. Detailed study is done for analyzing various factors like gas consumption for file transfer, computational overhead of various cryptography algorithms such as RSA, ECC and ElGamal. According to the findings of the experiments, computational overhead of ECC is less compared to other models and it also gives increased security.
Transaction and QoS-Driven Composition of Web Services Using Modified Grey Wolf Optimization with TOPSIS and AHP

Due to cloud computing and advanced web-based technologies, more and more service providers provide similar functionality web services with different QoS attribute values. The transactional behaviour of web services is described using transactional properties. Web services composition offers cost-effective construction of business applications. Finding a composite web service optimal in terms of QoS and transactional aspects is a multi-objective and challenging problem. Most of the existing optimization works proposed a single objective-based solution using weights assigned to various QoS attributes. They did not provide any information about weight assignment and used the weighted sum of QoS attributes as a fitness function. In this paper, we propose a modified grey wolf optimization approach for web services composition. Analytic Hierarchy Process (AHP) technique is used to assign weights to different QoS attributes. Fitness function is based on the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) technique. The proposed approach is compared with the genetic algorithm and Discrete Particle Swarm Optimization. Result analysis indicates the effectiveness of the proposed method.
Digital competencies of university professors in the 21st century

The latest developments in education caused by the 2019-nCOV pandemic and the transition from traditional to electronic teaching have influenced the special interest of teaching methodologists in determining the digital competencies of teachers in the 21st century. The changes have especially affected university professors, from whom the professional public expects to be the role models in the application of modern information and communication technologies. Those who previously understood the importance of computers and mobile phones in teaching went through the transition from one type of teaching to another more painlessly, while it was far more difficult for those who denied their functionality from the beginning. Such a factual situation has influenced a deeper understanding of their function in modernizing academic practice, analyzing official strategies for the development of education and the existing frameworks of necessary digital competencies. Therefore, the authors in this paper propose a framework for digital competencies of professors at higher education institutions in the Republic of Serbia, classifying them into six categories necessary for the successful implementation of academic teaching: 1) Basic information knowledge about modern information and communication technologies; 2) Creating accounts and using different online clients to establish communication in a virtual environment; 3) Managing a virtual learning environment; 4) Preparation of teaching materials with the help of application software; 5) Creating and managing an electronic portfolio; and 6) Safe use of online environment and ethics.
Localization Using DeepLab in Document Images Taken by Smartphones

Abstract The seamless integration of statistics from virtual and paper files could be very crucial for the know-how control of efficient. A handy manner to obtain that is to digitize a report from a picture. This calls for the localization of the report in the picture. Several approaches are deliberate to resolve this hassle; however, they are supported historical picture method strategies that are not robust to intense viewpoints and backgrounds. Deep Convolutional Neural Networks (CNNs), on the opposite hand, have been validated to be extraordinarily strong to versions in heritage and perspective of view for item detection and classification duties. Inspired by their robustness and generality, we advocate a CNN-primarily based totally technique for the correct localization of files in real-time. We advocate the new utilization of Neural Networks (NNs) for the localization hassle as a key factor detection hassle. The proposed technique ought to even localize snapshots that don't have a very square shape. Also, we used a newly amassed dataset that has extra tough duties internal and is in the direction of a slipshod user. The result is knowledgeable in 3 specific classes of snapshots and our proposed technique has 100% accuracy on easy one and 77% on average. The result is as compared with the maximum famous report localization strategies and cell applications.
Analisis Spektrofotometri Serapan Atom pada Penjernihan Air Sumur Bor di Desa Sekernan

Cloudy borehole water if done by filtering method with filtration by sand, charcoal, palm fiber, coral and husks can produce clear water. In the purification process can remove turbidity, color, metal Mn about 98.66 % and Fe around 99,50 %. Zeolite is a mineral that is used as an adsorbent, ion exchanger or catalyst. Zeolite in rice husk ash contains high silica content of 87%-97% and coal ash 50%-69% which is used as an ingredient in the manufacture of zeolite. This study aims to determine the zeolite made from rice husk ash and coal can reduce Fe and Mn levels. Based on the test results of the Health Laboratory Office, samples of water from drilled wells in Sekernan village, RT. 11 with the results of Fe content of 0.475 mg/l and Mn content of 0.380 mg/l. The use of borehole water which has a high metal content for drinking water, bathing and washing can cause a person to suffer from stomach ailments such as diarrhea, skin diseases, such as itching and so on. This research is an experimental study. The results of the water filtration process are left for 30-60 minutes. Furthermore, the water was tested for Fe and Mn levels using Atomic Absorption Spectrophotometry (AAS). The results of the AAS analysis showed that the previous Fe level decreased from 0.475 mg/l to &lt; 0.08 mg/l and the previous Mn level was 0.380 mg/l to &lt; 0.03 mg/l. The purification of borehole water with filtration method can produce clear water. Analysis of the AAS test made from zeolite can reduce the metal content of Mn by about 98.66% and Fe by about 99.50%.
Toward the morpho-syntactic annotation of an Old English corpus with universal dependencies

The aim of this article is to take the first steps toward the compilation of a treebank of Old English compatible with the framework of Universal Dependencies (UD). Such a treebank will comprise morphological and syntactic annotation of Old English texts adequate for cross-linguistic comparison, diachronic analysis and natural language processing. The article, therefore, engages in four tasks: (i) identifying the Old English exponents of UD lexical categories; (ii) selecting the Old English exponents of UD morphological features; (iii) finding the areas of Old English morphology that require token indexing in the UD format; and (iv) checking on the relevance of the universal set of dependency relations. The data have been extracted from ParCorOEv2, an open access annotated parallel corpus Old English-English. The main conclusions are that the annotation format calls for two additional fields (gloss and morphological relatedness) and that enhanced dependencies are required in order to account for some syntactic phenomena.