Reviewerly/exHarmony huggingface dataset:
Effects of memory organization on credit assignment in human reinforcement learning

Author(s): Prentis, Euan; Thompson-Schill, Sharon L. | Abstract: How does the similarity structure of memory influence credit assignment in reinforcement learning? Memory spaces vary in how integrated versus separable their constituent dimensions are, and how clustered versus distributed items are across dimensions. Greater integration may cause people to overattribute value to multiple dimensions, potentially leading to generalization errors. Greater clustering may bias people to attribute value to discrete category-centers (category-based learning) rather than map value continuously across the space (function learning). In this study, subjects complete a value-learning task in which stimuli are sampled from low-dimensional perceptual spaces, and reward is mapped to one dimension. Each space is intended to engender a different degree of integration and clustering in memory, such that effects of memory organization on learning can be probed. Additionally, we investigate how credit assignment on each of these artificial perceptual spaces differs to credit assignment on more complex spaces that define real-world semantic concepts.
TOM: The Assistant Robotic Tutor of Musicianship with Sound Peak Beat Detection

Most past literatures surrounding musical robots has focused on either the engineering design of physical robots that could produce musical sounds, or on the mixed mechatronics aspects of algorithms enhancement to design robots gaining musicianship skills. The recent combined both these research fields into one is called Robotic Musicianship, which is innovative but with limited literatures. Robot musician involved sound peak beat detection techniques to enable musical perception. Responding to the novelty demand, the study adapted design approach of onset beat detection, blended with positivists’ philosophical approaches, and deductive principles associated with these philosophies were adhered to. The study aim to conduct a conceptual design and implement the drum rudiment for a robotic assistant tutor to co-demo electronic snare drum. The design approach of onset beat detection is adapted. The fundamentals of this robot prototype and rudiment work well, although many improvements can be done on the functionality and fluidity, doing so should further enhance engagement and musical development, and achieve the long-term goal to branch out to higher levels of education for music.
Integrating a Graph Builder into Python Tutor

Analysing unknown source code to comprehend it is quite hard and expensive task. Therefore, the Program Comprehension (PC) subject has always been an area of interest as it helps to realize how a program works by identifying the code that implements each functionality. This means being able to map the problem domain with the program domain. PC is a complex area, but its importance for programmers is so high that many approaches and tools were proposed along the last two decades. Program Animation is one of those approaches requiring specialized techniques.For each programming language, there are already tools that enable us to execute a program step by step, visualize its execution path, observe the effect of each instruction on its data structures, and inspect the value of its variables at any point.In the present context, we sustain the idea that PC techniques and tools can also be of great value for students taking the first steps in programming using a specific language. To this end, we aim to improve Python Tutor, a well-known program visualization tool, with graph-based representations of source code such as Control Flow Graph (CFG), Data Flow Graph (DFG), Function Call Graph (FCG) and System Control Graph (SCG).This helps novice programmers to understand the source code analyzing not only the variable contents but also a set of automatically generated graph-based visualizations, that were not included in Python Tutor so far. This will allow the students to be focused on certain aspects of the program (depending on the graph), abstracting others such as details of its syntax.
Multiple Regression Tests and Prediction for Ethereum Transaction Value

Unlike Bitcoin which was designed as an alternative to national currency as a medium of exchange, Ethereum was originally intended to facilitate projects based on its own platform. However, as the popularity of Ethereum continued to grow, it began to compete with every other cryptocurrencies and has become the second largest cryptocurrency in the world. Thus, it becomes important to study ether, Ethereum’s currency, as a medium of exchange. In this research, we will employ several regression models, including two stage least square (2SLS), regression discontinuity, difference-in-difference, and autoregression, to study various factors influencing ether’s transaction value, which is a crucial indicator as a medium of exchange and try to predict its growth. Through this study, we can have a clearer and more profound understanding of Ethereum’s development process,as well as a basic estimate of its future development trend.
DICTATIONS AND THEIR TYPES

This article is about dictation and its types. It provides detailed information on the types of dictations. Here's a few basic facts about a stomp pad and how it is used. It explains the purpose of dictation from elementary school students.
Discussion on upgrading construction of environmental protection hotline driven by AI technology

Enhancing the public’s awareness of environmental protection is an important measure to achieve the goal of carbon peak and carbon neutrality. This article takes the improvement of public participation in environmental protection as the starting point, discusses the enthusiasm of the public in environmental protection and its legal channels for reporting and complaining of environmental pollution, summarizes the achievements and existing problems of the construction of complaint channels, and proposes to improve environmental protection reports. Channel countermeasures and suggestions, build a reporting channel of environmental protection with temperature through artificial intelligence technology, increase public awareness and willingness to participate, and promote the development of environmental protection.
ASSESSING THE PREDICTABILITY OF CRYPTOCURRENCY PRICES

The predictability of asset prices works against the notion of an efficient market where asset prices reflect all available and relevant information. This paper examined the predictability of Bitcoin and 51 other cryptocurrencies that have been classified into the following five categories: Application, Payment, Privacy, Platform, and Utility. Two market efficiency tests (Ljung-Box autocorrelation and Runs tests) were run on the daily returns of the 52 unique cryptocurrencies and the MSCI World index from 28 April 2013 to 30 June 2019. The results showed that Bitcoin was consistently efficient, whereas most of the other cryptocurrencies and even the MSCI World index were not, implying that their prices were predictable. Categorically, Payment altcoins were the most consistent in showing inefficiency. Since altcoins in this category also recorded the third highest risk-adjusted returns, investors with advanced technical trading strategies had a great chance of exploiting the market information to make extremely high abnormal returns.
Multi-Image Reconstruction in Multi-Contrast MRI

The acquisition of multiple contrast magnetic resonance images (MRI) has an important role in clinical diagnosis by increasing diagnostic knowledge. Long scanning durations, in which the patient must remain immobile, limits the acquisition of multiple contrast MRIs. Scanning times can be reduced by undersampled acquisitions and reconstruction of undersampled images. Common methods produce a fully sampled MR image of a single contrast from undersampled MR image of the same contrast. However, limited prior information of a single contrast MR image in the input data limits the reconstruction performance. Hence, reconstruction performance can be increased with the use of multiple contrast MRI as input data. In this study, a multi-contrast MRI reconstruction method is proposed which simultaneously produces fully sampled images from undersampled images of more than one contrast. This method is applied using generative adversarial network that produce highly realistic images by better recovering the high frequency details. The proposed method has been tested on a dataset containing multiple contrast brain MR images, and it has been demonstrated that it provides superior performance compared to the alternative single contrast reconstruction method as a result of numerical and visual evaluations.
Challenges and its Solutions with Blockchain Technology Adoption in Enterprises

Blockchain is going to be a very critical technology for future development of the world. This technology is useful in storing immutable data ensuring that no one will alter data. Since it is decentralized and traceable, no central authority can control it, making it less corruptible. The enterprises are starting to use Blockchain to achieve operational efficiency by automating business process or digitizing records, tracking and tracing physical and digital assets and securing sharing of information. The number of threat vectors grows exponentially as blockchain networks become more complex. While it significantly reduces traditional risk it also creates new risks. This paper highlights the risks and challenges with blockchain adoption, also proposes some of the solutions. Blockchain is an inter-organizational technology, so how do this technology bring people from different organization together is the biggest challenge. There are many other issues like interoperability, latency issues, smart contract vulnerabilities, security concerns and throughput issues etc. But people are still exploring and finding new ways of implementing blockchain technology in daily life due to its versatility resulting in new and innovative applications. So blockchain technology is revolutionary with the potential to improve or develop new systems in different industries and areas.
Categorization in the Wild: Category and Feature Learning across Languages

Author(s): Frermann, Lea; Lapata, Mirella | Abstract: Categories such as 'animal' or 'furniture' play a pivotal role in processing, organizing, and communicating world knowledge. Many theories and computational models of categorization exist, but evaluation has disproportionately focused on artificially simplified learning problems (e.g., by assuming a given set of relevant features or small data sets); and on English native speakers. This paper presents a large-scale computational study of category and feature learning. We approximate the learning environment with natural language text, and scale previous work in three ways: We (1) model the full complexity of the learning process, acquiring learning categories and structured features jointly; (2) study the generalizability of categorization models to five diverse languages; and (3) learn categorizations comprising hundreds of concepts and thousands of features. Our experiments show that meaningful representations emerge across languages. We further demonstrate a joint model of category and feature acquisition produces more relevant and coherent features than simpler models, suggesting it as an exploratory tool to support cross-cultural categorization studies.
Convolution Neural Network Model for Recognition of Speech for Words used in Mathematical Expression

Speech recognition is translation of audio signal into human readable form. Speech recognition plays a vital role in various areas such as in signal processing, dictation system, command and control, simple data entry.  Speech recognition in dictation system helps the disabled people. In this paper authors have performed the experiment for speech recognition of   mathematical words which is helpful to disabled people. Now a day’s the use of deep learning in various applications is challenging for the improvement of model. In this paper authors have used CNN model to improve the recognition accuracy. Authors have selected 17 mathematical words which are the most commonly used in mathematical expression. Rectified Linear unit activation function is used to train the CNN because of its fast computation. This paper evaluates the model for MFCC and Delta MFCC features for Adam and Adagrad optimizers. Result shows that Delta MFCC gives an accuracy of 83.33 % for both Adam and Adagrad optimizer. It indicates that Delta MFCC gives better result than MFCC. Result also shows that Adagrad with Delta MFCC trains the model earlier than Adam.
Is there a predictability hierarchy in reference resolution

Author(s): Xu, Weijie; Xiang, Ming | Abstract: The concept of accessibility has often been evoked to explain reference resolution. According to the Givenness Hierarchy theory, a referent’s accessibility in the mental state of a comprehender is encoded in the form of the referent as part of its lexical semantic representation. However, the current literature has not reached a consensus on what accessibility exactly means and how to best quantify it. The factors that modulate accessibility show a great extent of overlap with another independently motivated concept of predictability, raising the possibility of a “Predictability Hierarchy” that mirrors Givenness Hierarchy. In a self-paced reading study, the current study examines whether there is such “Predictability Hierarchy” by manipulating the predictability and the form of a referent presented to the participants. Our results indicate that although there is no strong evidence for approximating the Givenness Hierarchy with a “Predictability Hierarchy,” there is some preliminary evidence for a partial correlation between the form and the predictability of a referent.
IITK at SemEval-2021 Task 10: Source-Free Unsupervised Domain Adaptation using Class Prototypes

Recent progress in deep learning has primarily been fueled by the availability of large amounts of annotated data that is obtained from highly expensive manual annotating processes.To tackle this issue of availability of annotated data, a lot of research has been done on unsupervised domain adaptation that tries to generate systems for an unlabelled target domain data, given labelled source domain data.However, the availability of annotated or labelled source domain dataset can't always be guaranteed because of data-privacy issues.This is especially the case with medical data, as it may contain sensitive information of the patients.Source-free domain adaptation (SFDA) aims to resolve this issue by using models trained on the source data instead of using the original annotated source data.In this work, we try to build SFDA systems for semantic processing by specifically focusing on the negation detection subtask of the SemEval 2021 Task 10.We propose two approaches -ProtoAUG and Adapt-ProtoAUG that use the idea of self-entropy to choose reliable and high confidence samples, which are then used for data augmentation and subsequent training of the models.Our methods report an improvement of up to 7% in F1 score over the baseline for the Negation Detection subtask.
Effect of Missing Data Treatment on the Predictive Accuracy of C4.5 Classifier

Missing data is a common problem confronted by researchers in machine learning applications. Missing values affect both the performance of analysis tools, as well as the quality of the drawn decisions. This research aims to analyze the impact of four missing data treatment methods on the predictive accuracy of the C4.5 decision tree algorithm. It also investigates the imputation accuracy of each imputation method using a single dataset with missing values presented in a single variable. The work was performed under three missing data assumptions, namely, Missing Completely At Random (MCAR), Missing At Random (MAR) and Missing Not At Random (MNAR) with three missingness’ rates: 5%, 10%, and 15%. The methods used to treat the missing data are: lite-wise deletion, mean/mode imputation, K-nearest neighbor imputation, and decision tree imputation. The results of the experiments showed that the C4.5 classifier achieved better performance under the MCAR assumption. While the mean/mode imputation has the highest C4.5 predictive accuracy under MAR and MNAR assumptions. The k-nearest neighbor method obtained the most accurate imputation result under the MCAR assumption, whereas mean/mode imputation was the most accurate method under the MAR assumption. On the other hand, the lowest imputation accuracy levels were achieved under the MNAR assumption attributed to the mean/mode imputation method.
Electronic Evidence–Admissibility and Authentication: A Judicial Perception of Apex Court of India

We are living in the digital era. Disputes and crimes are inevitable in this era of technology. The investigation and judgment of the courts are highly dependent on the electronic evidence. The recent judgment of the Indian Apex Court has opined on the subject matter of admissibility of Digital Evidence under Section 65-B [Indian Evidence Act, 1872] in the Arujn Panditrao Khotkar case . The Apex Court of India had confirmed the decision of P.V. Anvar case  and overruled the decision of Shafhi Mohammad case . The Arjun Panditrao case  is the most up-to-date development in India regarding the admissibility of electronic evidence in India. The researchers examine the recent judgments on the admissibility of electronic evidence by using the doctrinal method of research. The researchers reflect the issues of admissibility of electronic evidence which are resolved by the Apex Court of India. The researchers also reveal the challenges for the admissibility of Electronic Evidence under Section 65-B of Evidence Act post- Arjun Panditrao case. The researchers also suggest future expectation from the Hon’ble Legislators and Judiciary to resolve the challenges for the admissibility of Electronic Evidence.
Hand Tracking by using Python and Machine Learning

Hand tracking is an important component in most practical gesture interaction. In this work, we present a real-time method for hand gesture recognition. We present another constant hand global positioning framework dependent on a solitary profundity camera. In our Project, the hand region is extracted from the background with the background removing method. Then, the palm and fingers are segmented so as to detect and recognize the fingers. In this paper, an automatic hand tracking and segmentation method based on depth information is proposed. Hand depth is determined and then exact hand region is obtained. In this manner, exact hand tracking is realized with very less time and regardless of the complex background. From the tracking process we extract several hand features that are fed to a finite state classifier which identifies the hand configuration. Experiments show the effectiveness of the hand tracking and dynamic gesture recognition. A research was based on a number of algorithms that could best explained a hand gesture. In the pre-processing state, a selfdeveloped algorithm eliminate the background of each training gesture. After that the image is transform into a binary image and the sums of all diagonal segments of the pictures are taken. A completely powerful hand gesture recognition system is still under development.
A Novel Quantum Communication Protocol and its Simulation in IBM Quantum Simulator

The paper gives the demonstrates of quantum super dense protocol and its related. Firstly, Qiskit's simulator is used to set-up and run the proposed quantum circuit, and then IBM quantum computer will be used to run the circuit for evaluation. In addition, we also introduce the two types of attacks which are possible on communication system and its simulation on IBM quantum computer via using quantum super dense protocol. The main findings are discussed in the wider setting of quantum cyber-security, where ways of hacking including the role of insider threats are discussed. Keywords—Quantum information, Quantum communication protocol, Quantum super dense coding, Quantum simulator, Quantum IBM Computer, Qiskit’s simulator
Clinical profile of malaria in children at a tertiary care hospital of Bihar and evaluation of parasite LDH based rapid diagnostic test for malaria

Background and Purpose: Malaria is a very common infection leading to high morbidity and mortality. We aimed to evaluate the clinical profile of Malaria in Bihar.We also tried to evaluate the efficacy of pLDH based rapid diagnostic test for Malaria. Materials and Methods: This was a hospital-based retrospective observational evaluation of record of patients diagnosed with Malaria between August 2018 to August 2020 at Indira Gandhi Institute of Medical Sciences, Patna, Bihar. Statistical significance of different clinical features was evaluated between the prevalent malarial species. pLDH based RDT was evaluated against the microscopic examination of Plasmodium species. Fischer’s exact test was used to determine” p value”. Results: 92 patients had confirmed diagnosis of malaria during the study period. The number of males was more 52(56.5%) and most patients were from the age group 5-10 years (37%). Vomiting, respiratory symptoms and jaundice were found significantly higher (p< 0.05) in falciparum Malaria cases. Sensitivity, specificity, PPV and NPV of pLDH based RDT were 93.8%, 87.5%, 76.2%, and 97.1% respectively when compared to microscopy. The positive likelihood ratio was 7.5 and the negative likelihood ratio was 0.07 Conclusion: The present study showed the clinical profile of Malaria in the state of Bihar, India. We were able to show that P. vivax infection is more common and P.falciparuminfection more dangerous. Rapid diagnostic tests were found to be effectiveneed to look for evidence of Malaria in sick children even without typical signs and symptoms. Keywords:  Malaria, RDT, Plasmodium, Thrombocytopenia.
STRUCTURE OF A SOFTWARE PRODUCT FOR SEMANTIC ANALYSIS OF TEXTUAL INFORMATION

The article is devoted to the development of software for semantic text analysis. Describes the General logic of the developed web application, which implements the classification of textual information by previously predefined semantic categories. This article provides usage and deployment diagrams, as well as a General diagram of the algorithm. The methods implemented in the app allow you to increase the speed of processing text information. The use of the developed software product allows you to quickly process text information of any content, as well as conduct a deeper semantic analysis of the text. The implemented method is relevant in many areas of activity where you have to work with text information: monitoring of emotional mood towards the company, marketing, social network analysis.
A Framework For Fall Activity Detection and Classification using Deep Learning Method

Detecting the fall of a person in indoor environment is a challenging issue especially for elderly and sick living alone. Cameras can continuously observes the scene and require little interaction with the user and therefore, are well suited for fall detection. In this paper, we propose a deep learning based framework for fall detection and classification. Further, we also apply different machine learning methods namely, Support Vector Machine (SVM) and decision tree after extracting important features to detect and classify the fall of a person. We compare our deep learning based framework with SVM and decision tree as well as other state-of-the-art methods. We have considered two different publicly available datasets for performing experiments. The experimental evaluation of our deep learning based proposed framework gives promising results and is comparable with other state-of-the-art methods.