Reviewerly/exHarmony huggingface dataset:
Computer Technology in Energy Efficiency Analysis

Due to the large scale and complex topology of ultra-dense network, the computational complexity of the algorithm is difficult to control. As an upgrade of reinforcement learning, EEA has significant advantages in solving optimization problems with large state and action space. This paper presents an EEA algorithm based on DNN, that is, an energy utilization analysis algorithm. This paper compares EAA with Q-learning algorithm in practicability and accuracy. Due to the accidental factors in the process of algorithm validation, this paper analyzes the algorithm iteratively for many times to verify the availability of the proposed EAA algorithm in the analysis of energy efficiency. The research shows that EEA algorithm can improve energy efficiency and ensure the user’s energy efficiency analysis ability. It can be seen from the Fig that the algorithm converges gradually after nearly 100 iterations. In the first 50 iterations, EEA performs worse than Q-learning. This is because, in the first 50 iterations, Q-learning can be learned from the initial feedback, while EEA only randomly selects actions and stores the feedback information in the playback experience pool. After 50 iterations, EEA began to learn from it. After 100 iterations, both EEA and Q-learning tend to be stable, and the performance of EEA is better than that of Q-learning. Compared with the typical Q-learning algorithm, the proposed EEA algorithm not only has faster convergence, but also has better performance index.
eLogbook: An Android-Based Logbook System

Events associated with the Student Industrial Work Experience Scheme (SIWES) in the developing countries, including Nigeria, are recorded manually. As a result, the effectiveness and accuracy of the scheme are often defeated. The paper presents the design and implementation of an android-based electronic logbook system for the administration of SIWES. The application eliminated the problems associated with the existing paper logbook, which include storage problems, complications associated with forgery or manipulation of contents. It also allows students, supervisors and lecturers to carry out their tasks seamlessly.
The Study on Dui-sentences in Modern Chinese

Dui-sentences have a wide range of uses and unique structure. this paper’s Dui is used as preposition, the inner structure consist of subject, the phrase of preposition and predicate. Adjunct and tipicalization of Dui-sentences are category of grammar, they weren’t studied in the meantime. this paper according to the generation grammar’s theory, we try to study Adjunct, the VP that consist of Dummy verb and topical ‘Dui+NP2’ in Dui-sentences. in addition, we examined the relation between the NP of VP and Dui closely. after we specially project Dui-sentences, we came to the following conclusions. NP1 have the nature of noun and predicate. they are made up of noun, pronoun and verb. sometimes the phrase of ‘subject+predicate’ is used as it. when NP1 are perdicate and the phrase of ‘subject+predicate’, they are an accident that express action. ‘Dui+NP2’ is located in front of predicate, it was used for adjunct in front of predicate, the adjunct of Dui-sentences was added to maximal projection, adjunct was projected in TP, vP, VP. according to Adjunct Projection Principle, it can’t be added to argument NP or CP. adjunct is generated in surface structure, it doesn’t need to move position, it is merged into the left in tree diagram, we can project like this paper picture3. dummy verb can be used for V, it forms VP with NP, it has the property of verb, and can add postposition ‘了, 过’ behind it. if it is a light verb, we can’t form the structure of vP-shell. NP2 is the object of preposition Dui, it isn’t the word that the object of dummy verb move, because ‘Dui+NP2’ adjunct is generated in surface structure, it doesn’t move position. ‘Dui+NP2’ is preposition phrase, it can be located in front of sentence, it was used for a topic, the contents of ‘Dui+NP2’ are the important information that agent want to emphasize. ‘Dui+NP2’ is used for adjunct, it moves in front of subject, and dummy verb isn’t used of light verb in Dui-sentences, this facts are the sole phenomenon of chinese, it is the parameter of language. we need to modify the definition of adjunct and light verb in generative grammar.
Monetary Policy and Exchange Market Pressure Dynamics in Sub-Saharan Africa

The hypothesis that a tighter monetary policy stance can lend strength to currency was tested in this study using Generalized Methods of Moments (GMM) estimation in a dynamic panel setting. Data on 20 SSA economies for which data were available for the period 1998 to 2017 are used. The study found a negative and significant relationship between monetary policy and exchange market pressure (EMP), implying that contractionary monetary policy can ease EMP. It also revealed significant relations between aggregate output, levels of public debt, the current account balance and terms of trade and exchange market pressure (EMP).
ARMFS Algorithm: A Multi-Filter Selection Approach based on Akaike Information Criterion and Roulette Wheel

Complex state estimation is facing urgent needs currently. Simply applying one state estimation method is not accurate and robust enough to deal with future state estimation tasks, which may lead to severe trouble for any system. This paper proposes a novel multi-filter selection algorithm based on the Akaike information criterion and Roulette wheel, named as ARMFS algorithm. Unscented Kalman Filter (UKF) and Elman neural network aided UKF (ENN-UKF) are selected as two base filters. The proposed ARMFS algorithm is tested in the field of vehicle driving state estimation. The experimental results demonstrate that the ARMFS algorithm can intelligently select between two base filters with a general accuracy of 71.24%, which is high enough to provide an optimized filter result. This study provides a reference and works as an attempt in applying multi-filter selection algorithm to complex state estimation.
Survey of Online Identity Management Techniques on Blockchain

Secure identity management is one of the controversial topics in the cybersecurity landscape. The user needs to rely on third-party providers for managing their personal information, which is a big security concern. Traditional identity systems typically suffer from single points of failure, lack of interoperability, and privacy issues such as encouraging mass data collection and user tracking. Blockchain technology has the potential to support novel data ownership and governance models with built-in control and consent mechanisms, which may benefit both users and businesses by alleviating these concerns. Blockchain-based systems provide the possibility for their users to insert their data in this distributed ledger. Users can trust the blockchain as it is leveraging consensus mechanisms to validate and gather the transactions in immutable blocks. The transparent and decentralized nature of the blockchain network enables the development of a non-refutable and unbreakable record of data, which is the fundamental feature of many applications, such as identity management.
융합 R&D 기획을 위한 글로벌 연구개발 과제 정보 체계 활용: 해안 침수 관련 융합 R&D 탐색을 중심으로

The paradigm of convergence R&D is shifting from the convergence of technologies to the convergence of solutions to solve the complex problems of scientific and social development. On the other hand, it is prevalent that there is a lack of convergence in our research field. Although Korea has invested heavily in fusion research and development, Korea has mainly focused on the application and development of technology, so failed to plan convergence R&D in line with the new paradigm. Therefore, in this study, we searched for convergence R&D area that is being done to solve social problems, and tried to make use of data-driven objective methods. For this purpose, we used the investment information of global R&D projects that had no retrospective properties and derived the convergence R&D area related to coastal flooding.
An MRF-UNet Product of Experts for Image Segmentation

While convolutional neural networks (CNNs) trained by back-propagation have seen unprecedented success at semantic segmentation tasks, they are known to struggle on out-of-distribution data. Markov random fields (MRFs) on the other hand, encode simpler distributions over labels that, although less flexible than UNets, are less prone to over-fitting. In this paper, we propose to fuse both strategies by computing the product of distributions of a UNet and an MRF. As this product is intractable, we solve for an approximate distribution using an iterative mean-field approach. The resulting MRF-UNet is trained jointly by back-propagation. Compared to other works using conditional random fields (CRFs), the MRF has no dependency on the imaging data, which should allow for less over-fitting. We show on 3D neuroimaging data that this novel network improves generalisation to out-of-distribution samples. Furthermore, it allows the overall number of parameters to be reduced while preserving high accuracy. These results suggest that a classic MRF smoothness prior can allow for less over-fitting when principally integrated into a CNN model. Our implementation is available at this https URL.
METHODS OF TEACHING COMPUTER SCIENCE AT THE UNIVERSITY

This article discusses the definition of teaching methods as a science, its object and subject, and features. The main components of the connection between the methodology of teaching computer science as a science and the main concepts of the educational process are also identified. The main focus is on generalizing the experience of teaching the discipline "Informatics" to students of humanitarian universities and highlighting interdisciplinary connections. The purpose of the article is to describe the intermediate results obtained during the study of the effective use of information technologies in the course of teaching students of a humanitarian university in the discipline "Informatics". The structure of the e-course in the self-study support system is presented. The place of the discipline "Informatics" in the training programs of students of the 1st year of the bachelor's degree in the areas of "Economics" is highlighted, the types of tasks for independent work of students are described, and an example of the design of educational materials for the case in the discipline "Informatics" is given. The main directions of improving the methodology of teaching the discipline "Informatics" are also outlined.
A Cellular Automata-Based Bilateral Filter for De-speckling in Ultrasound Imaging

The ultrasound imaging is much more recommended in the medical field for the clinical recognition than any other imaging mode, as it is of adaptable, befitting, safer, and viable nature. The images which are captured thereafter per-processed; as the process plays an important aspect in medical diagnosis. The accession of the images can incorporate different kinds of noise. The much the degraded the image is, the more is the problem arise in for the better disentangling the required features, recognition, studying clarity of edges. In this paper, the comparison is done in between the various filters like Gaussian, kuan, lee, etc., which were discussed in previous paper with the bilateral imposed together with the cellular automaton filter, both together has been imposed. The comparative result has been depicted in the table format as a result.
Increase the Output Efficiency of Photovoltaic (PV) Modules by Using Reflector

As it's known in worldwide the efficiency of photovoltaic modules is lower than otherrenewable energy devices efficiency, which converts the sunlight to electrical power.Inthis research two PV modules were employed, the first one with reflector and the otherone without reflector.Both modules have the same tilt angle; its performance was compared under the sameenvironmental conditions in Iraqi, (ambient temperature of 46 °C and solar radiation of730 W/m2). The mirror enhanced solar PV panel which tracked sun along the daytimefrom East to West in summer (July) 2019 with tilt angle 0.576 rad (33 degree)for paneland 1.20 rad (69 degree) for mirror which was calculated by experiment.The power increase percentages by 4% in the output of panel, with reflector comparedwith the panel without reflector in the same condition (sun light and the same solarsystem.
Offensive language identification in Dravidian code mixed social media text

Hate speech and offensive language recognition in social media platforms have been an active field of research over recent years. In non-native English spoken countries, social media texts are mostly in code mixed or script mixed/switched form. The current study presents extensive experiments using multiple machine learning, deep learning, and transfer learning models to detect offensive content on Twitter. The data set used for this study are in Tanglish (Tamil and English), Manglish (Malayalam and English) code-mixed, and Malayalam script-mixed. The experimental results showed that 1 to 6-gram character TF-IDF features are better for the said task. The best performing models were naive bayes, logistic regression, and vanilla neural network for the dataset Tamil code-mix, Malayalam code-mixed, and Malayalam script-mixed, respectively instead of more popular transfer learning models such as BERT and ULMFiT and hybrid deep models.
The Effect of Prior Knowledge on Sense of Agency: Using Expectation-Maximization to Reproduce the Moving Rubber-Hand Illusion

When humans make inferences that go beyond limited, noisy, or ambiguous input data, background knowledge is necessary to make generalizations. Such inferences are important for designing intelligent artificial agents. Bayesian inference, a statistical method commonly used as a cognitive model of the brain, formalizes the integration of background knowledge, i.e. priors, and sensory evidence. I research the effect of priors on sense of agency (SoA) in an artificial agent. In humans, SoA is the subjective experience of control over actions and their consequence. In robotics, developing an agent with SoA is a popular challenge and the first step towards the artificial self. This thesis designs an artificial agent where the same prior knowledge improves SoA in unambiguous environments and induces incorrect, illusory SoA in noisy, ambiguous environments. First, I provide a comprehensive overview on the role of priors in Bayesian inference and the computational principles of a SoA. Second, I define the ``point mass moving rubber hand illusion (mRHI), a simulation for an artificial agent that simplifies the human mRHI experiment. Third, I use two parameter estimators, ordinary least-squares (OLS) and expectation-maximization (EM), to calculate SoA in the point mass mRHI. I found that SoA requires a prior belief about the causal relationship between outcome and action. In the point mass mRHI, the agent has to identify which of the three point masses it can control with its force input. The agent has the prior knowledge that there is a causal relationship between its actions and the states of one point mass. Though with OLS the agent has correct and incorrect SoA, some of the results not comparable to the results from human mRHI experiment. A likely cause is that this simplest reproduction of the mRHI models SoA as a binary variable and does not use Bayesian inference. The (partially) Bayesian parameter estimator EM calculates SoA as a (continuous) posterior probability and finds results similar to the human mRHI experiment. Comparing an OLS algorithm without prior to the EM algorithm, I found that in unambiguous environments the Bayesian prior improves the agent's general SoA and SoA over time but does not improve the SoA for the point mass that the agent does control. In the ambiguous environments, the prior generally motivates the agent towards a SoA over the incorrect mass. However, in a noisy but slightly less ambiguous environment, the prior improves the agent's SoA. In short, the prior improves a SoA when the agent needs to make inferences that go beyond the data but can also induce an incorrect SoA in a noisy and ambiguous environment.
Negation Scope Resolution for Chinese as a Second Language

This paper studies Negation Scope Resolution (NSR) for Chinese as a Second Language (CSL), which shows many unique characteristics that distinguish itself from “standard” Chinese. We annotate a new moderate-sized corpus that covers two background L1 languages, viz. English and Japanese. We build a neural NSR system, which achieves a new state-of-the-art accuracy on English benchmark data. We leverage this system to gauge how successful NSR for CSL can be. Different native language backgrounds of language learners result in unequal cross-lingual transfer, which has a significant impact on processing second language data. In particular, manual annotation, empirical evaluation and error analysis indicate two non-obvious facts: 1) L2-Chinese, L1-Japanese data are more difficult to analyze and thus annotate than L2-Chinese, L1-English data; 2) computational models trained on L2-Chinese, L1-Japanese data perform better than models trained on L2-Chinese, L1-English data.
People: The Key to Agile Agencies

Agile software development involves collaboration between self-organizing, cross-functional teams to quickly create and release software. When fully implemented, agile software development can enab...
Mobile, Divisible, and Safe E-Cash System

Mobile, divisible, and safe e-cash system adapts on mobile terminals for e-payment which can circulate in multiple banks. The usage of the divisible e-cash does not need pass bank, which the bank has not the bottleneck of e-business. The author’s thesis discusses on the withdrawal protocol, payment protocol, transferable protocol, deposit protocol, and update of e-cash, based on elliptic curve cryptography (ECC).The system is simple, efficient, secure, and fit for the mobile e-payment terminals in which storage, power, operand capacity, and network bandwidth supply are extraordinarily restricted. The system can protect from double spending, non-frameability, eavesdropping, tampering, and “perfect crime” effectively too. It can save from mis-identity attacks, two-layer anonymity attacks, and linking attacks.
(Indirect) requests in Natural Language Processing: a preliminary theoretical proposal

This paper focuses on conventionalized and non-conventionalized indirect speech acts, and more concretely, on (indirect) requests. We do so within a Natural Language Processing environment called FunGramKB, which adheres to a cognitively-oriented Construction Grammar view of language. Here, conventionalized formulations like Can you X? are treated as constructions in their own right; that is, as entrenched form-meaning pairings and, thus, they are not considered indirect. By contrast, non-conventionalized formulations such as those instantiated by negative state remarks (e.g. I’m hungry) require degrees of inferencing for interpretation. Both types are computationally handled in different modules of FunGramKB. Our aim is to show that a cognitive version of Construction Grammar can offer a solution to the computational treatment of illocution.
Research Guides: Construction: Interlibrary Loan

This guide should assist students in the Construction program to locate Library materials for research and projects Use this page to request an Interlibrary Loan
SSNCSE_NLP@DravidianLangTech-EACL2021: Meme classification for Tamil using machine learning approach

Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes. A meme is an image or video that represents the thoughts and feelings of a specific audience. The challenge of dealing with memes is that they are region-specific and their meaning is often obscured in humour or sarcasm. A meme is a form of media that spreads an idea or emotion across the internet. The multi modal nature of memes, postings of hateful memes or related events like trolling, cyberbullying are increasing day by day. Memes make it even more challenging since they express humour and sarcasm in an implicit way, because of which the meme may not be offensive if we only consider the text or the image. In this paper we proposed a approach for meme classification for Tamil language that considers only the text present in the meme. This work explains the submissions made by SSNCSE NLP in DravidanLangTechEACL2021 task for meme classification in Tamil language. We achieve F1 scores of 0.50 using the proposed approach using the test-set.
In Defense of the Paper

The machine learning publication process is broken, of that there can be no doubt. Many of these flaws are attributed to the current workflow: LaTeX to PDF to reviewers to camera ready PDF. This has understandably resulted in the desire for new forms of publications; ones that can increase inclusively, accessibility and pedagogical strength. However, this venture fails to address the origins of these inadequacies in the contemporary paper workflow. The paper, being the basic unit of academic research, is merely how problems in the publication and research ecosystem manifest; but is not itself responsible for them. Not only will simply replacing or augmenting papers with different formats not fix existing problems; when used as a band-aid without systemic changes, will likely exacerbate the existing inequities. In this work, we argue that the root cause of hindrances in the accessibility of machine learning research lies not in the paper workflow but within the misaligned incentives behind the publishing and research processes. We discuss these problems and argue that the paper is the optimal workflow. We also highlight some potential solutions for the incentivization problems.
Spellchecking for Children in Web Search: a Natural Language Interface Case-study

Given the more widespread nature of natural language interfaces, it is increasingly important to understand who are accessing those interfaces, and how those interfaces are being used. In this paper, we explore spellchecking in the context of web search with children as the target audience. In particular, via a literature review we show that, while widely used, popular search tools are ill-designed for children. We then use spellcheckers as a case study to highlight the need for an interdisciplinary approach that brings together natural language processing, education, human-computer interaction to address a known information retrieval problem: query misspelling. We conclude that it is imperative that those for whom the interfaces are designed have a voice in the design process.