Reviewerly/exHarmony huggingface dataset:
Gradual Fine-Tuning for Low-Resource Domain Adaptation

Fine-tuning is known to improve NLP models by adapting an initial model trained on more plentiful but less domain-salient examples to data in a target domain. Such domain adaptation is typically done using one stage of fine-tuning. We demonstrate that gradually fine-tuning in a multi-step process can yield substantial further gains and can be applied without modifying the model or learning objective.
Redefining Data Dimensionality Through Dynamic Linkages in Data-Space Continuum

Dimensionality of data has been overly defined by scholars as the number of attributes a dataset holds. As such, high dimensional data is described as a curse to most analytics tools and algorithms; since, even a small increase in the number of attributes grows the predictive space significantly; so much so that the model is no longer as accurate as it ought to be. This study however seeks to redefine data dimensionality by first viewing data as a vector that exists within a data space continuum. Following the redefinition of dimensionality, this study proceeds to show the robustness and benefit of highly dimensional data in improving the quality of analytics. This is achieved in this study by introducing time as an important scalar in the data space continuum and showing the contribution of considering time during data analysis. This contribution explains the highly improved ability of the predictive space to not only being restricted to showing the how or to what extent attributes affect each other or the target output but also explains the pointer reasons why predicted events occur with relevance to a time series. This ability is thus deemed as a blessing to analysis following the redefinition of data dimensionality. The assumption made is that data points existing during different time stamps experience a variety of effects resulting from natural or artificial third parties.
Noise Robust Projection Rule for Klein Hopfield Neural Networks

Multistate Hopfield models, such as complex-valued Hopfield neural networks (CHNNs), have been used as multistate neural associative memories. Quaternion-valued Hopfield neural networks (QHNNs) reduce the number of weight parameters of CHNNs. The CHNNs and QHNNs have weak noise tolerance by the inherent property of rotational invariance. Klein Hopfield neural networks (KHNNs) improve the noise tolerance by resolving rotational invariance. However, the KHNNs have another disadvantage of self-feedback, a major factor of deterioration in noise tolerance. In this work, the stability conditions of KHNNs are extended. Moreover, the projection rule for KHNNs is modified using the extended conditions. The proposed projection rule improves the noise tolerance by a reduction in self-feedback. Computer simulations support that the proposed projection rule improves the noise tolerance of KHNNs.
Developing a motif finding algorithm using Suffix Tree and Hash Table

Motif searching is a widely used technique of Bioinformatics - an interdisciplinary field of Computer Science and Biology. It is implemented in various approaches to find out recurring patterns in DNA sequences that are presumed to have biological functions of humans. The motif is extensively used for drug design, human genome understanding project, and human disease research, etc. The main objective of this thesis is to develop an algorithm that would enable us to find the best motif from a DNA sequence within a short period of time. To construct a targeted algorithm, primarily a suffix tree and a hash table were used for finding the desired Motif and its frequency of occurrence in the particular DNA sequence. A remarkable result was obtained from the proposed model that requires a shorter period of time than other prevailing available algorithms. Consist of less complexity and short execution time ensures this algorithm practically viable as well as it would contribute tremendously in future research of Bioinformatics.
Eigen Value Analysis in Lower Bounding Uncertainty of Kalman Filter Estimates

In this paper we are concerned with the error-covariance lower-bounding problem in Kalman filtering: a sensor releases a set of measurements to the data fusion/estimation center, which has a perfect knowledge of the dynamic model, to allow it to estimate the states, while preventing it to estimate the states beyond a given accuracy. We propose a measurement noise manipulation scheme to ensure lower-bound on the estimation accuracy of states. Our proposed method ensures lower-bound on the steady state estimation error of Kalman filter, using mathematical tools from eigen value analysis.
Kayak Tracking using a Direct Lidar Model

This paper proposes a direct approach for extended object tracking (EOT) using light detection and ranging (lidar) measurements. The method does not use any clustering operations, but processes the individual laser beams directly in an extended Kalman filter (EKF), and resolves data association by means of techniques reminiscent of the probabilistic data association filter (PDAF). The method is particularly tailored to tracking of kayaks, and parameterizes the shape of the kayak as a stick whose length is part of the state vector. The proposed method is evaluated through a simulation study and tested on real lidar data.
Information Architecture for Industry Events: Intention, Diversity, and Inclusion

Industry events should reflect the diversity of their body of practitioners, but diverse, inclusive, and safe events don’t happen by accident; like the information-based structures that Information Architects create in the digital space, the information-sharing structure of a conference, workshop, or other events must be designed with intent and purpose. This case study outlines efforts undertaken by several Washington, DC-area events as well as the Information Architecture Conference during 2018 and 2019 to better architect Diversity and Inclusion within industry events. The purpose of these activities was to help event organizers to plan and execute better, more diverse, and more inclusive industry events. It describes the 2019 Diversity & Inclusion Workshop series that was undertaken at three of these events to inform diversity and inclusion efforts by the IA Conference, for which one of the authors served as 2019 diversity and inclusion co-chair, and shares the diversity, inclusion, safety, and accessibility topics identified by workshop participants. The chapter concludes with actionable steps that can be taken to improve industry events and their organizational bodies. The goal of this case study is to encourage and support conscious efforts to engineer better events, by leveraging information architecture (IA) and user experience (UX) techniques applied in larger contexts and information spaces.
A dataset to evaluate Hindi Word Embeddings

Abstract The current trend to solve different challenges of Natural Language Processing utilizes various online crawling methods to fetch the data and applying different shallow or deep learning methods to develop models for the respective tasks on this data. Word vectors generated using such methods are being applied for several NLP challenges and such vectors are being evaluated on word similarity task. Not only huge data is available but also multiple datasets are available for the English language to evaluate the performance of the developed models. However, the scenario is not the same for Indian languages specifically for Hindi. Focusing this challenge, we propose a dataset to check word similarity in Hindi. The construction process and afterwards annotation process are described in details. To construct this dataset, first, 353 word-pairs from the most popular English dataset are selected and translated. Their translations are verified by Hindi Experts. These word pairs are finally annotated independently by 11 native Hindi speakers. Multiple criteria have been set to select the annotators for this task. The final dataset has been evaluated on CBOW and Skip-gram models.
Assessment of the Impact of Publication Year and Keywords in Research Manuscript Quality Using Fuzzy Inference System

The start of good research is always through the good literature obtained in support of it. In today’s scenario, the researcher’s job of selecting the relevant work is a major complex task. The number of research works in the form of a research paper, review paper, etc., is abundantly available. There are various tools and algorithms available which are used in selecting and ranking the scientific publications. This paper mainly focuses on the impact of publication year and keywords on the selection of a research manuscript. The authors in this paper have proposed a fuzzy logic system for evaluating the impact of these parameters on the quality of the manuscript. The system developed was tested on the primary data collected by downloading the multiple research papers from various journals. The results obtained showed that the proposed system is efficiently supporting the objectives and correctly evaluating the impact of these parameters.
Optimal Model of Obstacle Avoidance of Two-Wheeled Robots

Obstacle Avoidance (OA) is a fundamental requirement for a mobile robot to prevent damaging itself. Also, Collision avoidance system is an important issue of the Self-driving car in the near future. Several robotic competitions in Taiwan challenge participants building and coding their mobile robots to avoid obstacles; and usually, the obstacles are plastic bottles. To do OA, be able to find the obstacle is the first requirement. At present, ranging sensors such as laser ranging, infrared ranging, ultrasonic sensors are commonly used to detect an obstacle. However, due to the transparency of plastic bottles, ultrasonic sensors is the only way to measure the distance of the plastic bottle. In this paper, an optimal OA method, shortest path without collision, is presented. Plenty experimental results show that the mathematical model presented in this paper is quite accurate and effective. Furthermore, the research results have been verified by participating in an international robotics competition, and finally winning the championship confirms the practical application value of this research.
Design and implementation software for mining association rules (market basket analysis) to design product layout desicions

Abstract A provided data from a transactional database, has supported technical development which can automatically find product association or items saved in the database. This finding association rules between saved product in database known as mining association rules. There are so many theories and algorithm developed for conducting mining association rules. One of algorithm developed is Apriori algorithm. This method, has a main goal to find the maximum frequent itemset. Next, this frequent itemset will be generated into associative rules which are not shown before in the database, become valuable information for considering materials in the decision process. Apriori algorithm is a interpretation technique of mining association rules, will be implemented into a web based software. On the software test which use in some different data, it’s concluded that time for mining association rules depends on the presence of every item in every transaction, total of transaction, minimum support and minimum confidence. For smaller value of minimum support and minimum confidence that entered, program will generate more association rules, vice versa.
Multi-time Scale Features for Anomaly Detection from Surveillance Videos

As the importance of surveillance in the world increases, the importance of surveillance cameras has increased. It has also increased the number of surveillance cameras installed. On the other hand, as the number of surveillance cameras increases, detecting abnormal events through monitoring requires higher labor intensity. The need for machine resources to detect abnormal events in stored videos has been expanded. Simultaneously, due to the rapid development of the computer vision field, a method has been developed in which the machine can detect abnormal events. To detect abnormal events, the importance of visual features that the machine can recognize has also increased. Previous methods used deep-learned visual features to detect anomalies, while feature extraction of visual features uses only a single stride and a single segment for the time scale. That is, abnormal events are detected by considering only a single scale. These features are spatiotemporal, but there is a possibility that the accuracy can be increased through various strides and segments. Therefore, we propose to consider various strides and various segments, i.e., multi-time scales. We evaluate performance with small datasets reconstructed from existing datasets.
A novel simple light-weight Neural Network for Road Segmentation

This study proposes simple methods to design a light-weight neural network. A deep learning domain has many state of the art neural networks so it is highly accurate for commonly used dataset such as ImageNet and Cifar-10, Cifar-100 and allows a rapid execution time and a small model. However, these state of the art neural networks are very complicated. This paper uses a VGG-16[1] model to reduce the size of the model and the inference time, but maintain accuracy. The semantic segmentation performance for the proposed method is compared to that for the VGG-16 model. The same full convolutional network (FCN) semantic segmentation algorithm [2] is used to compare the two models for the same semantic segmentation task. This study proposes an easier method to construct a light-weight neural network.
Communicative Artificial Intelligence in Multi-agent Gaming

In many modern game implementations, the intelligent agents are abstracted from their context and from each other. This leads to a disconnected experience that causes information to be lost from each agent once they are defeated. We wish to show the Communicative Artificial Intelligence system for multiagent games. This system provides adaptive AI across generations of agents that the player will encounter. In this scenario the player encounters the first wave of enemy AI near the perimeter of the target zone. The player must choose which weapon or weapons to fight with. If they defeat the enemy agents, these agents pass on their acquired knowledge to the next wave of enemy combatants. This second wave now has developed immunity to the same form of attack that was previously used by the player. As a result, the player must now choose a different attack. This information is passed along to the third wave of enemy agents who now have immunity to all weapons that the player has used thus far (collected from each previous generation of enemy AI). In this manner we hope to create a more strategic approach to the game wherein the player must reason about which weapons to use knowing that they will lose effectiveness as they progress. The goal is for the player to have to use the least powerful weaponry that is sufficient for the task at each level or they will be hopelessly outmatched as the game progresses. This research aims to close a gap in current games that is created by agents not acting as a part of the greater whole. In real-world scenarios the teams would communicate with each other and pass along such vital information. We also hope to draw the conclusion that this system can also inform more complicated systems (such as threat detection, intrusion thwarting, and more capable firewalls). We imagine multi-tiered security systems that pass along the techniques and knowledge about the attack or attacker to each progressively more challenging system thus providing thus providing a much more solid defense. We model such a system in our game as a means of evaluating this type of communicative AI.
Text, Intertext, Hypertext?

The concluding chapter draws together the arguments of the preceding chapters and looks ahead to potential further avenues of exploration. One of these is the use of the intertextual model to specifically look in more detail at language, for example, in the way metaphors work, and picks up on some of the ideas in the introduction concerning metamorphoses (of words/characters), in a conflation of some of the repeated elements of the texts studied. The chapter also surveys recent studies in adaptation that support the argument concerning the importance of intertextuality in textual analysis. It also outlines the possibility of aligning intertextual theory with that of hypertexts and proposes the construction of a hypertext model using early modern texts to act as an electronic model of intertextuality.
Literature Review: Overview of Cancer Treatment and Prediction Approaches Based on Machine Learning

The purpose of this chapter is to provide a literature survey through an overview of the research fields relevant to cancer treatment and prediction approaches based on machine learning. The past few years have witnessed an exponential growth in databases and repositories due to the increase in scientific knowledge and the massive data production. Biomedical domain represents one of the rich data domains. An extensive amount of biomedical data is currently available with wealth of information, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices El Houby (J Appl Biomed, 2018 []). One of the important biomedical research domains, epidemiological cancer research, who is of high priority across the world. Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. Every sixth death in the world is due to cancer, making it the second-leading cause of death (second only to cardiovascular diseases) Schutte (Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study, 2017 []). The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The vast amount of hidden data in huge databases related to cancer with all his variability has created tremendous interests in the field of data mining. While data mining is a discipline resulting from the combination of classical statistics and computer science algorithms, such as machine learning, aim to the extraction of new and useful knowledge from a large amount of data, it has become a useful instrument in bioinformatics. It can depict variation of cancer incidence and mortality by region, ethnicity, gender and socioeconomic factors that contribute to the assessment of population health needs, while it can contribute to the study of cancer burden. Furthermore, in-depth analysis of the patient’s profile using data mining methods may uncover hidden, previously unknown relations between patient profile, cancer treatment and surveillance. This chapter will present recent approaches and works related to this context. It presents also a synthesis of current and future trends for smart systems for E-health.
Cultivation of Intelligent Prefabricated Construction Talents in Higher Vocational Colleges Under the Information Age

The country issued a request for the promotion of intelligent and prefabricated buildings, and proposed innovative construction methods that adhere to standardized design, factory production, assembly construction, integrated decoration, information management, and intelligent applications; this detonated an extraordinary shortage of prefabricated construction talents the fuse. This article explores the related issues of practical teaching construction of prefabricated buildings in colleges and universities, in order to promote the comprehensive reform of the training mode of prefabricated building talents in colleges and universities, cultivate high-quality skilled talents that meet the actual work needs of employers, and enhance students’ employability.
식도 삼킴곤란의 약물 치료

Patients with esophageal dysphagia need a step-by-step approach for diagnosis and treatment. Endoscopic with biopsy and barium esophagogram are the essential tests evaluating anatomical abnormality and esophageal bolus stasis. Further imaging or esophageal function tests such as high-resolution esophageal manometry, functional endoluminal imaging probe, CT or endoscopic ultrasound are required. In the case of dysphagia due to esophageal motility disorder, whether it is the major motility disorder or minor motility disorder should be identified in high resolution manometry. Major motility disorders show esophagogastric junction obstruction or major peristaltic defects. In this case, the severity of the symptoms should be assessed and patients who need endoscopic or surgical treatment targeting lower esophageal sphincter should be screened. Impaired lower esophageal sphincter relaxation (achalasia, esophagogastric junction outflow obstruction, esophageal spasm or abnormal hypercontraction (jackhammer esophagus), hypotensive contraction (ineffective esophageal motility, failed peristalsis), esophageal hypersensitivity (noncardiac chest pain), gastroesophageal reflux disease and esophageal bolus stasis are the possible mechanisms causing dysphagia symptoms. The proper medical treatment depends on underlying mechanisms. (Korean J Gastroenterol 2021;77:71-76)
Synthesis of Ternary Reversible Circuits based on Permutation Cycles

The main motivation to use ternary logic systems is that the amount of information per circuit line is higher as compared to the corresponding binary logic representation, thereby leading to more compact circuit realizations. This is particularly attractive for quantum computing as qutrits are expensive resources and minimizing their number is one of the main objectives during synthesis process. Hence, the present work exploits the ternary logic concept and synthesizes the ternary reversible circuits using a cycle-based approach. A permutation representing the ternary reversible specification of a given function in given as an input. The permutation cycles are obtained from the input and then it is factored into smaller length cycles viz., 3-cycles and 2-cycles. The simpler cycles are then represented with the ternary reversible gates. The gate library such as Ternary Not gate, Multi-polarity Ternary Feynman Gate (\({\text {MPT}}_{\text {F}}\)) and Multi-polarity Ternary Toffoli Gate (\({\text {MPT}}_{\text {T}}\)) is used to synthesize 3-cycles and 2-cycles. These gates are then decomposed into elementary ternary quantum gates. The experimental evaluation indicates that there is an improvement in the present approach i.e 49 and 19% in terms of quantum cost and gate count as compared with the published works.
Determining the Best Geolocation For Business

The article is devoted to automatic search for a place on the map to open a business. The developed service displays competitors and analyzes the feasibility of starting a business in a given location. In the development process, Yandex tools were used, namely StaticAPI, search for organizations, and the API-geocoder. The analysis is performed based on two criteria: the number of transport hubs surrounds the point in which user is interested and the presence of competitors in the area. In this study, Static API forms the map image according to the values of parameters passed to the service in the URL based on user parameters. After that, you can use the "organization search" to identify similar geographical features. The search can be used for different types of businesses. The result is a program that allows a person to determine the profitability of a certain type of business, at a certain geographical point.