Reviewerly/exHarmony huggingface dataset:
Development of ET-DR Algorithm to Enhance Resolution for Gaze Correction of Low-Resolution Image Based Webcam Eye Tracking

Eye tracking is method of detection of a point on a screen that the user gazes at on CV technology.Eye tracking technology is one of the NUI technologies which enables controlling of GUI or inputting text, but it is also possible to analyze the user's psychological state or the user's understanding of the contents being gazed at through the user's eye gaze analysis.Eye tracking can be used for reading education, internet shopping, commercial advertising, etc.Furthermore, it can be used to detect cheating in online exams.The eye tracking system's permissible range varies depending not only on the motion of the user, but also on the camera, the quality of the acquired image, and the difficulty of the algorithm.Therefore, the accuracy of eye tracking is greatly influenced by how the eye tracking algorithm is implemented in addition to hardware variables.This paper presents Eye tracking Dead Reckoning (ET-DR) as a method to increase the resolution of the eyetracking system for reading education.DR is usually an algorithm used in navigation, but it has been modified and optimized to increase the resolution of eye tracking.To verify this algorithm, 100 Korean elementary school kids read Korean texts.At the time, the eye tracking success rate was assessed using a computer with an ET-DR algorithm and a computer without the algorithm, and a highly relevant result was achieved.
A Preliminary Study on AI for Telemetry Data Compression

Compression of telemetry streams is fundamental for both their storage and transmission. Recently, machine learning has been employed to enhance traditional data compression algorithms specifically for telemetry compression, both lossless and lossy. However, state-of-the-art telemetry compression algorithms are usually tailored to work with very specific datasets and can hardly generalize to different datasets. Moreover, much simpler traditional algorithms can often obtain better compression ratios with less computational complexity. In this work, we attempt a preliminary experiment aiming to verify the effectiveness of one of the most representative AI-based lossless telemetry compression algorithms against three different NASA datasets. Experimental results show that the model still struggles to perform better than traditional approaches, highlighting the necessity to design and study more sophisticated machine learning models for telemetry compression with a broader applicability.
Automated Grading of Regular Expressions

Abstract With the rapid transition to distance learning, automatic grading software becomes more important to both teachers and students. We study the problem of automatically grading the regular expressions submitted by students in courses related to automata and formal language theory. In order to utilize the semantic information of the regular expression, we define a declarative logic that can be described by regular language and at the same time has natural language characteristics, and use it for the following tasks: 1) to assign partial grades for incorrect regular expressions and 2) to provide helpful feedback to students to make them understand the reason for the grades and a way to revise the incorrect regular expressions into correct ones. We categorize the cases when students’ incorrect submissions deserve partial grades and suggest how to assign appropriate grades for each of the cases. In order to optimize the runtime complexity of the algorithm, two heuristics based on automata theory are proposed and evaluated on the dataset collected from undergraduate students. In addition, we suggest Regex2NL which translates regular expressions to natural language descriptions to give insight to students so that they can understand how the regular expressions work.
Pragmatic Gradual Polymorphism with References

Abstract Gradualizing System F has been widely discussed. A big challenge is to preserve relational parametricity and/or the gradual guarantee. Most past work has focused on the preservation of parametricity, but often without the gradual guarantee. A few recent works satisfy both properties by giving up System F syntax, or with some restrictions and the introduction of sophisticated mechanisms in the dynamic semantics. While parametricity is important for polymorphic languages, most mainstream languages typically do not satisfy it, for a variety of different reasons. In this paper, we explore the design space of polymorphic languages that satisfy the gradual guarantee, but do not preserve parametricity. When parametricity is not a goal, the design of polymorphic gradual languages can be considerably simplified. Moreover, it becomes easy to add features that are of practical importance, such as mutable references. We present a new gradually typed polymorphic calculus, called $$\lambda ^{G}_{gpr}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>λ</mml:mi> <mml:mrow> <mml:mi>gpr</mml:mi> </mml:mrow> <mml:mi>G</mml:mi> </mml:msubsup> </mml:math> , with mutable references and with an easy proof of the gradual guarantee. In addition, compared to other gradual polymorphism work, $$\lambda ^{G}_{gpr}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>λ</mml:mi> <mml:mrow> <mml:mi>gpr</mml:mi> </mml:mrow> <mml:mi>G</mml:mi> </mml:msubsup> </mml:math> is defined using a Type-Directed Operational Semantics (TDOS), which allows the dynamic semantics to be defined directly instead of elaborating to a target cast language. $$\lambda ^{G}_{gpr}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>λ</mml:mi> <mml:mrow> <mml:mi>gpr</mml:mi> </mml:mrow> <mml:mi>G</mml:mi> </mml:msubsup> </mml:math> and all the proofs in this paper are formalized in Coq.
Bunched Fuzz: Sensitivity for Vector Metrics

Abstract Program sensitivity measures the distance between the outputs of a program when run on two related inputs. This notion, which plays a key role in areas such as data privacy and optimization, has been the focus of several program analysis techniques introduced in recent years. Among the most successful ones, we can highlight type systems inspired by linear logic, as pioneered by Reed and Pierce in the Fuzz programming language. In Fuzz, each type is equipped with its own distance, and sensitivity analysis boils down to type checking. In particular, Fuzz features two product types, corresponding to two different notions of distance: the tensor product combines the distances of each component by adding them, while the with product takes their maximum . In this work, we show that these products can be generalized to arbitrary $$L^p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mi>p</mml:mi> </mml:msup> </mml:math> distances , metrics that are often used in privacy and optimization. The original Fuzz products, tensor and with, correspond to the special cases $$L^1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mn>1</mml:mn> </mml:msup> </mml:math> and $$L^\infty $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mi>∞</mml:mi> </mml:msup> </mml:math> . To ease the handling of such products, we extend the Fuzz type system with bunches —as in the logic of bunched implications—where the distances of different groups of variables can be combined using different $$L^p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>L</mml:mi> <mml:mi>p</mml:mi> </mml:msup> </mml:math> distances. We show that our extension can be used to reason about quantitative properties of probabilistic programs.
Builtin Types Viewed as Inductive Families

Abstract State of the art optimisation passes for dependently typed languages can help erase the redundant information typical of invariant-rich data structures and programs. These automated processes do not dramatically change the structure of the data, even though more efficient representations could be available. Using Quantitative Type Theory as implemented in Idris 2, we demonstrate how to define an invariant-rich, typechecking-time data structure packing an efficient runtime representation together with runtime irrelevant invariants. The compiler can then aggressively erase all such invariants during compilation. Unlike other approaches, the complexity of the resulting representation is entirely predictable, we do not require both representations to have the same structure, and yet we are able to seamlessly program as if we were using the high-level structure.
Interpreting Knowledge-based Programs

Abstract Knowledge-based programs specify multi-agent protocols with epistemic guards that abstract from how agents learn and record facts or information about other agents and the environment. Their interpretation involves a non-monotone mutual dependency between the evaluation of epistemic guards over the reachable states and the derivation of the reachable states depending on the evaluation of epistemic guards. We apply the technique of a must/cannot analysis invented for synchronous programming languages to the interpretation problem of knowledge-based programs and demonstrate that the resulting constructive interpretation is monotone and has a least fixed point. We relate our approach with existing interpretation schemes for both synchronous and asynchronous programs. Finally, we describe an implementation of the constructive interpretation and illustrate the procedure by several examples and an application to the Java memory model.
Characterizing Generic POI: A Novel Approach for Discovering Tourist Attractions

With the rapid spread of social networking services (SNS) and social media, tourism has become a more accessible leisure activity. While many tourists visit well-known tourist destinations, the number of tourists visiting lesser-known ones in non-urban or rural areas has been in decline, and the decline has been exacerbating various problems such as economic disparities. Under these circumstances, it is becoming increasingly important to attract tourists to lesser-known areas with few tourism resources, because attracting tourists brings various positive effects to local residents and municipalities. Existing efforts to attract tourists include studies on tourist spot mining, which search a tourist destination for sightseeing spots that are worth visiting but relatively less well known, based on user-generated content about spots on the Web, such as posts on SNS and images on photo-sharing services. However, as they rely on the amount of user-generated content about potential sightseeing spots to be mined, these methods are limited in discovering a new sightseeing spot that is not recognized as a tourist attraction in advance. To address these challenges, we are inspired by an interesting phenomenon: a previously little-known spot that looks similar to a famous place becomes a hot topic on social media, attracting many people and suddenly becoming a popular tourist attraction. In this paper, we call such a spot with a similar appearance to a well-known one, as generic point-of-interest (POI), and we provide a novel concept of discovering new sightseeing spots by computationally finding generic POIs; we give a new perspective of discovering a new tourist attraction by characterizing a commonplace spot that has a similar appearance to a well-known one as a new tourist attraction. We also propose a pipeline implemented for mining generic POIs, where we calculate the similarity between candidate spots of generic POIs and famous sightseeing spots. Furthermore, we evaluate the feasibility of our approach both through human evaluation and through a photo contest on the Web we held with Tourism Shikoku. The results prove that our proposed method can discover new tourist destinations. The primal advantage of our approach is that it gifts new tourism value to a spot with at least a single place photo similar to a famous sightseeing spot, and thus it discovers a new tourist attraction even in areas with a minimal number of user-submitted images. We hope that our novel concept will be the first step in efforts to attract tourists in various regions around the world, enabling utilization of local spots that even the locals may not be aware of.
&lt;i&gt;c&lt;/i&gt;-SNE: Deep Cross-modal Retrieval based on Subjective Information using Stochastic Neighbor Embedding

Cross-modal information retrieval based on subjective information aims to enable flexible media retrieval services, such as allowing users to specify, for example, an image to search audio clips. The resulting audio clips should have an impression similar to the specified image. Existing methods focus on building cross-media cross-modal relationships using objective information (such as the standard caption). However, such a relation can be built only between the pieces of media that are originally related, which limits the flexibility of cross-modal media retrieval. This research leverages subjective information in the media clips for similarity calculation to achieve more flexibility. We propose a novel cross-modal stochastic neighbor embedding technique called c-SNE. c-SNE can extract features of subjective information from pieces of media and map them in the common embedding space. It is a learning technique to bridge the heterogeneous gap between the modal distributions using label-weighted SNE. It allows users to find the media that share the same subjective information with a query medium. Our experimental results on the benchmark datasets demonstrate that the proposed method effectively performs in cross-modal distribution alignment and retrieval. Furthermore, our user study with ten users with 600 data points confirmed that c-SNE outperforms three related methods in the actual usage situation from the users' perspective.
Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts

Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy.In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study. Our research interest is not focused on standard performance metrics like precision, recall, and F-measure on the test and validation data. We discuss how with the help of software programming techniques the readability of rule-based (RB) information extraction (IE) pipelines can be improved, and therefore minimize the time to correct or update the rules, and efficiently import them to another programming language.The extract rules were manually programmed with training data of TNM classification and tested in two separate pipelines based on design specifications from domain experts and data curators. Firstly we implemented each rule directly in one line for each extraction item. Secondly, we reprogrammed them in a readable fashion through decomposition and intention-revealing names for the variable declaration. To measure the impact of both methods we measure the time for the fine-tuning and programming of the extractions through test data of semi-structured and unstructured texts.We analyze the benefits of improving through readability of the writing of rules, through parallel programming with regular expressions (REGEX), and the Apache Uima Ruta language (AURL). The time for correcting the readable rules in AURL and REGEX was significantly reduced. Complicated rules in REGEX are decomposed and intention-revealing declarations were reprogrammed in AURL in 5 min.We discuss the importance of factor readability and how can it be improved when programming RB text IE pipelines. Independent of the features of the programming language and the tools applied, a readable coding strategy can be proven beneficial for future maintenance and offer an interpretable solution for understanding the extraction and for transferring the rules to other domains and NLP pipelines.
Real Time Web-Based Smart Attendance System using AI

The performance of automatic face recognition (AFR) technology has improved in recent years. As a result, these systems are frequently employed for security and business purposes. a face recognition programme that runs automatically in the background while a university tracks employee attendance. Hence, Smart Attendance with Real Time Face Recognition might be a global solution for managing personnel on a daily basis. The work is incredibly challenging since the critical temporal background subtraction during an image remains a challenge. Real-time face detection is done using easy, quick Principal Component Analysis, which has a high rate of success in identifying the faces found.As a result, we have developed a module in which an employee's attendance will be recorded automatically by recognising their face using face recognition technology. Their face will be saved in the database after this one-time enrollment process. Since enrolling a face is a one-time activity, we need a mechanism. Every employee will have a unique employee ID that is their own roll number. Every employee's presence will be updated within a database. Results of the proposed system demonstrated that it performs better than the manual attendance system. The marking of attendance follows employee identification. This application provides the customer with considerably more solutions and accurate leads than the conventional attendance and leave.
The Comparison of Sentiment Analysis of Moon Knight Movie Reviews between Multinomial Naive Bayes and Support Vector Machine

Online movie streaming platforms have changed the current pattern of watching movies. Besides providing convenience in watching anywhere and anytime, this service is provided at a lower cost to moviegoers. The increase in moviegoers on online streaming platforms has resulted in easy-to-find reviews. This review can determine whether they decide to watch the film or not. The moviegoers' reviews can be easily and quickly found for analysis using sentiment analysis to find a film's worthiness. This study used sentiment analysis in classifying Twitter data predictions using the Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM). In the sentiment analysis of labeling with positive and negative categories, a distilled version of BERT (DistilBERT) was used in this study. With a little human assistance in preprocessing, the model worked objectively with an overall accuracy performance on the confusion matrix of 64.50% for the Multinomial Naive Bayes model and 64.12% for the Support Vector Machine model. Performance evaluation was also carried out by calculating the cross-validation accuracy, which resulted in an accuracy of 72.38% for the MNB. Meanwhile, the SVM model obtained an accuracy of 70.19%.
Equivalence of logical operations and other operations in Python programming language

In most programming languages, there are logical operators "logical NOT", "logical AND", "logical OR". There are also logical bitwise operators "bitwise NOT", "bitwise AND", "bitwise OR", "bitwise XOR". This article discusses the possibility of using other non-logical operators (comparison, arithmetic, shift, identification) to replace and conveniently shorthand other logical functions or logical operators in binary logic in a programming language and, on the other hand, code obfuscation capabilities. The considered examples cover the basic operations of the Python programming language and can be used in other programming languages. The problems of using other operations as logical ones are covered. The article also provides truth tables for all unary and binary Boolean functions and corresponding non-logical functions, as well as the generally accepted names of unary and binary Boolean functions and operations put together.
Novel groundtruth transformations for the aesthetic assessment problem

Aesthetic assessment evaluates the quality of a given image using subjective annotations, commonly user ratings, as a knowledge base. Rating complexity is usually relaxed in state-of-the-art works by employing a binary high/low quality label computed from the mean value of rating votes. Nevertheless, this approach introduces uncertainty to average-quality images, which may affect the performance of machine learning models trained from annotated data. In this work, we present a novel approach to aesthetic assessment based on redefining the rating-based groundtruths present in most datasets. Our intent is twofold: to reduce the rating uncertainty and to automatically group them into clusters reflecting high and low quality patterns, thus avoiding an arbitrary threshold like 5 in 1–10 ratings. The experimentation uses the well-known AVA dataset, which consists of more than 255,000 images, and we train several CNN models to test our new groundtruths against the baseline ones. The results show that our approach achieves significant performance gains, between 3% and 9% more balanced accuracy than the baseline groundtruths.
Consensus and fuzzy partition of dendrograms from a three-way dissimilarity array

In cluster analysis, a problem often addressed is finding a consensus on a set of hierarchical classifications of the same set of objects, named primary hierarchies (dendrograms). A unique consensus of the primary hierarchies, called a secondary hierarchy, is sufficient to synthesize the relevant clustering information only when the primary dendrograms are similar to one another. In contrast, when several differences are observed, more than one consensus secondary hierarchy is required to clearly synthesize the different primary hierarchies. Our methodology, PARoDENo3WD (PARtition of DENdrograms of a 3-Way Data array), aims to obtain a secondary fuzzy partition of the primary hierarchies, where hierarchies belonging to the same class are perceived as similar. Each class is associated with a consensus hierarchy. The fuzzy approach allows each primary hierarchy to contribute to the definition of all classes of the secondary partition, according to different membership degrees. In this way, the ‘clustering uncertainty’ is taken into account. Indeed, in several realistic applications, primary dendrograms belonging to different classes of the secondary partition may share some similar features. The performance of PARoDENo3WD is evaluated by an extended simulation study, generating 1800 three-way datasets. An application of PARoDENo3WD on a real data set is given.
Cost-Effective Solutions Using Smart Contracts in Blockchain for Commercial Residential Midrise Building Developers and Consumers

Developers of real estate projects such as commercial, official, and residential properties are keen to have high return on investment (ROI), and this study was carried out to demonstrate the need and implementation of smart contract using blockchain technology to enhance ROI. Blockchain technology, with the dual objective to achieve 25% ROI, in the design, construction, and sale of a midrise building, with design life of 50 years. All transactions involving stakeholders' critical activities of the project, such as acquisition of land, design, construction, marketing and selling of office floors, shop spaces, and residential flats in general and design and construction phase in particular, were identified and their influence in the project cost and project schedule are assessed. By the implementation of smart contract using blockchain technology in the construction and design phases of this project, it resulted in enhanced ROI value of 42%, which was 17% higher than the expected value, without sacrificing the quality, durability, and specifications, as per standards and regulations.