Reviewerly/exHarmony huggingface dataset:
Optimal Method of Vision Sensor Digital Image Edge Detection for Commercial Investment in Film Field

With the rapid development of the national economy, the film industry has risen rapidly under these conditions, and the development of film occupies a more important position in this process, which has led to the development of investment in the film field, an internationally recognized cultural space. This paper studies the commercial investment in the film field based on the edge detection of the digital image of the visual sensor. The purpose of the research is to point out the direction of commercial investment in the film industry by studying the effect of the edge detection of the digital image of the visual sensor on the application of the film object. The image edge detection algorithm based on wavelet transform and morphology and the application of these image edge detection algorithms such as wavelet theory analyze the shortcomings of movie, sound effects, and pictures and make a comparison before and after the improvement. Audiences, as the direct enjoyers of the cultural product of the film, their opinions, and evaluations of the film, largely determine the quality of the work. On the contrary, a good work can attract the public’s attention and bring a large impact. The data acquisition in this process is mainly based on the questionnaire survey of the audience. The experimental results show that before applying digital image edge detection to transform the clip, 7 people thought that the clip needed to be further improved to improve the quality of the film itself, and only 1 person affirmed the film; after the modification, 9 people proposed the film praise; in addition, 15 people audience watched the film processed by the edge detection method and gave high evaluations to the three aspects of sound effects, special effects, and editing, especially the editing part, with a score of 8.9.
Makerspace-the future of public libraries

Public libraries have always been regarded as the center of knowledge and information, but today the same public libraries have been challenged by the rise of mobile technology and digitization. Public libraries are facing a design shift in their focus from storing books to an active public space of learning which involves more engaging and reading. This thesis addresses the need for citizens to manifest the knowledge, skills, and attitudes required to take part in a complex and increasingly digitized world of personal and societal prosperity. Digital competence significantly involves communication, collaboration, content creation, and problem-solving. The definition of digital skill underscores confident, productive, creative and critical use of digital technologies for diverse purposes in various social contexts and with multiple tools (Ala-Mutka, 2011). Presently, there is a lack of knowledge, on creating learning opportunities for digital literacies that are inclusive for diverse learners with different capabilities and interests, which can accommodate different personal situations, objectives and combine, for example, formal and everyday learning practices (Erstad et al., 2016). This research aims at understanding makerspaces and public library makerspaces, including the people and practices involved in these spaces. Makerspaces align with the fact that public libraries meet the needs and interests of their local communities. This research employs site visits, case studies, and analysis to examine the journal articles and blog posts published from 2008 to 2015 that focus on makerspaces and public libraries. The first part of the research highlights the literature concerning the history of making through Maker concept, Maker Movement. The methodology reviews the mission of providing access to digital resources in public library makerspaces in order to meet the needs of diverse communities. The analysis highlights some of the benefits and issues that emerge through the new trend of making and makerspaces in the field of Public Libraries. The researcher provides a review of library makerspaces both in India and North America, which are supported by several case studies. This thesis reimagines a public library, by evaluating a design that engages and responds to the local community it supports.
Joint distribution in residue classes of polynomial-like multiplicative functions

Under fairly general conditions, we show that families of integer-valued polynomial-like multiplicative functions are uniformly distributed in coprime residue classes modulo $p$, where $p$ is a growing prime (or nearly prime) modulus. This can be seen as
RemoteVote and SAFE Vote: Towards Usable End-to-End Verification for Vote-by-Mail

Postal voting is growing rapidly in the U.S., with 43% of voters casting ballots by mail in 2020, yet until recently there has been little research about extending the protections of end-to-end verifiable (E2E-V) election schemes to vote-by-mail contexts. The first—and to date, only—framework to focus on this setting is STROBE, which has important usability limitations. In this work, we present two approaches, RemoteVote and SAFE Vote, that allow mail-in voters to benefit from E2E-V without changing the voter experience for those who choose not to participate in verification. To evaluate these systems and compare them with STROBE, we consider an expansive set of properties, including novel attributes of usability and verifiability, several of which have applicability beyond vote-by-mail contexts. We hope that our work will help catalyze further progress towards universal applicability of E2E-V for real-world elections.
SketchMeHow: Interactive Projection Guided Task Instruction with User Sketches

In this work, we propose an interactive general instruction framework SketchMeHow to guidance the common users to complete the daily tasks in real-time. In contrast to the conventional augmented reality-based instruction systems, the proposed framework utilizes the user sketches as system inputs to acquire the users’ production intentions from the drawing interfaces. Given the user sketches, the designated task instruction can be analyzed based on the subtask division and spatial localization for each task. The projector-camera system is adopted in the projection guidance to the end-users with the spatial augmented reality technology. To verify the proposed framework, we conducted two case studies of domino arrangement and bento production. From our user studies, the proposed systems can help novice users complete the tasks efficiently with user satisfaction. We believe the proposed SketchMeHow can broaden the research topics in sketch-based real-world applications in human-computer interaction.
Monitoring geometrical properties of word embeddings for detecting the emergence of new topics.

Slow emerging topic detection is a task between event detection, where we aggregate behaviors of different words on short period of time, and language evolution, where we monitor their long term evolution.In this work, we tackle the problem of early detection of slowly emerging new topics.To this end, we gather evidence of weak signals at the word level.We propose to monitor the behavior of words representation in an embedding space and use one of its geometrical properties to characterize the emergence of topics.As evaluation is typically hard for this kind of task, we present a framework for quantitative evaluation.We show positive results that outperform state-ofthe-art methods on two public datasets of press and scientific articles.
DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning

Kelly Kostopoulou was supported by the KAUST Visiting Student Research Program. The computing infrastructure was provided by the KAUST Super-computing Lab (KSL).
Matrix Thinking in the Fractal Digitalization of Education

This article is devoted to improving the motivation of students to study and, in general, the quality of education in the context of global digitalization. Along with numerous existing methods, the internal component of thinking and perception as an integral part of it plays a significant role. Research has shown that the mathematical logic used in the creation of most algorithms is not identical to the logic of thinking. The combined fuzzy and genetic logic that guides the student only partially includes clear logical structures. Moreover, this inclusion is manifested in different students with different degrees of belonging. If the teacher understands and uses the degree of such inclusion, then students will be able to show results of education that seem to exceed their own capabilities. Examples of the implementation of matrix thinking with students of military accounting, technical and creative specialties, as one of the ways to digitalize knowledge, are given. An example of the implementation of digitalization of training with students of applied programmers is considered on the example of creating software based on the material passed in a related IT discipline.
Green Computing and Blockchain Fundamentals

The evolution of technology has proven to be a worthwhile revolution for humankind. Technology has touched all aspects of this world from computers to our health; no places are left where we can’t use technology. But everything comes at some cost; technology bringing ease to our lives also brings threats to our lives by degrading the environment. Now, researchers are going on to use technology in a less waste-generating manner and moreover using it to promote greenness, and broadly this concept is known as green computing. So, this chapter discusses the concept of green computing, including the evolution of green computing like how pollution created by computers and peripherals has come into the awareness of people and what measures have been chosen to reduce or deduct the polluting elements. In the latter part, the role of industry and government is mentioned, as are the different approaches to green computing, such as virtualization, power management, and cloud computing. The benefits as well as disadvantages are also discussed. In the next part, information related to blockchain is discussed, consisting of the basic terminology, helping to understand its meaning. The history along with the development of blockchain is discussed and also the application of blockchain like cryptocurrency is discussed; various kinds of blockchain are mentioned as are the challenges which should be overcome to attain greater heights in blockchain technology. In the last part the interrelation of blockchain and green computing is mentioned – how blockchain can contribute in a greater percentage to promote green computing.
A Novel Data Standards Platform Using the ISO Core Components Technical Specification

Abstract It is generally observed in inter-organizational communication that present-day data exchange standards are too costly, large, slow to respond to industry demands, and complex to develop and use. These problems in data exchange are felt keenly by the manufacturing industry and its vast supply chains. In addressing these challenges, a successful attempt was recently made that involves two major developments. The first is the Core Components Technical Specification (CCTS), which is an ISO-approved meta-model for data exchange standards. CCTS introduces common data types, uniform structure for data models, and data usage semantics. The second is Score, which is a novel open-source software tool. Score was developed by National Institute of Standards and Technology researchers as a platform to take advantage of the CCTS for data exchange standards development and usage. This paper describes the potential of the CCTS and the Score platform, and describes the current status of the Score platform and Score-enabled industry interactions.
Sweet Spot: Displaying Interaction Areas on Everyday Home Surfaces using AR

In a typical home or office setting, a work desk provides a surface that primarily holds objects and is used for everyday tasks such as writing and typing. However, given its centrality and multiple surfaces, many of which are always unoccupied, an ordinary table may find secondary usage as an alternative interface to control facets of a smart home. For tabletop surfaces to work efficiently as alternative interfaces, there is a need to determine their most reachable zones and those that do not have too much clutter. We present Sweet Spots, an AR platform to visualize easily accessible and non-cluttered areas on table-like surfaces using augmented reality to assess suitable interaction areas. Sweet Spots provides designers and researchers with a tool to visualize their ideas and to take the exploration of key parameters that make an interaction area a Sweet Spot.
Analysis of Zero-Shot Crosslingual Learning between English and Korean for Named Entity Recognition

This paper presents a English-Korean parallel dataset that collects 381K news articles where 1,400 of them, comprising 10K sentences, are manually labeled for crosslingual named entity recognition (NER).The annotation guidelines for the two languages are developed in parallel, that yield the inter-annotator agreement scores of 91 and 88% for English and Korean respectively, indicating sublime quality annotation in our dataset.Three types of crosslingual learning approaches, direct model transfer, embedding projection, and annotation projection, are used to develop zero-shot Korean NER models.Our best model gives the F1-score of 51% that is very encouraging, considering the extremely distinct natures of these two languages.This is pioneering work that explores zero-shot crosslingual learning between English and Korean and provides rich parallel annotation for a core NLP task such as named entity recognition.
IT Audit of Cloud Accounting Platform Based on Big Data

Abstract So far, the level of science and technology in China has experienced unprecedented development, at the same time, the computer level of our country has improved significantly. At present, more and more Big data technologies are gradually entering the field of vision of people, in most industries of our country they are dedicated to the research and application of big data technology. Big data, these can provide people with more and more reliable information.At the same time, it also brings great convenience to people’s daily life and brings creativity to enterprises.With the development of the times and the progress of science and technology, the application of modern computer technology can achieve the digitization of information technology audit. That is, the audit project data ion of digital IT are kept and stored according to actual needs. According to the relationship between different electronic documents, link and combine them to improve the efficiency of IT audit and minimize audit costs. In recent years, Our country’s informatization process has become deeper and deeper, which has brought great convenience to all walks of life. In traditional IT auditing, it has been unable to meet the current development needs. This requires redesigning IT auditing thinking, establishing a new framework, and creating digital IT auditing to meet the needs of the times and improve IT auditing efficiency. In this article, we will study all kinds of data in the digital IT audit project, and we will discuss the application audit of the cloud-based computing platform.Big Data, mainly the audit of cloud computing platform data, and whether it is effective in the input, processing and output process, in order to achieve the effect of high speed and high quality.Finally, summarize the research results.The efficiency of IT verification has improved significantly.
Computational cognitive modeling in the social sciences

Computational cognitive models formalize structures and processes assumed to underlie human cognition and behavior in the form of executable computer programs. Although such models commonly target cognition of individuals, the social sciences may profit from considering cognitive models. Ultimately, social processes depend on the behavior of individuals and, for many pressing social problems, such as climate change, it has been argued that a deeper understanding of individual cognition and behavior is crucial for understanding the relevant social processes. It is social simulation, in particular, that may gain in comprehensiveness and correctness by incorporating computational cognitive models, because the models readily lend themselves to equip the agents in the simulation with reasonable cognition and behavior. At the same time, incorporating cognitive models into social simulation provides further insight into individual cognition by generating feedback about the correctness and scalability of the models in complex social settings. Consequently, computational cognitive models are a means to bridge the gap between individual-level questions about the human cognitive system and population-level sociological questions. In this chapter, I will give an overview of computational cognitive modeling as a method and its merits for the social sciences.
Applying and comparing multiple machine learning techniques to detect fraudulent credit card transactions

This experiment compared the performance of four machine learning algorithms in detecting bank card fraud. At the same time, the strong imbalance of the classes in the training sample was taken into account, as well as the difference in transaction amounts, and the ability of different machine learning methods to recognize fraudulent behavior was assessed taking into account these features. It has been found that a method that works well with indicators for assessing a classification is not necessarily the best in terms of assessing the magnitude of economic losses. Logistic regression is a good proof of this. The results of this work show that the problem of detecting fraud with bank cards cannot be regarded as a simple classification problem. AUC data is not the most appropriate metric for fraud detection tasks. The final choice of the model depends on the needs of the bank, that is, it is necessary to take into account which of the two types of errors (FN, FP) will lead to large economic losses for the bank. If the bank believes that the loss caused by identifying fraudulent transactions as regular transactions is the main one, it should choose the algorithm with the lowest FN value, which in this experiment is Adaboost. If the bank believes that the negative impact of identifying regular transactions as fraudulent is also very important, it should choose an algorithm with relatively small FN and FP data. In this experiment, the overall performance of the random forest is better. Further, by evaluating the economic losses caused by false positives (identifying an ordinary transaction as fraudulent), a quantitative analysis of the economic losses caused by each algorithm can be used to select the optimal algorithm model.
Team Enigma at ArgMining-EMNLP 2021: Leveraging Pre-trained Language Models for Key Point Matching

We present the system description for our submission towards the Key Point Analysis Shared Task at ArgMining 2021.Track 1 of the shared task requires participants to develop methods to predict the match score between each pair of arguments and keypoints, provided they belong to the same topic under the same stance.We leveraged existing state of the art pre-trained language models along with incorporating additional data and features extracted from the inputs (topics, key points, and arguments) to improve performance.We were able to achieve mAP strict and mAP relaxed score of 0.872 and 0.966 respectively in the evaluation phase, securing 5th place 1 on the leaderboard.In the post evaluation phase, we achieved a mAP strict and mAP relaxed score of 0.921 and 0.982 respectively.All the codes to generate reproducible results on our models are available on Github 2 .
Belief-Grounded Networks for AcceleratedRobot Learning under Partial Observability

Many important robotics problems are partially observable in the sense that asingle visual or force-feedback measurement is insufficient to reconstruct thestate. Standard approaches involve learning a policy over beliefs orobservation-action histories. However, both of these have drawbacks; it isexpensive to track the belief online, and it is hard to learn policies directlyover histories. We propose a method for policy learning under partialobservability called the Belief-Grounded Network (BGN) in which an auxiliarybelief-reconstruction loss incentivizes a neural network to concisely summarizeits input history. Since the resulting policy is a function of the historyrather than the belief, it can be executed easily at runtime. We compare BGNagainst several baselines on classic benchmark tasks as well as three novelrobotic touch-sensing tasks. BGN outperforms all other tested methods and itslearned policies work well when transferred onto a physical robot.
Tight Bounds for Differentially Private Anonymized Histograms

Previous chapter Next chapter Full AccessProceedings Symposium on Simplicity in Algorithms (SOSA)Tight Bounds for Differentially Private Anonymized HistogramsPasin ManurangsiPasin Manurangsipp.203 - 213Chapter DOI:https://doi.org/10.1137/1.9781611977066.14PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract In this note, we consider the problem of differentially privately (DP) computing an anonymized histogram, which is defined as the multiset of counts of the input dataset (without bucket labels). In the low-privacy regime ε ≥ 1, we give an ε-DP algorithm with an expected ℓ1-error bound of . In the high-privacy regime ε < 1, we give an lower bound on the expected ℓ1 error. In both cases, our bounds asymptotically match the previously known lower/upper bounds due to [Sur19]. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-706-6 https://doi.org/10.1137/1.9781611977066Book Series Name:ProceedingsBook Code:SOSA22Book Pages:v + 320
Distantly-Supervised Dense Retrieval Enables Open-Domain Question Answering without Evidence Annotation

Open-domain question answering answers a question based on evidence retrieved from a large corpus.State-of-the-art neural approaches require intermediate evidence annotations for training.However, such intermediate annotations are expensive, and methods that rely on them cannot transfer to the more common setting, where only questionanswer pairs are available.This paper investigates whether models can learn to find evidence from a large corpus, with only distant supervision from answer labels for model training, thereby generating no additional annotation cost.We introduce a novel approach (DISTDR) that iteratively improves over a weak retriever by alternately finding evidence from the up-to-date model and encouraging the model to learn the most likely evidence.Without using any evidence labels, DISTDR is on par with fully-supervised state-of-theart methods on both multi-hop and singlehop QA benchmarks.Our analysis confirms that DISTDR finds more accurate evidence over iterations, which leads to model improvements.
Online Path Planning Algorithm Base on Preset Points

Modern battlefield environment is complex and changeable. In order to improve the adaptability of aircraft to sudden threat, this paper proposes an online path planning algorithm. The algorithm is improved on the basis of A* algorithm. Firstly, the online search area is cut according to the maneuverability constraints of the aircraft, which reduces the amount of data in online planning. At the same time, in the face of sudden threat, the method of setting preset points is used to divide the local re-planning path into two parts for calculation. The simulation results can be seen that this method can reduce the amount of data in the process of local path re-planning, and ensure the real-time and effectiveness of online path planning.