Reviewerly/exHarmony huggingface dataset:
High-Quality Proposals for Weakly Supervised Object Detection

Despite significant efforts made so far for Weakly Supervised Object Detection (WSOD), proposal generation and proposal selection are still two major challenges. In this paper, we focus on addressing the two challenges by generating and selecting high-quality proposals. To be specific, for proposal generation, we combine selective search and a Gradient-weighted Class Activation Mapping (Grad-CAM) based technique to generate more proposals having higher Intersection-Over-Union (IOU) with ground truth boxes than those obtained by greedy search approaches, which can better envelop the entire objects. As regards proposal selection, for each object class, we choose as many confident positive proposals as possible and meanwhile only select class-specific hard negatives to focus training on more discriminative negative proposals by up-weighting their losses, which can make training more effective. The proposed proposal generation and proposal selection approaches are generic and thus can be broadly applied to many WSOD methods. In this work, we unify them into the framework of Online Instance Classifier Refinement (OICR). Experimental results on the PASCAL VOC 2007 and 2012 datasets and MS COCO dataset demonstrate that our method significantly improves the baseline method OICR by large margins (13.4% mAP and 11.6% CorLoc gains on the VOC 2007 dataset, 15.0% mAP and 8.9% CorLoc gains on the VOC 2012 dataset, and 6.4% mAP and 5.0% CorLoc gains on the COCO dataset) and achieves the state-of-the-art results compared with existing methods.
Dual Adversarial Network: Toward Real-World Noise Removal and Noise Generation

Real-world image noise removal is a long-standing yet very challenging task in computer vision. The success of deep neural network in denoising stimulates the research of noise generation, aiming at synthesizing more clean-noisy image pairs to facilitate the training of deep denoisers. In this work, we propose a novel unified framework to simultaneously deal with the noise removal and noise generation tasks. Instead of only inferring the posteriori distribution of the latent clean image conditioned on the observed noisy image in traditional MAP framework, our proposed method learns the joint distribution of the clean-noisy image pairs. Specifically, we approximate the joint distribution with two different factorized forms, which can be formulated as a denoiser mapping the noisy image to the clean one and a generator mapping the clean image to the noisy one. The learned joint distribution implicitly contains all the information between the noisy and clean images, avoiding the necessity of manually designing the image priors and noise assumptions as traditional. Besides, the performance of our denoiser can be further improved by augmenting the original training dataset with the learned generator. Moreover, we propose two metrics to assess the quality of the generated noisy image, for which, to the best of our knowledge, such metrics are firstly proposed along this research line. Extensive experiments have been conducted to demonstrate the superiority of our method over the state-of-the-arts both in the real noise removal and generation tasks. The training and testing code is available at https://github.com/zsyOAOA/DANet .
Bots and Misinformation Spread on Social Media: Implications for COVID-19

As of March 2021, the SARS-CoV-2 virus has been responsible for over 115 million cases of COVID-19 worldwide, resulting in over 2.5 million deaths. As the virus spread exponentially, so did its media coverage, resulting in a proliferation of conflicting information on social media platforms-a so-called "infodemic." In this viewpoint, we survey past literature investigating the role of automated accounts, or "bots," in spreading such misinformation, drawing connections to the COVID-19 pandemic. We also review strategies used by bots to spread (mis)information and examine the potential origins of bots. We conclude by conducting and presenting a secondary analysis of data sets of known bots in which we find that up to 66% of bots are discussing COVID-19. The proliferation of COVID-19 (mis)information by bots, coupled with human susceptibility to believing and sharing misinformation, may well impact the course of the pandemic.
Experimental Quantum Generative Adversarial Networks for Image Generation

Quantum machine learning is expected to be among the first practical applications of near-term quantum devices. Whether quantum generative adversarial networks (quantum GANs) implemented on near-term devices can actually solve real-world learning tasks, however, has remained unclear. The authors narrow this knowledge gap by designing a flexible quantum GAN scheme, and realizing this scheme on a superconducting quantum processor. Their system learns and generates images of real-world handwritten numerals, and exhibits competitive performance with classical GANs. This work opens up an avenue for exploring quantum advantage in various machine-learning tasks.
A General Survey on Attention Mechanisms in Deep Learning

Attention is an important mechanism that can be employed for a variety of deep learning models across many different domains and tasks. This survey provides an overview of the most important attention mechanisms proposed in the literature. The various attention mechanisms are explained by means of a framework consisting of a general attention model, uniform notation, and a comprehensive taxonomy of attention mechanisms. Furthermore, the various measures for evaluating attention models are reviewed, and methods to characterize the structure of attention models based on the proposed framework are discussed. Last, future work in the field of attention models is considered.
The alternative negative constructions in spoken and written Korean: Logistic regression analysis

Abstract Adopting quantitative corpus-based methods, this paper focuses on the alternative negative constructions in Korean, [ an V] and [V anhda ]. Logistic regression analyses for a mixed-effects model were carried out on data drawn from the Sejong Korean Corpus. Certain features of the verb or adjective in negative constructions significantly affect the use of the two negative constructions. A relevant factor is register/medium (spoken or written), among other significant interactions of factors. Furthermore, the fact that frequency is consistent with other relevant factors, together with certain diachronic facts of Korean, supports the claim that frequency of use plays an important role in linguistic changes. Another finding is that, notwithstanding noticeable differences between spoken and written language, the factors influencing the use of the two negative constructions in Korean are largely similar in the spoken and written registers.
Managing Publications According to Legal and Ethical Standards

Law and ethics originate in questions of right and wrong, good and bad, fair and unfair. Problems in law and ethics arise because the right, good, and fair often conflict with personal advantage—profit, advancement, convenience, or expediency. Resolving these conflicts is complex because profit is an incentive to development of improvements in thought, systems of organization, and products. Intellectual property includes products of research and creative work, such as publications, software, inventions, and manufacturing processes. Intellectual property law provides writers, inventors, researchers, and artists with the incentive to accept the risks and costs of development by giving them the right to profit from their work. Trademarks and patents, like copyrights, identify and protect intellectual property. A patent protects an invention, which may be an object, process, composition of matter, or design. A trade secret is information that is not generally known and that can provide its owner with a competitive advantage.
Web page design

This chapter discusses several basic web page design concepts and principles. The World Wide Web has become a de facto standard, and designing for web compatibility has become an important information system criterion. Rather than requiring the user to know Uniform resource locator (URL) for each desired page, the World Wide Web relies on symbolic, logical connections called hyperlinks. Navigation through a site is controlled by hyperlinks. The program that converts hyperlinks into associated URLs, requests pages from the Internet, and displays those pages is called a browser. Netscape and Microsoft’s Internet Explorer are the two best known browsers. A browser relies on hypertext markup language (html) to tell it how to map a page to the screen. Some pages are subdivided into frames, with each frame holding a separate html document. A page designer faces two, often conflicting objectives. The first objective is to catch user’s attention. The second is to provide enough content to satisfy the user’s needs.
Project networks, PERT, and CPM

A project network chart is a tool for graphically depicting a schedule and serves as a basis for Program Evaluation and Review Technique (PERT) and Critical path method. Project networks can be used to plan, record, and document a schedule and to track actual results against the schedule. PERT is useful in research and development projects where the times required to complete the various activities are uncertain. Creating a project network is a complex undertaking. The computations are straightforward, but non-trivial. Without appropriate software tools, maintaining or changing a project network can be difficult. The accuracy of the project network is no better than the estimated duration of each of the activities. The project network defines the dependency relationships between the events. Given a clear sense of the order in which events must occur, the analyst can prepare a schedule.
A NEW ERA FOR MANAGEMENT ACCOUNTANTS IN NIGERIA: A GUIDE TO ACHIEVEMENT OF ENHANCED ORGANIZATIONAL PERFORMANCE AND SUCCESS

Management accountants, in the conventional and contemporary age, have contributed to the development of organisations with regards organisational performance and success which has proven to be latent in Nigeria. The differences in development of organisational performance are dependent of the factors which have influenced both facets of roles of management accountants in Nigeria. This study compares the conventional and contemporary roles of management accountants and their contribution towards enhanced organisational performance and success in Nigeria. Secondary sources were analysed and information was gathered in the bid to produce literature on the issue of concern. The study shows that even with its current situation, management accountants, to varying extents, contribute to organisational performance and success. The study serves as a steer to academicians, potential employees, practicing management accountants and students in ensuring that these roles move with changes in the business environment for the sake of organisational performance and success.
Design and Materials in Fly Fishing

There is a common idyllic perception of fly fishing: a gentleman in tweed coat, a peaceful river meandering through green meadows, and a rhythmic waving of the flyline through the air. However, the sport itself is quite demanding and anglers are not there just to look good. They expect to catch fish and want to use fly fishing equipment that gives them the best opportunity. And, as in most sports, they turn to technological solutions to meet their high expectations.
Deterministic Spectral Analysis

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Uniform Data Fingerprint (UDF)

This document describes means of generating Uniform Data Fingerprint(UDF) values and their presentation as text sequences and as URIs.Uses of UDF fingerprints include but are not limited to creatingStrong Internet Names (SINs). Cryptographic digests provide a meansof uniquely identifying static data without the need for aregistration authority. A fingerprint is a form of presenting acryptographic digest that makes it suitable for use in applicationswhere human readability is required. The UDF fingerprint formatimproves over existing formats through the introduction of a compactalgorithm identifier affording an intentionally limited choice ofdigest algorithm and the inclusion of an IANA registered MIME Content-Type identifier within the scope of the digest input to allow the useof a single fingerprint format in multiple application domains.Alternative means of rendering fingerprint values are consideredincluding machine-readable codes, word and image lists. This documentis also available online at http://mathmesh.com/Documents/draft-hallambaker-udf.html [1] .
Coupled Local Minimizers: a new global optimization method

Global optimization technique, Coupled Local Minimizers, CLM methodABSTRACT:Anewefﬁcientglobaloptimization technique, namedCoupledLocalMinimizers(CLM),is presented in the paper. The CLMmethod uses aset of search points, initially spread over the searchspace. In each search point the function and derivative values are calculated and used to direct thesearch process. But instead of performing separate, independent searches from each of these points(i.e. multi-start local optimization), the set of optimizers are coupled during the search process inorder to create interaction between them, which results in a cooperative search mechanism. The com-bination of a fast convergence –due to the derivative information that is used– with the capabilityof ﬁnding the global minimum –resulting from the parallel strategy– guarantees an efﬁcient globaloptimization algorithm. The paper proposes an implementation based on the second-order Newtonmethod in order to increase the convergence speed. The CLM method and its implementation aredescribed extensively in the paper and are illustrated with a test function containing several localminima. The paper focusses on low-dimensional optimization problems only.1 INTRODUCTIONThe general purpose of
Validation of Locations Around a Planned Change

This document defines an extension to LoST (RFC5222) that allows aplanned change to the data in the LoST server to occur. Records thatpreviously were valid will become invalid at a date in the future, andnew locations will become valid after the date. The extension adds twoelements to the request: A URI to be used to inform theLIS that previously valid locations will be invalid after the plannedchange date, and add a date which requests the server to performvalidation as of the date specified. It also adds an optional Time-To-Live element to the response, which informs clients about the currentexpected lifetime of the validation. This document also provides aconventional XML schema for LoST, as backwards compatible alternativeto the RelaxNG schema in RFC5222
How Neural Networks (NN) Can (Hopefully) Learn Faster by Taking into Account Known Constraints

Neural networks are a very successful machine learning technique. At present, deep (multi-layer) neural networks are the most successful among the known machine learning techniques. However, they still have some limitations, One of their main limitations is that their learning process still too slow. The major reason why learning in neural networks is slow is that neural networks are currently unable to take prior knowledge into account. As a result, they simply ignore this knowledge and simulate learning “from scratch”. In this paper, we show how neural networks can take prior knowledge into account and thus, hopefully, learn faster.
A Global Optimization Algorithm for Sparse Mixed Membership Matrix Factorization

Mixed membership factorization is a popular approach for analyzing data sets that have within-sample heterogeneity. In recent years, several algorithms have been developed for mixed membership matrix factorization, but they only guarantee estimates from a local optimum. Here, we derive a global optimization algorithm that provides a guaranteed 𝜖-global optimum for a sparse mixed membership matrix factorization problem. We test the algorithm on simulated data and a small real gene expression dataset and find the algorithm always bounds the global optimum across random initializations and explores multiple modes efficiently.
Power law based foundation for the measurement of discrimination information for human knowledge representation

The discrimination information (DI) of keyword plays an important role in information retrieval and data mining. However, the measurement of DI is still a challenge because the existing methods cannot leverage the contradiction between accuracy and complexity. In this paper, a new model is proposed, does not need any prior knowledge and the computing complexity is O(nm) for a collection of m documents with n keywords. Firstly, we define three types of keywords according to the document frequency spectrum, which divides the spectrum of keywords into two monotonically spectrums that can give a qualitative analysis of DI. Secondly, in order to decrease the complexity, the power law function of keywords’ document frequencies is built. Thirdly, we propose an algorithm to classify keywords by using the distances between the adjacent points on the linear regression line. Finally, a piecewise function is used for computing DI according to the monotonically spectrums, which transforms DI into a scalable value to be used directly, thereby reducing the computing complexity of DI significantly. Moreover, a new weighting scheme of keywords based on DI is employed for document clustering, which shows that DI has a good prospect on the information retrieval area.
Concept Based Intelligent Information Retrieval Within Digital Library

A digital library is a type of information retrieval (IR) system. The existing information retrieval methodologies generally have problems on keyword-searching. We proposed a model to solve the problem by using concept-based approach (ontology) and metadata case base. This model consists of identifying domain concepts in userâ€™s query and applying expansion to them. The system aims at contributing to an improved relevance of results retrieved from digital libraries by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to import the concept of ontology, making use of its advantage of abundant semantics and standard concept. Domain specific ontology can be used to improve information retrieval from traditional level based on keyword to the lay based on knowledge (or concept) and change the process of retrieval from traditional keyword matching to semantics matching. One approach is query expansion techniques using domain ontology and the other would be introducing a case based similarity measure for metadata information retrieval using Case Based Reasoning (CBR) approach. Results show improvements over classic method, query expansion using general purpose ontology and a number of other approaches.
Review of Verloren Van Themaat (1972): Automatic Analysis of Dutch Compound Words

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Hypertext Transfer Protocol (HTTP/1.1): Activity Identifiers

It is very common that implementers of HTTP severs require the abilityto associate an identifier to an HTTP request and or response, thiscan be for a number of reasons which could include checking forduplicate requests, allowing the caller of an API to maintain a recordof their interaction with the server or to track client/serverrequests through a disparate system of services. In any case, theimplementer will quite often use a custom HTTP header and eitherassign a value itself or require the caller to supply the value. Thisdocument outlines a consistent storage mechanism for this identifierby way of a standard HTTP header and a new status code for when amandated identifier is omitted. The purpose is to create betterconsistency for clients of third-party HTTP servers and HTTP basedAPIs by introducing this standard request and response header.
Review of MORO (1997): The Raising Predicates Nouns Phrases and The Theory of Clause Structure

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Bespreking van BOLC (1978): Natural Language Communication with Computers

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Computational Data Mining For Feature Extraction In Health Informatics

This paper presents the methodologies and lessons learned from a cooperative effort within our institution involving the Health Science Center faculty, the Computer Science faculty, and senior/graduate students; the effort aimed at building a data mining module to input, process, and extract relevant information related to a pilot study on the effects of to Endocrine Disrupting Chemicals (EDCs) exposure on pregnancy, which was conducted by the Health Center and the School of Public Health.Interdisciplinary in nature, the project brought together biostatisticians, medical doctors, and computer and information scientists (CIS).On the medical side, the team was trying to assess human health risks from exposures to Endocrine Disrupting Chemicals, measuring both the exposure level and its ramifications in pregnant women of the Rio Grande Valley.To aid in the process from a computational and engineering point of view, a professor and two computer science and engineering majors were put in charge of taking the requirements and specifications from the medical side and converting them into a robust and flexible software application with a friendly graphical user interface.The study has progressed in a sequence of phases that included: obtaining approval of human subject research; preparing the necessary paperwork; recruiting subjects at local clinics, collecting blood and tissue samples, performing blood and tissue samples analysis, coding and entering data, constructing an integrated data base, performing statistical analysis, assessing human risk, and mining the database for trends, anomalies, and unusual cases.The educational experience and the interaction between the students and the medical/health team were invaluable.The CIS students, and their professors, benefited immensely from not only coding the design and requirements but also from learning about concepts such as getting a certificate of training on research on human subjects, conducting and inscribing surveys, extracting and visualizing basic factors and trends from the collected data.Our paper details the students' academic and professional experience in working with a real-life project with profound health and social impact on their local community.It also lays the foundation for continuous collaboration involving faculty and students between the involved departments.
The Effects of Money Laundering (ML) on Growth Application to the Gulf Countries

The strategic goal of this paper is to study the effects of the prevention policies against money laundering on growth in the gulf countries (Saudi Arabia, Kuwait, Qatar, Bahrain, UAE and Oman) from 1980 to 2014. Thus, the logistic regression (logit model) had given three fundamental results. The first had shown that the main policies in matter of fight against money laundering (anti money laundering law AMLL, suspicious transaction reporting STR, the criminalizing of terrorist financing CTF) have had positive effects on the increasing of probabilities to realize more growth. The second is that the said policies have had positive effects on the increasing of the degree of openness of the whole sample. The third is that the variable (proximity) had a positive and significant effect on anti-money laundering policies.