Reviewerly/exHarmony huggingface dataset:
Span-Based Nested Named Entity Recognition with Pretrained Language Model

Named Entity Recognition (NER) is generally regarded as a sequence labeling task, which faces a serious problem when the named entities are nested. In this paper, we propose a span-based model for nested NER, which enumerates all possible spans as potential entity mentions in a sentence and classifies them with pretrained BERT model. In view of the phenomenon that there are too many negative samples in all spans, we propose a multi-task learning method, which divides NER task into entity identification and entity classification task. In addition, we propose the entity IoU loss function to focus our model on the hard negative samples. We evaluate our model on three standard nested NER datasets: GENIA, ACE2004 and ACE2005, and the results show that our model outperforms other state-of-the-art models with the same pretrained language model, achieving 79.46%, 87.30% and 85.24% respectively in terms of F1 score.
Modeling in Systems Engineering: Conceptual Time Representation

The increasing importance of such fields as embedded systems, pervasive computing, and hybrid systems control is increasing attention to the time-dependent aspects of system modeling. In this paper, we focus on modeling conceptual time. Conceptual time is time represented in conceptual modeling, where the notion of time does not always play a major role. Time modeling in computing is far from exhibiting a unified and comprehensive framework, and is often handled in an ad hoc manner. This paper contributes to the establishment of a broader understanding of time in conceptual modeling based on a software and system engineering model denoted thinging machine (TM). TM modeling is founded on a one-category ontology called a thimac (thing/machine) that is used to elaborate the design and analysis of ontological presumptions. The issue under study is a sample of abstract modeling domains as exemplified by time. The goal is to provide better understanding of the TM model by supplementing it with a conceptualization of time aspects. The results reveal new characteristics of time and related notions such as space, events, and system behavior.
ImageAI: Comparison Study on Different Custom Image Recognition Algorithms

Today there are a considerable amount of algorithms that are used by scientists and developers to detect patterns in pictures. Due to the complexity of such analysis, with this paper, we want to understand and share the results of doing a model training based of a pool of pre-selected algorithms recurring to an accessible python library named ImageAI and the public cloud to perform such training. We used the Google platform Colaboraty to execute our tests.
Comparative Analysis of Secured Hash Algorithms for Blockchain Technology and Internet of Things

Cryptography algorithms play a vital role in Information Security and Management.To test the credibility, reliability of metadata exchanged between the sender and the recipient party of IoT applications different algorithms must be used.The hashing is also used for Electronic Signatures and based on how hard it is to hack them; various algorithms have different safety protocols.SHA-1, SHA-2, SHA3, MD4, and MD5, etc. are still the most accepted hash protocols.This article suggests the relevance of hash functions and the comparative study of different cryptographic techniques using blockchain technology.Cloud storage is amongst the most daunting issues, guaranteeing the confidentiality of encrypted data on virtual computers.Several protection challenges exist in the cloud, including encryption, integrity, and secrecy.Different encryption strategies are seeking to solve these problems of data protection to an immense degree.This article will focus on the comparative analysis of the SHA family and MD5 based on the speed of operation, its security concerns, and the need of using the Secure Hash Algorithm.
Kaleido-BERT: Vision-Language Pre-training on Fashion Domain

We present a new vision-language (VL) pre-training model dubbed Kaleido-BERT, which introduces a novel kaleido strategy for fashion cross-modality representations from transformers. In contrast to random masking strategy of recent VL models, we design alignment guided masking to jointly focus more on image-text semantic relations. To this end, we carry out five novel tasks, i.e., rotation, jigsaw, camouflage, grey-to-color, and blank-to-color for self-supervised VL pre-training at patches of different scale. Kaleido-BERT is conceptually simple and easy to extend to the existing BERT framework, it attains new state-of-the-art results by large margins on four downstream tasks, including text retrieval (R@1: 4.03% absolute improvement), image retrieval (R@1: 7.13% abs imv.), category recognition (ACC: 3.28% abs imv.), and fashion captioning (Bleu4: 1.2 abs imv.). We validate the efficiency of Kaleido-BERT on a wide range of e-commerical websites, demonstrating its broader potential in real-world applications.
Overview of Blockchain Technology: Applications and Use Cases

Blockchain technology has revolutionized agriculture, finance, education, supply chain, health care, and many sectors. Blockchain has extended its advantages to the non-financial sector too. Researchers in this area are constantly trying to explore many applications of blockchain that can provide fruitful results. This article discusses various use cases and real-time applications of blockchain technology in various sectors and subsequently, explores the state-of-the-art developments in blockchain use cases in various sectors and the scope for further research aspects in the future. It is intended for audience those who are interested to learn use cases and real-time applications of blockchain technology.
Serverless Predictions: 2021-2030

Within the next 10 years, advances on resource disaggregation will enable full transparency for most Cloud applications: to run unmodified single-machine applications over effectively unlimited remote computing resources. In this article, we present five serverless predictions for the next decade that will realize this vision of transparency -- equivalent to Tim Wagner's Serverless SuperComputer or AnyScale's Infinite Laptop proposals.
Data Mining and Analysis of Reddit User Data

Reddit is a popular social media forum where the focus is on community rather than a single user. It consists of many communities called subreddits where users discuss posts on pre-defined topics. We scraped through various posts on Reddit for data and combined it into single dataset. In this paper, we analyse the dataset containing the words that show the sentiments of the users for the various comments they have made on posts. Here, we analyse the prediction of the divergence of sentiments to the reactions to the news articles using Reddit as our information supply for news and comments. Through our analysis, we gain insight into the behavior of users, understand the dynamics of interaction between users and their opinions and the bias towards different issues on this platform.
Emotion-based Hierarchical Clustering of Romanian Poetry

Emotions play a central role in both writing and understanding literary works, and poetry is a genre rich in emotional content, vivid imagery and abstract language.This paper proposes a clustering-based approach to unsupervisedly mine emotional patterns in Mihai Eminescu's poetry.Lexicon-based emotion features are used for the clustering algorithm.Resulting clusters are assessed with regard to manually added characteristics of poems in the form of literary themes.There is a partial overlap between affective and thematic content, consistent with literary evaluations of the same works.Computational approaches have the advantage of being objective and replicable, with unsupervised techniques such as clustering representing a valuable tool in the exploration of literary works.Nonetheless, no specific emotional patterns, as determined by the proposed method, can be fully associated with particular literary themes.
Exploring Applicability of the InterCriteria Analysis to Evaluate the Performance of MOE and GOLD Scoring Functions

Molecular docking is the most commonly used technique in the structure-based approaches to in silico drug design. A key element in the algorithms for docking of bioactive molecules is the scoring function, the purpose of which is to calculate quickly and accurately the interaction energy in a protein-ligand complex and to allow for selection of appropriate ligand poses in the binding pocket. Despite the large number of comparative studies, the question of which docking programs and protocols are performing better is still unresolved and the results are often contradictory. InterCriteria analysis (ICrA), developed as a multi-criterion decision-making approach, has the capacity to assist in selecting the most appropriate scoring functions for outlining drug candidates. In this study the potential of ICrA is explored for the assessment of the performance of scoring functions available in two of the widely used software packages for docking, Molecular Operating Environment (MOE) and Genetic Optimization for Ligand Docking (GOLD). London dG and GoldScore scoring functions are in the focus of the study, being the default scoring functions in MOE and GOLD, respectively. Their performance has been examined based on the calculated binding energies and the experimental binding affinities of the benzamidine-type protease inhibitors in the protein targets of trypsin and thrombin. The obtained results have been subjected to ICrA and subsequently analyzed.
Strong Optimal Classification Trees.

Decision trees are among the most popular machine learning models and are used routinely in applications ranging from revenue management and medicine to bioinformatics. In this paper, we consider the problem of learning optimal binary classification trees. Literature on the topic has burgeoned in recent years, motivated both by the empirical suboptimality of heuristic approaches and the tremendous improvements in mixed-integer optimization (MIO) technology. Yet, existing MIO-based approaches from the literature do not leverage the power of MIO to its full extent: they rely on weak formulations, resulting in slow convergence and large optimality gaps. To fill this gap in the literature, we propose an intuitive flow-based MIO formulation for learning optimal binary classification trees. Our formulation can accommodate side constraints to enable the design of interpretable and fair decision trees. Moreover, we show that our formulation has a stronger linear optimization relaxation than existing methods. We exploit the decomposable structure of our formulation and max-flow/min-cut duality to derive a Benders' decomposition method to speed-up computation. We propose a tailored procedure for solving each decomposed subproblem that provably generates facets of the feasible set of the MIO as constraints to add to the main problem. We conduct extensive computational experiments on standard benchmark datasets on which we show that our proposed approaches are 31 times faster than state-of-the art MIO-based techniques and improve out of sample performance by up to 8%.
Ketrampilan Membaca pada Pembelajaran Bahasa Indonesia di SD/MI

Language is a system, meaning that the language is formed by a component that is permanently patterned and can be explored. As a system, language is not only systematic but also systemic. By systemic, it means that the language is arranged according to a certain pattern, not arranged randomly or carelessly. Whereas systemic means, the language system is not a single system but consists of subsystems. The ability to read skills is a very important skill in life, not only important in educational life, but also very important in social life. By reading students will know more about everything, students will also have broader knowledge and insights. Reading skills are the main capital of students. With this ability, students can learn other sciences, can communicate their ideas and be able to express themselves.
Modular Subset Sum, Dynamic Strings, and Zero-Sum Sets

The modular subset sum problem consists of deciding, given a modulus m, a multiset S of n integers in 0.m – 1, and a target integer t, whether there exists a subset of S with elements summing to t (mod m), and to report such a set if it exists. We give a simple O(m log m)-time with high probability (w.h.p.) algorithm for the modular subset sum problem. This builds on and improves on a previous O(m log7 m) w.h.p. algorithm from Axiotis, Backurs, Jin, Tzamos, and Wu (SODA 19). Our method utilizes the ADT of the dynamic strings structure of Gawrychowski et al. (SODA 18). However, as this structure is rather complicated we present a much simpler alternative which we call the Data Dependent Tree. As an application, we consider the computational version of a fundamental theorem in zero-sum Ramsey theory. The Erdős-Ginzburg-Ziv Theorem states that a multiset of 2n – 1 integers always contains a subset of cardinality exactly n whose values sum to a multiple of n. We give an algorithm for finding such a subset in time O(n log n) w.h.p. which improves on an O(n2) algorithm due to Del Lungo, Marini, and Mori (Disc. Math. 09).
Human Pose Estimation in the Browser

This chapter describes human pose estimation, a computer vision advancement that seeks to understand human movement through pictures and videos. The chapter discusses the various ways to estimate human poses through machine learning (ML) methods, with a focus on Dan Oved’s PoseNet method.
Efficient Attentions for Long Document Summarization

The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization. In this paper, we propose Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. We further conduct a systematic study of existing efficient self-attentions. Combined with Hepos, we are able to process ten times more tokens than existing models that use full attentions. For evaluation, we present a new dataset, GovReport, with significantly longer documents and summaries. Results show that our models produce significantly higher ROUGE scores than competitive comparisons, including new state-of-the-art results on PubMed. Human evaluation also shows that our models generate more informative summaries with fewer unfaithful errors.
The Image of Modern Russian Intelligentsia: A Representation of Self-Identification

The article gives an overview of researches, based on the methodological provisions of media psychology, psychosemiotics and narrative psychology, into the contemporary Russian intelligentsia, and determines the scientific and social significance of the intelligentsia as a «Russian specific phenomenon». Psychosemiotic and narrative analyses of the modern Russian intelligentsia were conducted on the basis of original texts of interviews with participants of the TV program «Posner». Psychosemiotic analysis has shown that modern mass media transform ideas about the contribution of the intelligentsia to the history of civilization, and its attitudes to the challenges of modernity. In the narrative, the dynamics of the States of the actors of the analyzed narrative text of the Respondent is established. It is determined that both methods largely complement each other, clarify and update the research of self-identification of the modern Russian intelligentsia. This article presents empirical results of psychosemantic representation of the image of the Russian intelligentsia. Ppsychosemantic analysis of public opinion regarding the image of the intellectual of the XIX–XX centuries and the image of the modern Russian intellectual was carried out using the author's specialized semantic differential. The sample consisted of the intelligentsia of the Angara region (scientists, musicians, artists, doctors, teachers) with a total number of 256 people. Based on the data obtained using a specialized semantic differential for assessing the images of the intelligentsia of the 19th and 20th centuries and the modern Russian intelligentsia, the leading factors that characterize the images of representatives of the intelligentsia of the 19th and 20th centuries are the following: social distance, voice of conscience, developed intellectual abilities, altruism, social elite, political leadership, patriotism; as for modern Russian intelligentsia they are: publicity, education, and social leadership. propensity to humanism, educated innovator, developed intellectual abilities, propensity to patriotism. There are differences in the images of the intelligentsia of the 19th and 20th centuries and modern Russian intelligentsia, which are manifested through the development of self-awareness, reflection on their place, role and purpose in life.
Research on the Financial Shared Service Center in the “Internet +" Era

Abstract At this stage, with our country’s social and economic development, market competition is becoming increasingly fierce. How to maintain its own healthy and stable development in the fierce market competition has become a major challenge facing modern enterprises. At present, with the advent of the “Internet+" era, Internet technology has gradually been popularized and promoted, and the application environment is becoming more and more mature. Information management of corporate finance has become an inevitable requirement for the development of the times. Financial sharing in the “Internet+" era is conducive to promoting the implementation of information management of corporate finance, and thus better promoting the optimization and upgrading of corporate accounting work, and maintaining the healthy and long-term development of enterprises.
Gaussian Process Regression for Derivative Portfolio Modeling and Application to CVA Computations

Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all the trades with each counterparty under both market and credit risk. We present a multi-Gaussian process regression approach, which is well suited for OTC derivative portfolio valuation involved in CVA computation. Our approach avoids nested simulation or simulation and regression of cash flows by learning a Gaussian metamodel for the mark-to-market cube of a derivative portfolio. We model the joint posterior of the derivatives as a Gaussian process over function space, with the spatial covariance structure imposed on the risk factors. Monte-Carlo simulation is then used to simulate the dynamics of the risk factors. The uncertainty in portfolio valuation arising from the Gaussian process approximation is quantified numerically. Numerical experiments demonstrate the accuracy and convergence properties of our approach for CVA computations.
Blockchain Technology-Based Good Distribution Practice Model of Pharmacy Industry in Indonesia

Distribution is the main activity in integrated product supply chain management.In the pharmaceutical industry, the process of drug distribution is important because of the handling, storage, and distribution of medicinal products with good standards and quality.The problem that occurs in the pharmaceutical industry is the circulation of counterfeit drugs by related parties, for example, unofficial or unregistered distributors or data collection for unregistered medicines circulated by distributors.Permits misused from drug manufacturing processes until they are distributed or circulated do not comply with the Food and Drug Supervisory Agency standard provisions.These problems must be resolved quickly with technological support to facilitate the distribution process in recording data distribution, providing data security, and traceability of transactions between related parties.This study proposes a good drug distribution model by applying blockchain technology.The Model development uses a qualitative approach and a user center design.The result of this study is a validated drug distribution model with blockchain technology.The model has the characteristics of transparency, security, traceability, decentralization, automation, immutability, and reliability.This model can help the government ensure public health and safety by ensuring that the drugs received are of good quality, thereby increasing the community safety and health and trust in drugs in circulation.
Holes in the Geofence: Privacy Vulnerabilities in “Smart” DNS Services

Abstract Smart DNS (SDNS) services advertise access to geofenced content (typically, video streaming sites such as Netflix or Hulu) that is normally inaccessible unless the client is within a prescribed geographic region. SDNS is simple to use and involves no software installation. Instead, it requires only that users modify their DNS settings to point to an SDNS resolver. The SDNS resolver “smartly” identifies geofenced domains and, in lieu of their proper DNS resolutions, returns IP addresses of proxy servers located within the geofence. These servers then transparently proxy traffic between the users and their intended destinations, allowing for the bypass of these geographic restrictions. This paper presents the first academic study of SDNS services. We identify a number of serious and pervasive privacy vulnerabilities that expose information about the users of these systems. These include architectural weaknesses that enable content providers to identify which requesting clients use SDNS. Worse, we identify flaws in the design of some SDNS services that allow any arbitrary third party to enumerate these services’ users (by IP address), even if said users are currently offline. We present mitigation strategies to these attacks that have been adopted by at least one SDNS provider in response to our findings.
Developers’ Responses to App Review Feedback – A Study of Communication Norms in App Development

Norms are general expectations of behavior in societies. Huge amount of computer-mediated interaction data available in the social media domain provides an opportunity to extract and study communication norms, both to understand their prevalence and to make informed decisions about adopting them. While interactions in social media platforms such as Twitter and Facebook have been widely studied, only recently researchers have started examining app reviews provided by users and the responses provided by developers in the domain of app development. In this vein, a lot of attention has been devoted to study the nature of user reviews, however, little is known about developer responses to such reviews. Additionally, no other prior work has scrutinized the nature of communication norms in this domain. Towards addressing these gaps, this work pursues three objectives using a dataset comprising user reviews and developer responses from Google’s top-20 apps used to track running with a total of 24,407 reviews and 2,668 responses. First, based on prior literature in computer-mediated interactions, the study identifies 12 norms in responses provided by developers in three categories (obligation norms, prohibition norms and domain-specific response norms). Second, it scrutinizes the awareness and adoption of these norms. Third, based on the results obtained, this study identifies the need for creating a response recommendation system that generates responses to user reviews either automatically, or with some help from the developers. The proposed response recommendation system is a normative system that will generate responses that abide by the norms identified in this work, and will also monitor potential norm violations (if the responses were to be modified by the developers). Development of such a system forms the focus of future work.