Reviewerly/exHarmony huggingface dataset:
Banking Regulation Classification in Portuguese

Products, services, among many other things in life have a quality standard, are inclusive, or do not harm customers. Regulations required from their manufacturers or providers make it possible. This type of requirement also exists in the finance sector. Governments, international agencies, or civil institutions are responsible for creating, applying, and inspecting these regulations. Regulators from all spheres (federal, state, and municipal) constantly demand changes in the finance sector to meet current needs adequately. This paper presents the constant evolution of a banking compliance application in Brazil. It aims to classify the relevance or irrelevance of regulatory documents published by more than 100 Brazilian regulators, affecting the businesses of more than 40 departments of Banco do Brasil. The application uses a hybrid strategy, combining machine learning and rules for a binary classification challenge involving each company department. This work also presents a particular type of corpus imbalance called The Imbalance Within Class.
Cd-Bcm:Cross-Domain Batch Certificates Management Based on Blockchain

With the development of information networks, network entities are interacting more and more frequently in different network domains. Identity management and cross-domain authentication are necessary in the multiple domains network environment. The current traditional architecture based on PKI has problems such as single point of failure, inefficient certificate revocation status management, and also lacks of privacy protection, which cannot meet the demand of cross-domain identity authentication. Blockchain is suitable for multi-participant cooperation requirement in multi-trust domain scenarios. In this paper, a cross-domain certificate management scheme CD-BCM is proposed. It solves the issue of CA’s single point of failure by designing multi-CA model with multi-signature. This certificate management structure based on the consortium blockchain can also be easily extended to general identity credentials. We also design a unified structure of batch certificates verification and conversion, which can optimize the identification efficiency of erroneous certificates. Finally, by comparing with current related schemes, our scheme achieves good functionality and scalability in the scenario of cross-domain certificate management.
The Macroeconomic Influence of Recent Political Conflicts in Africa: Generalized Synthetic Counterfactual Evidence

This paper measures the macroeconomic influence of recent political crisis, protest and uprisings in Africa with the generalized synthetic control method and evaluates the role played by natural resource dependence on the modulation of the nexus. We find that political crisis, protests and uprisings have a significant and negative nexus with economic growth while the nexus is positive with investment and price level. For economic growth, the deviation of the actual series from the counterfactual is negative, instantaneous, persistent and highly significant; indicating non-negligible costs of the shock. Indeed, dependence on natural resources amplifies the negative influence of political crisis, protests and uprisings on GDP. Finally, the more the treated country depends on natural resources, the more it becomes resilient to the investment losses caused by political crisis.
Introduction

Normally, we have many ways to explain machine learning. Before giving the definition, we would like to illustrate why we need machine learning. To give a solution of a computational problem, such as sorting, we need to carried out an algorithm to transform the problem’s input to an output. But sometimes, we may not have exactly algorithm to give a solutions for some tasks. For example, our task is to select spam emails from a massive undetected emails.
Evolution of Cryptocurrency

The avatar of currency has evolved over time, and 'cryptocurrency' is its latest incarnation. Cryptocurrency is a type of digital currency that allows peer-to-peer online payments without interference of financial institutions. Though experts are impressed with its growth trajectory, its decentralized and unregulated structure has stirred insecurity amongst governments across jurisdictions that has translated into bans and indecisiveness related to its status. While the concerns are not baseless and it is susceptible to cybercrimes, the focus must be to mitigate the issues rather than imposing a blanket prohibition that would be antithetical to the fundamental spirit and purpose of financial digitization, which is to promote the free flow of funds. In this chapter, the authors, firstly, analyze the practicality of cryptocurrency in light of the legal barriers and, secondly, assess the viability of a regulatory framework that ensures minimal control from the authorities but also checks concerns including cybercrime, illegal transactions, tax evasion, and lack of accountability.
Towards the Development of a Contingent Use of Systems Development Methodologies Model

Systems development methodologies (SDMs) are divided into two categories: plan-driven SDMs and agile SDMs. Research has shown that no single SDM is suitable for every systems development project situation. This paper aims to present aspects of a study that developed and empirically tested a contingent use of SDMs model to investigate contingent use of SDMs. The developed contingent use of SDMs model is tested using survey data collected from 155 systems development organizations. The findings show that SDMs are adopted and continuously tailored throughout the life cycle of a systems development project. This has theoretical and practical implications for SDM design and deployment. The empirical findings and the model presented in this study can assist researchers to investigate the contingent use of SDMs and improve their implementation in systems development projects. The findings provide insights on how theory and practice co-evolve and inform one another.
Hammer PDF: An Intelligent PDF Reader for Scientific Papers

It is the most important way for researchers to acquire academic progress via reading scientific papers, most of which are in PDF format. However, existing PDF Readers like Adobe Acrobat Reader and Foxit PDF Reader are usually only for reading by rendering PDF files as a whole, and do not consider the multi-granularity content understanding of a paper itself. Specifically, taking a paper as a basic and separate unit, existing PDF Readers cannot access extended information about the paper, such as corresponding videos, blogs and codes. Meanwhile, they cannot understand the academic content of a paper, such as terms, authors, and citations. To solve these problems, we introduce Hammer PDF, an intelligent PDF Reader for scientific papers. Apart from basic reading functions, Hammer PDF has the following four innovative features: (1) information extraction ability, which can locate and mark spans like terms and other entities; (2) information extension ability, which can present relevant academic content of a paper, such as citations, references, codes, videos, blogs, etc; (3) built-in Hammer Scholar, an academic search engine based on academic information collected from major academic databases; (4) built-in Q&A bot, which can find helpful conference information. The proposed Hammer PDF Reader can help researchers, especially those studying computer science, to improve the efficiency and experience of reading scientific papers. We have released Hammer PDF, available at https://pdf.hammerscholar.net/face.
Fatigue Damage estimation in vehicle from minimal instrumentation thanks to a mixed Engineering / Data Science approach

Monitoring damage and its potential causes in lab-tested structures requires extensive instrumentation that cannot be feasibly replicated in production assets. Since strain gauge instrumentation at a large scale is impractical, other proxy measurements for damage such as pressure, temperature or acceleration can be monitored and damage can be inferred from them. There is considerable difficulty in understanding not only which events lead to significant damage accumulation, but also in suitably and economically instrumenting assets in order to capture these data. We propose a general framework for exploring data which is inspired from the field of data science. Using a combination of engineering calculation packages and open-source data science tools, we show how engineers can further their understanding of the problem domain. We will present two case studies, each involving a different automotive component that is extensively instrumented with strain gauges, accelerometers and other sensors. We evaluate absolute damage from the strain data and, by using engineering indicators, data reduction, visualisations, correlations, we show how a minimal instrumentation subset can be identified for the purpose of damage approximation.
Mandating Data Rights Exercises

Data subject rights constitute critical tools for empowerment in the digitised society. These rights – especially those found in the General Data Protection Regulation (GDPR) – are intent agnostic and can be deployed to safeguard many interests and (fundamental) rights in a wide variety of contexts, not just individually, but also collectively. As data rights are increasingly exercised collectively and/or in relation to very complex data ecosystems, we can observe growing involvement of intermediating entities exercising rights on behalf of data subjects. Yet, the exact contours of whether and how data protection law allows these rights to be mandated in the first place remains an underexplored area. With this in mind, this article provides more clarity on the challenges and legal requirements of data rights mandates, specifically by looking at private law. The paper explains how data rights mandates relate to the GDPR, and explores the way in which private law arrangements structure - and constrain - such mandates.
Improving Text-Independent Speaker Verification with Auxiliary Speakers Using Graph

The paper presents a novel approach to refining similarity scores between input utterances for robust speaker verification. Given the embeddings from a pair of input utterances, a graph model is designed to incorporate additional information from a group of embeddings representing the so-called auxiliary speakers. The relations between the input utterances and the auxiliary speakers are represented by the edges and vertices in the graph. The similarity scores are refined by iteratively updating the values of the graph's vertices using an algorithm similar to the random walk algorithm on graphs. Through this updating process, the information of auxiliary speakers is involved in determining the relation between input utterances and hence contributing to the verification process. We propose to create a set of artificial embeddings through the model training process. Utilizing the generated embeddings as auxiliary speakers, no extra data are required for the graph model in the verification stage. The proposed model is trained in an end-to-end manner within the whole system. Experiments are carried out with the Voxceleb datasets. The results indicate that involving auxiliary speakers with graph is effective to improve speaker verification performance.
An Entity-Oriented Approach for Answering Topical Information Needs

In this dissertation, we adopt an entity-oriented approach to identify relevant materials for answering a topical keyword query such as "Cholera". To this end, we study the interplay between text and entities by addressing three related prediction problems: (1) Identify knowledge base entities that are relevant for the query, (2) Understand an entity's meaning in the context of the query, and (3) Identify text passages that elaborate the connection between the query and an entity. Through this dissertation, we aim to study some overarching questions in entity-oriented research such as the importance of query-specific entity descriptions, and the importance of entity salience and context-dependent entity similarity for modeling the query-specific context of an entity.
Live Object Detection in Real Time

While deep learning has achieved excellent results for the discovery of articles, there have been numerous issues identified with certifiable images such as clamor, obscuration, turning jitter, and so forth. These issues have a significant effect on the discovery of articles. The primary objective of this investigation is the identification of objects based on the You Only Look Once (YOLO) method. This method has a few advantages over other methods of determining article location. As YOLO anticipates jumping boxes using convolutional neural networks, a calculation made with difference algorithms, such as Convolutional Neural Network (CNN) and Fast-Convolutional Neural Network, does not consider the whole picture. We have used this calculation to identify different kinds of items.
Building a professional trajectory using the group argument method

The issues of the need to introduce advanced education in modern conditions are considered. The implementation of the concept of advanced education presupposes the formation of the ability of the education system to forecast. A method for constructing an individual professional trajectory by means of algorithms based on the self- organization approach is proposed. The method of group accounting of arguments was used as an example. The basic principles of algorithm design, optimality criteria, rules and main advantages of this method are presented. A mathematical forecasting model is presented. To test the performance of the predictive model using the method of group consideration of arguments, we took teachers engaged in career guidance work within the educational programs of additional pre-university education of the All-Russian Youth Center «Olymp» and collected statistical information about their publication activity. To build the model, a measuring sample was used for 30 years of the teacher’s work. The model was built and tested for teachers, not for students, because it is possible to use rather long-term statistics of their activities. It is concluded that the proposed model reflects the real-life professional trajectory of a teacher in terms of publication activity, which means that it can be used to predict individual professional trajectories of students.
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition

Neural architecture search (NAS) has attracted much attention and has been explored for automatic speech recognition (ASR). In this work, we focus on streaming ASR scenarios and propose the latency-controlled NAS for acoustic modeling. First, based on the vanilla neural architecture, normal cells are altered to causal cells to control the total latency of the architecture. Second, a revised operation space with a smaller receptive field is proposed to generate the final architecture with low latency. Extensive experiments show that: 1) Based on the proposed neural architecture, the neural networks with a medium latency of 550ms (millisecond) and a low latency of 190ms can be learned in the vanilla and revised operation space respectively. 2) For the low latency setting, the evaluation network can achieve more than 19% (average on the four test sets) relative improvements compared with the hybrid CLDNN baseline, on a 10k-hour large-scale dataset.
Transfer Visual Semantics into Knowledge Graph for Zero-Shot Recognition in Universal Datasets

Semantic fusion of combining attributes or text with knowledge graph has recently shown great potential for the task of Zero-shot Learning (ZSL). However, there is still a problem to be solved that insufficient expression of semantic features in embedding. Especially in universal datasets with an extreme challenge, such as ImageNet, these datasets are required to recognize unseen classes without corresponding annotation. To make ZSL more applicable in the real world, we found that efficiently understanding and using the image content is often the key to distinguishing objects in human recognition patterns. Thus we propose a method with transforming the visual information into semantics to alleviate the semantic gap between image and semantic description. This method allows us to exploit the Common Sense Knowledge Graph based on the hierarchical structure and the visual graph based on visual correlation concurrently. Compared with several state-of-the-art methods, the proposed method has achieved good performance in ImageNet.
On Rotation Gains Within and Beyond Perceptual Limitations for Seated VR

Head tracking in head-mounted displays (HMDs) enables users to explore a 360-degree virtual scene with free head movements. However, for seated use of HMDs such as users sitting on a chair or a couch, physically turning around 360-degree is not possible. Redirection techniques decouple tracked physical motion and virtual motion, allowing users to explore virtual environments with more flexibility. In seated situations with only head movements available, the difference of stimulus might cause the detection thresholds of rotation gains to differ from that of redirected walking. Therefore we present an experiment with a two-alternative forced-choice (2AFC) design to compare the thresholds for seated and standing situations. Results indicate that users are unable to discriminate rotation gains between 0.89 and 1.28, a smaller range compared to the standing condition. We further treated head amplification as an interaction technique and found that a gain of 2.5, though not a hard threshold, was near the largest gain that users consider applicable. Overall, our work aims to better understand human perception of rotation gains in seated VR and the results provide guidance for future design choices of its applications.
A Modified Data Clustering Algorithm for Uncertain Data in Peer to Peer Networks

A sensational rise of various applications and businesses has led to the rise of data being collected, it is getting harder to store this data on a single machine. It has become more feasible to manage dispersed data sets and organizations. That is the reason distributed data mining methods have become popular. Perhaps the main hurdle of data mining is to perform data clustering. While many clustering calculations exist for certain and uncertain data sets, there is need of productive calculations for distributed data sets. In this paper, a modified hierarchical clustering algorithm is proposed for uncertain dataset to improve timing in execution of the process.
Continual Learning for Sentiment Classification by Iterative Networks Combination

Deep neural networks have been reported to exhibit excellent performance on sentiment classification, where the impressive results are obtained with static model incapable of adapting their behavior to new domains over time. However, in practice, static models may become intractable quickly due to storage constraints or privacy issues. In this paper, we propose a novel continual learning approach for sentiment classification by iteratively combining (CSIC) the original network trained on old tasks and the fine-tuned network trained on new tasks with knowledge distillation, which adapts continually and keeps on learning over time without increasing the size of the network. We conduct extensive experiments on 16 popular review corpora. Experimental results demonstrate that the proposed CSIC method significantly outperforms the strong baselines for sentiment classification in continual learning scenario.
Multilayer Neural Network Technique for Parsing the Natural Language Sentences

In this article is presented an approach for parsing natural language sentences using neural networks. The pre-processing technique is applied to code the sentences into string of bits and after the training process is started, is formed into patterns available in the form of coded information. The multilayer feed forward networks are used here for training to classify the words into appropriate syntactical categories. The classified words represent the parsed information of the given sentences. The main function of the network is to assign the respective syntactical categories to each word of a sentence with a minimal error rate. The comparison between the two popular neural network approaches i.e. feed forward neural network and radial basis neural network is presented to analyze performance for the new and unknown sentences.
Review of deep learning network

research-article Share on Review of deep learning network Authors: Liming Chen School of Electronics and Materials Engineering,Leshan Normal University, China School of Electronics and Materials Engineering,Leshan Normal University, ChinaView Profile , Bin Xie Dean's office of the Engineering&Technical of Chengdu University of Technology, China Dean's office of the Engineering&Technical of Chengdu University of Technology, ChinaView Profile , YingChun Chen School of Electronics and Materials Engineering,Leshan Normal University, China School of Electronics and Materials Engineering,Leshan Normal University, ChinaView Profile Authors Info & Claims CSAI 2021: 2021 5th International Conference on Computer Science and Artificial IntelligenceDecember 2021 Pages 347–351https://doi.org/10.1145/3507548.3507601Online:09 March 2022Publication History 0citation28DownloadsMetricsTotal Citations0Total Downloads28Last 12 Months28Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
SPGC: Integration of Secure Multiparty Computation and Differential Privacy for Gradient Computation on Collaborative Learning

Achieving differential privacy and utilizing secure multiparty computation are the two primary approaches used for ensuring privacy in privacy-preserving machine learning. However, the privacy guarantee by existing integration protocols of both approaches for collaborative learning weakens when more participants join the protocols. In this work, we present Secure and Private Gradient Computation (SPGC), a novel collaborative learning framework with a strong privacy guarantee independent of the number of participants while still providing high accuracy. The main idea of SPGC is to create noise for the differential privacy within secure multiparty computation. We also created an implementation of SPGC and used it in experiments to measure its accuracy and training time. The results show that SPGC is more accurate than a naive protocol based on local differential privacy by up to 5.6%. We experimentally show that the training time increases in proportion to the noise generation and then demonstrate that the privacy guarantee is independent of the number of participants as well as the accuracy evaluation.
A Dual-Classifier Model for General Fine-Grained Event Detection Task

This paper presents a winning solution for the CCKS-2021 general fine-grained event detection task whose goal is to identify event triggers and the corresponding event types from the MAssive eVENt detection dataset (MAVEN). In this task, we focus on two challenging problems in MAVEN: event identification and event confusion. The former problem is that it is hard to determine whether the current trigger word triggers an event. The latter problem means that some events are prone to category confusion. To solve the event identification issue, we propose a dual-classifier event detection model, which combines event identification and event classification to enhance the ability to judge the existence of events. In addition, to solve the problem of event confusion, we introduce adversarial training strategies to enhance the robustness of event category boundaries. The approach achieves an F1-score of 0.7058, ranking the first place in the competition.