Reviewerly/exHarmony huggingface dataset:
Sequential Feature Filtering Classifier

The Ensemble and mixture of expertise method is the most intuitive and simple way to improve performance in the field of recognition using convolutional neural networks (CNNs). However, Ensemble is difficult to apply in real-time operation applications because the amount of computational overhead and parameters increase in proportion to the number of models. In another, a mixture of expertise that extracts various expertise and combines it is cumbersome to apply because it requires a large change in the network. In this study, we propose Sequential Feature Filtering Classifier (FFC), a simple but effective classifier for CNNs. Using sequential LayerNorm and ReLU, FFC zeroes out low-activation units and preserves high-activation units. The sequential feature filtering process generates multiple features, which are transmitted to a shared classifier, yielding multiple outputs (multiple expertise). FFC can be applied to any CNN with a classifier and it significantly improves the performance with negligible overhead. In this study, the efficacy of FFC is validated extensively on various tasks—ImageNet-1K classification, MS COCO detection, Cityscapes segmentation, and HMDB51 action recognition. Moreover, it is empirically established that FFC can further improve performances using additional techniques, including attention modules. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/seominseok0429/Sequential-Feature-Filtering-Classifier</uri> .
Making a Better Use of Caches for GCN Accelerators with Feature Slicing and Automatic Tile Morphing

GCNs (Graph Convolutional Networks) are becoming increasingly popular in the field of neural networks due to their ability to analyze many kinds of irregular data. Along with the rapid growth, there are various accelerators being proposed to mitigate the huge computational requirements. Often, the key bottleneck of executing GCNs is at the random accesses posed on the wide feature array. Vertex tiling is a popular technique to address the issue, but has a drawback of putting too much repetition on the data and being hard to tune parameters. In such regard, we propose feature slicing and automatic tile morphing, which greatly improves the cache behavior, and allows for easier tuning. Experimental results show that the proposed methods provide up to 40.1 percent overall execution time reduction, and automatically finds near-optimal tuning parameters.
Practical quantum encryption protocol with varying encryption configurations

Quantum communication is an important application that derives from the burgeoning field of quantum information and quantum computation. Focusing on secure communication, quantum cryptography has two major directions of development, namely quantum key distribution and quantum encryption. In this work we propose a quantum encryption protocol that utilizes a quantum algorithm to create blocks of ciphertexts based on quantum states. The main feature of our quantum encryption protocol is that the encryption configuration of each block is determined by the previous blocks, such that additional security is provided. We then demonstrate our method by an example model encrypting the English alphabet, with numerical simulation results showing the large error rate of a mock attack by a potential adversary. The safety of the encryption method is further demonstrated against several possible attack models. With the improvements against noises, our quantum encryption protocol is a capable addition to the toolbox of quantum cryptography.2 MoreReceived 24 February 2021Accepted 4 June 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.023251Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum communicationQuantum cryptographyQuantum Information
The fourth dimension in translation: time and disposability

The digital age has brought about exchange of information at unprecedented scales and speeds. Both within multilingual communities and across boundaries, translation is called upon to make information available with greater efficiency. While increasingly mature technology helps in this process, this paper argues for the discipline to put in context the translation phenomena emerging from strategies that have time taken as their prime concern. Specifically, it discusses the principle of disposability of information in determining minimum quality to be achieved as a way to systematically approach time-oriented translation methods. The discussion may have implications for how translation is practiced and understood and also represent a dimension in translation that can be further explored.
Revisit Raft Consistency Protocol on Private Blockchain System in High Network Latency

Raft’s good performance in the CFT system makes it a mainstream implementation solution for the consistency of the private chain system. However, we found in practice that Raft’s performance degradation is very serious when the network latency is greater than 1 ms. Therefore, we simulated the blockchain network environment and tested the TPS performance of Raft and Multi-raft. The results show that when the TPS is the same, Multi-raft reduces network traffic by 30% compared with Raft’s leader, and the CPU load increases slightly. Based on the experimental results, we discussed the optimization scheme of Raft on the geographically distributed system, and provided ideas for future research.
Research Data Management and Data Stewardship Competences in University Curriculum

Skills for data governance and management are critical for the wide adoption of Open Science practices and effective use of the data in research, industry, business and other economic sectors. The FAIR (Findable - Accessible - Interoperable - Reusable) data management principles and data stewardship provide a foundation for effective research data management. The 2018 “Turning FAIR into Reality” report and other documents recommend that data skills should be more widely included in university curricula and that a concerted effort should be made to coordinate and accelerate the pedagogy for professional data roles. Throughout Europe and beyond, many organisations, projects and initiatives work on providing training on FAIR data competences. However, wider adoption of the FAIR data culture can be achieved by including FAIR competences into university curricula. This paper presents the ongoing work of the FAIRsFAIR project to develop a Data Stewardship competence framework and to provide recommendations for implementing this framework in university curricula by means of defining the Data Stewardship Body of Knowledge Model Curricula. The proposed approach and identified competences and knowledge topics are supported by a job market analysis. The presented work is actively using the EDISON Data Science Framework as a basis for Data Stewardship competences definition and methodology for linking competences, skills, knowledge, and intended learning outcomes when designing curricula.
Generalized error path algorithm

Model selection with cross validation (CV) is very popular in machine learning. However, CV with grid and other common search strategies cannot guarantee to find the model with minimum CV error, which is often the ultimate goal of model selection. Recently, various solution path algorithms have been proposed for several important learning algorithms including support vector classification, Lasso, and so on. However, they still do not guarantee to find the model with minimum CV error. In this paper, we first show that the solution paths produced by various algorithms have the property of piecewise linearity. Then, we prove that a large class of error (or loss) functions are piecewise constant, linear, or quadratic w.r.t. the regularization parameter, based on the solution path. Finally, we propose a new generalized error path algorithm (GEP), and prove that it will find the model with minimum CV error in a finite number of steps for the entire range of the regularization parameter. The experimental results on a variety of datasets not only confirm our theoretical findings, but also show that the best model with our GEP has better generalization error on the test data, compared to the grid search, manual search, and random search.
Privacy and Transparency in CBDCs: A Regulation-by-Design AML/CFT Scheme

Central banks and governments all over the world are increasingly exploring digital versions of fiat money, known as retail Central Bank Digital Currencies (CBDCs). Most initiatives rely on Distributed Ledger Technologies and are presented as alternatives to physical cash. Consequently, anonymity-related regulatory questions arise in terms of Anti-Money Laundering and Counter-Terrorist Financing compliance. Against this backdrop, this paper provides a techno-legal taxonomy of approaches to balance privacy and transparency in CBDCs without thwarting accountability, but it also underlines cross-sectoral impacts. The contribution heeds regulation-by-design as its core methodological foundation, with Privacy-Enhancing Technologies as the relevant use case. Thus, it highlights that not only technology aids legal purposes, but also that some regulatory requirements ought to be designed into technology for one to reach agreed-upon results and/or standards.
MIRIAM: A Blockchain-based Web Application for Managing Professional Registrations of Medical Doctors in Brazil

Fiscal agencies have been facing several challenges regarding the legitimate exercise of professions. In particular, the context of medicine exercise poses as a crucial one, since the professionals involved in this field are responsible for dealing with the health of third parties. However, we may notice a number of incidents and frauds related to the professionals records of physicians, such as issuing forged diplomas or availability of information not validated. In order to avoid these frauds, one can highlight the relevance of investigating innovative solutions aiming to maintain in a safe and transparent way the professional history of each doctor. In this context, our objective and major contribution is to present MIRIAM, a blockchain-based web application based on the Hyperledger platform, that allows, in a decentralized and reliable fashion, the storage of relevant information necessary for managing registrations of medical professionals. In addition, we explored the Brazilian regulatory framework to clarify the solution's feasibility in a real-world workflow scenario.
Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression

Nested networks or slimmable networks are neural networks whose architectures can be adjusted instantly during testing time, e.g., based on computational constraints. Recent studies have focused on a "nested dropout" layer, which is able to order the nodes of a layer by importance during training, thus generating a nested set of subnetworks that are optimal for different configurations of resources. However, the dropout rate is fixed as a hyperparameter over different layers during the whole training process. Therefore, when nodes are removed, the performance decays in a human-specified trajectory rather than in a trajectory learned from data. Another drawback is the generated sub-networks are deterministic networks without well-calibrated uncertainty. To address these two problems, we develop a Bayesian approach to nested neural networks. We propose a variational ordering unit that draws samples for nested dropout at a low cost, from a proposed Downhill distribution, which provides useful gradients to the parameters of nested dropout. Based on this approach, we design a Bayesian nested neural network that learns the order knowledge of the node distributions. In experiments, we show that the proposed approach outperforms the nested network in terms of accuracy, calibration, and out-of-domain detection in classification tasks. It also outperforms the related approach on uncertainty-critical tasks in computer vision.
A Novel Technique for Earthquakes Magnitude Estimation

Earthquakes are natural phenomena whose consequences can be catastrophic. The introduction of new technologies such as intelligent transducers, which, being equipped with a communication interface, allow the exchange of information with other devices connected to the network, prompted researchers to study systems able to exploit data related to the first moments of the seismic event in an attempt to predict its characteristics. This idea is the base of Earthquake Early Warning (EEW) systems that aim to estimate the upcoming event's fundamental parameters to issue an alarm signal in case of danger, allowing the necessary precautions to be taken. There are many problems concerning parameters' estimation representing an indication of the phenomenon's danger in this context. In particular, the magnitude estimation and the compromise between the processing of reliable results and the time window's brevity in which the system algorithm must acquire and process the data to produce such results represent challenging aspects. This paper present new results of an approach, proposed by the authors that use a quotient of iterative filters. By choosing the right filter parameters, this quotient correlates with the earthquake magnitude by processing the incoming wave's first three seconds.
Towards Smart and Efficient Health Monitoring Using Edge-enabled Situational-awareness

IoT applications demand higher Quality of Experience (QoE) to deliver satisfactory services to the end-users. Variable user behavior, diverse application requirements, and dynamic system constraints exacerbate the QoE delivery challenge. Situational-awareness encapsulates the constraints at the user, application, and system levels, quantifiably reflecting system dynamics. Such models can manage diversity among multiple layers of the system stack while improving QoE. In this paper, we investigate the diverse set of requirements and opportunities in edge paradigm that vary over time using health monitoring applications. Towards address this challenge, we propose a situational-awareness model that enables intelligent orchestration of IoT applications across networked and distributed hardware platforms.
LexO: an open-source system for managing OntoLex-Lemon resources

The adoption of Semantic Web technologies and the Linked Data paradigm has been driven by the need to ensure the construction of resources that are at the same time interoperable, shareable and reusable by the scientific community. OntoLex-Lemon, which exploits the expressive power of ontologies, has come to be the de facto standard model for the representation of lexica and terminologies. The number of users potentially interested in editing or consuming OntoLex-Lemon data is thus very large. Unfortunately, the use of ontology editors for constructing such language resources can be very tedious due to the complexity and verbosity of the model, which heavily relies on specific modeling technicalities. This underlines the importance of developing tools and services that facilitate the creation and editing of resources and bring lexicography and terminology closer to the Semantic Web. In this paper we present LexO, a collaborative web editor for easily building and managing lexical and terminological resources in the context of the Semantic Web, based on the OntoLex-Lemon model. It makes the model accessible to users who do not possess the needed technical skills, thus allowing for wider adoption of new technological advances in the Semantic Web.
Multi-target tracking algorithm based on deep learning

Abstract With the continuous development of deep learning in multi-target tracking, the use of convolutional neural network for feature extraction has replaced the traditional feature extraction method, but the accuracy of target tracking needs to be improved. In order to further improve the accuracy of multi-target tracking, a new multi-target tracking algorithm based on RFB is proposed in this paper. The algorithm is mainly divided into three parts: multi-target detection, feature extraction and multi-target tracking. In the multi-target detection part, CenterNet was selected as the detection network to improve detection accuracy. In feature extraction part, RFBNET is combined with pedestrian re-recognition network to strengthen feature extraction capability. DeepSORT algorithm is used in multi - target tracking. Experimental results on MOT16 data set show that the proposed algorithm is more effective than other methods.
Experience Simple Transformer library in solving Mojaz Multi-Topic Labelling Task

This article describes the code that was used in a multi-topic labeling system, the code starts with loading the train, validation, and test data set then installing the pyarabic and Simple Transformers libraries." pyarabic" allows the system to manipulate Arabic letters. "Simple Transformers" is a Natural Language Processing (NLP) library designed to simplify the usage of Transformer models without having to compromise on utility. The model was used from the Simple Transformer library is "Multi Label Classification Model" with the model type "bert" and the model's name "asafaya/bert-base-arabic". In multi-label text classification, the target for a single article (row) from the training dataset is a list of 10 distinct binary labels. A transformer-based multi-label text classification model typically consists of a transformer model with a classification layer on top of it. The results were impressive compared to the short training time for two epochs, which is five minutes and 27 seconds. The accuracy results were as follows: F1 macro: 0.866, F1 micro: 0.869, competition website on Codalab: 0.8468.
How to Request Network Resources Just-in-Time using Smart Contracts

5G promises unprecedented levels of network connectivity to handle diverse applications, including life-critical applications such as remote surgery. However, to enable the adoption of such applications, it is important that customers trust the service quality provided. This can only be achieved through transparent Service Level Agreements (SLAs). Current resource provisioning systems are too general to handle such variety in applications. Moreover, service agreements are often opaque to customers, which can be an obstacle for 5G adoption for mission-critical services.In this work, we advocate short-term and specialised rather than long-term general service contracts and propose an end-to-end Permissioned Distributed Ledger (PDL) focused architecture; which allows operators to advertise their service contracts on a public portal backed by a PDL. These service contracts with clear Service Level Agreement (SLA) offers are deployed as smart contracts to enable transparent, automatic and immutable SLAs. To justify our choice of using a permissioned ledger instead of permissionless, we evaluated and compared contract execution times on both permissioned (i.e. Quorum and Hyperledger Fabric) and permissionless (i.e. Ropsten testnet) ledgers.
Off-chain Execution and Verification of Computationally Intensive Smart Contracts

We propose a novel framework for off-chain execution and verification of computationally-intensive smart contracts. Our framework is the first solution that avoids duplication of computing effort across multiple contractors, does not require trusted execution environments, supports computations that do not have deterministic results, and supports general-purpose computations written in a high-level language. Our experiments reveal that some intensive applications may require as much as 141 million gas, approximately 71x more than the current block gas limit for computation in Ethereum today, and can be avoided by utilizing the proposed framework.
Convolutional neural network-based ensemble methods to recognize Bangla handwritten character

In this era of advancements in deep learning, an autonomous system that recognizes handwritten characters and texts can be eventually integrated with the software to provide better user experience. Like other languages, Bangla handwritten text extraction also has various applications such as post-office automation, signboard recognition, and many more. A large-scale and efficient isolated Bangla handwritten character classifier can be the first building block to create such a system. This study aims to classify the handwritten Bangla characters. The proposed methods of this study are divided into three phases. In the first phase, seven convolutional neural networks i.e., CNN-based architectures are created. After that, the best performing CNN model is identified, and it is used as a feature extractor. Classifiers are then obtained by using shallow machine learning algorithms. In the last phase, five ensemble methods have been used to achieve better performance in the classification task. To systematically assess the outcomes of this study, a comparative analysis of the performances has also been carried out. Among all the methods, the stacked generalization ensemble method has achieved better performance than the other implemented methods. It has obtained accuracy, precision, and recall of 98.68%, 98.69%, and 98.68%, respectively on the Ekush dataset. Moreover, the use of CNN architectures and ensemble methods in large-scale Bangla handwritten character recognition has also been justified by obtaining consistent results on the BanglaLekha-Isolated dataset. Such efficient systems can move the handwritten recognition to the next level so that the handwriting can easily be automated.
Model Compression Hardens Deep Neural Networks: A New Perspective to Prevent Adversarial Attacks

Deep neural networks (DNNs) have been demonstrating phenomenal success in many real-world applications. However, recent works show that DNN's decision can be easily misguided by adversarial examples-the input with imperceptible perturbations crafted by an ill-disposed adversary, causing the ever-increasing security concerns for DNN-based systems. Unfortunately, current defense techniques face the following issues: 1) they are usually unable to mitigate all types of attacks, given that diversified attacks, which may occur in practical scenarios, have different natures and 2) most of them are subject to considerable implementation cost such as complete retraining. This prompts an urgent need of developing a comprehensive defense framework with low deployment costs. In this work, we reveal that "defensive decision boundary" and "small gradient" are two critical conditions to ease the effectiveness of adversarial examples with different properties. We propose to wisely use "hash compression" to reconstruct a low-cost "defensive hash classifier" to form the first line of our defense. We then propose a set of retraining-free "gradient inhibition" (GI) methods to extremely suppress and randomize the gradient used to craft adversarial examples. Finally, we develop a comprehensive defense framework by orchestrating "defensive hash classifier" and "GI." We evaluate our defense across traditional white-box, strong adaptive white-box, and black-box settings. Extensive studies show that our solution can enormously decrease the attack success rate of various adversarial attacks on the diverse dataset.