Reviewerly/exHarmony huggingface dataset:
On the Just-In-Time Discovery of Profit-Generating Transactions in DeFi Protocols

In this paper, we investigate two methods that allow us to automatically create profitable DeFi trades, one well-suited to arbitrage and the other applicable to more complicated settings. We first adopt the Bellman-Ford-Moore algorithm with DEFIPOSER-ARB and then create logical DeFi protocol models for a theorem prover in DEFIPOSER-SMT. While DEFIPOSER-ARB focuses on DeFi transactions that form a cycle and performs very well for arbitrage, DEFIPOSER-SMT can detect more complicated profitable transactions. We estimate that DEFIPOSER-ARB and DEFIPOSER-SMT can generate an average weekly revenue of 191.48ETH (76,592USD) and 72.44ETH (28,976USD) respectively, with the highest transaction revenue being 81.31ETH(32,524USD) and22.40ETH (8,960USD) respectively. We further show that DEFIPOSER-SMT finds the known economic bZx attack from February 2020, which yields 0.48M USD. Our forensic investigations show that this opportunity existed for 69 days and could have yielded more revenue if exploited one day earlier. Our evaluation spans 150 days, given 96 DeFi protocol actions, and 25 assets. Looking beyond the financial gains mentioned above, forks deteriorate the blockchain consensus security, as they increase the risks of double-spending and selfish mining. We explore the implications of DEFIPOSER-ARB and DEFIPOSER-SMT on blockchain consensus. Specifically, we show that the trades identified by our tools exceed the Ethereum block reward by up to 874x. Given optimal adversarial strategies provided by a Markov Decision Process (MDP), we quantify the value threshold at which a profitable transaction qualifies as Miner ExtractableValue (MEV) and would incentivize MEV-aware miners to fork the blockchain. For instance, we find that on Ethereum, a miner with a hash rate of 10% would fork the blockchain if an MEV opportunity exceeds 4x the block reward.
Active Semantic Relations in Layered Enterprise Architecture Development

Abstract Enterprise Architecture (EA) metamodels align an organisation’s business, information and technology resources so that these assets best meet the organisation’s purpose. The Layered EA Development (LEAD) Ontology enhances EA practices by a metamodel with layered metaobjects as its building blocks interconnected by semantic relations. Each metaobject connects to another metaobject by two semantic relations in opposing directions, thus highlighting how each metaobject views other metaobjects from its perspective. While the resulting two directed graphs reveal all the multiple pathways in the metamodel, more desirable would be to have one directed graph that focusses on the dependencies in the pathways. Towards this aim, using CG-FCA (where CG refers to Conceptual Graph and FCA to Formal Concept Analysis) and a LEAD case study, we determine an algorithm that elicits the active as opposed to the passive semantic relations between the metaobjects resulting in one directed graph metamodel. We also identified the general applicability of our algorithm to any metamodel that consists of triples of objects with active and passive relations.
Forensic analysis of private browsing mechanisms: Tracing internet activities

Forensic analysts are more than ever facing challenges upon conducting their deep investigative analysis on digital devices due to the technological progression. Of these are the difficulties present upon analyzing web browser artefacts as this became more complicated when web browser companies introduced private browsing mode, a feature aiming to protect users’ data upon opening a private browsing session, by leaving no traces of data on the local device used. Aiming to investigate whether the claims of web browser companies are true concerning the protection private browsing provides to the users and whether it really doesn’t leave any browsing data behind, the most popular desktop browsers in Windows were analyzed after surfing them regularly and privately. The results shown in this paper suggest that the privacy provided varies among different companies since evidence might be recovered from some of the browsers but not from others.
A Secure and Efficient Data Migration Over Cloud Computing

Abstract Migrations are complex and depend on the project and its requirements, so security is essential to ensure a safe migration to the cloud. There are many things to consider, such as compliance with industry regulations, performance, or internal security policies. When migrating to a cloud environment, security is a shared responsibility. The cloud is based on a shared responsibility model in which the Internet service provider is responsible for cloud security and the customer is responsible for cloud security. In this paper we proposed a high-security for transferring large scale data files to the cloud database management system and an inter-cloud knowledge migration process that provides quick intervals. Measuring the effectiveness of the proposed work we have compare the performance with exiting cloud migration services. In our proposed work communication plays an essential role in its success. It is necessary to ensure clear and efficient communication between the parties involved in the migration, from decision makers to IT experts, legal teams and security officials. By sharing the needs, goals, and threats among the parties involved, our prosed work creates a successful migration strategy for the business, thereby avoiding disruptions, data loss, and other potential risks.
Feature Selection Based on Distance Measurement

Every day we receive a large amount of information through different social media and software, and this data and information can be realized with the advent of data mining methods.In the process of data mining, to solve some high-dimensional problems, feature selection is carried out in limited training samples, and effective features are selected.This paper focuses on two Relief feature selection algorithms: Relief and ReliefF algorithm.The differences between them and their respective applicable scopes are analyzed.Based on Relief algorithm, the high weight feature subset is obtained, and the correlation between features is calculated according to the mutual information distance measure, and the high redundant features are removed to obtain the feature subset with higher quality.Experimental results on six datasets show the effectiveness of our method.
Adversarial Feature Augmentation and Normalization for Visual Recognition

Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings, instead of relying on computationally-expensive pixel-level perturbations. We propose Adversarial Feature Augmentation and Normalization (A-FAN), which (i) first augments visual recognition models with adversarial features that integrate flexible scales of perturbation strengths, (ii) then extracts adversarial feature statistics from batch normalization, and re-injects them into clean features through feature normalization. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks, including ResNets and EfficientNets for classification, Faster-RCNN for detection, and Deeplab V3+ for segmentation. Extensive experiments show that A-FAN yields consistent generalization improvement over strong baselines across various datasets for classification, detection and segmentation tasks, such as CIFAR-10, CIFAR-100, ImageNet, Pascal VOC2007, Pascal VOC2012, COCO2017, and Cityspaces. Comprehensive ablation studies and detailed analyses also demonstrate that adding perturbations to specific modules and layers of classification/detection/segmentation backbones yields optimal performance. Codes and pre-trained models will be made available at: https://github.com/VITA-Group/CV_A-FAN.
Design of classifiers based on ANN approximations of traditional methods

Abstract In this paper, we suggest basing the development of classification methods on traditional techniques but approximating them in whole or in part with artificial neural networks (ANNs). Compared to a direct ANN approach, the underlying traditional method is often easier to analyse. Classification failures can be better understood and corrected for, while at the same time faster execution can be obtained due to the parallel ANN structure. Furthermore, such a two‐step design philosophy partly eliminates the ‘guesswork’ associated with the design of ANNs. The expected gain is thus that the benefits from the traditional field, and the ANN field can be obtained by combining the best features from both fields. We illustrate our approach by working through an explicit example, namely, a Nearest Neighbour classifier applied to a subset of the MNIST database of handwritten digits. Two different approaches are discussed for how to translate the traditional method into an ANN. The first approach is based on a constructive implementation which directly reflects the original algorithm. The second approach uses ANNs to approximate the whole, or part of, the original method. An important part of the approach is to show how improvements can be introduced. In line with the presented philosophy, this is done by extending the traditional method in several ways followed by ANN approximation of the modified algorithms. The extensions are based on a windowed version of the nearest neighbour algorithm. We show that the improvements carry over to the ANN implementations. We further investigate the stability of the solutions by modifying the training set. It is shown that the errors do not change significantly. This also holds true for the ANN approximations providing confidence that the two‐step strategy is robust.
Controlled- <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Z</mml:mi></mml:math> operation versus the beam-splitter transformation: Errors of entangled operations

Using the quantum teleportation in continuous variables as a test scheme, we compare two entangled transformations---mixing of signals on a beam splitter and by the controlled-$Z$ (cz) operation. We evaluate these transformations in terms of the errors added to teleported oscillators. We have shown that the cz operation leads to the lower error of teleportation. This error can be further reduced by choosing appropriate weight coefficients for the cz transforms. We have compared the errors of the theoretical cz scheme and its practical implementation in the optical design. Although the cz optical scheme adds intrinsic noise to the overall transformation, it is nevertheless possible to specify the parameters which provides a gain in comparison with the traditional teleportation protocol.
Problem solution of optimal pathfinding for the movement of vehicles over rough mountainous areas

Abstract In recent years, a large number of studies were carried out in the field of finding and constructing the optimal path of movement. However, the task remains relevant due to the emergence of new aspects in this problem. The paper discusses and describes the existing algorithms for finding the shortest path on the graph. The material can be useful in building navigation systems and automated control in terms of the choice of optimal path.
Analysis and development of effective distance learning practices

he ways of developing such practices are determined taking into account a number of characteristic features of distance education: flexibility, modularity, innovative quality control of education, role repertoire of the teacher, economic benefits, etc. Describes specific principles of distance education, allocated based on the study of domestic and foreign authors, content updated by the authors: the principle of free access; the principle of remoteness; the principle of interactivity, the principle of identification; the principle of adequacy and expediency of using digital technologies in distance education. Methodological recommendations for teachers are given. The purpose of our research is to analyze how effective distance education and its forms are for students and primary school teachers who master additional professional training programs. Examples of questionnaires and survey data on the subject under study are given.
Text classification model for methamphetamine-related tweets in Southeast Asia using dual data preprocessing techniques

&lt;span&gt;Methamphetamine addiction is a prominent problem in Southeast Asia. Drug addicts often discuss illegal activities on popular social networking services. These individuals spread messages on social media as a means of both buying and selling drugs online. This paper proposes a model, the “text classification model of methamphetamine tweets in Southeast Asia” (TMTA), to identify whether a tweet from Southeast Asia is related to methamphetamine abuse. The research addresses the weakness of bag of words (BoW) by introducing BoW and Word2Vec feature selection (BWF) techniques. A domain-based feature selection method was performed using the BoW dataset and Word2Vec. The BWF dataset provided a smaller number of features than the BoW and TF–IDF dataset. We experimented with three candidate classifiers: Support vector machine (SVM), decision tree (J48) and naive bayes (NB). We found that the J48 classifier with the BWF dataset provided the best performance for the TMTA in terms of accuracy (0.815), F-measure (0.818), Kappa (0.528), Matthews correlation coefficient (0.529) and high area under the ROC Curve (0.763). Moreover, TMTA provided the lowest runtime (3.480 seconds) using the J48 with the BWF dataset.&lt;/span&gt;
NLP-CIC @ PRELEARN: Mastering Prerequisites Relations, from Handcrafted Features to Embeddings

We present our systems and findings for the prerequisite relation learning task (PRELEARN) at EVALITA 2020. The task aims to classify whether a pair of concepts hold a prerequisite relation or not. We model the problem using handcrafted features and embedding representations for in-domain and cross-domain scenarios. Our submissions ranked first place in both scenarios with average F1 score of 0.887 and 0.690 respectively across domains on the test sets. We made our code freely available1.
CZ-Base: A Database for Hand Gesture Recognition in Chinese Zither Intelligence Education

Training proper hand shapes is the key to learning a musical instrument, and a timely feedback plays an important role in improving the efficiency for trainees. In this paper, we establish a comprehensive database of Chinese zither performance gestures (CZ-Base) with a collection of hand shape subsets based on multi-view image acquisition. In addition, combined zither pedagogy with image analysis technology a classification standard is put forward. In our experiment, deep neural networks (VGG19, ResNet50 and InceptionV3) are used to realize the classification and recognition of zither playing gestures. The feasibility and practicability of the CZ-Base is thus verified. The tentative exploration of this paper is expected to inspire more interdisciplinary researches on science and fine arts.
Professional Development of Financial Managers

National economies impose new challenges on training competitive professionals who can succeed in high performance jobs. Financial management is one of them. This business demands professionals with innovative competences, and universities are responsible for training them. Professional development for financial managers, however, received little attention in recent publications. To bridge this gap, the article draws on the results of investigating the curricula for financial managers in universities. The paper aims at defining the theory and methodology underlying the professional development of financial managers and exploring the necessary changes to be introduced in the educational process, its content and forms. The following factors influencing the professional development of financial managers: awareness of personal psychological and physiological characteristics and capabilities, conscious professional choice, adaptability to university and profession, willingness for life-long learning, readiness for changes in profession. Based on these factors, the authors identified the following standards for the content of education for financial managers: student-centered approach to teaching management, marketing, human relations, entrepreneurship and business skills; project-based approach to teaching applied skills through real-world practice; close collaboration between the universities and employers. The survey results showed that the skills formed in university degree programs influence the professional development of financial managers and improve the competitiveness of the graduates. The survey revealed the basic competences of financial managers, including the innovative competences based on the professional standards and market demand. Further research can be aimed at developing special professional development programs for financial managers based on these findings.
A Philosophical Treatise and Educational Aspects on Fuzzy Control

Fuzzy Logic has found nowadays many applications to almost all sectors of human activity, withfuzzy control being one of the most important such applications. A control system regulates the behavior of adevice or another system with the help of a feedback controller. A fuzzy control system is a control system thatanalyses the input data in terms of variables which take continuous values in the interval [0, 1]. The presentarticle studies in detail the operation of fuzzy control systems and illustrates it by presenting an exampleof controlling a building’s central heating boiler.
Quantum algorithms for powering stable Hermitian matrices

Matrix powering is a fundamental computational primitive in linear algebra. It has widespread applications in scientific computing and engineering, and underlies the solution of time-homogeneous linear ordinary differential equations, simulation of discrete-time Markov chains, or discovering the spectral properties of matrices with iterative methods. In this paper, we investigate the possibility of speeding up matrix powering of sparse stable Hermitian matrices on a quantum computer. We present two quantum algorithms that can achieve speedup over the classical matrix powering algorithms -- (i) an adaption of quantum-walk based fast forwarding algorithm (ii) an algorithm based on Hamiltonian simulation. Furthermore, by mapping the N-bit parity determination problem to a matrix powering problem, we provide no-go theorems that limit the quantum speedups achievable in powering non-Hermitian matrices.
Review of Text Classification in Deep Learning

Text classification is an important research content in natural language processing.Compared with traditional manual processing, text classification based on deep learning improves both efficiency and accuracy.However, in the learning process, the content involved is very large and complex.In order to facilitate the research of more scholars, this paper summarizes the text classification of deep learning.The first part of this paper introduces the preprocessing of text classification.The second part introduces several feasible methods for deep learning text classification in detail.The third part introduces the test method of the model.The fourth part summarizes and analyzes the advantages and disadvantages of several methods to lay a foundation for further research.
Data Augmentation by Rubrics for Short Answer Grading

Short Answer Grading (SAG) is the task of scoring students' answers for applications such as examinations or e-learning. Most of the existing SAG systems predict scores based only on the answers, and critical evaluation criteria such as rubrics are ignored, which plays a crucial role in evaluating answers in real-world situations. In this paper, we propose a semi-supervised method to train a neural SAG model. We extract keyphrases that are highly related to answers scores from rubrics. Weights to words of answers are calculated as attention labels instead of manually annotated attention labels, based on span-wise alignments between answers and keyphrases. Only answers with highly weighed words are used as attention supervision. We evaluate the proposed model on two analytical assessment tasks of analytic score prediction and justification identification. Analytic score prediction is the task of predicting the score of a given answer for a prompt, and Justification identification involves identifying a justification cue in a given student answer for each analytic score. Our experimental results demonstrate that both performance of grading and justification identification is improved by integrating attention semi-supervised training, especially in a low-resource setting.
Learning to Optimize DAG Scheduling in Heterogeneous Environment

Scheduling job flows efficiently and rapidly on distributed computing clusters is one of huge challenges for daily operation of data centers. In a practical scenario, a single job consists of numerous stages with complex dependency relation represented as a Directed Acyclic Graph (DAG) structure. Nowadays a data center usually equips with a cluster of heterogeneous computing servers which are different in the hardware/software configuration. From both the cost saving and environmental friendliness, the data centers could benefit a lot from optimizing the job scheduling problems in the heterogeneous environment. Thus the problem has attracted more and more attention from both the industry and academy. In this paper, we propose a task-duplication based learning algorithm, namely \lachesis \footnote{The second of the Three Fates in ancient Greek mythology, who determines destiny.}, aiming to optimize the problem. In the proposed approach, it first perceives the topological dependencies between jobs using a reinforcement learning framework and a specially designed graph neural network (GNN) to select the most promising task to be executed. Then the task is assigned to a specific executor with the consideration of duplicating all its precedent tasks according to an expert-designed rules. We have conducted extensive experiments over standard workloads to evaluate the proposed solution. The experimental results suggest that \lachesisquad can achieve at most 26.7\% reduction of makespan and 35.2\% improvement of speedup ratio over seven strong baseline algorithms, including the state-of-the-art heuristics methods and a variety of deep reinforcement learning based algorithms.