Reviewerly/exHarmony huggingface dataset:
Code is the (F)Law: Demystifying and Mitigating Blockchain Inconsistency Attacks Caused by Software Bugs

Blockchains promise to provide a tamper-proof medium for transactions, and thus enable many applications including cryptocurrency. As a system built on consensus, the correctness of a blockchain heavily relies on the consistency of states between its nodes. But consensus protocols of blockchains only guarantee the consistency in the transaction sequence rather than nodes' internal states. Instead, nodes must replay and exe-cute all transactions to maintain their local states independently. When executing transactions, any different execution result could cause a node out-of-sync and thus gets isolated from other nodes.After systematically modeling the transaction execution process in blockchains, we present a new attack INCITE, which can lead different nodes to different states. Specifically, attackers could invoke an ambiguous transaction of a vulnerable smart contract, utilize software bugs in smart contracts to lead nodes that execute this transaction into different states. Unlike attacks that bring short-term inconsistencies, such as fork attacks, INCITE can cause nodes in the blockchain to fall into a long-term inconsistent state, which further leads to great damages to the chain (e.g., double-spending attacks and expelling mining power). We have discovered 7 0day vulnerabilities in 5 popular blockchains which can enable this attack. We also proposed a defense solution to mitigate this threat. Experiments showed that it is effective and lightweight.
Speckle Deformation Measurement Based on Pixel Correlation Search Method

Digital Image Correlation Method (DICM) is a non-contact deformation measurement method based on digital image processing technology and optical measurement methods. Because of its non-contact, high-precision, full-field and other advantages, it is widely used in civil engineering, aerospace and other fields. The whole-pixel correlation search method is a fast and high-precision deformation measurement method in DICM. This article first investigates the composition of the DIC measurement system, and studies the performance and unimodality of the shape function and related functions, and selects reasonable function model. Finally, the three whole-pixel search algorithms of point-by-point search method, hill-climbing method and differential evolution algorithm are experimentally analyzed. Finally, it is found that the performance of the hill-climbing method based on the zero mean normalized least square distance sum function is the best.
Statistical and Similarity Features Based Recognition of Offline Characters

This paper uses a blend of similarity and statistical based features for the recognition of offline alphabetic characters in noisy and noiseless environment. The complete representation of the characters is based on the combination of these two different families of features and recognition by different classifiers. The main strategy is to extract complementary similarity measure (CSM) as a feature vector and combined with grey level co-occurrence matrix (GLCM) features. A standard dataset is taken into consideration and recognition is done by artificial neural network (ANN), support vector machine (SVM), Naïve Bayes (NB) classifier and random forest (RF) classifier. The highest average recognition accuracy of all characters is recorded as 94.05% using RF in noiseless environment. In noisy environment, the highest accuracy is recorded as 75.8% by neural network. The analysis proves that the combination of feature works on various types of printed characters in noisy and noiseless environment irrespective of the font of characters.
Abstractive Text Summarization with Hierarchical Multi-scale Abstraction Modeling and Dynamic Memory

In this paper, we propose a novel abstractive text summarization method with hierarchical multi-scale abstraction modeling and dynamic memory (called MADY). First, we propose a hierarchical multi-scale abstraction modeling method to capture the temporal dependencies of the document from multiple hierarchical levels of abstraction, which mimics the process of how human beings comprehend an article by learning fine timescales for low-level abstraction layers and coarse timescales for high-level abstraction layers. By applying this adaptive updating mechanism, the high-level abstraction layers are updated less frequently and expected to remember the long-term dependency better than the low-level abstraction layer. Second, we propose a dynamic key-value memory-augmented attention network to keep track of the attention history and comprehensive context information for the salient facets within the input document. In this way, our model can avoid generating repetitive words and faultiness summaries. Extensive experiments on two widely-used datasets demonstrate the effectiveness of the proposed MADY model in terms of both automatic evaluation and human evaluation. For reproducibility, we submit the code and data at: https://github.com/siat-nlp/MADY.git.
Russia on the Global Artificial Intelligence Scene

Artificial Intelligence (AI) is a very active research area with a number of applications in business, communication, healthcare, etc. AI attracts great attention in Russia and in late 2019 was included in the country strategy till 2030. In this paper we analyze Russia’s position in the space of international scholarly publications on AI. While AI research is interdisciplinary, its core fits the computer science field, where most of the original results are published in conference proceedings and not in journals. Therefore, we consider the list of key AI conferences based on the Australian CORE and Microsoft Academic rankings. We conduct a comparative analysis Russia vs World based on the Scopus data. The metrics used in the analysis allow us to tackle both quantity and quality dimensions. The results of the study are essential for developing measures for the further implementation of the National Strategy for the Development of Artificial Intelligence in Russia and better understanding its position in the international scientific community.
Quantum process discrimination with restricted strategies

The discrimination of quantum processes, including quantum states, channels, and superchannels, is a fundamental topic in quantum information theory. It is often of interest to analyze the optimal performance that can be achieved when discrimination strategies are restricted to a given subset of all strategies allowed by quantum mechanics. In this paper, we present a general formulation of the task of finding the maximum success probability for discriminating quantum processes as a convex optimization problem whose Lagrange dual problem exhibits zero duality gap. The proposed formulation can be applied to any restricted strategy. We also derive necessary and sufficient conditions for an optimal restricted strategy to be optimal within the set of all strategies. We provide a simple example in which the dual problem given by our formulation can be much easier to solve than the original problem. We also show that the optimal performance of each restricted process discrimination problem can be written in terms of a certain robustness measure. This finding has the potential to provide a deeper insight into the discrimination performance of various restricted strategies.
Birds Classification Based on Deep Transfer Learning

At present, there is no huge data set on bird classification, and the classic CUB-200-2011 data set has only 11 788 images, which are still unable to train a generalized classification and recognition model compared with ImageNet and other large data sets with millions of data. Therefore, using deep transfer learning, after tuning for bird recognition, is very valuable with large data sets training model parameters. In this paper, by comparing the training effect of common benchmark models in CUB-200-2011 dataset, ResNeXt model is selected as the transfer learning benchmark model for its well performance. Through optimizing the loss function and reducing the learning rate, the proposed model provides better performance for the data augmentation and adding the full connection layer. Compared with the benchmark model, the recognition rate of the proposed model can reach 84.43%.
Multi-Scale Oriented Basic Image Features Column for Handwritten Digit Recognition

Handwritten digits recognition is a key research problem in the domain of image analysis and pattern recognition. Specifically, the appearance approaches based on feature extraction have been proposed to solve many research issues. This paper presents a novel way of extending the oriented Basic Image Features column (oBIFs column) to multi-scale features. Moreover, this method is very efficient and robust for hand handwritten digit recognition as the changes in size, slant and shape have little effect on the accuracy. The proposed method is carried in two steps: first, we concatenate oBIF image for scale parameter at two or more scales to provide Multi-scale oBIFs. Second, the two variation Multi-Scale oBIFs are crossed to form columns at each location. Finally, a histogram is created from the occurrences of different patterns. The CVL dataset was considered to evaluate our approach, where the digits used where from different fonts, widths, and directions. We managed to achieve a good recognition of unnormalized digits in almost 96% of the cases, which is comparable with the state-of-the-art approaches.
Forensic Analysis of Copy-Move Attack with Robust Duplication Detection

Copy-move is one type of attack to forge a digital image where the attacker duplicates several areas of the image and paste them in different places to conceal a particular object on the original image. After the forgery, advanced methods such as noise addition and blurring, are often performed in the forged image to make it more challenging to recognize the attack. Therefore, it is required to do a preprocessing before conducting the detection. The preprocessing can be eliminated using a copy-move detection that is more resistant to noise addition and blurring. This paper proposes a new, flexible, and robust method that perform forensic analysis of both regular and advanced copy-move using modification and addition from two methods. The first method is designed to identify a regular copy-move attack, while the second one is effective for an advanced attack. The proposed method combines these two methods, can adapt to the forged image condition, and no preprocessing is required.
An Autonomous Galactic Swarm Optimization Algorithm Supported by Hidden Markov Model

In this work we implemented a version of the Galactic Swarm Optimization metaheuristic algorithm tuned by a hidden Markov model. The Galactic Swarm Optimization algorithm is an abstraction of the motion of stars within galaxies on the first level, and galaxies within a cluster of galaxies on the second level. We address the problem of controlling the metaheuristic parameters by identifying the state of the algorithm at each iteration i, using the Hidden Markov Model framework and updating the Galactic Swarm Optimization parameters accordingly. The results obtained show an improvement compared to the original algorithm using the fixed parameters found in the literature. In addition, the results are compared against other algorithms that use different techniques and hybridizations to solve the same problem, showing an improvement in performance with a similar quality for the solutions obtained.
A truss‐based approach for densest homogeneous subgraph mining in node‐attributed graphs

Abstract In a wide range of graph analysis tasks such as community detection and event detection, densest subgraph mining is important and primitive. With the development of social network, densest subgraph mining not only need to consider the structural data but also the attributes information, which descripts the features of nodes or edges. However, there are few researches on densest subgraph mining with attribute description. In this article, we only focus on the node‐attributed graph. According to the properties of structure and attribute in node‐attributed graphs, we define a novel dense subgraph pattern, called hybridized k ‐truss in attribute‐augmented graph. A hybridized k ‐truss is a subgraph that consists of structural nodes and attribute nodes, of which there are at least ( k − 2) common neighbors between any two connected nodes. We introduce the densest hybridized truss problem, and the densest hybridized truss mapping to a densely connected subgraph with homogenous attributes in the original graph. We propose a densest hybridized truss extraction (DHTE) algorithm for node‐attributed graphs, to automatically find the densest subgraph with high density and homogenous attributes at the same time. Extensive experimental results of 21 real world datasets demonstrate the effectiveness and efficiency of DHTE over state‐of‐the‐art methods, through comparison about structural cohesiveness and attributive homogeneity.
Development an Accurate Neural Network for Coin Recognition

This paper presents recognition system for coins of the USSR that is based on a convolutional neural network and database that stores images of coins and information about each coin. In addition, two algorithms for processing the incoming image were investigated, which occurs in four stages: cropping the background of the image, finding the center of the coin by rotating it, reducing the size of the coin image to increase the recognition speed of the neural network and find the maximum number of collinear vectors.
Designing and building an information system for food distributor companies

Abstract Business developments demanded food distributor XYZ Company to change the management of data from manually recording to computerized. Company information data is stored on the computer so that the company can manage data effectively and quickly. The analysis is conducted on the company’s data flow system and is depicted with the Data Flow Diagram. The data object relationships in the company are pointed through the Entity Relationship Diagram. The development of programming applications for companies is done on Windows platforms based on visuals and graphics. Identification of the registration of goods in the company’s old system and presented with images and proposals covering the performance, information, economy, control, efficiency, and service sections of the company’s old systems. The expected result of this research is that companies can obtain a more convenient data recording system, reduce errors in the data recording process. This application is expected to facilitate the reprocessing of sales and purchase data at food distributor companies.
Bradley Conditionals and Dynamic Choice

Abstract One of the main contributions of Richard Bradley’s book is an elegant extension of Jeffrey’s Logic of Decision that countenances the evaluation of conditional prospects. This extension offers a promising new setting in which to model dynamic choice. In Bradley’s framework, plans can be understood as conditionals of an appropriate sort, while dynamic consistency can be viewed as providing a constraint on the evaluation of conditionals across time. In this paper, we study connections between planning conditionals and dynamic consistency.
Didactic Complexity Estimation of the General Physics Course Sections

Improving the effectiveness of teaching physics involves determining the didactic complexity (DС) of various elements of the educational material. The proposed method of key concepts is used, which consists in identifying the most complex concepts in the text, formulas and calculating their average information capacity. To assess the semantic complexity of terms, we take into account the number of words in their definitions, the term's entry into the language picture of the world, and its abstraction degree. The applied methodology for evaluating the physics textbook DС is as follows: 1) for each sub-paragraph write out 10 key terms, knowledge of which is necessary for understanding the text and is an indicator of complete assimilation of the educational material; 2) collect all the selected keywords in a text file and arrange them into several categories of complexity; 3) with the help of a special program Slozhnost.pas accessing the dictionary Slovar.txt, analyze the files and determine the total amount of information contained in key concepts from different paragraphs, and their average information capacity; 4) for each paragraph, estimate the proportion of mathematical and other formulas in relation to the total volume of the text; 5) for each sub-paragraph, write out 5 key terms of maximum complexity used in mathematical formulas; 6) for each paragraph, create files containing keywords included in the formulas; 7) evaluate the complexity of each term and use the complexity program; 8) for each paragraph, determine the total amount of information in keywords and the coefficient of information folding; 9) randomly select sentences from different sections of the textbook, determine the average length of words and the average number of syllables in words, and find an complexity indicator of the sentence structure; 10) for each paragraph, calculate the differential and integral didactic complexity for text and formula information.
Designing a mixed reality interface for autonomous robot-based change detection

Robots, equipped with powerful modern sensors and perception algorithms, have enormous potential to use what they perceive to provide enhanced situational awareness to their human teammates. One such type of information is changes that the robot detects in the environment that have occurred since a previous observation. A major challenge for sharing this information from the robot to the human is the interface. This includes how to properly aggregate change detection data, present it succinctly for the human to interpret, and allow the human to interact with the detected changes, e.g., to label, discard, or even to task the robot to investigate, for the purposes of enhanced situational awareness and decision making. In this work we address this challenge through the design of an augmented reality interface for aggregating, displaying, and interacting with changes detected by an autonomous robot teammate. We believe the outcomes of this work could have significant applications to Soldiers interacting with any type of high-volume, autonomously-generated information in Multi-Domain Operations.
Michman: an Orchestrator to deploy distributed services in cloud environments

Big data storing and processing require specialized distributed services and significant amounts of computing resources. Cloud environments allow one to deploy virtual clusters with the requested characteristics and install the necessary services, but manual configuration is too laborious and inefficient. To use IaaS clouds one should be a highly knowledgeable in the field of system administration. This article presents a Michman tool, which allows on-demand deployment of clusters with a set of services in the cloud IaaS, stores information about deployed systems and provides management of their life cycle. The tool is implemented as a service with a system of user groups, REST API carries access to the system.
Security Implications of IIoT Architectures for Oil &amp; Gas Operations

Abstract Implementing the IIoT paradigm into the classical oil &amp; gas field OT systems is one of the essential concepts for Digital Oilfield 2.0. The transition in architecture and the corresponding technology changes can create a new cyber-physical security risk profile through alterations in the digital information structure of the oilfield OT system. With the onset of IIoT implementations in the industry, it is an opportune time to review and assess the emerging cyber-physical risk landscape. In the paper, we identified and compared the current oilfield OT logical structures with the designs emerging through the IIoT implementations. The analysis includes extensive reviews of developing standards, such as those proposed by Industrial Internet Consortium, and ongoing published experiences to find the primary points of transition. The security risks stemming from the IIoT implementation appear to raise significant concerns with regard to potentially severe cybersecurity outcomes, which could materially impact the integrity and safety of oilfieldoperations. The study concentrated on the cybersecurity threats that could pose negative physical and operational conditions resulting from loss of visibility and / or loss of control of the operational processes in field facilities. Extensive literature reviews were the basis for identifying the implications of cybersecurity risks in the ongoing stages of integrating the IIoT into the field. The reviews identified the modified strategies for cyber-physical systems, including potential threats and counter measurements for the field IIoT model. However, these proposed strategies still miss a fundamental denominator - the assessments generally ignore that it is the fundamental nature of IIoT structure itself that creates cyber-security vulnerabilities. To investigate further, we performed a contrasting analysis based on specific case studies of field IIoT devices such as the pump-off controller and OT architectures. Three foundational threat implications emerged on the transformation of IIoT architecture into the oilfield: 1)The exponential growth of connected distributed artificial intelligence (DAI) devices enormously increases the complexity of designing the software of each facility and system. 2)The cutting-edge Machine to Machine (M2M) characteristic in the IIoT model pushes the human out of the traditional control and monitor loop. 3)The widespread scale of DAI devices with the unique IP address in the network shifts cybersecurity risks to each connected endpoint. The cornerstone of the distinctive IIoT attributes illustrated in the paper contributes to the potential loss of control, leading to potential for serious damaging operational outcomes in the field. The goal of this paper is to aid oilfield security planning and design processes through animproved recognition of the cyber-physical security impacts emerging from the implementation of IIoT architectures and technologies integration into field OT domains.
Temporal Sensitivity and Latency During Teleoperation: Using Track Clearance to Understand Errors in Future Projection

This study investigates the role of individual differences in time perception on task performance during teleoperation with latency.Long distance teleoperation induces latency, causing performance issues for the operator. Previous research demonstrated that individual differences in time perception predicted performance on a similar task, having participants navigate a radio controlled (RC) car around a track. This work extends the relationship into routes of varying course width to test whether differences in time perception predict movement over-/underestimation.Participants completed a time estimation task and a route navigation task while experiencing latency. In the time estimation task, participants estimated the duration of multiple visual stimuli (2 s or less). In the route navigation task, participants moved a virtual cube across a route. Each trial varied in the amount of latency and the amount of horizontal clearance in the track (4-10 m for a 1.2-m-long/wide cube).The results showed fairly consistent latency by time estimation and latency by clearance interaction effects on a wide set of trial-level variables, such as completion time, and action-level performance variables, such as time spent moving per move event. However, the results were not consistently in the predicted direction.Results suggest that clearance and timing affect performance across latency, at both the overall level (i.e., trial completion time) and at the action level (time spent moving). An open question remains as to how these contextual factors affect movement strategy selection.