Reviewerly/exHarmony huggingface dataset:
Optimization of Action Recognition Model Based on Multi-Task Learning and Boundary Gradient

Recently, people’s demand for action recognition has extended from the initial high classification accuracy to the high accuracy of the temporal action detection. It is challenging to meet the two requirements simultaneously. The key to behavior recognition lies in the quantity and quality of the extracted features. In this paper, a two-stream convolutional network is used. A three-dimensional convolutional neural network (3D-CNN) is used to extract spatiotemporal features from the consecutive frames. A two-dimensional convolutional neural network (2D-CNN) is used to extract spatial features from the key-frames. The integration of the two networks is excellent for improving the model’s accuracy and can complete the task of distinguishing the start–stop frame. In this paper, a multi-scale feature extraction method is presented to extract more abundant feature information. At the same time, a multi-task learning model is introduced. It can further improve the accuracy of classification via sharing the data between multiple tasks. The experimental result shows that the accuracy of the modified model is improved by 10%. Meanwhile, we propose the confidence gradient, which can optimize the distinguishing method of the start–stop frame to improve the temporal action detection accuracy. The experimental result shows that the accuracy has been enhanced by 11%.
Influence of Organizational Factors and Forms of Accounting Outsourcing on Enterprise Cybersecurity

Advances in computer communications and shift to a digital economy necessitate the outsourcing of accounting operations, that is delegation of accounting functions to third parties. It is proved that introduction of accounting outsourcing leads to emergence of significant cyber risks to the operation of the economic entity. It is substantiated that when choosing an optimal form of outsourcing, enterprise management should be focused on the organizational features of accounting foremost. The influence of organizational accounting factors on the cybersecurity of the enterprise in the context of outsourcing and its various forms is determined. The methodology for assessing potential cyber risks of accounting outsourcing is developed in terms of delegating accounting functions to a specialized outsourcing firm, an independent accounting expert, an embedded accountant-outstaffer, or a cloud information processing service. The proposals can be used beneficially during the selection of organizational forms of outsourcing in order to ensure enterprise cybersecurity.
Security index based on perfectly undetectable attacks: Graph-theoretic conditions

The notion of security index quantifies the least effort involved in conducting perfectly undetectable attacks. Thus, the security index enables a systems operator to assess the vulnerability of a component, informs sensor placement strategies, and helps in deciding the feasibility of secure estimators and fault detectors. In this paper, we investigate the (possible) variation in this index as a consequence of variation in the system parameters. To this end, we adopt a structured systems approach, typically represented by a directed graph, with the edges of the said graph being in one-to-one correspondence with the system parameters. We first show that the security index is generic. That is, for almost all choices of edge weights, the security index of a component remains the same. We refer to such an index as the generic security index. Secondly, we derive graph-theoretic conditions (and based on those an algorithm) for computing the generic security index. Third, we provide graph-theoretic conditions for computing lower (resp. upper) bounds on the values that the security index of a component can take for all nonzero choices of the edge weights of the directed graph. Finally, we provide a brute force search method for calculating the said bounds.
SeLFVi: Self-supervised Light-Field Video Reconstruction from Stereo Video

Light-field imaging is appealing to the mobile devices market because of its capability for intuitive post-capture processing. Acquiring light field (LF) data with high angular, spatial and temporal resolution poses significant challenges, especially with space constraints preventing bulky optics. At the same time, stereo video capture, now available on many consumer devices, can be interpreted as a sparse LF-capture. We explore the application of small baseline stereo videos for reconstructing high fidelity LF videos.We propose a self-supervised learning-based algorithm for LF video reconstruction from stereo video. The self- supervised LF video reconstruction is guided via the geometric information from the individual stereo pairs and the temporal information from the video sequence. LF estimation is further regularized by a low-rank constraint based on layered LF displays. The proposed self-supervised algorithm facilitates advantages such as post-training finetuning on test sequences and variable angular view interpolation and extrapolation. Quantitatively the reconstructed LF videos show higher fidelity than previously proposed unsupervised approaches. We demonstrate our results via LF videos generated from publicly available stereo videos acquired from commercially available stereoscopic cameras. Finally, we demonstrate that our reconstructed LF videos allow applications such as post-capture focus control and region-of-interest (RoI) based focus tracking for videos.
Empirical Analysis of Phrase-Based Statistical Machine Translation System for English to <i>Hindi</i> Language

Hindi is the national language of India. However, most of the Government records, resolutions, news, etc. are documented in English which remote villagers may not understand. This fact motivates to develop an automatic language translation system from English to Hindi. Machine translation is the process of translating a text in one natural language into another natural language using computer system. Grammatical structure of Hindi language is very much complex than English language. The structural difference between English and Hindi language makes it difficult to achieve good quality translation results. In this paper, phrase-based statistical machine translation approach (PBSMT) is used for translation. Translation, reordering and language model are main working components of a PBSMT system. This paper evaluates the impact of various combinations of these PBSMT system parameters on automated English to Hindi language translation quality. Freely available n-gram-based BLEU metric and TER metric are used for evaluating the results.
PATHATTACK: Attacking Shortest Paths in Complex Networks

Shortest paths in complex networks play key roles in many applications. Examples include routing packets in a computer network, routing traffic on a transportation network, and inferring semantic distances between concepts on the World Wide Web. An adversary with the capability to perturb the graph might make the shortest path between two nodes route traffic through advantageous portions of the graph (e.g., a toll road he owns). In this paper, we introduce the Force Path Cut problem, in which there is a specific route the adversary wants to promote by removing a low-cost set of edges in the graph. We show that Force Path Cut is NP-complete. It can be recast as an instance of the Weighted Set Cover problem, enabling the use of approximation algorithms. The size of the universe for the set cover problem is potentially factorial in the number of nodes. To overcome this hurdle, we propose the PATHATTACK algorithm, which via constraint generation considers only a small subset of paths—at most 5% of the number of edges in 99% of our experiments. Across a diverse set of synthetic and real networks, the linear programming formulation of Weighted Set Cover yields the optimal solution in over 98% of cases. We also demonstrate running time vs. cost tradeoff using two approximation algorithms and greedy baseline methods. This work expands the area of adversarial graph mining beyond recent work on node classification and embedding.
Differential Fault Attack on Lightweight Block Cipher PIPO

With the recent development of Internet of Things (IoT) devices, related security issues are also increasing. In particular, the possibility of accessing and hijacking cryptographic devices is also increasing due to the rapid increase in usage of these devices. Therefore, research on cryptographic technologies that can provide a safe environment even in resource-constrained environments has been actively conducted. Among them, there are increasing security issues of side-channel analysis for devices due to their physical accessibility. The lightweight block cipher PIPO was recently proposed in ICISC 2020 to address these issues. The PIPO has the characteristic of providing robust security strength while having less overhead when using the side-channel analysis countermeasures. A differential fault attack is a type of side-channel analysis that induces fault in cryptographic operations and utilizes difference information that occurs. Differential fault attacks on the PIPO have not yet been studied. This paper proposed a single-bit flip-based differential fault attack on the lightweight block cipher PIPO for the first time. We show that simulations enable the recovery of the correct secret key with about 98% probability through 64 fault ciphertexts. Therefore, the PIPO does not provide security against differential fault attacks. When using the PIPO cipher on IoT devices, designers must apply appropriate countermeasures against fault-injection attacks.
Risk-Aware Optimal Control for Automated Overtaking With Safety Guarantees

This article proposes a solution to the overtaking control problem where an automated vehicle tries to overtake another vehicle with uncertain motion. Our solution allows the automated vehicle to robustly overtake a human-driven vehicle under certain assumptions. Uncertainty in the predicted motion makes the automated overtaking problem hard to solve due to feasibility issues that arise from the fact that the overtaken vehicle (e.g., a vehicle driven by an aggressive driver) may accelerate to prevent the overtaking maneuver. To counteract them, we introduce the weak assumption that the predicted velocity of the overtaken vehicle respects a supermartingale, meaning that its velocity is not increasing in expectation during the maneuver. We show that this formulation presents a natural notion of risk. Based on the martingale assumption, we perform a risk-aware reachability analysis by analytically characterizing the predicted collision probability. Then, we design a risk-aware optimal overtaking algorithm with guaranteed levels of collision avoidance. Finally, we illustrate the effectiveness of the proposed algorithm with a simulated example.
Learning Robot Exploration Strategy With 4D Point-Clouds-Like Information as Observations

Being able to explore unknown environments is a requirement for fully autonomous robots. Many learning-based methods have been proposed to learn an exploration strategy. In the frontier-based exploration, learning algorithms tend to learn the optimal or near-optimal frontier to explore. Most of these methods represent the environments as fixed size images and take these as inputs to neural networks. However, the size of environments is usually unknown, which makes these methods fail to generalize to real world scenarios. To address this issue, we present a novel state representation method based on 4D point-clouds-like information, including the locations, frontier, and distance information. We also design a neural network that can process these 4D point-clouds-like information and generate the estimated value for each frontier. Then this neural network is trained using the typical reinforcement learning framework. We test the performance of our proposed method by comparing it with other five methods and test its scalability on maps that are much larger than maps in the training set. The experiment results demonstrate that our proposed method needs shorter average traveling distances to explore whole environments and can be adopted in maps with arbitrarily sizes.
Towards Compact Modeling of Noisy Quantum Computers: A Molecular-Spin-Qubit Case of Study

Classical simulation of Noisy Intermediate Scale Quantum computers is a crucial task for testing the expected performance of real hardware. The standard approach, based on solving Schrödinger and Lindblad equations, is demanding when scaling the number of qubits in terms of both execution time and memory. In this article, attempts in defining compact models for the simulation of quantum hardware are proposed, ensuring results close to those obtained with standard formalism. Molecular Nuclear Magnetic Resonance quantum hardware is the target technology, where three non-ideality phenomena—common to other quantum technologies—are taken into account: decoherence, off-resonance qubit evolution, and undesired qubit-qubit residual interaction. A model for each non-ideality phenomenon is embedded into a MATLAB simulation infrastructure of noisy quantum computers. The accuracy of the models is tested on a benchmark of quantum circuits, in the expected operating ranges of quantum hardware. The corresponding outcomes are compared with those obtained via numeric integration of the Schrödinger equation and the Qiskit’s QASMSimulator. The achieved results give evidence that this work is a step forward towards the definition of compact models able to provide fast results close to those obtained with the traditional physical simulation strategies, thus paving the way for their integration into a classical simulator of quantum computers.
Spatiotemporal estimation of photovoltaic system adopters using fuzzy logic

A considerable amount of governmental agencies have sought to improve their inhabitants' quality of life in recent years, encouraging new sustainable technologies. The characterization of how these new technologies are disseminated in urban areas can help planners carry out campaigns or incentive policies that help achieve decarbonization goals. This paper presents a methodology based on fuzzy logic to characterize the spatiotemporal dynamics as to the adopters of photovoltaic systems in residential subareas. This characterization model proposes to demonstrate the influence that the first inhabitants, who adopted the technology of photovoltaic systems, can exert on the inhabitants of adjacent sub-areas. Considering that the degree of adherence to photovoltaic technology is related to subjective characteristics, the proposal uses three linguistic variables: low, medium, and high to model such adherence using fuzzy sets. The proposed methodology results present heat maps that identify the subareas with more inhabitants adopting photovoltaic systems. Hence, it has been applied in a medium-sized city in the state of São Paulo, Brazil. The obtained spatial distribution of photovoltaic adopters is heterogeneous, with subareas presenting medium and high values of photovoltaic adopters for each study year. The percentages of categorized subareas as high in the years 2017, 2018, 2019 are 8,3%, 19,3%, 19,7%, respectively. These results indicate that planners should promote more PV technology, especially in the city's outskirt subareas. A spatiotemporal estimation method and the history of recording installations of photovoltaic systems are used for comparison purposes. The comparison of results shows that the proposed method better characterizes the PV diffusion installed in the first years. This class of results can help planners better assess the diffusion goals of photovoltaic systems in urban areas, which are being used in the integrated planning of sustainable cities and provide information for planners to reach decarbonization goals.
A Source-to-Target Constraint Rewriting for Direct Mapping

Most of the existing structured digital information today is still stored in relational databases. That’s why it is important for the Semantic Web effort to expose the information in relational databases as RDF, or allow to query it using SPARQL. Direct mapping is a fully automated approach for converting well-structured relational data to RDF that does not require formulating explicit mapping rules [, ]. Along with the mapped RDF data, it is desirable to have a description of that data. Previous work [, ] has attempted to describe the RDF graph in terms of OWL axioms, which is problematic, partly due to the open world semantics of OWL. We start from the direct mapping suggested by Sequeda et al. [], which integrates and extends the functionalities of proposal [] and the W3C recommendation [], and present a source-to-target semantics preserving rewriting of constraints in an SQL database schema to equivalent SHACL [] constraints on the RDF graph. We thus provide a SHACL description of the RDF data generated by the direct mapping without the need to perform a costly validation of those constraints on the generated data. Following the approach of [], we define the rewriting from SQL constraints to SHACL by a set of Datalog rules. We prove that our source-to-target rewriting of constraints is constraint preserving and weakly semantics preserving.
A Homomorphic Encryption-based Adaptive Image Filter Using Division Over Encrypted Data

Homomorphic encryption (HE) is an important cryptographic technique that allows one to directly perform computation on encrypted data without decryption. In HE-based applications using digital images, a user often encrypts a private image captured on a local device. This image can contain noise that negatively affects the results of HE-based applications. To solve this problem, this paper proposes an HE-based adaptive image filter. For small-sized encrypted input data, pixels that have no dependency when sliding a window are encoded into the same ciphertext. For division in the adaptive filter, which is not supported by conventional HE schemes, a numerical approach is adopted. To the best of the authors’ knowledge, this paper is the first work that applies division over encrypted data to an image processing algorithm. We implemented the proposed HE-based adaptive filter as a proof-of-concept client-server model. The proposed design can address important privacy issues in image processing applications in internet-of-things and cyber-physical systems, where many devices are connected through a vulnerable network.
Optimization methods for deep neural networks

This paper reviews the optimization methods for the training of deep neural networks, particularly with complex problems. The primary goal of the optimizer is to speed up the training and helps to boost the efficiency of the model. Optimization methods are the engines underlying deep neural networks that enable them to learn from data. This review paper covers the fundamentals of gradient-based optimization methods and their application to training neural networks. The major topics reviewed in this paper are gradient descent, momentum methods, 2nd order methods, and stochastic methods. The strengths of the optimization methods and their shortcomings, potential research directions, and open topics are being addressed and highlighted.
Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining

The solution space of a frequent itemset generally presents exponential explosive growth because of the high-dimensional attributes of big data. However, the premise of the big data association rule analysis is to mine the frequent itemset in high-dimensional transaction sets. Traditional and classical algorithms such as the Apriori and FP-Growth algorithms, as well as their derivative algorithms, are unacceptable in practical big data analysis in an explosive solution space because of their huge consumption of storage space and running time. A multi-objective optimization algorithm was proposed to mine the frequent itemset of high-dimensional data. First, all frequent 2-itemsets were generated by scanning transaction sets based on which new items were added in as the objects of population evolution. Algorithms aim to search for the maximal frequent itemset to gather more non-void subsets because non-void subsets of frequent itemsets are all properties of frequent itemsets. During the operation of algorithms, lethal gene fragments in individuals were recorded and eliminated so that individuals may resurge. Finally, the set of the Pareto optimal solution of the frequent itemset was gained. All non-void subsets of these solutions were frequent itemsets, and all supersets are non-frequent itemsets. Finally, the practicability and validity of the proposed algorithm in big data were proven by experiments.
Global Heisenberg scaling in noisy and practical phase estimation

Heisenberg scaling characterizes the ultimate precision of parameter estimation enabled by quantum mechanics, which represents an important quantum advantage of both theoretical and technological interest. Here, we study the attainability of strong, global notions of Heisenberg scaling in the fundamental problem of phase estimation, from a practical standpoint. A main message of this work is an asymptotic noise "threshold" for global Heisenberg scaling. We first demonstrate that Heisenberg scaling is fragile to noises in the sense that it cannot be achieved in the presence of phase damping noise with strength above a stringent scaling in the system size. Nevertheless, we show that when the noise does not exceed this threshold, the global Heisenberg scaling in terms of limiting distribution (which we highlight as a practically important figure of merit) as well as average error can indeed be achieved. Furthermore, we provide a practical adaptive protocol using one qubit only, which achieves global Heisenberg scaling in terms of limiting distribution under such noise.
Identification of Private ICS Protocols Based on Raw Traffic

With the development of the Industrial Internet in recent years, security issues have been a hot topic of the industrial control system (ICS) network management. Identifying the protocol traffic in the communication process of the ICS is an important prerequisite to avoid security problems, especially in ICSs that use many private protocols. The private protocols cannot be analyzed due to the unknown internal structure of the protocols, which makes the ICS protocol identification work more difficult. However, the Internet-oriented protocol identification method is not applicable to the scenario of the private ICS protocols network environment. With this problem in mind, this paper proposes a method of ICS protocol identification based on the raw traffic payload. The method firstly performs data preprocessing such as data selection, interception, cleaning conversion, and labeling on the raw traffic of the protocol based on the characteristics of the industrial control protocol. Then it uses an AM-1DCNN + LSTM deep learning model to extract temporal and spatial features of the ICS raw traffic, and performs protocol identification. This method can effectively extract ICS protocol features in scenarios where protocol parsing is impossible compared with existing methods. We constructed a dataset for ICS protocol identification based on open-source data and tested the proposed method for experiments, and the identification accuracy rate reached 93%.