Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiobjective memetic algorithm for vital nodes identification in complex networks

Vital nodes identification, that is, finding a set of nodes whose absence would cause a collapse of the network, is a significant project in network science. Despite there are a plenty of methods to identify the vital nodes, two major problems still need to be solved, that is how to select these nodes and how to determine the number of them. In this study, we focus on dealing with these two problems via proposing a multiobjective memetic algorithm for vital nodes identification task. First, vital nodes identification task is modeled as a biobjective optimization problem by analyzing the characteristic of vital nodes. Then, a memetic strategy and specific evolutionary operators inspired by multiple centralities are designed to execute local and global search. In addition, a long-tail property is found from the Pareto front of this bi-objective optimization problem, and the simulation results always show an obvious knee region. Hence, an adaptive learning method to determine the size of vital nodes is designed by searching for the knee point. At last, the proposed framework is tested on the scale free networks and real-life networks, and the simulation results validate its effectivity in contrast with the state-of-art greedy methods.
An All-Flexible Sensing Sole For Legged Robots

Recently, major efforts are made to employ robotic devices and especially also legged robots in industrial environments. Such robots can take over dull, dreary and dangerous tasks from humans. Development of legged robots suitable for industrial operation will be a major driver of industry 5.0. In this work, we present the fabrication and evaluation of the concept of an all-flexible sole for legged robots. The sole is made from a variant of Polysiloxane with integrated liquid electrodes. The electrodes are produced by dispensing Carbonloaded Poly(dimethylsiloxane) into cavities at certain areas of the sole. Through the employed capacitive sensing principle, it is possible to retrieve proximity (and also pressure) information from the environment, in this case, especially the ground or pavement structure. Through the employed materials and their composition, the sole is robust to mechanical and thermal stresses and wear. At the same time it still conforms to uneven and unstructured terrain.
A High Speed Low Power Pipelined SAR Analog to Digital Converter

By combining the advantages of the high speed and high resolution pipelined ADC and the low power SAR ADC, the two stages pipelined SAR ADC is proposed. It mainly reduces the power hungry operation amplifier by removing the front-end sample-and-hold circuit of the capacitor array in SAR ADCs and the sampling switch. Hence, the whole circuit only requires one operational amplifier which is used in the MDAC circuit. The capacitor arrays used in the SAR ADC adopt the monotonic switching procedure to achieve a better energy efficiency and a higher operation speed. An additional comparator for the MSB of the proposed ADC is designed for the ADC using in the sample phasing. It can enhance the sampling rate and can relax the design difficulty of the operation amplifier in the MDAC. The simulated results show that the proposed pipelined SAR ADC can be operated at 80MS/s with 11.17 ENOB and is implemented in TSMC 0.18um process and 1.8V supply voltage.
Power Consumption and Delay in Wired Parts of Fog Computing Networks

In the last decade Cloud computing has seen a surge of popularity. Clouds, with their scale and high functionality, are used to outsource various infrastructure, platform, and software services. However, relaying solely on distant Cloud Data Centers (DCs) can be inefficient for many applications concerning mobile devices and Internet of Things (IoT) in general. A more decentralized Fog computing paradigm has been proposed to augment Cloud availability and execution. This work addresses latency and power consumption in Fog computing networks. Models for power consumption and delay are proposed. Performance of Fog computing is estimated using parameters setting based on real-world equipment and traffic. Our results tackle the balance between Fog and Cloud. Applications requiring heavy computations (relative to size of offloaded data) are best served by Cloud DCs, while it is faster (and more power-efficient) to compute “lighter” requests in the Fog Nodes (FNs). However, where is the trade-off between power consumption and delay in the context of Fog and Cloud? We answer this question modeling multiple architectures and using various network scenarios.
The Solution of Key Transmission in Multi-level Network Steganography

In recent years, multi-level steganography has become a new research direction in the network steganography. In order to ensure transmitting the key and the ciphertext covertly in multi-level steganography, a solution of key transmission is proposed in this paper. The proposed solution consists of 2 methods. The upper-level method is used to transmit the ciphertext. The lower-level is used to deliver the key covertly. The experimental results showed that this solution can realize synchronous transmission of the ciphertext and the key, which has a high steganographic bandwidth and a low steganographic cost, improving the security of network steganography.
Detecting Malicious Android Apps using the Popularity and Relations of APIs

Accurate malware detection is important to protect Android users against the growing number of sophisticated malwares. In this paper, we propose a simple but efficient malware detection methodology that identifies the subset of Android APIs as classification features. Since each app needs to use a set of Android APIs to fulfill its main objective, the list of APIs used in an app represents the app’s characteristic. Our methodology constructs two ranked lists of Android APIs, namely benign_API_list and malicious_API_list. The benign_API_list contains the most commonly invoked APIs among benign apps and the malicious_API_list contains the most commonly invoked APIs among malicious apps. Then, for a given suspicious app, we compute the sum of inverse values of the rankings of the used benign APIs and also the sum of inverse value s of the ranking of the used malicious APIs. We determine whether the app is benign or malicious by comparing the two sums. More specifically, if the sum of inverse values based on benign apps is larger than the one based on malicious apps, we determine that the given app is benign. Otherwise, we determine that the given app is malicious. Our experimental evaluation shows that the proposed methodology achieves an accuracy of 87.35%~89.93% for Android malware detection. The proposed method can be possibly utilized in features selections in machine learning-based malware detection algorithms.
Multi-Session Mapping for Indoor Substation Environment Using a Head-Mounted RGB-D Sensor

Three-dimensional model of environment combined with workflow information can provide intuitive and effective guidance for wearable-assisted maintenance and inspection in substations. Incremental mapping and sub-map stitching are essential for building a large-scale substation environment. In this paper, a multi-session mapping method using head-mounted RGB-D sensor is introduced for simultaneous localization and mapping (SLAM) in unknown substation environment. A hierarchical map structure is designed to store the sub-map models of the environment and equipment information, which allows further hierarchical query and visual display. Transformations among sub-maps are calculated based on cross-sub-map loop closure detection and ICP algorithm, followed by sub-maps alignment and stitching. A wearable visual SLAM system is developed, which integrates dense-mapping of electrical equipment as well as semi-dense mapping of large-scale substation environment. The result of experiment verifies the effectiveness and practicability of the proposed method.
QPPDs: Querying Property Paths Over Distributed RDF Datasets

A key property of linked data, i.e., the web-based representation and publication of data as interconnected labeled graphs, is that it enables querying and navigating through datasets distributed across the network. SPARQL1.1, the current standard query language for RDF-based linked data, defines a construct—called property paths (PP)—to navigate between the entities of a graph. This is potentially very useful in a number of use cases, e.g., in the biomedical domain, where large datasets are available as linked data graphs. However, the use of PP in SPARQL 1.1. is possible only on a single local graph, requiring us to merge all distributed datasets into one large, centrally stored graph, therefore reducing the value of using linked data in the first place. We propose an index-based approach—called QPPDs—for answering queries for paths distributed across multiple, distributed datasets. We provide a heuristic-based source selection mechanism to select the relevant datasets (also called data sources) for a given path query, and a technique that federates queries to selected sources, and assembles (merges) the paths (i.e., partial or complete) retrieved from those remote datasets. We demonstrate our approach on a genomics use-case, where the description of biological entities (e.g., genes, diseases, and drugs) is scattered across multiple datasets. In our preliminary investigation, we evaluate the QPPDs approach with real-world path queries—on biological data that are very heterogeneous in nature—in terms of performance (overall path retrieval time) and result completeness, i.e., the number of paths retrieved.
Integrated financial data stream mining based on memory curve

Abstract Integrated financial data stream mining is an important method to study financial data stream with the sample drift. In view of the defects of the traditional integrated financial data stream mining, human memory curve is introduced into the financial data stream mining, and the memorizing based financial data stream mining (MFSM) is proposed. The model regards the base classifier as the knowledge gained by the system, through the “Memory” mechanism, it not only makes the base classifier useful in history be kept in the “Memory” attributing to the strong memory capacity, and improve the stability of the prediction, but also select the base classifier with good classification effect from the “Memory bank” to participate in the integrated prediction, so as to improve the adaptability to the sample variation. Based on the MFSM model, a memorizing based integrated financial data stream mining (MISM) is put forward, which makes use of the Ebbinghaus memory curve to design the memory curve of the system, and utilizes the selective integration to simulate human’s “Memory” mechanism. After compared with 4 typical financial data stream mining algorithms, the results show that: The MISM algorithm has high classification accuracy and strong overall adaptability to the sample drift, especially for the sample drift that ocurrs repeated and the complex sample drift existing in the practical application. It not only can adapt to the new sample variations quickly, but also can effectively resist the impact of the random sample fluctuations on the system performance.
Dynamic Power Sharing and Autonomous Voltage Regulation in Islanded DC Microgrids

This paper proposes a coordinated control strategy for islanded microgrids (MGs) including bus voltage compensation and dynamic power sharing. The droop control is used to realize power sharing among the distributed generations (DGs) in the primary control layer. On this basis, a distributed secondary control with voltage regulation control and current correction control is proposed. This control strategy compensates the DC bus voltage drop caused by the traditional droop control, thus restoring the bus voltage to the rated value. Moreover, the droop coefficient is designed to be adjusted constantly, which achieves high precision of power sharing. The simulations under different operation modes are conducted to validate the performance of the proposed method, where the results demonstrate its effectiveness.
A Surrogate-Assisted Cooperative Co-evolutionary Algorithm Using Recursive Differential Grouping as Decomposition Strategy

Cooperative co-evolutionary algorithms, especially those able to uncover interaction structure between variables, have a great potential in optimizing large-scale problems. Nevertheless, they are expensive in terms of number of function evaluations and this issue can be quite problematic when dealing with computationally expensive optimization problems. An effective approach to deal with such problems lies in the exploitation of surrogate models. The latter ones work as cheap-to-evaluate alternatives to the expensive function reducing the computational cost, while still providing improved designs. This process, called surrogate-assisted optimization, is very effective on small-dimensional problems but is not suitable to solve large-scale problems due to the curse of dimensionality. In this paper, a new algorithm, taking benefit from cooperative coevolution and surrogate models, is introduced to efficiently solve high-dimensional, expensive and black-box problems. The proposed algorithm uses recursive differential grouping to perform an accurate problem decomposition. Experimental results are provided on a set of 1000-dimensional problems and show promising results.
Vulnerabilities of Line Current Differential Relays to Cyber-Attacks

Being fast and highly reliable under different conditions, line current differential relays (LCDRs) are high-end protective devices that are deployed to protect DC and critical AC lines. LCDRs, however, are vulnerable to cyber attacks, since this type of relay is dependent on communication infrastructure and the global positioning system (GPS). This susceptibility may enable attackers to fool LCDRs into issuing unwarranted trip signals, and potentially to create an instability if several attacks are coordinated. Through case-studies, this paper shows how coordinated attacks against LCDRs in DC and AC networks can create a voltage collapse in the system. The case-studies are carried on the IEEE 14-bus AC network, as well as on a DC microgrid.
Prediction of Hour of Coincident Daily Peak Load

To manage the power system well, it is important to keep the balance between the electricity supply and its demand. For many utilities, consumers need to pay additional money for the electricity during the peak time on days with the highest demands – coincident peak pricing. Therefore, if the consumers are informed the daily load peak a day or hours in advance, such surcharges may be avoided. The accurate forecasting of load peak time not only helps to provide a reliable electricity supply, but also be useful to reduce the cost of electricity for consumers. In this paper, we study the historical data and then build the classification models for winter and summer to predict the time of daily peaks 24 hours ahead. Several classification algorithms, including Naïve Bayes, SVM, Random Forest, AdaBoost, CNN, LSTM, Stacked Autoencoder, are applied to solve this problem. Finally, the performance of these methods have been examined and compared with LSTM having the best overall accuracy, precision, and recall.
TRICKS—Time TRIggered Covert Key Sharing for Controller Area Networks

There are dozens of proposals for securing the controller area network (CAN); however, only a few of them are concerned on how to share secret keys between CAN nodes. Recently, some works have used the non-destructive property of CAN arbitration in order to exchange a secret key and achieve information theoretic security for the key exchange. In our proposals, we exploit both delays and the non-destructive arbitration of CAN to achieve a secure key exchange. While our approach is less efficient when it comes to bandwidth, we do not require any kind of additional hardware and we build our implementation on the software layer which is accessible for any CAN-based application. To boost efficiency, we finally bootstrap secret keys by means of the guessing-resilient protocols, such as encrypted-key-exchange (EKE) and simple password exponential key exchange (SPEKE). In principle, a few dozen frames suffice for a secure key-exchange between two CAN nodes. We discuss several protocol versions and extensions for the case of more than two parties. We also present the experimental results on modern automotive-grade controllers to prove the performance of our solution.
Memetic Multi-agent Optimization in High Dimensions using Random Embeddings

In this paper, we propose a memetic multi-agent optimization (MeMAO) paradigm to enhance the search efficacy of classical EAs (i.e., Differential Evolution (DE)) in solving the complex optimization problems. The essential backbone of MeMAO is a recently proposed memetic multi-agent learning system wherein agents acquire increasing learning capabilities by interacting with the environment mainly in a reinforcement learning manner. Differing from MeMAS, the particular interest of MeMAO is placed on addressing the specific challenges when applying classical EAs to optimize the high dimensional optimization problems with a "low effective dimensionality". To achieve this, the target optimization problem is firstly re-formulated into multiple low dimensional tasks via random embedding methods. Further, MeMAO employs DE as the fundamental populationbased evolutionary solver for multiple agents to optimize multiple low dimensional tasks in a multi-agent scenario. Importantly, MeMAO constructs the social interaction mechanisms among multiple agents, hence improves their convergence speed for solving the target optimization problem by sharing the beneficial information across multiple agents. Lastly, to testify the efficacy of the proposed MeMAO, comprehensive empirical studies on 8 synthetic optimization problems with a dimensionality of 2,000 are provided.
10Gbps Length adaptive on-chip RF serial link for Network on Chips and Multiprocessor chips applications

This paper introduces the use of duo-binary modulation for a 10-Gbps RF on-chip long range serial link. RF signaling is used to reduce the delay so that a 5 mm line is crossed in less than 50 ps. Furthermore, the duo-binary approach is used to reach higher bandwidth efficiency since this modulation uses a two-time smaller bandwidth for the transmission. The RF design complexity is also reduced thanks to duo-binary modulation.
A Survey of Virtual Machine Management in Edge Computing

Many edge computing systems rely on virtual machines (VMs) to deliver their services. It is challenging, however, to deploy the virtualization mechanisms on edge computing hardware infrastructures. In this paper, we introduce the engineering and research trends of achieving efficient VM management in edge computing. We elaborate on: 1) the virtualization frameworks for edge computing developed in both the industry and the academia; 2) the virtualization techniques tailored for edge computing; 3) the placement and scheduling algorithms optimized for edge computing; and 4) the research problems in security related to virtualization of edge computing.
A Provably Secure Anonymous Biometrics-Based Authentication Scheme for Wireless Sensor Networks Using Chaotic Map

The rapid growth of wireless sensor networks (WSNs) has opened new doors to realize remote monitoring in various areas. However, designing authentication protocol for resource-constrained WSNs is a challenging task. Recently, Aghili et al. introduced an efficient three-factor authentication scheme for WSNs using hash function, and argued that the scheme is immune to known attacks. However, we discover that their scheme suffers from a few serious weaknesses, such as session key disclosure attack, desynchronization attack, sensor node impersonation attack, and session-specific temporary information attack, and does not provide forward secrecy. In order to overcome the deficiency of this scheme, we propose an enhanced biometric-based authentication scheme for WSNs using Chebyshev chaotic map. We demonstrate that the proposed scheme is provably secure in the random oracle model. Furthermore, Burrows–Abadi–Needham logic analysis shows that the proposed scheme achieves mutual authentication and session key agreement. The informal analysis demonstrates that the proposed scheme is resistant to various attacks and has desirable attributes, such as forward secrecy and three-factor secrecy. Finally, the security and performance comparison show that the proposed scheme is more practical.
Design and Implementation of a Concurrency Benchmark Tool for Cloud Storage Systems

File sharing and collaboration is a common functionality in cloud storage systems, which allow multiple users to concurrently manipulate files in shared folders or workspaces. However, in the face of concurrency, existing cloud storage systems may deliver unexpected results to end-users. The goal of our research is to reveal the results delivered (but never explicitly specified) by the existing cloud storage systems in concurrent environments and make these results publically accessible, which could benefit both cloud storage end-users and service providers. To reach this research goal, we developed an automated benchmark tool, which allows people to freely test the four major cloud storage systems, including Dropbox, Box, Mircosoft OneDrive, and Google Drive, with well-designed concurrency test cases, and access the testing results, at anytime from anywhere. In this paper, we detail the design and implementation of the tool and present technical solutions to the issues in implementing the tool and report part of benchmarking results.
Volt-Var Optimization of Distribution Systems for Coordinating Utility Voltage Control with Smart Inverters

The Volt-Var control of smart inverters has drawn wide attention as one of the solutions to the voltage regulation of distribution systems with high PV penetration. Unlike utility voltage control devices, such as on-load tap changers and switched capacitors, smart inverters can provide fast and continuous voltage regulation. This paper describes a new Volt-Var optimization scheme that coordinates the utility control of switched capacitors and on-load tap changers with Volt-Var droop control of smart PV inverters. A voltage-load sensitivity matrix is used to form a linear relationship between control variables and bus voltages. A successive linear programming technique is applied to transform the original mixed-integer nonconvex programing problem into a sequence of mixed-integer linear programming problems. The proposed Volt-Var optimization model is validated on a 47-bus distribution circuit and the results show that the method is effective in improving voltage regulation as well as reducing the operations of on-load tap changers.
STAHL: A Novel Scan-Test-Aware Hardened Latch Design

As modern technology nodes become more susceptible to soft errors, many radiation hardened latch designs have been proposed. However, redundant circuitry used to tolerate soft errors in such hardened latches also reduces the test coverage of cell-internal manufacturing defects. To avoid potential test escapes that lead to soft error vulner-ability and reliability issues, this paper proposes a novel Scan-Test-Aware Hardened Latch (STAHL). Simulation results show that STAHL has superior defect coverage compared to previous hardened latches while maintaining full radiation hardening in function mode.