Reviewerly/exHarmony huggingface dataset:
Runtime Analysis of the $$(\mu + 1)$$-EA on the Dynamic BinVal Function

We study evolutionary algorithms in a dynamic setting, where for each generation a different fitness function is chosen, and selection is performed with respect to the current fitness function. Specifically, we consider Dynamic BinVal, in which the fitness functions for each generation is given by the linear function BinVal, but in each generation the order of bits is randomly permuted. For the $$(1 + 1)$$ -EA it was known that there is an efficiency threshold $$c_0$$ for the mutation parameter, at which the runtime switches from quasilinear to exponential. Previous empirical evidence suggested that for larger population size $$\mu $$ , the threshold may increase. We prove rigorously that this is at least the case in an $$\varepsilon $$ -neighborhood around the optimum: the threshold of the $$(\mu + 1)$$ -EA becomes arbitrarily large if the $$\mu $$ is chosen large enough. However, the most surprising result is obtained by a second order analysis for $$\mu =2$$ : the threshold increases with increasing proximity to the optimum. In particular, the hardest region for optimization is not around the optimum. (Extended Abstract. A full version is available on arxiv at [17].)
Usability Evaluation Through Fuzzy AHP-TOPSIS Approach: Security Requirement Perspective

Most of the security strategies today are primarily designed to provide security protection, rather than to solve one of the basic security issues related to adequate software product architecture. Several models, frameworks ... | Find, read and cite all the research you need on Tech Science Press
Automated and Formal Synthesis of Neural Barrier Certificates for Dynamical Models

Abstract We introduce an automated, formal, counterexample-based approach to synthesise Barrier Certificates (BC) for the safety verification of continuous and hybrid dynamical models. The approach is underpinned by an inductive framework: this is structured as a sequential loop between a learner, which manipulates a candidate BC structured as a neural network, and a sound verifier, which either certifies the candidate’s validity or generates counter-examples to further guide the learner. We compare the approach against state-of-the-art techniques, over polynomial and non-polynomial dynamical models: the outcomes show that we can synthesise sound BCs up to two orders of magnitude faster, with in particular a stark speedup on the verification engine (up to three orders less), whilst needing a far smaller data set (up to three orders less) for the learning part. Beyond improvements over the state of the art, we further challenge the new approach on a hybrid dynamical model and on larger-dimensional models, and showcase the numerical robustness of our algorithms and codebase.
Does Financial Development Really Matter for Poverty Reduction in Africa?

This paper revisits the financial development–poverty nexus. The study builds a robust measure of financial development that captures the state of financial development in Africa. We adopt the measure for examining the relationship between 1996 and 2015. Our results agree with a priori expectation. Overall, we find a reducing effect of financial development on absolute poverty but this does not affect relative poverty. Private credit has a poverty-reducing effect; however, total financial development and financial inclusion do not affect poverty in African countries. Also, stability and efficiency increase poverty levels. Considering the low level of financial inclusion in the continent, it is likely that financial development will reduce poverty in the continent if the poor can access credits. Our results should be interpreted with caution, the commercial banking model may not adequately address poverty in Africa. Hence, much should not be expected in policy circles from financial development for poverty reduction in the present state of financial development in Africa.
Hard class rectification for domain adaptation

Domain adaptation (DA) aims to transfer knowledge from a label-rich and related domain (source domain) to a label-scare domain (target domain). Pseudo-labeling has recently been widely explored and used in DA. However, this line of research is still confined to the inaccuracy of pseudo labels. In this paper, we explore the imbalance issue of performance among classes in-depth and observe that the worse performances among all classes are likely to further deteriorate in the pseudo-labeling, which not only harms the overall transfer performance but also restricts the application of DA. In this paper, we propose a novel framework, called Hard Class Rectification Pseudo-labeling (HCRPL), to alleviate this problem from two aspects. First, we propose a simple yet effective scheme, named Adaptive Prediction Calibration (APC), to calibrate predictions of target samples. Then, we further consider the predictions of calibrated ones, especially for those belonging to the hard classes, which are vulnerable to perturbations. To prevent these samples to be misclassified easily, we introduce Temporal-Ensembling (TE) and Self-Ensembling (SE) to obtain consistent predictions. The proposed method is evaluated on both unsupervised domain adaptation (UDA) and semi-supervised domain adaptation (SSDA). Experimental results on several real-world cross-domain benchmarks, including ImageCLEF, Office-31, Office+Caltech, and Office-Home, substantiate the superiority of the proposed method.
Anomaly Detection, Localization and Classification for Railway Inspection

The ability to detect, localize and classify objects that are anomalies is a challenging task in the computer vision community. In this paper, we tackle these tasks developing a framework to automatically inspect the railway during the night. Specifically, it is able to predict the presence, the image coordinates and the class of obstacles. To deal with the low-light environment, the framework is based on thermal images and consists of three different modules that address the problem of detecting anomalies, predicting their image coordinates and classifying them. Moreover, due to the absolute lack of publicly-released datasets collected in the railway context for anomaly detection, we introduce a new multi-modal dataset, acquired from a rail drone, used to evaluate the proposed framework. Experimental results confirm the accuracy of the framework and its suitability, in terms of computational load, performance, and inference time, to be implemented on a self-powered inspection system.
Computer Vision-Control-Based CNN-PID for Mobile Robot

With the development of artificial intelligence technology, various sectors of industry have developed. Among them, the autonomous vehicle industry has developed considerably, and research on self-driving control systems using artificial intelligence has been extensively conducted. Studies on the use of image-based deep learning to monitor autonomous driving systems have recently been performed. In this paper, we propose an advanced control for a serving robot. A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified directions. The robot should be able to follow the trajectory with speed control. Two controllers were used simultaneously to achieve this. Convolutional neural networks (CNNs) are used for target tracking and trajectory prediction, and a proportional-integral-derivative controller is designed for automatic steering and speed control. This study makes use of a Raspberry PI, which is responsible for controlling the robot car and performing inference using CNN, based on its current image input.
ECONOMIC POLICY UNCERTAINTY AND THE BITCOIN MARKET: AN INVESTIGATION IN THE COVID-19 PANDEMIC WITH TRANSFER ENTROPY

This paper investigates the prediction power of economic policy uncertainty on Bitcoin trading (return, volume, and volatility) over the period from May 2013 to June 2019. We employ the Transfer Entropy model with the following two different regimes (i) stationary and (ii) nonstationary assumption. We construct different algorithm calculations for returns, volume and volatility to test how this proxy impacts. We find that the global Economic Policy Uncertainty negatively causes Bitcoin volumes and volatilities. Therefore, under uncertain regimes, investors are risk-averse to trade, which makes the market less volatile. Our findings confirm the existence of pessimistic risk premium, the theory of deteriorating liquidity and the widen bid-ask spread, which lead to a decline in trading volume under uncertainties in the Bitcoin market. By using different reliable data sources as well as expanding timeframe until May 2020 with COVID-19 pandemic, our results remain robust. Hence, the practical implications will be the useful tools for different parties in the Bitcoin market in the financial turbulence context.
Empirical Evaluation of a Method for Monitoring Cloud Services Based on Models at Runtime

Cloud computing is being adopted by commercial and governmental organizations driven by the need to reduce the operational cost of their information technology resources and search for a scalable and flexible way to provide and release their software services. In this computing model, the Quality of Services (QoS) is agreed between service providers and their customers through Service Level Agreements (SLA). There is thus a need for systematic approaches with which to assess the quality of cloud services and their compliance with the SLA. In previous work, we introduced a generic method for Monitoring cloud Services using models at RunTime (MoS@RT), which allows the monitoring requirements or the metric operationalizations of these requirements to be changed at runtime without the modification of the underlying infrastructure. In this paper, we present the design of a monitoring infrastructure that supports the proposed method with its instantiation to a specific platform and reports the results of an experiment carried out to evaluate the perceived efficacy of 58 undergraduate students when using the infrastructure to configure the monitoring of cloud services deployed on the Microsoft Azure platform. The results show that the participants perceived MoS@RT to be easy to use, useful, and they also expressed their intention to use the method in the future. Although further experiments must be carried out to strengthen these results, MoS@RT has proved to be a promising monitoring method for cloud services.
Transfer Learning for Automated Test Case Prioritization Using XCSF

With the rise of test automation, companies start to rely on large amounts of test cases. However, there are situations where it is unfeasible to perform every test case as only a limited amount of time is available. Under such circumstances a set of crucial tests has to be compiled. Recent research has shown that reinforcement learning methods such as XCSF classifier systems are well-suited for this task. This work investigates whether reusing knowledge of XCSF-based agents is beneficial for prioritizing test cases and subsequently selecting test suites in terms of performance. We developed a simplistic population transformation and evaluate it in a series of experiments. Our evaluation shows that XCSF may indeed benefit from transfer learning for this use case.
Post-Streaming Wastage Analysis – A Data Wastage Aware Framework in Mobile Video Streaming

Mobile video streaming is now ubiquitous among mobile users. This work investigates a less studied and yet significant problem in mobile video streaming – data wastage, i.e., some downloaded video data may not be played back but discarded by video players due to early departure or video skip, thus the bandwidth consumed in transferring them is wasted. Our measurements show that data wastage is significant in practice, e.g., 25.2 percent∼51.7 percent of video data downloaded are in fact wasted. Moreover, substantial data wastage exists not only in current commercial streaming platforms, but also in state-of-the-art adaptive streaming systems proposed in the literature. This work develops a new post-streaming wastage analysis (PSWA) framework to tackle this problem by converting existing adaptive streaming algorithms into data wastage aware versions. PSWA enables the streaming vendors to explicitly control the tradeoff between data wastage and quality-of-experience (QoE). Extensive evaluations show that PSWA can reduce data wastage significantly (e.g., 80 percent) without any adverse impact on QoE. Moreover, it has strong robustness to perform consistently across a wide range of networks. PSWA can be readily implemented into current streaming platforms, and thus offers a practical solution to data wastage for mobile streaming services.
BAXTER: Bi-Modal Aerial-Terrestrial Hybrid Vehicle for Long-Endurance Versatile Mobility

Unmanned aerial vehicles are rapidly evolving within the field of robotics. However, their performance is often limited by payload capacity, operational time, and robustness to impact and collision. These limitations of aerial vehicles become more acute for missions in challenging environments such as subterranean structures which may require extended autonomous operation in confined spaces. While software solutions for aerial robots are developing rapidly, improvements to hardware are critical to applying advanced planners and algorithms in large and dangerous environments where the short range and high susceptibility to collisions of most modern aerial robots make applications in realistic subterranean missions infeasible. To provide such hardware capabilities, one needs to design and implement a hardware solution that takes into the account the Size, Weight, and Power (SWaP) constraints. This work focuses on providing a robust and versatile hybrid platform that improves payload capacity, operation time, endurance, and versatility. The Bi-modal Aerial and Terrestrial hybrid vehicle (BAXTER) is a solution that provides two modes of operation, aerial and terrestrial. BAXTER employs two novel hardware mechanisms: the M-Suspension and the Decoupled Transmission which together provide resilience during landing and crashes and efficient terrestrial operation. Extensive flight tests were conducted to characterize the vehicle’s capabilities, including robustness and endurance. Additionally, we propose Agile Mode Transfer (AMT), a transition from aerial to terrestrial operation that seeks to minimize impulses during impact to the ground which is a quick and simple transition process that exploits BAXTER’s resilience to impact.
Security Enhancement of Forensic Evidences Using Blockchain

In today's digital era, data is most important in every phase of work. The storage and processing on data with security is the need of each and every application field. Data need to be tamper resistant due to possibility of alteration. Data can be represented and stored in heterogeneous format. There are chances of attack on information which is vital for particular organization. With rapid increase in cyber crime, attackers behave maliciously to alter those data. But it is having great impact on forensic evidences which is required for provenance. Therefore, it is required to maintain the reliability and provenance of digital evidences as it travels through various stages during forensic investigation. In this approach, there is a forensic chain in which generated report passes through various levels or intermediaries such as pathology laboratory, doctor, police department etc. To build the transparent system with immutability of forensic evidences, blockchain technology is more suitable. Blockchain technology provides the transfer of assets or evidence reports in transparent environment without central authority. In this paper blockchain based secure system for forensic evidences is proposed. The proposed system is implemented on Ethereum platform. The tampering of forensic evidence can be easily traced at any stage by anyone in the forensic chain. The security enhancement of forensic evidences is achieved through implementation on Ethereum platform with high integrity, traceability and immutability.
Security Enhancement of Digital Signatures for Blockchain using EdDSA Algorithm

Blockchain will be the most used technology in the next decade with the key features of distributed ledger and high security. To provide security in the transactions process of hashing, blockchain technology was using the Elliptic Curve Digital Signature Algorithm (ECDSA). Using the ECDSA Algorithm, if any error has happened, this may lead to identify the private key value that which can acquire matching signatures for various documents. To avoid that problem, we suggest using the Edwards-curve Digital Signature Algorithm (EdDSA) Algorithm in generating the hash functions among the transactions, which provides the high-level speed, getting the best performance, and independence in generating the random number. Our suggestion provides improved security while compared to the ECDSA Algorithm.
RBDN: Residual Bottleneck Dense Network for Image Super-Resolution

Recent studies have shown that a super-resolution generative adversarial network (SRGAN) can significantly improve the quality of single-image super-resolution. However, existing SRGAN methods also have certain drawbacks, such as an insufficient feature utilization, a large number of parameters. To further enhance the visual quality, we thoroughly studied three key components of SRGAN, i.e., the network architecture, adversarial loss, and perceptual loss, and propose a DenseNet with Residual-in-Residual Bottleneck Block (RRBB), called a residual bottleneck dense network (RBDN), for single-image super-resolution. First, to improve the utilization of features between the various layers of the network, we adopted a dense cascading connection between layers. At the same time, to reduce the computational cost, we added a bottleneck structure to each layer, greatly reducing the number of network parameters and accelerating the convergence speed of the training process. Second, the proposed RRBB, as the basic network building unit, removes the batch normalization (BN) layer and employs the ELU function to reduce the opposite effects in the absence of BN. In addition, we applied an improved overall loss function during the model training process to stably train the model and further improve the realism of the reconstructed high-resolution image. To prove the superiority of our proposed model, we conducted a comprehensive and objective evaluation of the Peak Signal-to-Noise Ratio, structural similarity, learned perceptual image patch similarity, and other evaluation indicators obtained from the three test sets, i.e., Set5, Set14, and BSD100, from the recent state-of-the-art model. Finally, we conducted qualitative and quantitative analyses of the results obtained in terms of the evaluation indicators, the authenticity of the restored HR images, and textural details, which show the superiority of the RBDN model.
Demonstration of Shor Encoding on a Trapped-Ion Quantum Computer

Fault-tolerant quantum error correction (QEC) is crucial for unlocking the true power of quantum computers. QEC codes use multiple physical qubits to encode a logical qubit, which is protected against errors at the physical qubit level. Here we use a trapped ion system to experimentally prepare $m$-qubit GHZ states and sample the measurement results to construct $m\times m$ logical states of the $[[m^2,1,m]]$ Shor code, up to $m=7$. The synthetic logical fidelity shows how deeper encoding can compensate for additional gate errors in state preparation for larger logical states. However, the optimal code size depends on the physical error rate and we find that $m=5$ has the best performance in our system. We further realize the direct logical encoding of the $[[9,1,3]]$ Shor code on nine qubits in a thirteen-ion chain for comparison, with $98.8(1)\%$ and $98.5(1)\%$ fidelity for state $\left\vert\pm\right\rangle_L$, respectively.
A Fuzzy Logic-Based Adaptive Dynamic Window Approach for Path Planning of Automated Driving Mining Truck

Automated Driving for open-pit mines has become a trend in recent years, and one of the most challenging scenario is parking path planning based on the demand of the excavator. In path planning field, dynamic window approach (DWA) is widely used. However, such method negalects expected direction at the destination, which makes it difficult to be implemented for mining trucks parking. To deal with this problem, we proposed a fuzzy logic-based adaptive DWA by considering truck kinematics. Firstly, an improved DWA considering obstacle avoidance is designing by taking Ackermann steering constraint into account. In the approach, we design a novel objective function to regulate the truck direction. Secondly, the detail of fuzzy logic is presented to guarantee the robustness against different situations. Simulations performed with the fuzzy logic-based adaptive DWA show that the mining truck can drive to the destination in high direction accuracy in different scenarios.
Lightweight Voice Anonymization Based on Data-Driven Optimization of Cascaded Voice Modification Modules

In this paper, we propose a voice anonymization framework based on data-driven optimization of cascaded voice modification modules. With increasing opportunities to use speech dialogue with machines nowadays, research regarding privacy protection of speaker information encapsulated in speech data is attracting attention. Anonymization, which is one of the methods for privacy protection, is based on signal processing manners, and the other one based on machine learning ones. Both approaches have a trade off between intelligibility of speech and degree of anonymization. The proposed voice anonymization framework utilizes advantages of machine learning and signal processing-based approaches to find the optimized trade off between the two. We use signal processing methods with training data for optimizing hyperparameters in a data-driven manner. The speech is modified using cascaded lightweight signal processing methods and then evaluated using black-box ASR and ASV, respectively. Our proposed method succeeded in deteriorating the speaker recognition rate by approximately 22% while simultaneously improved the speech recognition rate by over 3% compared to a signal processing-based conventional method.
Macro-micro mutual learning inside compositional model for human pose estimation

In this paper, we propose to perform mutual learning inside the compositional model for human pose estimation. We conduct inference of the human pose in a compositional model which is composed of parts and subparts networks to better capture intrinsic prior knowledge. A macro–micro mutual learning mechanism is put forward to promote the information interaction between human limbs (parts) and joints (subparts). At first, a macro mutual learning module is proposed to conduct the information interaction macroscopically. Features representing the whole human body are leveraged in this case. Secondly, a micro mutual learning module is proposed to promote the information interaction within each limb triplet group to refine corresponding features microcosmically. Combining the macro and micro mutual learning modules promotes the information interaction across different levels. Besides, a novel Balanced Masked Mean Square Error (BMMSE) loss is put forward to solve the positive–negative samples imbalance problem which is more apparent in compositional model. Effectiveness of the method is evaluated on MPII, LSP and COCO benchmarks. Our algorithm achieves leading positions on the leader board of these three datasets.