Reviewerly/exHarmony huggingface dataset:
Multi-robot path planning in wireless sensor networks based on jump mechanism PSO and safety gap obstacle avoidance

In order to meet the real-time and accurate requirements of multi-robot path planning in dynamic environment, this paper adopted wireless sensor network to locate robots and obstacles and used an improved artificial intelligent algorithm to plan path. In this paper, a jumping mechanism particle swarm optimization (JPSO) algorithm and a safety gap obstacle avoidance algorithm (SGOA) algorithm were proposed. Compared with canonical PSO algorithm, JPSO algorithm has three improvement strategies: Fitness value evaluation function, new learning sample and jumping strategy. The JPSO algorithm updates the particles with poor comprehensive quality by jumping and adjusts the inertia weight adaptively according to fitness value evaluation function. With the cooperation of new learning samples, the global searching ability and precision of the algorithm can be improved. SGOA algorithm is mainly aimed at the problem that robots with low priority are stuck in a long wait and cannot continue to walk when avoiding obstacles. By implementing the SGOA algorithm, a new collision-free safety path can be optimized for the robot with low priority. In order to verify JPSO and SGOA algorithm, a lot of experiments were done. JPSO algorithm was compared with two other improved PSO algorithms with 6 standard test functions. The path planning and obstacle avoidance experiments of six robots were realized using the JPSO and SGOA algorithm. The experimental results show that JPSO algorithm has higher accuracy and faster convergence speed than the other two improved PSO algorithms, and SGOA algorithm can solve the dynamic obstacle avoidance problem in the path planning of multiple robots well.
Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters

Abstract The ubiquitous diffusion of cloud computing requires suitable management policies to face the workload while guaranteeing quality constraints and mitigating costs. The typical trade-off is between the used power and the adherence to a service-level metric subscribed by customers. To this aim, a possible idea is to use an optimization-based placement mechanism to select the servers where to deploy virtual machines. Unfortunately, high packing factors could lead to performance and security issues, e.g., virtual machines can compete for hardware resources or collude to leak data. Therefore, we introduce a multi-objective approach to compute optimal placement strategies considering different goals, such as the impact of hardware outages, the power required by the datacenter, and the performance perceived by users. Placement strategies are found by using a deep reinforcement learning framework to select the best placement heuristic for each virtual machine composing the workload. Results indicate that our method outperforms bin packing heuristics widely used in the literature when considering either synthetic or real workloads.
Classifying Metaheuristics: Towards a unified multi-level classification system

Abstract Metaheuristics provide the means to approximately solve complex optimisation problems when exact optimisers cannot be utilised. This led to an explosion in the number of novel metaheuristics, most of them metaphor-based, using nature as a source of inspiration. Thus, keeping track of their capabilities and innovative components is an increasingly difficult task. This can be resolved by an exhaustive classification system. Trying to classify metaheuristics is common in research, but no consensus on a classification system and the necessary criteria has been established so far. Furthermore, a proposed classification system can not be deemed complete if inherently different metaheuristics are assigned to the same class by the system. In this paper we provide the basis for a new comprehensive classification system for metaheuristics. We first summarise and discuss previous classification attempts and the utilised criteria. Then we present a multi-level architecture and suitable criteria for the task of classifying metaheuristics. A classification system of this kind can solve three main problems when applied to metaheuristics: organise the huge set of existing metaheuristics, clarify the innovation in novel metaheuristics and identify metaheuristics suitable to solve specific optimisation tasks.
Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer

DNA repair deficiency (DRD) is an important driver of carcinogenesis and an efficient target for anti-tumor therapies to improve patient survival. Thus, detection of DRD in tumors is paramount. Currently, determination of DRD in tumors is dependent on wet-lab assays. Here we describe an efficient machine learning algorithm which can predict DRD from histopathological images. The utility of this algorithm is demonstrated with data obtained from 1445 cancer patients. Our method performs rather well when trained on breast cancer specimens with homologous recombination deficiency (HRD), AUC (area under curve) = 0.80. Results for an independent breast cancer cohort achieved an AUC = 0.70. The utility of our method was further shown by considering the detection of mismatch repair deficiency (MMRD) in gastric cancer, yielding an AUC = 0.81. Our results demonstrate the capacity of our learning-base system as a low-cost tool for DRD detection.
Secure Surveillance System Using Chaotic Image Encryption Technique

Abstract In the current data transmission situation, digital images constitute a large part of visual communication. Their security is thus an essential field of concern. This paper analyses several chaotic maps for the encryption of images and discusses their advantages and disadvantages. The characteristics of chaotic maps such as stochastic, ergodicity and highly sensitive initial conditions allow them reliable to encrypt images. Many of the previously proposed imaging approaches used chaotic, low-dimensional charts that display the lowest security and have very less potential to handle force and attacks. To solve this challenge, scientists have proposed multiple broad chaotic charts. In this paper the characteristics and techniques of some chaotic maps used to encrypt images were reviewed. Also for images like boat, airplane, peppers, lake, house chaotic encryption is applied and analysed.
Mapping concentrated solar power site suitability in Algeria

The investment in solar thermal power technologies has become increasingly attractive, despite their still perceived high costs. Algeria presented an ambitious plan for increasing the participation of renewable energy sources (RES) in the power system, with significant investments foreseen for solar power technologies. To achieve this objective, it is necessary to identify optimal sites for the implementation of these plants, as well as others where implementation is highly inadvisable from the economic, social, or environmental points of view. The main goal of this study is to present and apply a methodology to identify adequate locations for the installation of solar power plants in Algeria. The study addressed the particular case of concentrated solar power (CSP) and proposed a hybrid approach combining multi-criteria decision making and Geographic Information System. The approach allowed mapping and visualizing unfeasible areas and ranking the feasible sites. The results showed that more than 51% of the territory of the country is unfeasible for the implementation of CSP, mainly due to criteria related to topographic aspects, water availability, and distance to the grid. The results demonstrated that relying only on Direct Normal Irradiation (DNI) values may result in a reductionist vision for energy planning and thus other criteria can play a fundamental role in the decision process. The model allowed also to identify the best regions for CSP investment and opens routes for more detailed studies for the exact site selection.
Amanuensis: Information provenance for health-data systems

Mobile health (mHealth) apps and devices are increasingly popular for health research, clinical treatment, and personal wellness, as they offer the ability to continuously monitor aspects of individuals' health as they go about their everyday activities. Combining the data produced by these mHealth devices may give healthcare providers a more holistic view of a patient's health, increase the level of patient care, and reduce operating costs. Creating a trusted and secure data sharing ecosystem for mHealth devices is difficult, however, as devices are implemented with different technologies and managed by different organizations. To address these issues, we present Amanuensis: a concept for a secure, integrated healthcare data system that leverages Blockchain and Trusted Execution Environment (TEE) technologies to achieve information provenance for mHealth data. By using a blockchain to record and enforce data-access policies, we remove the need to trust a single entity with gate-keeping the health data. Instead, participating organizations form a consortium to share responsibility for verifying data integrity and enforcing access policies for data stored in private data silos. Data accesses and computations take place inside of TEEs to preserve data confidentiality and to provide a verifiable attestation report that can be stored on the blockchain for the purpose of information provenance. We evaluate a prototype implementation of Amanuensis – built using Intel SGX trusted execution hardware and the VeChain Thor blockchain platform – which shows that Amanuensis is capable of supporting up to 14,256,000 mHealth data sources at $0.07 per data source per day.
Unsupervised Feature Selection via Adaptive Graph Learning and Constraint

The performance of graph-based feature selection methods relies heavily on the quality of the construction of the similarity matrix. However, most of the graphs on these methods are initially fixed, where few of them are constrained. Once the graph is determined, it will remain constant in the whole optimization process. In other words, in case that the graph constructed on the raw data is not appropriate, it will drag down the entire algorithm. Aiming to tackle this defect, a novel unsupervised feature selection via adaptive graph learning and constraint (EGCFS) is proposed to select the uncorrelated yet discriminative features by exploiting the embedded graph learning and constraint. The adaptive graph learning method incorporates the structure of the similarity matrix into the optimization process, which not only learns the graph structure adaptively but also obtains the closed-form solution of the graph coefficient. Special graph constraint is embedded with the feature selection process to connect nearer data points with larger probability. The idea of maximizing between-class scatter matrix and the adaptive graph structure is integrated into a uniform framework to obtain excellent structural performance. Moreover, the proposed embedded graph constraint not only performs with manifold structure but also validates the link between graph-based approach and k -means from a unique perspective. Experiments on several benchmark data sets verify the effectiveness and superiority of the proposed method.
Particle swarm optimization with an enhanced learning strategy and crossover operator

Particle Swarm Optimization (PSO) is a well-known swarm intelligence (SI) algorithm employed for solving various optimization problems. This algorithm suffers from premature convergence to local optima. Accordingly, a number of PSO variants have been proposed in the literature. These algorithms exploited different schemes to improve performance. In this paper, we propose a new variant of PSO with an enhanced Learning strategy and Crossover operator (PSOLC). This algorithm applies three strategies, comprising altering the exemplar particles, updating the PSO parameters, and integrating PSO with Genetic Algorithm (GA). In the proposed learning strategy, each particle is guided by the best positions (pbests) of all particles, which improves its search capability. Furthermore, the proposed parameter updating scheme computes the self-cognition coefficient for each particle based on the quality of the pbests. Finally, the proposed crossover operator injects randomness to particles to improve the global search ability. The proposed improvements in PSOLC increase its exploration capability at the early stages of the search process and its exploitation ability at the end. The derived outcome from applying PSOLC and other variants of PSO to the benchmark functions verify the superiority of the proposed algorithm in terms of accuracy and convergence speed.
LoGAN: Latent Graph Co-Attention Network for Weakly-Supervised Video Moment Retrieval

The goal of weakly-supervised video moment retrieval is to localize the video segment most relevant to a description without access to temporal annotations during training. Prior work uses co-attention mechanisms to understand relationships between the vision and language data, but they lack contextual information between video frames that can be useful to determine how well a segment relates to the query. To address this, we propose an efficient Latent Graph Co-Attention Network (LoGAN) that exploits fine-grained frame-by-word interactions to jointly reason about the correspondences between all possible pairs of frames, providing context cues absent in prior work. Experiments on the DiDeMo and Charades-STA datasets demonstrate the effectiveness of our approach, where we improve Recall@1 by 520% over prior weakly-supervised methods, even boasting an 11% gain over strongly-supervised methods on DiDeMo, while also using significantly fewer model parameters than other co-attention mechanisms.
Multiscale fused network with additive channel–spatial attention for image segmentation

Class imbalance is a challenge in image segmentation. Deep learning has demonstrated potential advantages as a promising approach in solving such challenging problems because of its powerful ability to extract features. To treat class imbalance in medical image segmentation effectively, in this study, deep learning technology is employed. Further, a new approach, referred to as multiscale fused network with additive channel–spatial attention (MSF-ACSA), is developed. In particular, using the U-shape convolutional network as the structure of the main body network, an additive channel–spatial attention (ACSA) module, which exploits high-level features to guide low-level features for optimal responses, is designed. Therefore, the proposed MSF-ACSA can availably focus on regions of interest while adaptively recalibrating the channel significance of feature maps. Simultaneously, a multiscale fusion with deep supervision module is introduced to incorporate fine-grained details with coarse-grained semantics from diverse scale features. Furthermore, a focal weighted Tversky loss function is proposed to mitigate class imbalance issues. Numerous experimental results indicate that the proposed MSF-ACSA achieves the best performance when compared with state-of-the-art approaches for medical image segmentation.
Weaponized AI for cyber attacks

Artificial intelligence (AI)-based technologies are actively used for purposes of cyber defense. With the passage of time and with decreasing complexity in implementing AI-based solutions, the usage of AI-based technologies for offensive purposes has begun to appear in the world. These attacks vary from tampering with medical images using adversarial machine learning for false identification of cancer to the generation of adversarial traffic signals for influencing the safety of autonomous vehicles. In this research, we investigated recent cyberattacks that utilize AI-based techniques and identified various mitigation strategies that are helpful in handling such attacks. Further, we identified existing methods and techniques that are used in executing AI-based cyberattacks and what probable future scenarios will be plausible to control such attacks by identifying existing trends in AI-based cyberattacks.
A survey of bacterial foraging optimization

Bacterial foraging optimization algorithm (BFO) is a biological-inspired swarm intelligence optimization algorithm that simulates the foraging behavior of bacteria to obtain maximal energy during the searching process. Since its inception, it has evoked wide attention from researchers. The number of articles about BFO and its variants has grown significantly over the past decades and continues to grow. Hence, there is a clear need for a scientific and comparative review of extant BFO literature based on a common framework that offers a quick glimpse of the development of BFO. In light of this, we develop a bibliometric review via using CiteSpace to construct a knowledge mapping, and then conduct a thorough literature review of BFO research. Based on the analytical results, we present the influential researchers, journals or proceedings, and articles, and discuss various key issues of BFO. Finally, we suggest serval research directions that require further attention. This survey is expected to exhibit a lucid outline and useful guidance for the researchers of BFO.
DRBFT: Delegated randomization Byzantine fault tolerance consensus protocol for blockchains

Blockchain, as a potentially revolutionary technology, has been used in cryptocurrency to record transactions chronologically among multiple parties. Due to the fast development of the blockchain and its de-centralization, blockchain technology has been applied in broader scenarios, such as smart factories, supply chains, and smart cities. Consensus protocol plays a vital role in the blockchain, which addresses the issue of reaching consensus on transaction results among involved participants. Nevertheless, with the complexity of the network environment and growing amount of network users, the advance of blockchain is gradually restricted by the efficiency, security and reliability of consensus protocols. In this paper, we propose a delegated randomization Byzantine fault tolerance consensus protocol named DRBFT based on Practical Byzantine Fault Tolerance (PBFT) to enhance the efficiency and reliability of the consensus procedure. Specifically, a random selection algorithm called RS is developed to cooperate with the voting mechanism, which can effectively reduce the number of nodes participating in the consensus process. Our proposed scheme is characterized by the unpredictability, randomicity and impartiality, which accelerate the system to reach consensus on the premise of ensuring the system activity. Furthermore, the feasibility of our proposed scheme is also proved by both theoretical analysis and experimental evaluations.
Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing

In virtualized cloud computing systems, energy reduction is a serious concern since it can offer many major advantages, such as reducing running costs, increasing system efficiency, and protecting the environment. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping cloud resources to user requests to achieve good performance by minimizing the energy consumption of cloud resources within a user-defined deadline is a huge challenge. This paper proposes Energy and Performance-Efficient Task Scheduling Algorithm (EPETS) in a heterogeneous virtualized cloud to resolve the issue of energy consumption. There are two stages in the proposed algorithm: initial scheduling helps to reduce execution time and satisfy task deadlines without considering energy consumption, and the second stage task reassignment scheduling to find the best execution location within the deadline limit with less energy consumption. Moreover, to make a reasonable balance between task scheduling and energy saving, we suggest an energy-efficient task priority system. The simulation results show that, compared to current energy-efficient scheduling methods of RC-GA, AMTS, and E-PAGA, the proposed solution helps to reduce significant energy consumption and improve performance by 5%–20% with deadline constraint satisfied.
Homodyne-based quantum random number generator at 2.9 Gbps secure against quantum side-information

Abstract Quantum random number generators promise perfectly unpredictable random numbers. A popular approach to quantum random number generation is homodyne measurements of the vacuum state, the ground state of the electro-magnetic field. Here we experimentally implement such a quantum random number generator, and derive a security proof that considers quantum side-information instead of classical side-information only. Based on the assumptions of Gaussianity and stationarity of noise processes, our security analysis furthermore includes correlations between consecutive measurement outcomes due to finite detection bandwidth, as well as analog-to-digital converter imperfections. We characterize our experimental realization by bounding measured parameters of the stochastic model determining the min-entropy of the system’s measurement outcomes, and we demonstrate a real-time generation rate of 2.9 Gbit/s. Our generator follows a trusted, device-dependent, approach. By treating side-information quantum mechanically an important restriction on adversaries is removed, which usually was reserved to semi-device-independent and device-independent schemes.
Trends in VR/AR technology-supporting language learning from 2008 to 2019: a research perspective

This paper systematically reviews 150 (VR/AR)-supporting language learning articles from 2008 to 2019 and summarizes the development trends. The definition of VR/AR is given firstly, and then the model of VR/AR supporting learning is proposed. This model’s language types, research participants, learning devices, learning goals, research issues, research methods, research domains and learning strategies act as a coding scheme here. Meanwhile, through review and analysis, empirical study has been proven to be in its peak period; the VR/AR is increasingly applied, and larger numbers of immersive devices are being used. Under such circumstances, higher education is the principal of research, and speaking/expressing and words are still the most significant learning goals. As for research issues, the first period (2008–2013), which focuses more on learners’ opinions, learning behaviours, learning attitudes and learning performance, is still the main scheme even though the second period (2014–2019) has more research issues and wider research scale. Right now, research about teaching and learning has already drawn the most attention, and task-based learning, situated learning, and game-based learning are the most commonly used learning strategies. Finally, this paper analyses VR/AR used in language learning through SWOT analysis to provide more profound suggestions and further discussion.
Metamorphic object insertion for testing object detection systems

Recent advances in deep neural networks (DNNs) have led to object detectors (ODs) that can rapidly process pictures or videos, and recognize the objects that they contain. Despite the promising progress by industrial manufacturers such as Amazon and Google in commercializing deep learning-based ODs as a standard computer vision service, ODs --- similar to traditional software --- may still produce incorrect results. These errors, in turn, can lead to severe negative outcomes for the users. For instance, an autonomous driving system that fails to detect pedestrians can cause accidents or even fatalities. However, despite their importance, principled, systematic methods for testing ODs do not yet exist.