Reviewerly/exHarmony huggingface dataset:
Evolutionary Optimization of Baba Is You Agents

Baba is You is a challenging puzzle game in which the player can modify the rules of the game. This yields a large variety of puzzles and an enormous state space to be searched through. Recently, the Feature Space Search algorithm has shown great results in Sokoban, which apart from the rule modification shares many similarities to Baba is You. It uses multiple heuristics to guide the search into promising regions of the search space. In this work, we are proposing a similar concept for solving Baba is You based on multiple heuristics that are aggregated for guiding a tree-based search process. However, finding parameters for weighting/prioritizing the different heuristics is a non-trivial task. This process is done, by applying evolutionary algorithms for single- and multi-objective optimization. Specifically, we compare the effects of these different optimization schemes on the agents' general level-solving capabilities. In all cases, the agent was able to adapt well to the training and test set with no significant differences among the optimization schemes. Compared with state-of-the-art Baba is You agents our search-based approach shows an improved performance in terms of the number of levels being solved, as well as a reduction in the average time required to solve a level.
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service

Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://appflx.link
Ensemble Methods for Probabilistic Solar Power Forecasting: A Comparative Study

To guide the selection of probabilistic solar power forecasting methods for day-ahead power grid operations, the performance of four methods, i.e., Bayesian model averaging (BMA), Analog ensemble (AnEn), ensemble learning method (ELM), and persistence ensemble (PerEn) is compared in this paper. A real-world hourly solar generation dataset from a rooftop solar plant is used to train and validate the methods under clear, partially cloudy, and overcast weather conditions. Comparisons have been made on a one-year testing set using popular performance metrics for probabilistic forecasts. It is found that the ELM method outperforms other methods by offering better reliability, higher resolution, and narrower prediction interval width under all weather conditions with a slight compromise in accuracy. The BMA method performs well under overcast and partially cloudy weather conditions, although it is outperformed by the ELM method under clear conditions.
Brain-like Intelligent Data Mining Mechanism Based on Convolutional Neural Network

Many important advances in artificial intelligence research in recent years reflect a trend: inspired by brain science, even partial references can effectively improve the intelligence level of existing artificial intelligence models and systems. However, in order to truly approach or even surpass human-level artificial intelligence, more in-depth research and reference on the brain information processing mechanism is needed. The goal of brain-inspired intelligence research is to realize the next-generation artificial intelligence system with brain-like mechanism and human-like behavior by learning from the neural structure and information processing mechanism of the brain. Because brain-inspired intelligence data has a variety of different cognitive abilities and has strong adaptive ability to complex environments, it is necessary to establish a brain-inspired intelligence data mining mechanism. However, when the current algorithm is used to establish a mining mechanism, it is difficult to eliminate the redundancy in the data accurately, and there is a problem of large data mining errors. Therefore, a brain-like intelligence data mining mechanism based on opportunistic cognition is proposed. The above methods are combined with the time granularity. First, the time series of the original data is segmented to complete the preprocessing of the data, and the correlation between the variables of the brain-like intelligence data is adjusted according to the Mahalanobis distance, and its eigenvalues and characteristics are obtained through the covariance matrix. Vector, which reduces the attributes of the discretized intelligent data on the basis of rough set theory, and eliminates redundant fragments in the table without losing the original brain-like intelligence data information, thus forming a class based on opportunistic cognition. Brain intelligence data mining mechanism. The experimental results show that the proposed method performs better in brain-like intelligent data mining, and the mining efficiency is faster.
Project Presentation: Recursive Functions and Engineering Probabilistic Ontologies

Artificial Intelligence is developing using statistical and logical approaches. Considerable efforts have been devoted to combining logical and probabilistic methods in a single framework, which influenced the development of several formalisms and programming tools. Such formalisms allow representation and reasoning about uncertain knowledge. Uncertainty happens in many areas, like medicine, manufacturing, weather forecasts, prediction (of e.g. voting intentions, natural disaster), etc. Ontologies are machine-processable formalisms for knowledge representation. Their purpose is to describe objects according to domain of interests. This knowledge is used by (automated) reasoning systems for query answering. Probabilistic ontologies are obtained by adding a probabilistic interpretation to the constraints forming the ontology, and adapting corresponding reasoning methods to handle these probabilities. The RFEPO is an interdisciplinary project and aims at formulating unification and matching problems used in probabilistic ontology reasoning, and to search and compare algorithms for their solution. Additionally, when there is no algorithm for solving them, our project aims to study the algebraic structures of degrees induced by Turing and other algorithmic reducibilities.
Design of Face Recognition Fatigue Driving Detection System Based on Improved YOLO Algorithm

Face recognition is a very important step in driver fatigue driving detection, which is directly related to the subsequent driver’s eye detection and location, the judgment of driver’s eye state, etc. In recent years, DL (Deep learning) has made great progress in face recognition. For face recognition in natural scenes, researchers have made long-term research and substantial progress. In this paper, the research of face recognition fatigue driving detection system based on improved YOLO algorithm is carried out, and a face recognition fatigue driving detection system is established. In this paper, the YOLOv5 network structure is adopted, and the Label Smoothing method is introduced into the Prediction layer of YOLOv5. By defining a central loss function, the intra class variation is minimized while keeping different features separable, thus improving the inter class separability. The results show that the accuracy is 97.421% when the threshold is set to 0.5. The accuracy obtained is 4.871% higher than the original YOLOv5 algorithm. It can meet the requirements of practical application.
An Auto-Scaling Framework for Predictable Open Source FaaS Function Chains

Function as a Service (FaaS) is a novel approach for application virtualization in the cloud. Complex cloud native applications may be implemented as chains of functions. Due to the inter-dependencies between the functions of such chains, their performance is harder to predict. This also means that the design of a resource efficient scaling mechanism is a challenging task, the goal is to avoid wasteful scaling strategies. In this paper, we propose an auto-scaling framework that keeps the amount of resources, allocated for the functions in the chain, at a minimum level, while meeting the latency requirements of an application implemented as a function chain. Then, we show how to integrate our auto-scaling framework with a general open source FaaS system, as well as with, OpenFaaS. We also introduce our modifications on OpenFaaS that enable to use various load-balancing algorithms for distributing the requests between the function instances. We compare our auto-scaling framework to the auto-scaler of OpenFaaS by the completion time of our python3 based image processing test function chain, as well as, by the amount of allocated resources.
The Impact of Directed Pod Eviction on Kubernetes Resource Utilization

One of the promises of container orchestration technologies is that they enable effective utilization of hardware resources and thus reduce infrastructure costs. However, as applications scale up and down over time in a Kubernetes-managed environment, the cluster may enter a state of resource fragmentation. In such a state, the cluster's hardware cannot be used to its full potential because resources become locked by poor Pod to Node mappings. This paper shows that such problems are common as workload requirements approach a cluster's resource capacity. Additionally, we present an experimental analysis of directed Pod eviction as a technique to combat the issue of resource fragmentation. Our findings show that directed Pod eviction can reduce resource fragmentation and allow clusters to run a given workload using 16.7% fewer Nodes than is consistently possible using standard Kubernetes scheduling. These findings are unaffected by applying input shaking to the experimental analysis, strengthening confidence in the generality of these resource utilization improvements.
Future of Kernel Object-Based Memory Forensics

There are various techniques (String Search, Signature, List Traversal, Kernel Object, etc.) to perform memory forensics. Among them, Kernel Object-based memory forensics techniques that utilize the object structure of the kernel are considered the most reliable. Kernel Object-based memory forensics techniques require prior knowledge of the object structure of the operating system kernel used in the memory dump. However, reverse engineering the kernel for a vast number of operating system versions and architectures to identify the object structure is labor- and time-consuming. To solve this problem, academic researchers have developed methods to efficiently identify the structure of various kernel objects. Various studies have been conducted to identify key features that kernel objects leave in memory, or to use automation technology. We will review these works and discuss what further research can be done and the challenges that need to be considered.
Secure Lattice-Based Ciphertext-Policy Attribute-Based Encryption from Module-LWE for Cloud Storage

Ciphertext-Policy Attribute-Based Encryption (CPABE) is the most promising scheme to achieve fine-grained access control and data privacy in a cloud environment by offering oneto-many encryption. However, most existing CP-ABE schemes are fragile to quantum attacks because they are constructed using bilinear pairing assumptions. This paper proposes a Secure Lattice-Based CP-ABE scheme (SL-CP-ABE) from the Module Learning With Errors (M-LWE) to defend against quantum attacks and to realize fine-grained access control and data privacy in the cloud storage. The security of SL-CP-ABE proved against the Indistinguishable Selective Chosen Plaintext Attack (INDsCPA) under the hardness of the M-LWE. The performance analysis demonstrates that the SL-CP-ABE scheme is efficient and practical.
Surrogate-Assisted Morphology Optimization by Genetic Algorithms

Deep reinforcement learning has attracted wide interest because of its extraordinary capabilities in multiple fields. However, morphology optimization by using evolutionary computation techniques has not been intensively investigated. In this paper, we explore the use of genetic algorithms (GA) to automatically design the morphology of an agent. Evaluating the performance of an agent is very time-consuming because it needs to be trained from scratch. Moreover, it is computationally infeasible to train separate controllers for all possible different morphologies of agents to identify the optimal ones and is difficult to obtain the accurate cumulative reward of an agent to estimate the performance of the morphologies. To address these issues, we use a morphology comparator as a surrogate model to estimate the probability of one morphology being better than the other, instead of directly predicting the performance of each morphology. A set of surrogate models based on a radial basis function network are developed before evolution to make full use of the data to guide the search. Experimental results indicate that the proposed method is able to efficiently find out optimal morphologies to achieve better performance than the default morphology.
Security Concerns of Adversarial Attack for LSTM/BiLSTM Based Solar Power Forecasting

Solar photovoltaics (PV) power generation forecasting has become more crucial with the high use of solar PV resources and high impact on grid stability and reliability. The integration of solar PV can cause extreme stability and reliability issues due to its high dependence on weather conditions. This study investigates the impact of adversarial attack and training methods on Long Short-Term Memory (LSTM) and Bidirectional (BiLSTM) based solar power generation forecasts. Results are evaluated for models' responses against adversarial attacks with and without adversarial training in terms of the selected performance metrics. The results show that LSTM and BiLSTM-based models are dramatically vulnerable to adversarial attacks without adversarial training. It is also indicated that the adversarial training method effectively defends the LSTM and BiLSTM-based solar power generation models against adversarial attacks.
A Data-Centric Approach to Parameter Tuning, an Application to Differential Evolution

Algorithms such as Differential Evolution are currently state of the art in many fields, however, the performance of Differential Evolution is strongly influenced by the chosen values of its parameters. The most relevant parameters in Differential Evolution are the size of the population, the crossover probability, and the mutation factor. In this research, we present a novel way of tuning these parameters using neural networks. We collect data characterizing the optimization process and associate it with the result of modifying each parameter independently. We use this information to train several classification models on how to adjust each parameter. The trained models are then used to adjust the parameters after consecutive executions of Differential Evolution. Computational results using the CEC'13 benchmark suite, show that this approach is very effective and leads to a significant improvement in performance.
Randomness-Enhanced Grey Wolf Optimizer for Inverse Kinematics Solution of Redundant Robotic Manipulators

In this study, a randomness-enhanced grey wolf optimizer (REGWO) is proposed to efficiently solve the inverse kinematics (IK) of redundant manipulators. REGWO is an improved version of the grey wolf optimizer (GWO), and it overcomes its shortcomings concerning premature convergence and low population diversity. In the REGWO, more random individuals are introduced to affect population updates, which is beneficial for the global search of the algorithm. A fitness function composed of position and orientation errors is proposed to solve the optimization problem. An eight-degree-of-freedom serial manipulator was applied to verify the superiority of the REGWO. The results were compared with the classical GWO and other four state-of-the-art algorithm results. The simulation results demonstrate that the proposed REGWO can quickly and accurately solve the IK problem of a redundant manipulator.
Is CC-(1+1) EA More Efficient than (1+1) EA on Separable and Inseparable Problems?

Many Real-world optimisation tasks are increasingly large-scale optimisation (LSO) problems. Cooperative co-evolutionary algorithms (CoEAs), which involve more than two populations and utilise the divide-and-conquer approach, have been introduced to solve LSO problems more efficiently. However, the behaviour of cooperative CoEAs is not fully understood because the interactions between two or more populations make analysis challenging. Runtime analysis has improved the under-standing of traditional EAs. We argue that using runtime analysis of CoEAs could provide helpful insights, e.g., how their expected runtime depends on alaorithmic design decisions. In this paper, we show that the expected optimisation time of the basic cooperative co-evolutionary (1+1) EA (CC-(1+1) EA) on linear functions is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Theta(n\log n)$</tex> . This solves an open conjecture by (Jansen and Wiegand, 2004). Moreover, empirical analysis is conducted on two more complicated problems: NK − LANDSCAPE and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -MAXSAT problems. Our results show that the CC-(1+1) EA perform similarly to the (1+1) EA on these problems. However, adjusting block length allows us to optimise its performance on the NK − LANDSCAPE problem. Our results provide a more precise bound for the expected runtime of CC-(1+1) EA and a more detailed empirical analysis of its behaviour on more complicated inseparable problems.
FWA-RL: Fireworks Algorithm with Policy Gradient for Reinforcement Learning

Evolutionary and swarm intelligence-based black-box optimization algorithms have been widely utilized to solve an increasing number of machine learning problems in recent years. Despite the effectiveness of these algorithms, the low sample efficiency has become one of the main obstacles due to their sampling-based nature. Thus, it is of great interest to improve the sample efficiency of traditional black-box optimization algorithms while keeping their merits. In this paper, the population-based fireworks algorithm (FWA) is equipped with policy gradient (PG) information, leading to the development of a novel and effective algorithm called FWA-RL. The main idea is to enhance the explosion operator with policy gradient-guided explosion and conduct firework cooperation using distillation-based cooperation. Experimental studies show the proposed algorithm can outperform state-of-the-art pure reinforcement learning (RL) algorithms and other hybrid evolutionary reinforcement learning algorithms (EARL) on the standard MuJoCo benchmark suite for continuous control. With efficient parallel implementation, FWA-RL may serve as a competitive algorithm for solving more even complex problems.
Zero-Cost In-Depth Enforcement of Network Policies for Low-Latency Cloud-Native Systems

Packaging applications in containers and managing them dynamically using a cluster orchestrator is the de-facto approach for deployment of cloud-native applications. When containers run inside virtual machines (VMs) to protect infrastructural assets, network policies (NPs) at the container layer and security groups (SGs) at the VM layer provide complementary firewall mechanisms that strengthen defenses against lateral movement of attackers. However, least-privilege NPs at the container layer may not always be consistent with statically defined, over-permissive SGs at the VM layer. This is especially a problem with low-latency configuration of container networking solutions that requires every opened container protocol, port and traffic direction also to be opened at the VM layer. In any post-exploitation scenario where attackers escape from within an already compromised or infected container, such over-permissive SGs do not prevent the attacker from spreading across VMs to find powerful tokens for accessing the cluster orchestrator. In this paper, we introduce GrassHopper (GH), a fast and dynamic cross-layer enforcement approach for NPs, which automatically generates SG configurations from dynamically verified NPs. Given the low-latency context, the design of GH must ensure that dynamically generated SG rules are applied fast before the newly scheduled containers become ready to serve traffic. We evaluate GH on a Kubernetes cluster running on OpenStack. For a wide range of relevant low-latency applications and cluster setups, GH can reduce the network attack surface between VMs at a ratio of 75-to-99% while causing no application level performance overhead with respect to latency, throughput, and CPU utilization.
IoT Service Composition — An Estimation of Distribution Algorithm with Adaptive Bias

Service composition in Internet of Things (SCIoT), as an emerging topic in service computing, aims to select optimal services to complete user requests according to various user requirements such as minimizing energy consumption and response time. To solve this NP-hard problem, numerous heuristic methods, e.g., local search and population-based algorithms, have been proposed, wherein Estimation of Distribution Algorithm (EDA) gains increasing attention because of its explicit global probabilistic nature. However, existing EDAs increase solution diversity by using fixed bias, yet interfere the stability of the learned distribution in the later stage of optimization. Therefore, this paper proposes an EDA with an adaptive bias strategy (EDA-AdaBias) to solve the service composition in IoT problem. The decreasing bias value is added onto the probability values for all choices of each solution variable over generations, which improves diversity of sampled solutions and avoids dramatic change of constructed distribution. Experiments indicate that EDA-AdaBias presents promising performance compared to other competitive methods on this problem.
Darly: Deep Reinforcement Learning for QoS-aware scheduling under resource heterogeneity Optimizing serverless video analytics

Today, video analytics are becoming extremely popular due to the increasing need for extracting valuable information from videos available in public sharing services through camera-driven streams. Typically, video analytics are organized as a set of separate tasks, each of which has different resource requirements (e.g., computational- vs. memory-intensive tasks). The serverless computing paradigm forms a very promising approach for mapping such types of applications, as it enables fine-grained deployment and management in a per-function manner. However, modern serverless frameworks suffer from performance variability issues, due to i) the interference introduced due to co-location of third-party workloads with the serverless funcations and ii) the increasing hardware heterogeneity introduced in public clouds. To this end, this work introduces Darly, a QoS- and heterogeneity-aware Deep Reinforcement Learning-based Scheduler for serverless video analytics deployments. The proposed framework incorporates a DRL agent which exploits low-level performance counters to identify the levels of interference and the degree of heterogeneity in the underlying infrastructure and combines this information along with user-defined QoS requirements to dynamically optimize resource allocations by deciding the placement, migration, or horizontal scaling of serverless functions. Promising results are produced withing our experiments, which are accompanied with the intent to further build upon this groundwork.