Reviewerly/exHarmony huggingface dataset:
Deep Reinforcement Learning Enhanced Greedy Algorithm for Online Scheduling of Batched Tasks in Cloud in Cloud HPC Systems

In a large cloud data center HPC system, a critical problem is how to allocate the submitted tasks to heterogenous servers for achieving the goal of maximize systems net gain defined as the value of completed tasks minus system operation cost. We consider this problem in the online setting that tasks arrive in batches and propose a novel deep reinforcement learning (DRL) enhanced greedy algorithm of two-stage scheduling interacting task sequencing and task allocation. For task sequencing we deploy a DRL module to make prediction for the best allocation sequence for each arriving batch of tasks based on knowledge (allocation strategies) learnt from prior batches. For task allocation, we propose a greedy strategy that allocates tasks to servers one by one online following the allocation sequence to maximally increase the total gain. We show that our greedy strategy has a performance guarantee of competitive ratio 1/(1+k) to the optimal offline solution, which improves the existing result for the same problem, where k is upper bounded by the maximum cost-to-gain ratio of each task. While our DRL module enhances the greedy by providing the likely-optimal allocation sequence for each batch of arriving tasks, our greedy strategy bounds DRLs prediction error within a proven performance guarantee for any allocation sequence, enabling a better solution quality than that obtainable from both DRL and greedy optimization alone. Extensive experiment evaluation results in both simulation and real application environments demonstrate the effectiveness and efficiency of our proposed algorithm. Compared with the state-of-the-art baselines, our algorithm increases the system gain by about 10% to 30%. Our algorithm provides an interesting example of joining machine-learning and greedy optimization techniques to improve ML-based solutions with a worst-case performance guarantee for solving hard optimization problems.
Image Classification Based on Convolutional Neural Network

The main goal of this research work is to use Neural Networks to recognize and categorize images. As an input, a fixed dataset will be selected. Further, this research work will consider two distinct types of creatures and determine the animal species. The difficulty of recognizing images of animals is used to solve the CAPTCHA challenge. It is simple and easy, but data shows that cats and dogs are particularly difficult to distinguish automatically. CNNs are the simple neural networks with at least one layer that use convolution rather than generic environment multiplication. We shall visualize and distinguish a code made up of 0's and 1's that is taken- cat as 0 and Dog as 1. This research work aims to train the neural network and recognize the animals. As a result, it demonstrates that such animal finding may be completed by a convolution neural network.
SMOTE-OB: Combining SMOTE and Online Bagging for Continuous Rebalancing of Evolving Data Streams

The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that combines the challenges of class-imbalanced data streams and concept drift. In this paper, we propose the Synthetic Minority Oversampling TEchnique with Online Bagging (SMOTE-OB). It is a novel cost-sensitive ensemble strategy that uses Online Bagging and a new sketched version of SMOTE to over/undersample the minority and majority classes. We benchmarked SMOTE-OB on synthetic and real data streams containing different concept drifts, imbalance levels, and class distributions. We bring statistical evidence that the SMOTE-OB ensemble achieves minority class performance that are better than the state-of-the-art ones. Moreover, we perform a time/memory consumption analysis.
Hybridized Ranking Model for Prioritizing Functional Software Requirements

Many difficulties exist when defining and deciding which requirements should be implemented first in an ultra-large-scale multi-stakeholder involved system. This often leads to system failure and product dissatisfaction. This paper established a suitable method supporting more precise and accurate decision-making in prioritizing requirements. The authors collected and analyzed a large number of software requirements in a case study, which was based on real-life practices and processes. Structured interviews and questionnaires were used to collect data from 600 stakeholders. They formulated a model based on the analyzed requirements using the CBRanking and the MACBETH approaches. They ranked the requirements and considered the requirements' relative importance according to the stakeholders' opinions. Thus, a hybridized mathematical model was proposed for prioritizing these functional requirements and evaluating its performance for consistency and completeness. The results showed the software's best functional requirements concerning the customers' expectations.
Towards Situation-Aware Meta-Optimization of Adaptation Planning Strategies

Platooning is a promising approach for optimizing the usage of the existing road infrastructure by driving in convoys with low inter-vehicle distances. Platooning coordination fosters advantages like an increased road throughput and reduced fuel consumption. Diverse so-called platooning coordination strategies exist in the literature and according to the no-free-lunch theorem, each has individual assets and drawbacks, making them best applicable for different traffic situations. This paper proposes a layered system model and a feedback loop for meta-optimization of self-adaptive systems. We apply our concept on platooning coordination as a case study and provide an experience report. The platooning coordination strategy is exchangeable and its input parameters are tuned to fit the current traffic situation. Our evaluation results show that the choice of the platooning coordination strategy is situation-dependent. Further, our meta-optimization of the input parameters of these strategies for the traffic situation is favorable compared to a static approach.
Adversarial Machine Learning for Enhanced Spread Spectrum Communications

Recently deep learning has demonstrated much success within the fields of image and natural language processing, facial recognition, and computer vision. The success is attributed to large, accessible databases and deep learning's ability to learn highly accurate models. Thus, deep learning is being investigated as a viable end-to-end approach to digital communications design. This work investigates the use of adversarial deep learning to ensure that a radio can communicate covertly, via Direct Sequence Spread Spectrum (DSSS), with another while a third (the adversary) is actively attempting to detect, intercept and exploit their communications. The adversary's ability to detect and exploit the DSSS signals is hindered by: (i) generating a set of spreading codes that are balanced and result in low side lobes as well as (ii) actively adapting the encoding scheme. Lastly, DSSS communications performance is assessed using energy constrained devices to accurately portray IoT and IoBT device limitations.
Strategies of Eliciting Young People’s Affective and Quick Participation in a Youth Magazine’s Instagram Community

Traditional media have merged with social media and pursue to produce engaging content and form relationships with online audiences. At the same time, social media has allowed young people a venue to practice their basic needs of interaction, identity, and self-expression, and provides communities of shared issues and social/political aspirations. By analysing the Instagram post caption data of the largest Finnish (girls’) youth magazine, this case study investigated what posting strategies used by the magazine elicited youth participation, and how do the strategies and participation align with young people’s media and online communication preferences. The study found the magazine to use various strategies; mostly affective topics and emoji/tag suggestion appeals. From those perceived as general posting strategies, affective topics and contests and ballots, and from more specific framing choices, a positive approach and topics providing information, peer-support and close topic angles for young women, elicited the most participation. Additionally, a suggestion to comment with a quick emoji/tag was the most engaging appeal suggestion. Young people preferred the image-dominated expressive participation towards both everyday/personal and civic/political topics. The study produces valuable, detailed information regarding young people’s online participation preferences and the potential of traditional media in engaging with online youth audiences.
AnomalyHop: An SSL-based Image Anomaly Localization Method

An image anomaly localization method based on the successive subspace learning (SSL) framework, called Anomaly-Hop, is proposed in this work. AnomalyHop consists of three modules: 1) feature extraction via successive subspace learning (SSL), 2) normality feature distributions modeling via Gaussian models, and 3) anomaly map generation and fusion. Comparing with state-of-the-art image anomaly localization methods based on deep neural networks (DNNs), AnomalyHop is mathematically transparent, easy to train, and fast in its inference speed. Besides, its area under the ROC curve (ROC-AUC) performance on the MVTec AD dataset is 95.9%, which is among the best of several benchmarking methods.
Adaptation<sup>2</sup>: Adapting Specification Learners in Assured Adaptive Systems

Specification learning and controller synthesis are two methods that promise to provide control systems with assured adaptive capabilities at run-time. Specification learning can automatically update specifications in light of violation traces observed within the operational environment. Controller synthesis can then automatically generate implementations that are guaranteed to satisfy these specifications in every environment.Specification learning is implemented using general-purpose AI systems. These systems are highly configurable, and the configuration choice heavily affects the effectiveness. Setting configuration parameters is far from obvious as they bear no clear semantic relation with the adaptation task. State of the art requires configurations to be set by domain experts at design time for each application domain.In this paper, we argue that to create assured control systems that can effectively and efficiently adapt at run-time, the learning systems upon which they are built must also have adaptive learning strategies for determining configurations at runtime. We demonstrate this idea with a proof-of-concept that computes domain-dependent policies using reinforcement learning.
Learning Causal Fuzzy Logic Rules by Leveraging Markov Blankets

An important property of fuzzy systems is the interpretability provided by their rules. However, if a fuzzy system is derived through machine learning algorithms, its interpretability is often greatly diminished as membership functions and rules are adjusted to minimize error in respect to a data set. To address part of this challenge, we propose a novel two-step fuzzy rule generation framework leveraging the concept of the Markov blanket, i.e., the set of variables which are causally related to a target variable – such as a system’s output. By estimating the Markov blanket for a given application, we restrict rule learning to (only) the variables which are causally linked to the system output, thus minimising the generation of spurious rules (based on spurious correlations of variables). This decreases the complexity of fuzzy systems and maintains a causal link between rules’ antecedents and consequent(s) – as expected by humans when viewing rules. The proposed framework can improve the interpretability of fuzzy rule based systems which are tuned using machine learning techniques, while also providing performance advantages as are commonly associated with feature selection techniques. Experiment results show that even the initial implementation of the framework proposed here can generate more concise and interpretable rule bases without compromising performance.
Binary Code Similarity Detection

Binary code similarity detection is to detect the similarity of code at binary (assembly) level without source code. Existing work still has their limitation when dealing with mutated binary code with different compiling options. We proposed a novel approach to address this problem. By inspecting the binary code, we found that generally, within a function, some instructions aim to calculate (prepare) value for some other instructions. The latter instructions are defined by us the key instructions. Currently, we define four categories of key instructions: calling subfunctions, comparing instruction, returning instruction, and memory address writing instruction. Thus if we symbolically execute similar binary codes, the symbolic value at these key instructions should be similar. We implemented our idea into a tool. This prototype tool can: 1. symbolically execute binary code, 2. extract symbolic values at key instructions into a graph, and 3. compare the symbolic graph similarity. In our implementation, we also address some problems, including path explosion and loop handling.
Results of analysis of multimedia traffic of elements of the Internet of Things

The article offers options for reference models for the construction and interaction of Internet of Things (IoT) systems. The results of solving the problem are presented, namely, data acquisition and traffic analysis of devices used in smart home systems - smart sockets. Sockets manufactured by TP-Link and Redmond are considered. For more accurate traffic data, both the data generated and received by the outlets are considered. The obtained results of information about the operation of the presented devices can be used for a deeper study in order to predict jumps in the volume of traffic and the behavior of the system as a whole, as well as for directly computer models of networks of intelligent devices.
GAWD

Given a set of node-labeled directed weighted graphs, how to find the most anomalous ones? How can we summarize the normal behavior in the database without losing information? We propose GAWD, for detecting anomalous graphs in directed weighted graph databases. The idea is to (1) iteratively identify the "best" substructure (i.e., subgraph or motif) that yields the largest compression when each of its occurrences is replaced by a super-node, and (2) score each graph by how much it compresses over iterations --- the more the compression, the lower the anomaly score. Different from existing work [1] on which we build, GAWD exhibits (i) a lossless graph encoding scheme, (ii) ability to handle numeric edge weights, (iii) interpretability by common patterns, and (iv) scalability with running time linear in input size. Experiments on four datasets injected with anomalies show that GAWD achieves significantly better results than state-of-the-art baselines.
Dynamic channel bonding in WLANs by hierarchical laser chaos decision maker

Laser chaos decision-maker has been demonstrated to enable ultrahigh-speed decision-making in solving multi-armed bandit (MAB) problems in the GHz order. In addition to recent intensive studies of photonic information processing devices and systems, the pursuit of novel applications is important, which is also demanded from future technologies, including Beyond 5G context. In this paper, we examine the applicability to dynamic channel bonding (DCB), which has been introduced in wireless local area networks (WLANs), and demonstrate a method for achieving higher data rate transmissions while avoiding interference. First, we propose a DCB method utilizing laser chaos decision-maker. Second, we design two hierarchical trees for decision making, that is, DCB selection. Third, we experimentally implement our proposed methods in a practical WLAN and confirm its operational ability. We analyze the parameter of the proposed method and compare the proposed method with conventional decision-making algorithms of ε-greedy and UCB1-tuned. We show that our proposed method demonstrates better DCB decisions than the other decision-making algorithms. Furthermore, we demonstrate that in DCB, the design method of the hierarchical trees or the parameter for the proposed method influences the performance of decision making.
Multi-Layered Blockchain Governance Game

The research designs a new integrated system for the security enhancement of a decentralized network by preventing damages from attackers, particularly for the 51 percent attack. The concept of multiple layered design based on Blockchain Governance Games frameworks could handle multiple number of networks analytically. The Multi-Layered Blockchain Governance Game is an innovative analytical model to find the best strategies for executing a safety operation to protect whole multiple layered network systems from attackers. This research fully analyzes a complex network with the compact mathematical forms and theoretically tractable results for predicting the moment of a safety operation execution are fully obtained. Additionally, simulation results are demonstrated to obtain the optimal values of configuring parameters of a blockchain-based security network. The Matlab codes for the simulations are publicly available to help those whom are constructing an enhanced decentralized security network architecture through this proposed integrated theoretical framework.
Deep Learning-Based Blind Image Super-Resolution using Iterative Networks

Deep learning-based single image super-resolution (SR) consistently shows superior performance compared to the traditional SR methods. However, most of these methods assume that the blur kernel used to generate the low-resolution (LR) image is known and fixed (e.g. bicubic). Since blur kernels involved in real-life scenarios are complex and unknown, per-formance of these SR methods is greatly reduced for real blurry images. Reconstruction of high-resolution (HR) images from randomly blurred and noisy LR images remains a challenging task. Typical blind SR approaches involve two sequential stages: i) kernel estimation; ii) SR image reconstruction based on estimated kernel. However, due to the ill-posed nature of this problem, an iterative refinement could be beneficial for both kernel and SR image estimate. With this observation, in this paper, we propose an image SR method based on deep learning with iterative kernel estimation and image reconstruction. Simulation results show that the proposed method outperforms state-of-the-art in blind image SR and produces visually superior results as well.
Unsupervised Denoising Feature Learning for Classification of Corrupted Images

Numerous corrupted or noisy images are generated owing to the problems in sampling and transmission. Facing the category of high-dimensional image data, directly operating like classification is inefficient and the accuracy is normally not ideal. Consequently, dimensionality reduction is a critical process. After dimensionality reduction by classical algorithms, the classification efficiency can be improved. However, the problem of low accuracy still exists. In this paper, an unsupervised denoising feature learning model is proposed to learn a discriminative feature representation from corrupted images. First, the relationship between initial data and low-dimensional feature representation is explored. We introduce several classical dimensionality reduction algorithms. Second, the working mechanism of denoising feature learning model is proved. Due to its structural characteristics, denoising model can extract robust features from partially destroyed data to represent the original data. It demonstrates proposed model can extract essential information from noisy image to decrease reconstruction error. Finally, extensive experiments demonstrate that the features representation obtained by the proposed method outperforms those obtained by the existing classical dimensionality reduction algorithms.
A Wearable Acoustic Sensor Based Driver Distraction Behaviour Recognition

Distraction behavior of drivers could impair driving safety. Therefore, behaviour monitoring of drivers is essential for Intelligent vehicle and autonomous driving system. Prevalent driver behavior recognition methods mostly employed machine vision techniques where images and videos of drivers and passengers inside are collected by in-car cameras. In fact, these methods suffer from controversial issues of privacy-violations and possible spoofing. Also, behavior like yawning is significant for driver drowsiness detection, it is difficult for vision based methods to distinguish yawning from other regular behavior as they are similar in terms of facial features. In this work, we put forward a novel driver distracting behavior monitoring system via wearable acoustic sensor incorporating with Mel-spectrum transform and deep learning. We provide detailed analysis of our framework. Experimental results confirm that our method yields quality inference on typical driving safety affecting behaviours.
A new method for measuring 3D rotation angle of spherical joint

A new method for measuring 3D rotation angle of precision spherical joint based on eddy current sensors is proposed in this study. A pseudorandom code is used to generate a 2D plane absolute code and then mapped to the sphere. The 3D absolute code is formed on the ball head through precision cutting, and an eddy current sensor array is arranged in the ball socket to identify the code. The experimental tests were conducted on the parameter-optimized prototype, and the experimental results showed that the mean squared error of the 3D rotation angle measurement was approximately 25′ in the range. The proposed new measurement principle provides a new approach to the precision measurement of spherical rotary motion, which is expected to produce a breakthrough in the theory and technology of spherical-angle sensor measurement. The proposed new principle also provides a theoretical basis and technical experience for the integration and development of intelligent devices.
Hyperspectral Image Stripe Removal Network With Cross-Frequency Feature Interaction

Remote sensing images, especially hyperspectral images (HSIs), are extremely vulnerable to random noise and stripe noise. As a key aspect of HSI data quality improvement, stripe noise removal has always been a pervasive issue in remote sensing image processing. Convolutional neural networks have been applied for HSI data destriping. However, the existing methods lose the stripe-free component of the original image to a certain extent. These models also ignore the global spatial context of images and the correlation between spatial information and spectral information. Therefore, we propose a novel destriping convolutional network to overcome the problems with the existing methods. Octave convolution is used to extract cross-frequency features, and separate and compress the low-frequency information of the images, while dilation convolution (Dila-Conv) is used to reduce the amount of required calculation and also preserve the key image information. In addition, Dila-Conv can expand the receptive field to obtain multiscale features. Finally, a cross-channel enhanced spatial–spectral feature fusion module is used to acquire and integrate spatial context information and interchannel dependencies on a global scale as auxiliary information so that the network model can learn and pay attention to key feature information, specifically, “what to look for” and “where to look at,” which can facilitate the distinction between stripe and stripe-free components. Experimental results obtained using multiple datasets demonstrated that the proposed method can outperform the existing comparable methods and can produce satisfactory results in terms of visual effects and quantitative evaluation.