Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning assisted Loop Distribution for Locality and Vectorization

Loop distribution (loop fission) is a well known compiler optimization that splits the loop into multiple loops. Loop distribution can be seen as an enabler of various other optimizations with different goals, like, the parallelizability of the loop, the vectorizability of the loop, its locality characteristics. In this work, we present a Reinforcement Learning (RL) based approach to efficiently perform loop-distribution with the twin goals of optimizing for both vectorization as well as locality. Broadly, we generate the SCC Dependence Graph (SDG) for each loop of the program. Our RL model learns to predict the distribution order of the loop by performing topological walk of the graph. The reward to our model is computed using the instruction cost and number of cache misses of the loop. As the RL models need data for training purposes, we also propose a novel strategy to extend the training set by generating new loops. We show our results on x86 architecture on various benchmarks: from TSVC, LLVM-Test-Suite, PolyBench and PolyBench-NN. Our framework achieves an average improvement of 3.63% on TSVC, 4.61% on LLVM-Test-Suite Microbenchmarks, 1.78% on PolyBench and 1.95% on PolyBench-NN benchmark suites for execution time. The baseline is O3 compiler option of LLVM. We also show the improvements of our method on other performance metrics like Instruction Per Cycle (IPC), Number of loops distributed & vectorized, and L1 cache performance.
A Fitness Landscape Analysis of the LeNet-5 Loss Function

Fitness landscape analysis (FLA) refers to a set of techniques that allow for the characterisation, visualisation and comprehension of the trends of objective functions within their decision spaces. Two of the important features estimated through FLA are ruggedness, i.e. the number and distribution of optima within the decision space, and neutrality, i.e. the width, distribution and frequency of areas with one objective function value. Recent studies have proposed the application of FLA to the training problem in supervised learning. The present paper extends previous results on ruggedness and neutrality to investigate the loss landscape of LeNet-5. More specifically, we demonstrate that the neutrality threshold is an important metaparameter and that its optimisation is required to correctly assess the ruggedness and neutrality of a problem. Furthermore, this study investigates the difference between the landscape perceived by a learning algorithm and the actual landscape. The obtained numerical results indicate that the division of datasets into batches causes an increase in perceived ruggedness. As a result, modern training algorithms that make use of division into batches, such as Adam, overestimate ruggedness.
Deep Convolutional Autoencoder for Assessment of Drive-Cycle Anomalies in Connected Vehicle Sensor Data

This work investigates a practical and novel method for automated unsupervised fault detection in vehicles using a fully convolutional autoencoder. The results demonstrate the algorithm we developed can detect anomalies which correspond to powertrain faults by learning patterns in the multivariate time-series data of hybrid-electric vehicle powertrain sensors. Data was collected by engineers at Ford Motor Company from numerous sensors over several drive cycle variations. This study provides evidence of the anomaly detecting capability of our trained autoencoder and investigates the suitability of our autoencoder relative to other unsupervised methods for automatic fault detection in this data set. Preliminary results of testing the autoencoder on the powertrain sensor data indicate the data reconstruction approach availed by the autoencoder is a robust technique for identifying the abnormal sequences in the multivariate series. These results support that irregularities in hybrid-electric vehicles' powertrains are conveyed via sensor signals in the embedded electronic communication system, and therefore can be identified mechanistically with a trained algorithm. Additional unsupervised methods are tested and show the autoencoder performs better at fault detection than outlier detectors and other novel deep learning techniques.
A Meta Heuristic Genetic Algorithm for Multi-Depot Routing in Autonomous Bridge Inspection

We attack the problem of routing autonomous climbing robots for steel truss bridge inspection using a metaheuristic genetic algorithm. These robots, deployed from four depots at the four corners of the bridge, must traverse every member of the bridge truss while minimizing distance traveled and balancing tours among robots. This problem maps to the well known NP-Hard Min-Max Multi-Depot k (robot) Chinese Postman Problem. We constructed 20 benchmark bridge instances of four different types of truss configurations using realistic dimensions as a testbed for comparison. Compared to the best known direct encoded genetic algorithm approach, our metaheuristic genetic algorithm produces routes that are on average 25% better quality, and does so 22 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> faster. On the four depot version of the problem, the MetaGA on average performs 42% better. These results on our benchmarks show evidence our metaheuristic genetic algorithm provides high-quality tours in realistic time for real-world robot bridge inspection scenarios and has the potential to generalize to vehicle routing and the broader class of arc-routing problems.
Spatial Super Resolution Technology of FY-3D MWRI With Deep Internal Learning

The data of Microwave Radiation Imager(MWRI) on FY3D satellite suffers from numerous impacts during data acquisition, resulting in a loss in spatial resolution. However, existing methods such as wiener filtering and Lucy Richardson are unable to enhance spatial resolution under complicated deterioration conditions. In this paper, an unsupervised method using the “zero-shot” super-resolution(ZSSR) framework is applied to microwave image processing. Experimental results on simulated images and real MWRI images have demonstrated the validity and effectiveness of the method.
“Even if …” – Diverse Semifactual Explanations of Reject

Machine learning based decision making systems applied in safety critical areas require reliable high certainty predictions. For this purpose, the system can be extended by an reject option which allows the system to reject inputs where only a prediction with an unacceptably low certainty would be possible. While being able to reject uncertain samples is important, it is also of importance to be able to explain why a particular sample was rejected. With the ongoing rise of eXplainable AI (XAI), a lot of explanation methodologies for machine learning based systems have been developed - explaining reject options, however, is still a novel field where only very little prior work exists. In this work, we propose to explain rejects by semifactual explanations, an instance of example-based explanation methods, which them self have not been widely considered in the XAI community yet. We propose a conceptual modeling of semifactual explanations for arbitrary reject options and empirically evaluate a specific implementation on a conformal prediction based reject option.
Solar Irradiance Forecast using Long Short-Term Memory: A Comparative Analysis of Different Activation Functions

Microgrid consists of multiple Distribution Generations (DGs) such as solar PV and wind turbines which are weather dependent. As DGs are weather dependent, a collection of weather parameters from the Singapore weather station is used as the dataset for solar irradiance forecasting. It is essential to have an accurate solar irradiance tool to efficiently manage microgrid renewable power generation. In this paper, a vanilla Long Short-Term Memory model is implemented using different activation functions, including ReLU, ELU, Leaky-ReLU, SELU, and GELU, in the dense layer to forecast short-term solar irradiance. Activation functions introduce the non-linearity and learn the relationship between the input and output values. The importance of the activation function is to support on the model learning and execution of difficult tasks. With the activation function, the model creates stacking capabilities of multiple layers of neurons to develop the deep neural networks to learn complex datasets. The impact of the proposed methods is evaluated in terms of efficiency, robustness, and accuracy of each activation function.
Adversarially Robust Deepfake Video Detection

Fake videos have been in circulation on mainstream media since long. However, with increased popularity of online social networks, it is becoming many times easier to spread such videos and achieve virality. Recent advancements in deep learning has further fuelled this menace as the so called deepfake videos are hard to differentiate from the genuine ones. While deepfake video detection techniques attempt to identify the fake videos from real videos, these are now being subjected to adversarial attacks, thus undermining their efficacy. In this paper, we show that accuracy of deepfake detectors can be considerably improved by incorporating an adversarial learning step during model building. We use a recently proposed deep network architecture, namely VGG19, as deepfake detector supplemented with adversarial training using Iterative Fast Gradient Sign Method (I-FGSM). To further improve non-adversarial accuracy ensemble of models were used. Extensive experiments on a large deepfake video corpus under different white box adversarial attacks demonstrate significant adversarial robustness of the proposed method.
A Large-Scale Group Decision-Making Method based on Sentiment Analysis for the Detection of Cooperative Group

Group Decision-Making is a process by which a set of experts sort a set of alternatives. When there are a large number of experts, the process is called a Large-Scale Group Decision-Making process. In this kind of environment, there is a great variety of attitudes when discussing the set of alternatives. For instance, there may be experts who may be more aggressive or more peaceful. Aggressiveness is directly related to the degree of cooperativeness of the experts because if an expert is aggressive it implies that he/she will be less cooperative. Consequently, this paper develops a Large-Scale Group Decision-Making method that classifies experts according to their cooperativeness. This classification is based on using sentiment analysis to detect the degree of aggressiveness of each expert. Thus, it is possible to determine whether a specific behavior implies similar valuations and also to detect which experts are more cooperative and are able to reach a consensual ranking of alternatives and which ones would be less willing to cooperate to reach a consensual decision.
Cross-domain Algorithm Selection: Algorithm Selection across Selection Hyper-heuristics

The present study introduces algorithm selection on selection hyper-heuristics. Hyper-heuristics are known as problem-independent methods utilized to solve different instances from varying problem domains. In the literature, there has been effective hyper-heuristic designs providing a certain level of generality in problem solving. Still, the relevant existing research indicates that there is no single hyper-heuristic which performs always the best on different problem solving scenarios. Algorithm selection has been investigated essentially to address this issue, mainly for the problem-specific algorithms, by automatically identifying the (near) best algorithm(s) for each given problem instance. This paper performs algorithm selection on selection hyper-heuristics, for the first time, delivering cross-domain algorithm selection. For this purpose, a suite of problem-independent features is initially introduced. Then, algorithm selection is examined across 9 single-objective combinatorial optimization problems with 6 online selection hyper-heuristics. The experimental results carried out on these problems indicated that algorithm selection is effective for choosing hyper-heuristics while offering improved generality and robustness.
Comparison of Edge Computing Methods for Environmental Monitoring IoT Sensors Using Neural Networks and Wavelet Transforms

IoT sensor solutions have become ubiquitous in recent years. This rapid growth has resulted in large amounts of real-time data being transmitted to a cloud. Cloud computing paradigms have extensive requirements on data rates in a communications channel. The current trend is to use edge computing techniques to process data files primarily on the edge of a network. The present paper compares neural networks and wavelet transforms which apply compression methods to environmental parameter datasets. Three selected parameters (temperature, air pressure and wind speed) were compressed at ratios of 1:2, 1:4, 1:8 and 1:12. In addition, three different types of wavelet were compared in the wavelet transform. The results showed that the best RMSE compression result can be achieved by using a Biorthogonal wavelet, which compressed up to 93 % of the volume of data. In using a neural network to compress data, much depends on the nature of the data and also the amount of training data. With appropriately selected data, a volume reduction of 88 % and value of RMSE 0.5356 can be achieved.
Ensemble Learning based on Classifier Prediction Confidence and Comprehensive Learning Particle Swarm Optimisation for Medical Image Segmentation

Segmentation, a process of partitioning an image into multiple segments to locate objects and boundaries, is considered one of the most essential medical imaging process. In recent years, Deep Neural Networks (DNN) have achieved many notable successes in medical image analysis, including image segmentation. Due to the fact that medical imaging applications require robust, reliable results, it is necessary to devise effective DNN models for medical applications. One solution is to combine multiple DNN models in an ensemble system to obtain better results than using each single DNN model. Ensemble learning is a popular machine learning technique in which multiple models are combined to improve the final results and has been widely used in medical image analysis. In this paper, we propose to measure the confidence in the prediction of each model in the ensemble system and then use an associate threshold to determine whether the confidence is acceptable or not. A segmentation model is selected based on the comparison between the confidence and its associated threshold. The optimal threshold for each segmentation model is found by using Comprehensive Learning Particle Swarm Optimisation (CLPSO), a swarm intelligence algorithm. The Dice coefficient, a popular performance metric for image segmentation, is used as the fitness criteria. The experimental results on three medical image segmentation datasets confirm that our ensemble achieves better results compared to some well-known segmentation models.
Revisiting Echo State Networks for Continuous Gesture Recognition

Smartphones are equipped with Inertial Measurement Units (IMUs) that can capture user gesture data. Continuous gesture recognition is essential as it can be utilized and enhance human-computer interaction. Echo State Networks (ESNs) and Long Short-Term Memory (LSTM) models are well suited to performing this task. They have been successfully applied to the task in previous research, with LSTMs outperforming ESNs while having a considerably longer training time. However, the application of ESNs to continuous gesture recognition has not been fully explored as only the leaky integrator ESN has been used without hyperparameter optimization. In this study, we attempt to improve the ESN performance on the continuous gesture recognition task by experimenting with different model architectures and hyperparameter tuning. The performance of ESN models is significantly enhanced in terms of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> -score to 0.88, which is higher than the previously best performance of 0.87 using an LSTM model on continuous gesture recognition. The significant improvement is in training time, which is approximately 13 seconds for the ESN model compared to 89 seconds for the LSTM model in past research.
Efficient Video Summarization for Smart Surveillance Systems

In surveillance systems, vast amounts of data are collected from different sources to monitor ongoing video activities. Usually, video data is passively captured by visual sensors and forwarded to the command center, without intelligent Edge functionalities to select essential video information and locally detect abnormal events therein. These shortcomings often seen in practical surveillance scenarios lead to a wastage of storage resources and make data management, retrieval, and informed decision complex and time-consuming. Therefore, endowing visual sensors with video summarization capabilities is of utmost importance for smarter surveillance systems. This study departs from this rationale to propose an efficient neural networks-based video summarization method for surveillance systems. The proposed approach learns how to optimally segment a video by measuring informative features from the data flow, followed by memorability and entropy to maintain the relevance and diversity of the video summary produced on the Edge. Experimental results over benchmark datasets reveal that the proposed scheme outperforms other state-of-the-art counterparts and proves the effectiveness of our method for video summarization in smart cities.
Reusable Toolkit for Natural Language Processing in an Ambient Intelligence Environment

Computational natural language processing (NLP) is indispensable in a humanized ambience intelligence environment. NLP facilitates ambient intelligence by making machines understand, and be understood by, humans. This in turn makes machines behave more human-like than they typically are today. Technological advances in machine learning (ML), and especially deep learning (DL), have been a key enabler of NLP research. This paper begins with a survey of recent developments of ML/DL for NLP. It then identifies some of the most promising techniques reported in recent literature. These most promising techniques are then assembled into a reusable toolkit for computational NLP. The adaptable nature of the assembled toolkit allows it to be reused in a broad range of NLP applications. The paper then describes experimental evaluation of our implemented solutions for comparative analysis. Two specific NLP applications form the basis of comparative evaluation. The first involves identifying one of M English sentences that does not make sense. The second, which is harder than the first, involves choosing from among N sentences the one that best explains why a presented sentence is invalid. Human baseline accuracies for these applications are 99.1% and 97.8%, respectively. The observation that these results are somewhat less than perfect demonstrates that even humans can occasionally find these tasks difficult. It further underscores the difficulties involved in some of these computational NLP applications. Experiments conducted on benchmark data show that advanced ML/DL can achieve near-human performance in both computational NLP applications with accuracy scores of 96.1% and 93.7%, respectively.
Efficient CNN with uncorrelated Bag of Features pooling

Despite the superior performance of CNN, deploying them on low computational power devices is still limited as they are typically computationally expensive. One key cause of the high complexity is the connection between the convolution layers and the fully connected layers, which typically requires a high number of parameters. To alleviate this issue, Bag of Features (BoF) pooling has been recently proposed. BoF learns a dictionary, that is used to compile a histogram representation of the input. In this paper, we propose an approach that builds on top of BoF pooling to boost its efficiency by ensuring that the items of the learned dictionary are non-redundant. We propose an additional loss term, based on the pair-wise correlation of the items of the dictionary, which complements the standard loss to explicitly regularize the model to learn a more diverse and rich dictionary. The proposed strategy yields an efficient variant of BoF and further boosts its performance, without any additional parameters.
Real-Time Human Fall Recognition based on Deep Learning Methods and Single Depth Image with Privacy Requirements

With the decline in physical function of the elderly, the risk of falling down increases and threatening their health. In the traditional fall recognition methods, multi-frame RGB image sequences are obtained to recognize falls, which may lead to problems of a high computational cost, real-time recognition, privacy leakages. In this paper, a novel fall recognition algorithm based on a single frame human binary image is proposed. Especially, the method is suitable for scenes with a basically unchanged background and privacy protection requirements, such as bathroom and toilet. The experimental results show that the precision and recall of the model achieves 97.3% and 99.4% on the public URFD dataset, which meets the practical application requirements. Furthermore, a posture recognition algorithm based on yolov5s and depth images is designed to distinguish between falling and lying down, which can be applied to bedrooms, living rooms and other places. On the basis of these algorithms, a depth image fall recognition software based on the flask framework is developed and deployed to the Alibaba cloud platform. Finally, the proposed fall recognition approach is validated by experiments and realized in real time.
Tangible User Interface to Learn about Voronoi Diagrams

This paper proposes a tangible user interface to introduce Voronoi diagrams, a geometric model that partitions a plane into particular regions. Although Voronoi diagrams have number of applications, their characteristics and drawing scheme is not familiar to ordinary people. Our tangible environment offers an interactive Voronoi diagram which is manipulated with small Styrofoam objects to indicate Voronoi sites on the tabletop. Two applications, giraffe patches and store placement on a map, were studied with visitors at a science museum. From the results of our user study at a science museum, the proposed tangible learning system turned to be highly effective for users to enhance their interest and understanding of Voronoi diagrams. Our future work includes to conduct a series of user experiments with more visitors in local museums to evaluate usability of the proposed system. Development of other learning applications is also of high importance.
Capture and Recognition of Bead Weaving Activities using Hand Skeletal Data and an LSTM Deep Neural Network

Several factors lead to the extinction of tangible cultural heritage, such as globalization, urbanization, intention and accidental neglect, modernization, mechanization, limited usage, migration, and the minimization of skilled practitioners/craft educators and owners. We, the Gesture and Form group, aim to preserve the bead weaving craft, which plays an essential role in culture. We aim to use Augmented reality (AR) and Machine Learning (ML) to preserve this craft. Preservation in this context means passing the technique from one generation to the other, even without a skilled craftsperson. To achieve this, we aim to use an LSTM deep neural network to classify micro activities in the Peyote stitch with the hand skeletal data from the Microsoft Hololens 2. In this paper, we present our preliminary work and a workflow of the ML process involved with a mini project we called "Thumbs-up gesture classification." Using a single subject participant for the gesture classification, we achieve 100% accuracy on the training and test set, and new data from the device. Finally, we present a future direction for our research.