Reviewerly/exHarmony huggingface dataset:
Computational Operations and Hardware Resource Estimation in a Convolutional Neural Network Architecture

The convolutional neural network (CNN) models have proved to be very advantageous in computer vision and image processing applications. Recently, due to the increased accuracy of the CNNs on an extensive variety of classification and recognition tasks, the demand for real-time hardware implementations has dramatically increased. They involve intensive processing operations and memory bandwidth for achieving desired performance. The hardware resources and approximate performance estimation of a target system at a higher level of abstraction is very important for optimized hardware implementation. In this paper, initially we developed an 'Optimized CNN model', and then we explored the approximate operations and hardware resource estimation for this CNN model along with suitable hardware implementation process. We also compared the computed operations and hardware resource estimation of few published CNN architectures, which shows that optimization process highly helps in reducing the hardware resources along with providing a similar accuracy. This research has mainly focused on the computational complexity of the convolutional and fully connected layers of our implemented CNN model.
RSG-Net: A Recurrent Similarity Network With Ghost Convolution for Wheelset Laser Stripe Image Inpainting

Wheelset fault detection with high accuracy is challenging due to poor image quality. Specifically, the wheelset images are collected dynamically outdoors and suffer from diffuse reflection and environmental interference. Thus, the images contain light stripe adhesions (light flairs) and local fractures to be inpainted. The existing inpainting models are inapplicable to restore grayscale wheelset images. They are also too heavy to be deployed in an embedded wheelset monitoring equipment. In this paper, we propose a lightweight high-precision inpainting model that consists of a recurrent similarity network with the ghost convolution (RSG-Net) to remove light flairs and repair local fractures. RSG-Net replaces standard Pconv (partial convolutional) layers with soft-coding ones that can improve the feature representational ability. To reduce the influence of the background region features on image restoration, an asymmetrical similarity measure is designed to calculate not only the angle difference between the target and the source feature vectors but also the activation of the source ones. The multi-scale structural similarity (MS-SSIM) loss term is introduced to precisely guide the structural information restoration, such as the stripe edges. Moreover, the ghost convolution is introduced in RSG-Net to realize the model compression that can retain the core features of wheelset images and remove the redundant features. We conduct three groups of experiments that demonstrate the accuracy superiority of the proposed RSG-Net over the baseline methods, and the number of parameters is reduced by about 50%.
MASS: A Multisource Domain Adaptation Network for Cross-Subject Touch Gesture Recognition

Touch gesture recognition (TGR) plays a pivotal role in many applications, such as socially assistive robots and embodied telecommunication. However, one obstacle to practicality of existing TGR methods is the individual disparities across subjects. Moreover, a deep neural network trained with multiple existing subjects can easily lead to overfitting for a new subject. Hence, how to mitigate the discrepancies between the new and existing subjects and establish a generalized network for TGR is a significant task to realize reliable human–robot tactile interaction. In this article, a novel framework for Multisource domain Adaptation via Shared-Specific feature projection (MASS) is proposed, which incorporates intradomain discriminant, multidomain discriminant, and cross-domain consistency into a deep learning network for cross-subject TGR. Specifically, the MASS method first extracts the shared features in the common feature space of training subjects, with which a domain-general classifier is built. Then, the specific features of each pair of training and testing subjects are mapped and aligned in their common feature space, and multiple domain-specific classifiers are trained with the specific features. Finally, the domain-general classifier and domain-specific classifiers are ensembled to predict the label for the touch samples of a new subject. Experimental results performed on two datasets show that our proposed MASS method achieves remarkable results for cross-subject TGR. The code of MASS is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/AI-touch/MASS</uri> .
Differentially-Private “Draw and Discard” Machine Learning: Training Distributed Model from Enormous Crowds

The setting of our problem is a distributed architecture facing an enormous user set, where events are repeating and evolving over time, and we want to absorb the stream of events into the model: first local model, then absorb it in the global one, and also care about user privacy. Naturally, we learn a phenomenon which happens distributedly in many places (like malware spread over smartphones, user behavior to operation and UX of an app, or other such events). To this end, we considered a configuration where the learning server is built to deal with the possibly high frequency high-volume environment in a natural distributed fashion, while taking care of statistical convergence and privacy properties of the setting as well. We propose a novel framework for privacy-preserving client-distributed machine learning. It is based on the desire to allow differential privacy guarantees in the local model of privacy in a way that satisfies systems constraints using high number of asynchronous client-server communication (i.e., not much coordination among separate clients, which is a simple communication model to implement, which in some settings already exist, i.e., in apps facing users), and provides attractive model learning properties. We develop a generic randomized learning algorithm “Draw and Discard” because it relies on random sampling and discarding of models for load distribution and scalability, which also provides additional server-side privacy protections and improved model quality through averaging. The model is general and we show its applicability to Generalized Linear models. We analyze the statistical stability and privacy guarantees provided by our approach against faults and against several types of adversaries. We then showcase experimental results. Our framework (first reported in [28]) has been experimentally deployed in a real industrial setting. We view the result as an initial combination of ML and of distributed systems, and we believe it poses numerous directions for further developments.
Doughnut Computing in City Planning for Achieving Human and Planetary Rights

Doughnut economics provides a framework for appraising how to improve social behaviour through new relations and goals in the way humanity uses (and suffers from the lack of) resources for services. Municipalities used to have several competences and responsibilities either in the use of resources and the provision of (social) services to citizens. Usually they hold a huge amount of data and experiences that can be used to provide better planning of their responsibilities to citizens. Cities abstract social behaviour for regeneration and distribution in two aspects: i) those related to the interaction of individuals inside big communities, basically cities, and the global beneficials obtained by social services and solidarity, and ii) the social relationship of these communities (cities and citizens) with nature, in the sense of intertwining with sustainability and reasonable use of (natural) resources. In [9], Dahl proposed and justified Doughnut Computing, an AI methodology based on inferential programming that, using the Doughnut Economics model as a compass [21]), can help us achieve, both locally and globally, the goal of enabling social and ecological well-being. In this paper, we discuss how to specialize this methodology, always guided by the human and sustainability goals of Doughnut economics, to analize cities’ social behaviour as communities in order to perform planning of municipal services to improve social relations, sustainability and quality of life. This paper reports our first experiences in three different cities across the world, showing a very promising interplay between computation, sustainability, and social services.
Winner Determination Algorithms for Graph Games with Matching Structures

Cram, Domineering, and Arc Kayles are well-studied combinatorial games. They are interpreted as edge-selecting-type games on graphs, and the selected edges during a game form a matching. In this paper, we define a generalized game called Colored Arc Kayles, which includes these games. Colored Arc Kayles is played on a graph whose edges are colored in black, white, or gray, and black (resp., white) edges can be selected only by the black (resp., white) player, although gray edges can be selected by both black and white players. We first observe that the winner determination for Colored Arc Kayles can be done in $$O^*(2^n)$$ time by a simple algorithm, where n is the order of a graph. We then focus on the vertex cover number, which is linearly related to the number of turns, and show that Colored Arc Kayles, BW-Arc Kayles, and Arc Kayles are solved in time $$O^*(1.4143^{{\tau }^2+3.17{\tau }})$$ , $$O^*(1.3161^{{\tau }^2+4{{\tau }}})$$ , and $$O^*(1.1893^{{\tau }^2+6.34{{\tau }}})$$ , respectively, where $${\tau }$$ is the vertex cover number. Furthermore, we present an $$O^*((n/{\nu }+1)^{{\nu }})$$ -time algorithm for Arc Kayles, where $${\nu }$$ is neighborhood diversity. We finally show that Arc Kayles on trees can be solved in $$O^* (2^{n/2})(=O(1.4143^n))$$ time, which improves $$O^*(3^{n/3})(=O(1.4423^n))$$ by a direct adjustment of the analysis of Bodlaender et al.'s $$O^*(3^{n/3})$$ -time algorithm for Node Kayles.
An Effective Generative Model Based Channel Estimation Method With Reduced Overhead

The traditional channel estimation for OFDM systems suffers from big overheads caused by pilots. Current channel estimation methods only consider the correlation among pilots within a time-frequency grid, which is a kind of local channel information. In this article, we propose a Generative Adversarial Network (GAN) based channel estimation method to utilize not only local channel information but also correlation across multiple frames, thus much fewer pilots are needed. We propose to explore GAN to establish a mapping from a low dimensional space to a high dimensional channel space. Then based on very few pilots, we solve an optimization problem to localize the low dimensional vector corresponding to the specified channel and generate the corresponding channel. Furthermore, we propose an improved conditional GAN (cGAN), which takes the multi-path number as conditional information, making the proposed generative channel model more robust in various scenarios. Experiments demonstrate that the proposed cGAN-based method can be applied to multiple channel profiles once trained offline, and it is robust to the mismatch of multi-path number. Besides, the proposed method outperforms the ideal ALMMSE and ChannelNet methods under the same pilot density. In the best case, it can achieve comparable performance with only about half the number of pilots.
A Scenario Approach for Synthesizing <i>k</i>-Inductive Barrier Certificates

The notion of k-inductive barrier certificates generalize the idea of k-induction to verification of discrete-time continuous-state dynamical systems by requiring restrictions over k-grams (sequence of k-states in evolution) of the system transitions. The promise of k-inductive barrier certificates is in the simplicity of the form of barrier certificates (lower-degree of polynomials) at the cost of more complex non-convex constraints involving logical implications. Recent breakthroughs in convex robust programming via the scenario approach deliver a sampling-based randomized algorithm for the computation of barrier certificates. In the absence of system dynamics (a.k.a. black box models), extending scenario approach to k-inductive barrier certificates faces challenges due to the resulting lack of convexity. This paper overcomes non-convexity challenges by providing a sound approach for data-driven computation of k-inductive barrier certificates. We present computational methods to solve the resulting scenario programs for k-inductive barrier certificates, provide out-of-sample performance guarantees, and experimentally demonstrate the effectiveness of the proposed results.
A Unified Mechanism for Cloud Scheduling of Scientific Workflows

Scheduling plays a vital role in the efficient utilization of the available resources in clouds.This paper investigates the capabilities of the current scheduling algorithms of WorkFlowSim framework for processing scientific workflows.These investigations used four different sizes of workloads each, for, five well-known workflows.It was revealed that none of the existing algorithms is capable of efficiently executing all the four sizes of workload for the complete set of workflows.Different algorithms performed better, when they were applied to various workloads of a particular workflow.This fact was used in developing an improved unified mechanism, which is capable of using an existing algorithm that performed well in the past, against the given workload.Evaluation results showed that the proposed mechanism improved over the existing algorithms for 4 out of 5 workflows (Epigenomics, Inspiral, Cyber Shake, and Montage), when tested against an aggregated load of all sizes, in terms of simulation time.For the workflow named SIPHT, however, it responded exactly the same as Max-Min algorithm.The minimum and maximum improvements, against the existing best and worst algorithms, in percentage, for Epigenomics, Inspiral, SIPHT, Cyber Shake and Montage were 16-63, 30-68, 0-69, 30-68, and 9-71 in corresponding order.This work has an additional overhead in terms of a dedicated module to find and store algorithmic performance.It is, however, required once and, thus, the increase in execution time might be marginal.The future work intends to check the impact of compute time towards optimization parameters such as makespan, pricing and deadlines.
Generative Adversarial Network for Car Following Trajectory Generation and Anomaly Detection

Vehicle trajectory generation and anomaly detection are critical in the sensing and prediction module for automated driving. However, developing models that capture realistic trajectory data distribution and detect anomalous driving behaviors could be challenging. This paper proposes ‘TrajGAN,’ an unsupervised approach based on the Generative Adversarial Network (GAN) to exploit vehicle trajectory data for generation and anomaly detection. TrajGAN consists of an encoder-decoder Long Short-Term Memory (LSTM)based generator and an LSTM-multilayer perceptron (MLP) based discriminator. Trained with the Next Generation Simulation (NGSIM) dataset, TrajGAN can generate realistic trajectories with a similar distribution of training data and identify a manifold of anomalous trajectories based on an anomaly scoring scheme. Applied to new trajectory data, the model scores trajectory sections at the microscopic level, indicating their fit into the learned distribution. Simulated experiments validate that the approach can effectively reproduce artificial trajectories and identify anomalous driving behaviors.
Mining Implication in the Geochemical Exploration of Fluvial Sediments of the East Nyambaka Volcanic Zone (Adamawa-Cameroon)

Geochemical characterization in stream sediments from Nyambaka locality was carried out on ten samples collected from the study area watershed.The distribution of trace metals in the sediments showed that some lithophiles (Be, Sc, V, Cr, Sr, Th, U), chalcophiles (Cu, Zn, Ga, As) and siderophiles (Mn, Co, Ni, Mo) have high average contents in the sediments compared to their average concentration in the upper crust.From the factorial analysis of the components, the total variance explained made it possible to represent and capture the main information on the data and to represent them in a graphical frame.Thus, using this graphical frame, the trace elements were divided into four groups opposed to each other.They show an enrichment of stream sediments in group 1 (Cr, Cu, Mn, Be, Ni, Co, Zr, Sr, Ba, Mg) and group 2 (Mo, As, Hg, V, Ag) elements more than group 3 (Ga, Sc, U) and group 4 (Au, Th).Rare earth analysis indicates a high abundance of Lanthanide (28.1 ppm to 42.4 ppm), a high concentration of light rare earth (LREE), and depletion of heavy rare earth (HREE).The variable LREE/HREE (7.24 < LREE/ HREE < 12.22) and (La/Yb)N (6.70 < (La/Yb)N < 11.72) ratios suggest an effect of heterogeneous sorting of heavy minerals with variable enrichment of dense and resistant minerals.
Innovations in professional activity: what students of specialty “Preschool Education” think about it

Contemporary challenges of society, its dynamic development cause changes in all spheres of life, particularly in education. To obtain a university education in the Ukrainian state, higher Education Standard in specialty 012 Preschool Education for the second (master’s) level of higher education (2020) was approved, which provides for the formation of relevant competencies. The purpose of the study was to analyze the essence of innovations in the professional activities of teachers of preschool educational institutions (PEI). To find out the attitude of students to the implementation of innovations in professional activities, a thematic survey was conducted. The selection of questions and respond options is based on all competencies of the Standard and the materials of theoretical analysis. The sample of the online survey covers the students’ responses from two state universities of Ukraine ( n =159), who are obtaining the second (master’s) level of higher education on the educational-professional program “Preschool Education”. Respondents were grouped according to the mode of study. The obtained quantitative and qualitative data revealed the influence of the experience of practical experience in the PEI on the choice of the respond. This determined the necessity to teach students the educational component “Innovation activities in PEI” and the development of corresponding tasks during their teaching practicum in the system of preschool education.
Anomaly Detection in Thermal Images of Perishable Items Using Deep Learning

In this article, deep learning architecture has been used to detect anomalies on thermal images of perishable items like fruits and vegetables. Three experiments have been conducted, and their performances have been studied. In first experiment, two different deep learning models have been applied for classifying presence or absence of anomaly and Deep-CNN (Deep-Convolutional Neural Networks) performs well. In second experiment, YoLoV4 and YoLoV5 have been used to classify and localize the anomalies; YoLoV5 localizes the anomalies effectively with a mAP (Mean Average Precision) of 95.67%. In third experiment, YoLoV5 and Faster-RCNN (Faster Region Based Convolutional Neural Network) have been trained on the dataset in which each image contains multiple objects with and without anomalies to detect and localize them; Faster-RCNN gives mAP of 96.55% for detecting and localizing anomalies. From the experiments, it is observed that proposed deep learning models can be used for anomaly detection in thermal images.
CNNs with Compact Activation Function

Activation function plays an important role in neural networks. We propose to use hat activation function, namely the first order B-spline, as activation function for CNNs including MgNet and ResNet. Different from commonly used activation functions like ReLU, the hat function has a compact support and no obvious spectral bias. Although spectral bias is thought to be beneficial for generalization, we show that MgNet and ResNet with hat function still exhibit a slightly better generalization performance than CNNs with ReLU function by our experiments of classification on MNIST, CIFAR10/100 and ImageNet datasets. This indicates that CNNs without spectral bias can have a good generalization capability. We also illustrate that although hat function has a small activation area which is more likely to induce vanishing gradient problem, hat CNNs with various initialization methods still works well.
Better Exploiting BERT for Few-Shot Event Detection

Recent approaches for event detection rely on deep supervised learning, which requires large annotated corpora. Few-shot learning approaches, such as the meta-learning paradigm, can be used to address this issue. We focus in this paper on the use of prototypical networks with a BERT encoder for event detection. More specifically, we optimize the use of the information contained in the different layers of a pre-trained BERT model and show that simple strategies for combining BERT layers can outperform the current state-of-the-art for this task.
Explaining Graph Neural Networks With Topology-Aware Node Selection: Application in Air Quality Inference

Graph neural networks (GNNs) have proven their ability in modelling graph-structured data in diverse domains, including natural language processing and computer vision.However, like other deep learning models, the lack of explainability is becoming a major drawback for GNNs, especially in health-related applications such as air pollution estimation, where a model's predictions might directly affect humans' health and habits.In this paper, we present a novel post-hoc explainability framework for GNNbased models.More concretely, we propose a novel topology-aware kernelised node selection method, which we apply over the graph structural and air pollution information.Thanks to the proposed model, we are able to effectively capture the graph topology and, for a certain graph node, infer its most relevant nodes.Additionally, we propose a novel topological node embedding for each node, capturing in a vector-shape the graph walks with respect to every other graph node.To prove the effectiveness of our explanation method, we include commonly employed evaluation metrics as well as fidelity, sparsity and contrastivity, and adapt them to evaluate explainability on a regression task.Extensive experiments on two real-world air pollution data sets demonstrate and visually show the effectiveness of the proposed method.
Graph-Based Generative Face Anonymisation with Pose Preservation

We propose AnonyGAN, a GAN-based solution for face anonymisation which replaces the visual information corresponding to a source identity with a condition identity provided as any single image. With the goal to maintain the geometric attributes of the source face, i.e., the facial pose and expression, and to promote more natural face generation, we propose to exploit a Bipartite Graph to explicitly model the relations between the facial landmarks of the source identity and the ones of the condition identity through a deep model. We further propose a landmark attention model to relax the manual selection of facial landmarks, allowing the network to weight the landmarks for the best visual naturalness and pose preservation. Finally, to facilitate the appearance learning, we propose a hybrid training strategy to address the challenge caused by the lack of direct pixel-level supervision. We evaluate our method and its variants on two public datasets, CelebA and LFW, in terms of visual naturalness, facial pose preservation and of its impacts on face detection and re-identification. We prove that AnonyGAN significantly outperforms the state-of-the-art methods in terms of visual naturalness, face detection and pose preservation. Code and pretrained model are available at https://github.com/Fodark/anonygan .