Reviewerly/exHarmony huggingface dataset:
Investigating Transfer Learning in Graph Neural Networks

Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning has proven extremely successful for traditional deep learning problems, resulting in faster training and improved performance. Despite the increasing interest in GNNs and their use cases, there is little research on their transferability. This research demonstrates that transfer learning is effective with GNNs, and describes how source tasks and the choice of GNN impact the ability to learn generalisable knowledge. We perform experiments using real-world and synthetic data within the contexts of node classification and graph classification. To this end, we also provide a general methodology for transfer learning experimentation and present a novel algorithm for generating synthetic graph classification tasks. We compare the performance of GCN, GraphSAGE and GIN across both synthetic and real-world datasets. Our results demonstrate empirically that GNNs with inductive operations yield statistically significantly improved transfer. Further, we show that similarity in community structure between source and target tasks support statistically significant improvements in transfer over and above the use of only the node attributes.
Smart Electronic Assistive Device for Visually Impaired Individual through Image Processing

According to World Health Organization, it is assessed that around 1.3 billion individuals live with some distance or near vision impairment. People with visual impairment encounter different issues in their everyday life as advanced assistive devices are frequently not meeting the consumer prerequisites in terms of price and level of assistance. The paper’s primary goal is to build a smart electronic assistive device specifically for visually impaired individuals. The study consists of two devices, which are the glasses and the smart cane. The glasses are responsible for text detection and recognition, which falls under image processing. At the same time, the smart cane is the one responsible for obstacle detection using combination sensors known as VL53L0X and Ultrasonic. This study found that the smart cane and smart glass functions well as intended, in alerting users through a voice alert about the obstacles around and navigation and reading the text captured by the camera. The smart cane’s obstacle detection accuracy is 100%, while the smart glasses’ overall accuracy is 98.13% for document text and 91.33% for natural scene text.
GRACGE: Graph Signal Clustering and Multiple Graph Estimation

In graph signal processing (GSP), complex datasets arise from several underlying graphs and in the presence of heterogeneity. Graph learning from heterogeneous graph signals often results in challenging high-dimensional multiple graph estimation problems, and prior information regarding which graph the data was observed is typically unknown. To address the above challenges, we develop a novel framework called GRACGE ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GRAph signal Clustering and multiple Graph Estimation</i> ) to partition the graph signals into clusters and jointly learn the multiple underlying graphs for each of the clusters. GRACGE advocates a regularized EM (rEM) algorithm where a structure fusion penalty with adaptive regularization parameters is imposed on the M-step. Such a penalty can exploit the structural similarities among graphs to overcome the curse of dimensionality. Moreover, we provide a non-asymptotic bound on the estimation error of the GRACGE algorithm, which establishes its computational and statistical guarantees. Furthermore, this theoretical analysis motivates us to adaptively re-weight the regularization parameters. With the adaptive regularization scheme, the final estimates of GRACGE will geometrically converge to the true parameters within statistical precision. Finally, experimental results on both synthetic and real data demonstrate the performance of the proposed GRACGE algorithm.
Drone Detection with Visual Transformer

The rapid growth of drones presents potential threats to public security and personal privacy, and it is vital to effectively detect the intruding drones. Prior work on visual-based drone detection using convolutional networks regards the drone detection task as a regression problem on a large set of human-defined components, i.e., proposals and anchors. These components bring a huge number of predictions to be selected, which pose the challenges to drone detection. In this paper, we propose a Deformable DETR-based drone detector with visual transformer, which eliminates the human-defined components to pursue high-accuracy detection performance. Specifically, to detect remote drones at a higher accuracy, the resolution of the features in backbone is enhanced. Meanwhile, two data augmentation methods including illumination jittering and multi-blurring are developed to cope with the time-varying illumination and the changeable weather, based on which the environmental robustness of the proposed detector is thus maintained. The field experiments are carried out, and it is demonstrated that a higher detection accuracy is achieved for the proposed drone detector.
Predicting Politeness Variations in Goal-Oriented Conversations

Politeness is an expression in language that eases the conversation toward a positive undertone. If there is a display of rudeness, even the finest communication can fall through. In addition, if lathered with politeness, even the most angst-prone scenario can be expressed with far less hurt. In this article, we address the task of identifying politeness in goal-oriented dialog systems. In this regard, we create politeness-annotated conversational data (PACD) utilizing Microsoft Dialogue Challenge and DSTC1 datasets. For correctly identifying the politeness, we employ a hierarchical transformer network that effectively captures the contextual information (i.e., previous utterances) and current input for predicting the politeness in a given utterance of a dialog. Empirical results demonstrate that our proposed approach outperforms all the defined baselines. Furthermore, through in- and cross-domain experiments, we show the necessity of a PACD to mitigate acts such as rude requests or insults for both socially interactive and task-oriented dialog systems.
Dynamic Threshold Setting for VM Migration

Cloud data centres require efficient management of resources and robust methods that consider SLA violations, node utilisation and simplify the adaptation decision making process. Thus resource management should be ideally solved in an online manner. To address this, approaches have been presented in the literature to set thresholds that trigger VM migration. One challenge with these approaches is they typically use node metrics (e.g., CPU and memory) as an indicator of VM performance and do not factor in VM performance metrics when setting the CPU migration threshold. A hypothesis is that migrating VMs without factoring in VM performance metrics, e.g., response time can lead to either early or delayed migration of VMs. We present an approach to discover the CPU utilization level for VM migration dynamically. This approach monitors VM response time and discovers the CPU threshold where response time would increase beyond a defined SLA level and uses this threshold for VM migration. We use reinforcement learning (RL) to learn when it is rewarding to migrate a VM. The RL reward function drives a policy towards high CPU utilisation and attaches a penalty to overachieving SLAs. We use simulation to evaluate the approach and compare it to 4 heuristics: Static, Interquartile Range, Median Absolute Deviation, Local Regression. The results show a significant reduction in SLA violations and an increase in CPU utilization of active nodes.
Concept Drift Detection Delay Index

Data streams may encounter data distribution changes, which can significantly impair the accuracy of models. Concept drift detection tracks data distribution changes and signals when to update models. Many drift detection methods apply thresholds to distinguish between drift or non-drift streams and to claim their method outperforms others with non-aligned drift thresholds. We consider that selecting a proper drift threshold could be more important than developing a new drift detection algorithm, and different drift detection algorithms may end up with very similar performance with aligned drift thresholds. To better understand this process, we propose a novel threshold selection algorithm to align the drift thresholds of a set of algorithms so that they are all at the same sensitivity level. Based on comprehensive experiment evaluations, we observed that several state-of-the-art drift detection algorithms could achieve similar results by aligning their thresholds, providing a novel insight to explain how drift detection algorithms contribute to data stream learning. We noticed that a higher detection sensitivity improves accuracy for data streams with frequent distribution change. The evaluation results are showing that drift thresholds should not be fixed during stream learning. Rather, they should adjust dynamically based on the prevailing conditions of the data stream.
Efficient Runtime Profiling for Black-box Machine Learning Services on Sensor Streams

In highly distributed environments such as cloud, edge and fog computing, the application of machine learning for automating and optimizing processes is on the rise. Machine learning jobs are frequently applied in streaming conditions, where models are used to analyze data streams originating from e.g. sensory data. Often the results for particular data samples need to be provided in time before the arrival of next data. Thus, enough resources must be provided to ensure the just-in-time processing for the specific data stream.This paper focuses on proposing a runtime modeling strategy for containerized machine learning jobs, which enables the optimization and adaptive adjustment of resources per job and component. Our black-box approach assembles multiple techniques into an efficient runtime profiling method, while making no assumptions about underlying hardware, data streams, or applied machine learning jobs. The results show that our method is able to capture the general runtime behaviour of different machine learning jobs already after a short profiling phase.
A Survey on Multi-Objective Tasks and Workflow Scheduling Algorithms in Cloud Computing

The challenge in cloud services is scheduling and allocating resources due to the exponential growth in demand and diversity of cloud resources. Scheduling is to allocate tasks across cloud resources so that scheduling algorithms reduce power consumption and offer cloud providers maximum return by reducing execution time. Various QoS parameters (such as makespan, load balancing, costs, etc.) are considered for efficient scheduling to reduce workload and enhance performance. Through this framework, multi-objective scheduling is a decision-making problem with multiple attributes considering the trade-off between the conflicting and competing parameters mentioned in the SLA between users and providers. This paper summarizes various multi-objective scheduling algorithms that consider contradictory and competing parameters or constraints to be optimized simultaneously. These algorithms are finally tabulated, presenting their advantages and disadvantages with cloud simulation tools and other QoS related parameters.
Overview of Scene Text Detection and Recognition

With the advent of the intelligent age, different natural scenes contain rich text information, resulting in a wide range of applications. Although many algorithms for scene text detection and recognition have been proposed, they still face great challenges, such as Different shapes, multilingual recognition, severe blurring, irregular aspect ratio, etc. This paper first summarizes the detection, recognition and end-to-end algorithms proposed in recent years, focuses on the current mainstream deep learning algorithm, and then introduces some common data sets in the experiment. Compared with the algorithm under the benchmark data set, some experimental results with good robustness are given. Finally, the future research field that hope to achieve further development is prospected.
Digital black hole: Undeclared internet policy in Pakistan’s Pashtun periphery

Due to the State’s militarized policies in the war-torn Pashtun areas (ex-FATA) bordering Afghanistan, the Pakistan government’s undeclared internet policy in this region has become an obstacle in the way of both exercising the freedom of expression and the attainment of digital access as a basic human right. This is in line with the colonial legacy of keeping this area a buffer-zone, strategically rendering it an information black hole. But the criminalization of digital freedom has also triggered local protests for media-making rights. Based on interviews with ‘tribal’ students, and using critical discourse analysis in combination with Achille Mbembe’s concept of necropolitics, we examine how the Pakistani State denies the tribal students their digital rights resulting in the loss of their time and the consequences of this collective loss. We found that the local frustration could not be limited to the lack of internet. Entailing an element of passive defiance, the tribal students are deserting their ancestral land to reclaim their bodies from the goliath of the State, hampering its necro behemoth that is using them as a complicit body in a disabled space.
SUPREAR-NET: Supervised Resolution Enhancement and Recognition Network

Heterogeneous face recognition is a challenging problem where the probe and gallery images belong to different modalities such as, low and high resolution, visible and near-infrared spectrum. A Generative Adversarial Network (GAN) enables us to learn an image to image transformation model for enhancing the resolution of a face image. Such a model would be helpful in a heterogeneous face recognition scenario. However, unsupervised GAN based transformation methods in their native formulation might alter useful discriminative information in the transformed face images. This affects the performance of face recognition algorithms when applied on the transformed images. We propose a Supervised Resolution Enhancement and Recognition Network (SUPREAR-NET), which does not corrupt the useful class-specific information of the face image and transforms a low resolution probe image into a high resolution one, followed by effective matching with the gallery using a trained discriminative model. We show the results for cross-resolution face recognition on three datasets including the FaceSurv face dataset, containing poor quality low resolution videos captured at a standoff distance up to 10 meters from the camera. On the FaceSurv, NIST MEDS and CMU MultiPIE datasets, the proposed algorithm outperforms recent unsupervised and supervised GAN algorithms.
A global Lyapunov function for the coherent Ising machine

Two classical Ising machine schemes, the Oscillator Ising Machine (OIM) and the Bistable Latch Ising Machine (BLIM), have been shown to feature global Lyapunov functions, i.e., continuous "energy-like" functions whose local minima are naturally found by the physics of these schemes. We show that the Coherent Ising Machine (CIM), an optical scheme that predated OIM and BLIM, also has a global Lyapunov function that approximates the Ising Hamiltonian at stable equilibrium points. Our result sharpens understanding of CIM operation, revealing that its mechanism for breaking out of local minima is a purely probabilistic classical one, similar to Gibbs sampling.
Decentralized Trading of High-Proportion Renewable Energy Microgrid Based on Master-Slave Game and Slime Mould Algorithm (SMA)

Microgrids has always been the hotspot of academic research, and the decentralized trading of electrical energy through artificial intelligence algorithms has been widely applied in recent years. However, the randomness and volatility of wind power and photovoltaic have brought huge uncertainty to the transaction. For this reason, this paper proposes a decentralized trading model based on the master-slave game. Aiming at maximizing the interests of agents and users in the microgrid, a master-slave game model is constructed. Through Karush-Kuhn-Tucker optimality condition and duality theorem, the multi-objective nonlinear optimization model is transformed into a mixed linear programming problem for solution. The simulation results show that the real-time electricity price is 0.32 yuan/kWh higher than the negotiated electricity price based on the optimal electricity price strategy, power grid can reduce energy storage pressure. The results proved that the transaction model is feasible and effective in terms of operational efficiency and economic welfare.
A lateral-scanning white-light interferometer for topography measurements on rotating objects in process environments

This paper presents the enhancement of the surface measurement ability of lateral-scanning white-light interferometry (LSWLI) for applications with industrial environments including fast-rotating objects and vibrations. For this purpose, a LSWLI with an integrated laser-speckle-based measurement of the lateral displacement is realised, which enables a vibration compensation and a decoupling of the measurement speed from the surface speed. The approach is validated and characterised in a test setup meeting the measurement conditions of a rotating sheet metal roller during surface reconditioning. The promising results open up the possibility for a high-resolution optical inspection of component topographies close to industrial manufacturing processes.
Active learning technologies in the context of distance education

The currently implemented Federal state educational standards of higher education (FSES HE 3+, FSES HE 3++) provide for the use of active learning technologies in educational activities in order to enhance the cognitive activity of students. The transition to a distance learning format as a result of the coronavirus pandemic has actualized the feasibility of conducting training sessions using active learning methods that make it possible to build an educational trajectory of interaction between teachers and students in an online format more ergonomically. The research problem is What active learning technologies are effective in distance education? The purpose of the research is to substantiate the use of active learning technologies in distance education as a means of enhancing the educational process. The research objectives are to substantiate the importance of active learning technologies in the current conditions of distance education; to reveal the concept of "technologies of active learning"; to present the practice of using active learning methods in distance education. The methodology and research methods. The theoretical basis of the research is the development of active learning methods, the empirical base is the data of an online survey. The research methods are analysis, generalization, survey, description. The results of the research are the following: prerequisites for distance education and the importance of active learning technologies have been substantiated; the concept of "technologies of active learning" have been disclosed; the practice of using active teaching methods in distance education has been described. Key conclusions: the prerequisites for the introduction of distance learning into the educational practice are the widespread use of information and communication technologies, as well as the unfavorable sanitary and epidemiological situation in the world caused by the spread of Covid-19; active learning technologies include methods that activate the learning activities of students. The following active methods are optimal for distance learning: interview, essay, project, case study, group discussion.
Shattering Distribution for Active Learning

Active learning (AL) aims to maximize the learning performance of the current hypothesis by drawing as few labels as possible from an input distribution. Generally, most existing AL algorithms prune the hypothesis set via querying labels of unlabeled samples and could be deemed as a hypothesis-pruning strategy. However, this process critically depends on the initial hypothesis and its subsequent updates. This article presents a distribution-shattering strategy without an estimation of hypotheses by shattering the number density of the input distribution. For any hypothesis class, we halve the number density of an input distribution to obtain a shattered distribution, which characterizes any hypothesis with a lower bound on VC dimension. Our analysis shows that sampling in a shattered distribution reduces label complexity and error disagreement. With this paradigm guarantee, in an input distribution, a Shattered Distribution-based AL (SDAL) algorithm is derived to continuously split the shattered distribution into a number of representative samples. An empirical evaluation of benchmark data sets further verifies the effectiveness of the halving and querying abilities of SDAL in real-world AL tasks with limited labels. Experiments on active querying with adversarial examples and noisy labels further verify our theoretical insights on the performance disagreement of the hypothesis-pruning and distribution-shattering strategies. Our code is available at https://github.com/XiaofengCao-MachineLearning/Shattering-Distribution-for-Active-Learning.
ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning

Building assistive interfaces for controlling robots through arbitrary, high-dimensional, noisy inputs (e.g., webcam images of eye gaze) can be challenging, especially when it involves inferring the user's desired action in the absence of a natural ‘default’ interface. Reinforcement learning from online user feedback on the system's performance presents a natural solution to this problem, and enables the interface to adapt to individual users. However, this approach tends to require a large amount of human-in-the-loop training data, especially when feedback is sparse. We propose a hierarchical solution that learns efficiently from sparse user feedback: we use offline pre-training to acquire a latent embedding space of useful, high-level robot behaviors, which, in turn, enables the system to focus on using online user feedback to learn a mapping from user inputs to desired high-level behaviors. The key insight is that access to a pre-trained policy enables the system to learn more from sparse rewards than a naïve RL algorithm: using the pre-trained policy, the system can make use of successful task executions to relabel, in hindsight, what the user actually meant to do during unsuccessful executions. We evaluate our method primarily through a user study with 12 participants who perform tasks in three simulated robotic manipulation domains using a webcam and their eye gaze: flipping light switches, opening a shelf door to reach objects inside, and rotating a valve. The results show that our method successfully learns to map 128-dimensional gaze features to 7-dimensional joint torques from sparse rewards in under 10 minutes of online training, and seamlessly helps users who employ different gaze strategies, while adapting to distributional shift in webcam inputs, tasks, and environments