Reviewerly/exHarmony huggingface dataset:
MEDA: Meta-Learning with Data Augmentation for Few-Shot Text Classification

Meta-learning has recently emerged as a promising technique to address the challenge of few-shot learning. However, standard meta-learning methods mainly focus on visual tasks, which makes it hard for them to deal with diverse text data directly. In this paper, we introduce a novel framework for few-shot text classification, which is named as MEta-learning with Data Augmentation (MEDA). MEDA is composed of two modules, a ball generator and a meta-learner, which are learned jointly. The ball generator is to increase the number of shots per class by generating more samples, so that meta-learner can be trained with both original and augmented samples. It is worth noting that ball generator is agnostic to the choice of the meta-learning methods. Experiment results show that on both datasets, MEDA outperforms existing state-of-the-art methods and significantly improves the performance of meta-learning on few-shot text classification.
Data Augmentation Using Background Replacement for Automated Sorting of Littered Waste

The introduction of sophisticated waste treatment plants is making the process of trash sorting and recycling more and more effective and eco-friendly. Studies on Automated Waste Sorting (AWS) are greatly contributing to making the whole recycling process more efficient. However, a relevant issue, which remains unsolved, is how to deal with the large amount of waste that is littered in the environment instead of being collected properly. In this paper, we introduce BackRep: a method for building waste recognizers that can be used for identifying and sorting littered waste directly where it is found. BackRep consists of a data-augmentation procedure, which expands existing datasets by cropping solid waste in images taken on a uniform (white) background and superimposing it on more realistic backgrounds. For our purpose, realistic backgrounds are those representing places where solid waste is usually littered. To experiment with our data-augmentation procedure, we produced a new dataset in realistic settings. We observed that waste recognizers trained on augmented data actually outperform those trained on existing datasets. Hence, our data-augmentation procedure seems a viable approach to support the development of waste recognizers for urban and wild environments.
Visual Sensor Fusion Based Autonomous Robotic System for Assistive Drinking

People with severe motor impairments like tetraplegia are restricted in activities of daily living (ADL) and are dependent on continuous human assistance. Assistive robots perform physical tasks in the context of ADLs to support people in need of assistance. In this work a sensor fusion algorithm and a robot control algorithm for localizing the user’s mouth and autonomously navigating a robot arm are proposed for the assistive drinking task. The sensor fusion algorithm is implemented in a visual tracking system which consists of a 2-D camera and a single point time-of-flight distance sensor. The sensor fusion algorithm utilizes computer vision to combine camera images and distance measurements to achieve reliable localization of the user’s mouth. The robot control algorithm uses visual servoing to navigate a robot-handled drinking cup to the mouth and establish physical contact with the lips. This system features an abort command that is triggered by turning the head and unambiguous tracking of multiple faces which enable safe human robot interaction. A study with nine able-bodied test subjects shows that the proposed system reliably localizes the mouth and is able to autonomously navigate the cup to establish physical contact with the mouth.
A State-of-the-Art Survey on Context-Aware Recommender Systems and Applications

In the digital transformation era, increasingly more individuals and organizations use or create services in digital spaces. Many business transactions have been moving from the offline to online mode. For example, sellers intend to introduce their products on e-commerce platforms rather than display them on store shelves as in traditional business. Although this new format business has advantages, such as more space for product displays, more efficient searches for a specific item, and providing a good tool for both buyers and sellers to manage their products, it is also accompanied by the obviously important problem that users are confused when choosing an appropriate item due to a large amount of information. For this reason, the need for a recommendation system appears. Informally, a recommender system is similar to an information filtering system that helps identify a set of items that best satisfy users' demands based on their preference profiles. The integration of contextual information (e.g., location, weather conditions, and user's mood) into recommender systems to improve their performance has recently received considerable attention in the research literature. However, incorporating such contextual information into recommendation models is a challenging task because of the increase in both the dimensionality and sparsity of the model. Different approaches with their own advantages and disadvantages have been proposed. This paper provides a comprehensive survey on context-aware recommender systems in recent years. In particular, the authors pay more attention to journal and conference proceedings papers published from 2016 to 2020. In addition, this paper also presents open issues for context-aware recommender systems and discuss promising directions for future research.
Comparison of Distance Models on K-Nearest Neighbor Algorithm in Stroke Disease Detection

Stroke is a cardiovascular (CVD) disease caused by the failure of brain cells to get oxygen supply to pose a risk of ischemic damage and result in death. This Disease can detect based on the similarity of symptoms experienced by the sufferer so that early steps can be taking with appropriate counseling and treatment. Stroke detecting requires a machine learning method. In this research, the author used one of the supervised learning classification methods, namely K-Nearest Neighbor (K-NN). K-NN is a classification method based on calculating the distance to training data. This research compares the Euclidean, Minkowski, Manhattan, Chebyshev distance models to obtain optimal results. The distance models have been tested using the stroke dataset sourced from the Kaggle repository. Based on the test results, the Chebyshev model has the highest levels of accuracy compared to the other three distance models with an average accuracy value of 95.49%, the highest accuracy of 96.03%, at K = 10. The Euclidean and Minkowski distance models have the same level of accuracy at each K value with an average accuracy value of 95.45%, the highest accuracy of 95.93% at K = 10. Meanwhile, Manhattan has the lowest average compared to the other distance models, which is 95.42% but has the highest accuracy of 96.03% at the value of K = 6
Heterogeneous Graph Information Bottleneck

Most attempts on extending Graph Neural Networks (GNNs) to Heterogeneous Information Networks (HINs) implicitly take the direct assumption that the multiple homogeneous attributed networks induced by different meta-paths are complementary. The doubts about the hypothesis of complementary motivate an alternative assumption of consensus. That is, the aggregated node attributes shared by multiple homogeneous attributed networks are essential for node representations, while the specific ones in each homogeneous attributed network should be discarded. In this paper, a novel Heterogeneous Graph Information Bottleneck (HGIB) is proposed to implement the consensus hypothesis in an unsupervised manner. To this end, information bottleneck (IB) is extended to unsupervised representation learning by leveraging self-supervision strategy. Specifically, HGIB simultaneously maximizes the mutual information between one homogeneous network and the representation learned from another homogeneous network, while minimizes the mutual information between the specific information contained in one homogeneous network and the representation learned from this homogeneous network. Model analysis reveals that the two extreme cases of HGIB correspond to the supervised heterogeneous GNN and the infomax on homogeneous graph, respectively. Extensive experiments on real datasets demonstrate that the consensus-based unsupervised HGIB significantly outperforms most semi-supervised SOTA methods based on complementary assumption.
Horizontal scaling method for a hyperconverged network

Today cloud platforms are gradually being replaced by hyperconverged. With a hyperconverged infrastructure, the servers, networks, storage and computing power are combined. This is done through specific software. The main advantage of hyperconverged network is to reduce operating costs. However, system performance can be reduced due to the standardization of hardware modules and centralization of control, and, consequently, QoS deteriorates. At the same time, companies that operate computer systems are attracted by the ability to quick horizontal scaling and lower operating costs. Hyperconverged networks often use addition of a new hardware modules to meet QoS requirements using a simple horizontal scaling implementation. The purpose of the paper is to develop such a horizontal scaling method, which allows decreasing transaction processing time while total cost of data transmission does not exceed the specified values. The method also allows determining the optimal characteristics of additional hardware.
Experimental detection of qubit-environment entanglement without accessing the environment

Decoherence is an unavoidable effect of the quantum system coupled to an environment, which leads to the system evolving from a pure state to a mixed one. A critical issue here is that the reduction of purity is quantum or classical, which corresponds to whether or not entanglement is generated between the system and environment. We report a proof-of-principle experimental investigation of such entanglement in a simple but paradigmatic case, in which a qubit system interacts with a pure dephasing channel. The pure dephasing process is realized in a linear photonic system, and the experiment is a validation in a very controlled setting. By adopting previous methods to detect qubit-environment entanglement, we prove that such entanglement can be detected by performing local measurements on only one subsystem, even when the other subsystem in the pair cannot be detected. Our experiment paves the way for investigating features of open quantum system dynamics.
Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation

Virtual Reality (VR) games that feature physical activities have been shown to increase players' motivation to do physical exercise. However, for such exercises to have a positive healthcare effect, they have to be repeated several times a week. To maintain player motivation over longer periods of time, games often employ Dynamic Difficulty Adjustment (DDA) to adapt the game's challenge according to the player's capabilities. For exercise games, this is mostly done by tuning specific in-game parameters like the speed of objects. In this work, we propose to use experience-driven Procedural Content Generation for DDA in VR exercise games by procedurally generating levels that match the player's current capabilities. Not only finetuning specific parameters but creating completely new levels has the potential to decrease repetition over longer time periods and allows for the simultaneous adaptation of the cognitive and physical challenge of the exergame. As a proof-of-concept, we implement an initial prototype in which the player must traverse a maze that includes several exercise rooms, whereby the generation of the maze is realized by a neural network. Passing those exercise rooms requires the player to perform physical activities. To match the player's capabilities, we use Deep Reinforcement Learning to adjust the structure of the maze and to decide which exercise rooms to include in the maze. We evaluate our prototype in an exploratory user study utilizing both biodata and subjective questionnaires.
On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

Automating the analysis of digital microscopic images to identify the cell sub-types or the presence of illness has assumed a great importance since it aids the laborious manual process of review and diagnosis. In this paper, we have focused on the analysis of white blood cells. They are the body’s main defence against infections and diseases and, therefore, their reliable classification is very important. Current systems for leukocyte analysis are mainly dedicated to: counting, sub-types classification, disease detection or classification. Although these tasks seem very different, they share many steps in the analysis process, especially those dedicated to the detection of cells in blood smears. A very accurate detection step gives accurate results in the classification of white blood cells. Conversely, when detection is not accurate, it can adversely affect classification performance. However, it is very common in real-world applications that work on inaccurate or non-accurate regions. Many problems can affect detection results. They can be related to the quality of the blood smear images, e.g., colour and lighting conditions, absence of standards, or even density and presence of overlapping cells. To this end, we performed an in-depth investigation of the above scenario, simulating the regions produced by detection-based systems. We exploit various image descriptors combined with different classifiers, including CNNs, in order to evaluate which is the most suitable in such a scenario, when performing two different tasks: Classification of WBC subtypes and Leukaemia detection. Experimental results have shown that Convolutional Neural Networks are very robust in such a scenario, outperforming common machine learning techniques combined with hand-crafted descriptors. However, when exploiting appropriate images for model training, even simpler approaches can lead to accurate results in both tasks.
A renewable energy-aware power allocation for cloud data centers: A game theory approach

With the rapid emerging of Internet of Things (IoT) devices and the proliferation of cloud-based applications, the cloud computing industry is becoming a vital element for ensuring our daily services. However, cloud computing uses large scale data centers equipped with energy-hungry servers and huge power facilities that massively consume power. This presents a real challenge which can negatively influence the power grid, while exposing the environment to global warming issues. Therefore, minimizing cloud data center power consumption is a challenging problem and has to be addressed. In this paper, we look at renewable energy in the context of a smart grid–cloud architecture and investigate the issue of grid power dispatching to cloud data centers. Since cloud data centers have a non-cooperative nature regarding power demand from the power stations, we model our power allocation problem as a non-cooperative game. Afterwards, we prove the existence and the uniqueness of Nash equilibrium. Moreover, we formulate the payoff function of our game as a non-linear optimization problem before resolving it using Lagrange multipliers and Karush–Kuhn–Tucker (KKT) conditions. Thus, we determine the assigned optimal quantity to each data center based on three main criteria : renewable energy usage, number of critical running applications and workload charge. Extensive simulations are performed by comparing our scheme with an existing work. Results show that our scheme outperforms the comparing approach with a percentage of 31.2% in terms of power load rate and significantly reduces emissions of carbon dioxide.
Convolutional Neural Network Based In-Loop Filter For VVC Intra Coding

In the emerging Versatile Video Coding (VVC) standard, there are three in-loop filters known as deblocking, sample adaptive offset (SAO), and adaptive loop filter (ALF) for suppressing compression artifacts as well as reducing distortion. However, those handcrafted filters are insufficient to deal with the complicated compression artifacts. Deep - filtering has demonstrated overwhelming successes in the field of image restoration. In this paper, we propose a convolutional neural network-based filter for enhancing the quality of VVC intra coded frames. The proposed filter takes auxiliary information including partitioning and prediction information as input. For chroma, auxiliary information further includes luma samples. Regarding the training, we find that data augmentation and loss function selection are not trivial. We conduct extensive experiments to evaluate the effectiveness of each design. When testing on top of VTM-10.0 under all intra configuration, the proposed filter achieves state-of-the-art performance, i.e. on average 7.57%, 13.18%, and 12.50% BD-rate reductions for Y, Cb, and Cr, respectively. The proposed filter ranks first among neural network-based in-loop filters at the 20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> meeting of the Joint Video Exploration Team (JVET).
Artificial Neural Networks

Artificial neural networks (ANNs) aim at learning discrete-valued, real-valued, and vector-valued representations in the machine learning (ML) field. Algorithms such as backpropagation based on gradient descent method for tuning network parameters in order to best fit a training dataset have already shown remarkable results in the field of ML. The ANN learning is robust to errors and has been effectively applied to different problems in the area of speech recognition, speaker recognition, and other domains of speech processing. The main emphasis of this chapter is on neural network (NN) representations and defining suitable problems for NN learning. The rest of the chapter covers numerous substitute designs for the primitive units making up an ANN such as perceptron units, sigmoid units, and linear units. This chapter also covers the learning algorithms for training single units. Later, the backpropagation algorithm for multilayer perceptron training is described in detail. Also, the general issues such as the representational capabilities of ANNs, overfitting problems, and substitutes to the backpropagation algorithm are also explained. Lastly, the concepts of convergence, local minima, generalization, overfitting, and stopping criterion are covered in detail.
DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers

We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations such as blur, haze, and changes in image contrast. While the robustness of DNN classifiers has been the subject of intense research in recent years, the solutions delivered by this research focus on verifying DNN robustness to small perturbations in the images being classified, with perturbation magnitude measured using established \(L_p\) norms. This is useful for identifying potential adversarial attacks on DNN image classifiers, but cannot verify DNN robustness to contextually relevant image perturbations, which are typically not small when expressed with \(L_p\) norms. DeepCert addresses this underexplored verification problem by supporting: (1) the encoding of real-world image perturbations; (2) the systematic evaluation of contextually relevant DNN robustness, using both testing and formal verification; (3) the generation of contextually relevant counterexamples; and, through these, (4) the selection of DNN image classifiers suitable for the operational context (i) envisaged when a potentially safety-critical system is designed, or (ii) observed by a deployed system. We demonstrate the effectiveness of DeepCert by showing how it can be used to verify the robustness of DNN image classifiers build for two benchmark datasets ('German Traffic Sign' and 'CIFAR-10') to multiple contextually relevant perturbations.
Multi-Level Transfer Learning from Near-Field to Far-Field Speaker Verification

In far-field speaker verification, the performance of speaker embeddings is susceptible to degradation when there is a mismatch between the conditions of enrollment and test speech. To solve this problem, we propose the feature-level and instance-level transfer learning in the teacher-student framework to learn a domain-invariant embedding space. For the feature-level knowledge transfer, we develop the contrastive loss to transfer knowledge from teacher model to student model, which can not only decrease the intra-class distance, but also enlarge the inter-class distance. Moreover, we propose the instance-level pairwise distance transfer method to force the student model to preserve pairwise instances distance from the well optimized embedding space of the teacher model. On FFSVC 2020 evaluation set, our EER on Full-eval trials is relatively reduced by 13.9% compared with the fusion system result on Partial-eval trials of Task2. On Task1, compared with the winner's DenseNet result on Partial-eval trials, our minDCF on Full-eval trials is relatively reduced by 6.3%. On Task3, the EER and minDCF of our proposed method on Full-eval trials are very close to the result of the fusion system on Partial-eval trials. Our results also outperform other competitive domain adaptation methods.
Looking at Looking: The Amorphous Fixation Measure

This chapter describes an eye movement recording device presently in use and the construction of a computer algorithm which defines a fixation. Also, it underlines the importance of researchers reporting their fixation criteria, examples are provided of how subtle changes in some fixation criteria may dramatically affect the processed fixation data output. Video recording devices are limited to a sampling rate of 60 per second, which is too slow to measure either the short voluntary eye jumps (saccades) between fixations or the more rapid involuntary eye movements that occur during a fixation. Other recording devices are more suitable for measuring eye motion per se, but our interest is in measuring fixation positions and their time history. In general, all researchers must arrive at a data partitioning scheme custom tailored to their own specific needs. However, the summary measures of fixation frequency, duration, and location generated by any particular algorithm are quite common and likely to be taken for granted.
Block color image encryption algorithm based on elementary cellular automata and DNA sequence operations

A block color image encryption algorithm based on chaotic system, elementary cellular automata (ECA), and deoxyribonucleic acid (DNA) sequence operations is proposed. The architecture is comprised of forward-diffusion, permutation, and backward-diffusion. First, the red, green, and blue components of a color image are converted into three DNA matrices by DNA encoding rule. Exclusive OR (XOR) operation is applied between the DNA coding chaotic sequence generated by the improved Henon map and three DNA matrices. Second, the large DNA matrix, composed of three DNA matrices, is divided into several small blocks with unequal sizes. ECA is employed to scramble the positions of these blocks. Finally, DNA computations are introduced to diffuse three permutated DNA matrices. In addition, initial values and system parameters of the chaotic system are calculated from the secure hash algorithm (SHA-256) hash value of the plain image and the given parameters. Both DNA encoding and ECA order rules rely on plaintext information. Therefore, the proposed algorithm is highly sensitive to plain image. Simulation results and security analyses demonstrate that our algorithm achieves excellent security performance and acceptable running time.