Reviewerly/exHarmony huggingface dataset:
Entropy-aware self-training for graph convolutional networks

Recently, graph convolutional networks (GCNs) have achieved significant success in many graph-based learning tasks, especially for node classification, due to its excellent ability in representation learning. Nevertheless, it remains challenging for GCN models to obtain satisfying predictions on graphs where only few nodes are with known labels. In this paper, we propose a novel entropy-aware self-training algorithm to boost semi-supervised node classification on graphs with little supervised information. Firstly, an entropy-aggregation layer is developed to strengthen the reasoning ability of GCN models. To the best of our knowledge, this is the first work to combine the entropy-based random walk theory with GCN design. Furthermore, we propose an ingenious checking part to add new nodes as supervision after each training round to enhance node prediction. In particular, the checking part is designed based on aggregated features, which is demonstrated more effective than previous methods and boosts node classification significantly. The proposed algorithm is validated on six public benchmarks in comparison with several state-of-the-art baseline algorithms, and the results illustrate its excellent performance.
User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model

Handwriting analysis is playing an important role in user authentication or online writer identification for more than a decade. It has a significant role in different applications such as e-security, signature biometrics, e-health, gesture analysis, diagnosis system of Parkinson’s disease, Attention-deficit/hyperactivity disorders, analysis of vulnerable people (stressed, elderly, or drugged), prediction of gender, handedness and so on. Classical authentication systems are image-based, text-dependent, and password or fingerprint-based where the former one has the risk of information leakage. Alternatively, image processing and pattern-analysis-based systems are vulnerable to camera attributes, camera frames, light effect, and the quality of the image or pattern. Thus, in this paper, we concentrate on real-time and context-free handwriting data analysis for robust user authentication systems using digital pen-tablet sensor data. Most of the state-of-the-art authentication models show suboptimal performance for improper features. This research proposed a robust and efficient user identification system using an optimal feature selection technique based on the features from the sensor’s signal of pen and tablet devices. The proposed system includes more genuine and accurate numerical data which are used for features extraction model based on both the kinematic and statistical features of individual handwritings. Sensor data of digital pen-tablet devices generate high dimensional feature vectors for user identification. However, all the features do not play equal contribution to identify a user. Hence, to find out the optimal features, we utilized a hybrid feature selection model. Extracted features are then fed to the popular machine learning (ML) algorithms to generate a nonlinear classifier through training and testing phases. The experimental result analysis shows that the proposed model achieves more accurate and satisfactory results which ensure the practicality of our system for user identification with low computational cost.
A Position-Aware Transformer for Image Captioning

Image captioning aims to generate a corresponding description of an image. In recent years, neural encoder-decoder models have been the dominant approaches, in which the Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) are used to translate an image into a natural language description. Among these approaches, the visual attention mechanisms are widely used to enable deeper image understanding through fine-grained analysis and even multiple steps of reasoning. However, most conventional visual attention mechanisms are based on high-level image features, ignoring the effects of other image features, and giving insufficient consideration to the relative positions between image features. In this work, we propose a Position-Aware Transformer model with image-feature attention and position-aware attention mechanisms for the above problems. The image-feature attention firstly extracts multi-level features by using Feature Pyramid Network (FPN), then utilizes the scaled-dot-product to fuse these features, which enables our model to detect objects of different scales in the image more effectively without increasing parameters. In the position-aware attention mechanism, the relative positions between image features are obtained at first, afterwards the relative positions are incorporated into the original image features to generate captions more accurately. Experiments are carried out on the MSCOCO dataset and our approach achieves competitive BLEU-4, METEOR, ROUGE-L, CIDEr scores compared with some state-of-the-art approaches, demonstrating the effectiveness of our approach.
ReCLive: Real-Time Classification and QoE Inference of Live Video Streaming Services

Social media, professional sports, and video games are driving rapid growth in live video streaming, on platforms such as Twitch and YouTube Live. Live streaming experience is very susceptible to short-time-scale network congestion since client playback buffers are often no more than a few seconds. Unfortunately, identifying such streams and measuring their QoE for network management is challenging, since content providers largely use the same delivery infrastructure for live and video-on-demand (VoD) streaming, and packet inspection techniques (including SNI/DNS query monitoring) cannot always distinguish between the two. In this paper, we design and develop ReCLive: a machine learning method for live video detection and QoE measurement based on network-level behavioral characteristics.
Authorship Attribution of Modern Standard Arabic Short Texts

Text data, including short texts, constitute a major share of web content. The availability of this data to billions of users triggers frequent plagiarism attacks. Authorship Attribution (AA) seeks to identify the most probable author of a given text based on similarity to the writing style of potential authors. In this paper, we approach AA as a writing style profile generation process, where we group text instances for each author into a single profile. We use Twitter as the source for our short Modern Standard Arabic (MSA) texts. Numerous experiments with various training approaches, tools and features allowed us to settle on a text representation method that relies on text concatenation of Arabic tweets to form chunks, which are then duplicated to reach a precalculated length. These chunks are used to train machine learning models for our 45 author profiles. This allowed us to achieve accuracies up to 99%, which compares favorably with the best results reported in the literature.
Differentially Private Federated Learning with Shuffling and Client Self-Sampling

This paper studies a distributed optimization problem in the federated learning (FL) framework under differential privacy constraints, whereby a set of clients having local samples are connected to an untrusted server, who wants to learn a global model while preserving the privacy of clients' local datasets. We propose a new client sampling called self-sampling that reflects the random availability of clients in the learning process in FL. We analyze the differential privacy of the SGD with client self-sampling by composing amplification by sub-sampling along with amplification by shuffling. Furthermore, we analyze the convergence of the proposed SGD algorithm showing that we can get a reasonable learning performance while preserving the privacy of clients' data even with client self-sampling.
Guarding Machine Learning Hardware Against Physical Side-channel Attacks

Machine learning (ML) models can be trade secrets due to their development cost. Hence, they need protection against malicious forms of reverse engineering (e.g., in IP piracy). With a growing shift of ML to the edge devices, in part for performance and in part for privacy benefits, the models have become susceptible to the so-called physical side-channel attacks. ML being a relatively new target compared to cryptography poses the problem of side-channel analysis in a context that lacks published literature. The gap between the burgeoning edge-based ML devices and the research on adequate defenses to provide side-channel security for them thus motivates our study. Our work develops and combines different flavors of side-channel defenses for ML models in the hardware blocks. We propose and optimize the first defense based on Boolean masking . We first implement all the masked hardware blocks. We then present an adder optimization to reduce the area and latency overheads. Finally, we couple it with a shuffle-based defense. We quantify that the area-delay overhead of masking ranges from 5.4× to 4.7× depending on the adder topology used and demonstrate a first-order side-channel security of millions of power traces. Additionally, the shuffle countermeasure impedes a straightforward second-order attack on our first-order masked implementation.
Fusion of dynamic predictive block adaptive quantization and vector quantization for staggered SAR data compression

Staggered synthetic aperture radar (SAR) is an advanced concept for high-resolution wide-swath (HRWS) imaging, which overcomes the problem of blind ranges by continuously varying the pulse repetition frequency (PRF). For better performance of information recovery in blind ranges, data are typically highly oversampled in azimuth for staggered SAR system. Thus a huge data volume onboard is required and the demands for internal data storage and downlink capacity are increasing. In this paper, we investigate a novel method, named Dynamic Predictive Block Adaptive Vector Quantization (DP-BAVQ), to reduce the volume of downlinked data for staggered SAR. It firstly compresses the difference of the raw data and their predictions with the dynamic predictive block adaptive quantization (DP-BAQ). Then a secondary compression is performed with vector quantization (VQ). The simulation results and the experiments with real data show that the proposed method achieves an improvement in the signal-to-quantization noise ratio (SQNR) compared with DP-BAQ. Moreover, DP-BAVQ at 2 bits/sample provides a higher SQNR compared with standard block adaptive quantization (BAQ) at 3 bits/sample. Thus the proposed method allows for a significant reduction of data volume.
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations

Multi-label activity recognition is designed for recognizing multiple activities that are performed simultaneously or sequentially in each video. Most recent activity recognition networks focus on single-activities, that assume only one activity in each video. These networks extract shared features for all the activities, which are not designed for multi-label activities. We introduce an approach to multi-label activity recognition that extracts independent feature descriptors for each activity and learns activity correlations. This structure can be trained end-to-end and plugged into any existing network structures for video classification. Our method outperformed state-of-the-art approaches on four multi-label activity recognition datasets. To better understand the activity-specific features that the system generated, we visualized these activity-specific features in the Charades dataset. The code will be released later.
Introducing NYTK-NerKor, A Gold Standard Hungarian Named Entity Annotated Corpus

Here we present NYTK-NerKor, a gold standard Hungarian named entity annotated corpus containing 1 million tokens. This is the largest corpus ever in its kind. It contains balanced text selection from five genres: fiction, legal, news, web, and Wikipedia. A ca. 200,000 tokens subcorpus contains gold standard morphological annotation besides NE labels. We provide official train, development and test datasets in a proportion of 80%-10%-10%. All sets provide a balanced selection from all genres and sources, while the morphologically annotated subcorpus is also represented in all sets in a balanced way. The format of data files are CoNLL-U Plus, in which the NE annotation follows the CoNLL2002 labelling standard, while morphological information is encoded using the well-known Universal Dependencies POS tags and morphosyntactic features. The novelty of NYTK-NerKor as opposed to similar existing corpora is that it is: by an order of magnitude larger, freely available for any purposes, containing text material from different genres and sources, and following international standards in its format and tagset. The corpus is available under the license CC-BY-SA 4.0 from its GitHub repository: https://github.com/nytud/NYTK-NerKor.
A Computational Dual-Process Model of Fixation-Duration Control in Natural Scene Viewing

Abstract Research on eye-movement control during natural scene viewing has investigated the degree to which the duration of individual fixations can be immediately adjusted to ongoing visual-cognitive processing demands. Results from several studies using the fixation-contingent scene quality paradigm suggest that the timing of fixations adapts to stimulus changes that occur on a fixation-to-fixation basis. Analysis of fixation-duration distributions has revealed that saccade-contingent degradations and enhancements of the scene stimulus have two qualitatively distinct types of influence. The surprise effect begins early in a fixation and is tied to surprising visual events such as unexpected stimulus changes. The encoding effect is tied to difficulties in visual-cognitive processing and occurs relatively late within a fixation. Here, we formalize an existing descriptive account of these two effects (referred to as the dual-process account) by using stochastic simulations. In the computational model, surprise and encoding related influences are implemented as time-dependent changes in the rate at which saccade timing and programming are completed during critical fixations. The model was tested on data from two experiments in which the luminance of the scene image was either decreased or increased during selected critical fixations (Walshe &amp; Nuthmann, Vision Research , 100, 38–46, 2014). A counterfactual method was used to remove model components and to identify their specific influence on the fixation-duration distributions. The results suggest that the computational dual-process model provides a good account for the data from the luminance-change studies. We describe how the simulations can be generalized to explain a diverse set of experimental results.
Overview of Machine Learning Process Modelling

Much research has been conducted in the area of machine learning algorithms; however, the question of a general description of an artificial learner’s (empirical) performance has mainly remained unanswered. A general, restrictions-free theory on its performance has not been developed yet. In this study, we investigate which function most appropriately describes learning curves produced by several machine learning algorithms, and how well these curves can predict the future performance of an algorithm. Decision trees, neural networks, Naïve Bayes, and Support Vector Machines were applied to 130 datasets from publicly available repositories. Three different functions (power, logarithmic, and exponential) were fit to the measured outputs. Using rigorous statistical methods and two measures for the goodness-of-fit, the power law model proved to be the most appropriate model for describing the learning curve produced by the algorithms in terms of goodness-of-fit and prediction capabilities. The presented study, first of its kind in scale and rigour, provides results (and methods) that can be used to assess the performance of novel or existing artificial learners and forecast their ‘capacity to learn’ based on the amount of available or desired data.
MVSN: A Multi-view stack network for human parsing

The human parsing task is dedicated to allocating pixel-level fine-grained semantic labels. However, recent solutions are limited to fully utilize the prior information (poses and edges) and the potential information of the image data, remaining problems with ambiguous boundaries, incomplete human parts, and redundant labels. To solve the above problems, we propose a novel Multi-view Stack Network (MVSN), which is constructed by three stacks with multiple views of features included parts, edges, and pre-segmentation. Meanwhile, a channel correlator is developed to acquire the correlation between local and global information better. Comprehensive experiments and corresponding results on three public datasets show that the proposed MVSN performs favorably against the state-of-the-art methods.
Defending Against Model Inversion Attack by Adversarial Examples

Model inversion (MI) attacks aim to infer and reconstruct the input data from the output of a neural network, which poses a severe threat to the privacy of input data. Inspired by adversarial examples, we propose defending against MI attacks by adding adversarial noise to the output. The critical challenge is finding a noise vector that maximizes the inversion error and introduces negligible utility loss to the target model. We propose an algorithm to craft such noise vectors, which also incorporates utility-loss constraints. Specifically, our algorithm takes advantage of the gradient of an inversion model we train to mimic the adversary and compute a noise vector to turn the output into an adversarial example that can maximize the reconstruction error of the inversion model. Then we apply a label modifier that keeps the label unchanged to achieve zero accuracy loss of the target model. Our defense does not tamper with the training process or need the private training dataset. Thus it can be easily applied to any current neural networks or APIs. We evaluate our method under both standard and adaptive attack settings. Our empirical results show our approach is effective against state-of-the-art MI attacks due to the transferability of adversarial examples and outperforms existing defenses. Furthermore, it causes more reconstruction errors while introducing zero accuracy loss and less distortion than existing defenses.
On the Use of Deep Reinforcement Learning for Visual Tracking: A Survey

This paper aims at highlighting cutting-edge research results in the field of visual tracking by deep reinforcement learning.Deep reinforcement learning (DRL) is an emerging area combining recent progress in deep and reinforcement learning.It is showing interesting results in the computer vision field and, recently, it has been applied to the visual tracking problem yielding to the rapid development of novel tracking strategies.After providing an introduction to reinforcement learning, this paper compares recent visual tracking approaches based on deep reinforcement learning.Analysis of the state-of-the-art suggests that reinforcement learning allows modeling varying parts of the tracking system including target bounding box regression, appearance model selection, and tracking hyper-parameter optimization.The DRL framework is elegant and intriguing, and most of the DRL-based trackers achieve state-of-the-art results.
On coresets for support vector machines

We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a model trained on the coreset is provably competitive with that trained on the original data set. Since the size of the coreset is generally much smaller than the original set, our preprocess-then-train scheme has potential to lead to significant speedups when training SVM models. We prove lower and upper bounds on the size of the coreset required to obtain small data summaries for the SVM problem. As a corollary, we show that our algorithm can be used to extend the applicability of any off-the-shelf SVM solver to streaming, distributed, and dynamic data settings. We evaluate the performance of our algorithm on real-world and synthetic data sets. Our experimental results reaffirm the favorable theoretical properties of our algorithm and demonstrate its practical effectiveness in accelerating SVM training.
Malaria Blood Smear Classification Using Deep Learning and Best Features Selection

Malaria is a critical health condition that affects both sultry and frigid region worldwide, giving rise to millions of cases of disease and thousands of deaths over the years. Malaria is caused by parasites that enter the human red blood cells, grow there, and damage them over time. Therefore, it is diagnosed by a detailed examination of blood cells under the microscope. This is the most extensively used malaria diagnosis technique, but it yields limited and unreliable results due to the manual human involvement. In this work, an automated malaria blood smear classification model is proposed, which takes images of both infected and healthy cells and preprocesses them in the L*a*b* color space by employing several contrast enhancement methods. Feature extraction is performed using two pretrained deep convolutional neural networks, DarkNet-53 and DenseNet-201. The features are subsequently agglutinated to be optimized through a nature-based feature reduction method called the whale optimization algorithm. Several classifiers are effectuated on the reduced features, and the achieved results excel in both accuracy and time compared to previously proposed methods.
Applying Real-Time Object Shapes Detection To Automotive Traffic Roads Signs

This paper deals with a topical issue for researchers, namely the detection of objects in real-time. This is an important topic in the field of computer vision, as the detection and recognition of objects from images require very powerful algorithms. There are different ways to detect objects, for example, based on either the color or shape of the objects. The proposed approach consists in the realization of an algorithm to detect an object in the shape of a triangle, square, and rectangle, applied on traffic road signs for the automotive sector. Many detection methods can be adapted to progress efficiently and robustly, combining these different studies to find the best strategy. Besides, the use of numerous images in the test phase can be used to increase the accuracy of object detection. This approach could be interesting, can be established in the industrial sector, for the conception or development of embedded systems such as Advanced Driver Assistance Systems (ADAS) integrated into autonomous vehicles. The objective of this approach is to help systems based on artificial intelligence for road safety, to have a high degree of accuracy with negligible errors for detecting traffic road signs.