Reviewerly/exHarmony huggingface dataset:
Deep Adaptive Inference Networks for Single Image Super-Resolution

Recent years have witnessed tremendous progress in single image super-resolution (SISR) owing to the deployment of deep convolutional neural networks (CNNs). For most existing methods, the computational cost of each SISR model is irrelevant to local image content, hardware platform and application scenario. Nonetheless, content and resource adaptive model is more preferred, and it is encouraging to apply simpler and efficient networks to the easier regions with less details and the scenarios with restricted efficiency constraints. In this paper, we take a step forward to address this issue by leveraging the adaptive inference networks for deep SISR (AdaDSR). In particular, our AdaDSR involves an SISR model as backbone and a lightweight adapter module which takes image features and resource constraint as input and predicts a map of local network depth. Adaptive inference can then be performed with the support of efficient sparse convolution, where only a fraction of the layers in the backbone is performed at a given position according to its predicted depth. The network learning can be formulated as joint optimization of reconstruction and network depth losses. In the inference stage, the average depth can be flexibly tuned to meet a range of efficiency constraints. Experiments demonstrate the effectiveness and adaptability of our AdaDSR in contrast to its counterparts (e.g., EDSR and RCAN). Code is available at https://github.com/csmliu/AdaDSR .
SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced Hyperspectral Data Classification

Conventional classification algorithms have shown great success in balanced hyperspectral data classification. However, the imbalanced class distribution is a fundamental problem of hyperspectral data, and it is regarded as one of the great challenges in classification tasks. To solve this problem, a non-ANN based deep learning, namely SMOTE-Based Weighted Deep Rotation Forest (SMOTE-WDRoF) is proposed in this paper. First, the neighboring pixels of instances are introduced as the spatial information and balanced datasets are created by using the SMOTE algorithm. Second, these datasets are fed into the WDRoF model that consists of the rotation forest and the multi-level cascaded random forests. Specifically, the rotation forest is used to generate rotation feature vectors, which are input into the subsequent cascade forest. Furthermore, the output probability of each level and the original data are stacked as the dataset of the next level. And the sample weights are automatically adjusted according to the dynamic weight function constructed by the classification results of each level. Compared with the traditional deep learning approaches, the proposed method consumes much less training time. The experimental results on four public hyperspectral data demonstrate that the proposed method can get better performance than support vector machine, random forest, rotation forest, SMOTE combined rotation forest, convolutional neural network, and rotation-based deep forest in multiclass imbalance learning.
Automatic Face Detection and Recognition for Attendance Maintenance

This paper focuses on building a deep learning based efficient attendance capturing system. Contemporary world is heading towards AI where every second creates a new vision with an enormous change. In Artificial Intelligence (AI), face recognition is one of the fastest growing domains. Instead of using traditional methods for marking attendance, we propose to automate it by identifying human faces with their unique face features known as Face Recognition. Face detection is a prerequisite process for face recognition which aims to identify and locate all faces irrespective of their position, scale, orientation, lighting conditions, expression etc. We created a system architectural solution using YOLO, MTCNN, FaceNet embeddings by applying multiple augmentations, picture quality check and de-noise methods to get a better attendance system with less maintenance, low cost hardware (Google Colab - Free Version), better performance and accuracy.
Effect of Working Experience on Air Traffic Controller Eye Movement

Eye movement is an important indicator of information-seeking behavior and provides insight into cognitive strategies which are vital for decision-making. Various measures based on eye movements have been proposed to capture humans' ability to process information in a complex environment. The effectiveness of these measures has not yet been fully explored in the field of air traffic management. This paper presents a comparative study on eye-movement measures in air traffic controllers with different levels of working experience. Two commonly investigated oculomotor behaviors, fixation and saccades, together with gaze entropy, are examined. By comparing the statistical properties of the relevant metrics, it is shown that working experience has a notable effect on eye-movement patterns. Both fixation and saccades differ between qualified and novice controllers, with the former type of controller employing more efficient searching strategies. These findings are useful in enhancing the quality of controller training and contributing to an understanding of the information-seeking mechanisms humans use when executing complex tasks
Acceleration of Intrusion Detection in Encrypted Network Traffic Using Heterogeneous Hardware

More than 75% of Internet traffic is now encrypted, and this percentage is constantly increasing. The majority of communications are secured using common encryption protocols such as SSL/TLS and IPsec to ensure security and protect the privacy of Internet users. However, encryption can be exploited to hide malicious activities, camouflaged into normal network traffic. Traditionally, network traffic inspection is based on techniques like deep packet inspection (DPI). Common applications for DPI include but are not limited to firewalls, intrusion detection and prevention systems, L7 filtering, and packet forwarding. With the widespread adoption of network encryption though, DPI tools that rely on packet payload content are becoming less effective, demanding the development of more sophisticated techniques in order to adapt to current network encryption trends. In this work, we present HeaderHunter, a fast signature-based intrusion detection system even for encrypted network traffic. We generate signatures using only network packet metadata extracted from packet headers. In addition, we examine the processing acceleration of the intrusion detection engine using different heterogeneous hardware architectures.
Few-shot fine-grained classification with Spatial Attentive Comparison

The main goal of this paper is to propose a novel model, named Spatial Attentive Comparison Network (SACN), which is used to address a problem, termed few-shot fine-grained recognition (FSFG). FSFG is to recognize fine-grained examples with only a few samples, which is challenging for deep neural networks. SACN is made up of three modules, namely feature extraction module, selective-comparison similarity module (SCSM), and classification module: feature extraction module extracts the distinctive information into feature maps, SCSM is used to fuse the features of support set with those of the query set based on selective comparison. Considering the noisy background and tiny differences between different categories, we apply SCSM to fuse these features by arranging different weights pixel by pixel, and all these weights are learned automatically. Moreover, we apply pyramid structure to enrich the features. By conducting comprehensive experiments on three fine-grained datasets, namely CUB-200-2011 (CUB Birds), Stanford Dogs Dataset, and Stanford Cars Dataset, we demonstrate that the proposed method achieves superior performance over the competing baselines.
Extractors and Secret Sharing Against Bounded Collusion Protocols

In a recent work, Kumar, Meka, and Sahai (FOCS 2019) introduced the notion of bounded collusion protocols (BCPs). BCPs are multiparty communication protocols in which N parties, holding n bits each, attempt to compute some joint function of their inputs, f:({0,1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> →{0,1}. In each round, p parties (the collusion bound) work together to write a single bit on a public blackboard, and the protocol continues until every party knows the value of f. BCPs are a natural generalization of the well-studied number-in-hand (NIH) and number-on-forehead (NOF) models, which are just endpoints on this rich spectrum of protocols (corresponding to p=1 and p=N-1, respectively). In this work, we investigate BCPs more thoroughly, and answer questions about them in the context of communication complexity, randomness extractors, and secret sharing. 1.First, we provide explicit lower bounds against BCPs. Our lower bounds offer a tradeoff between collusion and complexity, and are of the form n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ω(1)</sup> when p=0.99N parties collude. This bound is independent of the relationship between N, n, whereas all previous bounds became trivial when . 2.Second, we provide explicit leakage-resilient extractors against BCPs. Also known as cylinder-intersection extractors, these objects are multi-source extractors of the form Ext: ({0,1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> →{0,1}, whose output looks uniform even conditioned on the bits produced (“leaked”) by a BCP executed over the inputs of the extractor. Our extractors work for sources with min-entropy k ≥ polylog(n) against BCPs with collusion p ≤ N-2. Previously, all such extractors required min-entropy k ≥ 0.99n even when p ≤ O(1). 3.Third, we provide efficient leakage-resilient secret sharing schemes against BCPs. These cryptographic primitives are standard t-out-of- N secret sharing schemes, equipped with an additional guarantee that the secret remains hidden even if the individuals participate in a BCP using their shares. Our schemes can handle collusion up to p ≤ O(t/logt), whereas the previous best scheme required p ≤ O(logN). Along the way, we also construct objects that are more general than those listed above (i.e., compilers), objects that are more specialized (and stronger) than those listed above, and resolve open questions posed by Goyal and Kumar (STOC 2018) and Kumar, Meka, and Sahai (FOCS 2019).
FLAS: Computation and Communication Efficient Federated Learning via Adaptive Sampling

Federated learning enables collaborative deep learning over multiple clients without sharing their local data, and it becomes increasingly popular due to the good balance between data privacy and model usability. Generally, it faces the heavy communication overhead when a large number of clients are involved and the low convergence rate incurred by non-IID data. However, few existing solutions cannot simultaneously address the communication and statistic challenges. In this paper, we propose a computation and communication efficient federated learning via adaptive sampling. By capturing different data distribution among clients, we utilize the concept of self-paced learning to adaptively adjust thresholds to filter training data for each client and also to select suitable clients to be involved in each global learning round. We prove its correctness through theoretical analysis and also evaluate its performance through experimental evaluations on real-world datasets. Detailed experimental results show that it can effectively reduce the communication cost while achieving the good trade-off between accuracy and efficiency.
Face Detection based on SSD and CamShift

A face detection method combining SSD target detection algorithm and CamShift tracking algorithm was designed for fatigue driving detection. ResNet50 was used to replace the feature extraction network of the original SSD target detection algorithm to improve the accuracy of face location. CamShift and Kalman filter algorithm were used to track the face area to improve the detection speed and reduce the burden of system operation. The real vehicle test shows that the strategy combining SSD network and improved CamShift algorithm significantly improves the detection efficiency, and has a strong robustness to the effects of light change, occlusion loss and skin-like interference.
AVN: An Adversarial Variation Network Model for Handwritten Signature Verification

Handwritten signature verification is a crucial yet challenging problem. While previous studies have made great progress in this problem, they learn signature features passively from given existing data. In this paper, we propose a novel adversarial variation network (AVN) model for handwritten signature verification which mines effective features by actively varying existing data and generating new data. Powered by a proposed novel variation consistency mechanism, the AVN contains three different types of modules unified under one end-to-end framework: the extractor seeks to extract deep discriminative features of handwritten signatures, the discriminator aims to make verification decisions based on the extracted features, and the variator is designed to actively generate signature variants for constructing a more discriminative model. The proposed model is trained in an adversarial way with a min-max loss function, by which the three modules cooperate and compete to enhance the entire model’s ability and therefore the signature verification performance is improved. We test the proposed method on four challenging signature datasets of different languages: CEDAR, BHSig-Hindi, BHSig-Bengali, and GPDS Synthetic Signature. Extensive experiments with in-depth discussions validate the effectiveness of the proposed method.
A Mutually Auxiliary Multitask Model With Self-Distillation for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE), which aims to extract emotions and the corresponding causes in documents, has a wide range of applications in network public opinion analysis.Current two-stage methods first extract emotion and cause clauses, and then pair them.However, there are two problems in these methods: 1) the unidirectional enhancement between emotion and cause extraction fails to make full use of the correlation between them; 2) the errors from the first stage directly degrade the performance of the second stage.To address these problems, we firstly propose a mutually auxiliary multitask model to promote the extraction of emotion and cause clauses by adding two auxiliary tasks which are identical to the original tasks.The proposed model uses the predicted results generated by the two auxiliary tasks as extra features of each other's main tasks, so as to establish the bidirectional correlation between emotion and cause extraction.Secondly, to reduce the influence of error propagation on the second stage, we design a self-distillation method for pairwise tasks to train the proposed model, which further improve the accuracy of emotion and cause extraction.Experimental results on the ECPE benchmark dataset show that the proposed model has achieved good performance on emotion-cause pair extraction, outperforming the baseline models by 1.92% in F1 score.
Re-evaluation of the D21-digital-index assessment instrument for measuring higher-level digital competences

The development of reliable tools for assessing digital competences is of great importance. This is why we set out a re-evaluation of the measurement quality of the D21-Digital-Index assessment instrument. The D21-Digital-Index is a biannual and influential study held in Germany. The instrument used in the D21-surveys is based on the theoretical framework DigComp. In our analyses we used data of 1142 participants from vocational training and higher education institutions to estimate item parameters and the quality of the instrument using Item Response Theory. Because choosing an appropriate IRT-model is crucial for instrument evaluation, we calculated and compared two types of models, the Rasch and the Birnbaum model of which the latter turned out to achieve the better fit. In a unidimensional analysis the five scales of the instrument with 24 items in total yield acceptable measures. Multidimensional analysis shows a dimensional separation and hence confirms the construct validity of the instrument.
Age and Gender Predictions using Artificial Intelligence Algorithm

Gender is still a central aspect of personality, and in social life it is still an important factor. Gender and age projections for artificial intelligence can be used in many areas, such as the development of smart human-machine interfaces, fitness, cosmetics, e-commerce, etc. The prediction of age and gender is an ongoing and active research question for individuals from their facial images. A number of approaches to solving this issue have been suggested by the researchers, but the criteria and actual performance are still insufficient. This paper proposes a mathematical approach to recognition patterns in order to solve this problem. The Convolution Neural Network (ConvNet / CNN) deep learning algorithm is used as a feature extractor in the proposed solution. CNN takes input images and assigns value to and can distinguish between various aspects / objects (learnable weights and biases) of the image. ConvNet needs much less pre-processing than other classification algorithms. While the filters are hand-made in primitive methods, ConvNet can learn these filters / features with adequate training. In this research, face images of individuals have been trained with convolution neural networks, and age and sex with a high rate of success have been predicted. More than 20,000 images are containing age, gender and ethnicity annotations. The images cover a wide range of poses, facial expression, lighting, occlusion, and resolution.
Discovering Sentimental Interaction via Graph Convolutional Network for Visual Sentiment Prediction

With the popularity of online opinion expressing, automatic sentiment analysis of images has gained considerable attention. Most methods focus on effectively extracting the sentimental features of images, such as enhancing local features through saliency detection or instance segmentation tools. However, as a high-level abstraction, the sentiment is difficult to accurately capture with the visual element because of the “affective gap”. Previous works have overlooked the contribution of the interaction among objects to the image sentiment. We aim to utilize interactive characteristics of objects in the sentimental space, inspired by human sentimental principles that each object contributes to the sentiment. To achieve this goal, we propose a framework to leverage the sentimental interaction characteristic based on a Graph Convolutional Network (GCN). We first utilize an off-the-shelf tool to recognize objects and build a graph over them. Visual features represent nodes, and the emotional distances between objects act as edges. Then, we employ GCNs to obtain the interaction features among objects, which are fused with the CNN output of the whole image to predict the final results. Experimental results show that our method exceeds the state-of-the-art algorithm. Demonstrating that the rational use of interaction features can improve performance for sentiment analysis.
Defending Water Treatment Networks: Exploiting Spatio-Temporal Effects for Cyber Attack Detection

While Water Treatment Networks (WTNs) are critical infrastructures for local communities and public health, WTNs are vulnerable to cyber attacks. Effective detection of attacks can defend WTNs against discharging contaminated water, denying access, destroying equipment, and causing public fear. While there are extensive studies in WTNs attack detection, they only exploit the data characteristics partially to detect cyber attacks. After preliminary exploring the sensing data of WTNs, we find that integrating spatio-temporal knowledge, representation learning, and detection algorithms can improve attack detection accuracy. To this end, we propose a structured anomaly detection framework to defend WTNs by modeling the spatiotemporal characteristics of cyber attacks in WTNs. In particular, we propose a spatio-temporal representation framework specially tailored to cyber attacks after separating the sensing data of WTNs into a sequence of time segments. This framework has two key components. The first component is a temporal embedding module to preserve temporal patterns within a time segment by projecting the time segment of a sensor into a temporal embedding vector. We then construct Spatio-Temporal Graphs (STGs), where a node is a sensor and an attribute is the temporal embedding vector of the sensor, to describe the state of the WTNs. The second component is a spatial embedding module, which learns the final fused embedding of the WTNs from STGs. In addition, we devise an improved one class-SVM model that utilizes a new designed pairwise kernel to detect cyber attacks. The devised pairwise kernel augments the distance between normal and attack patterns in the fused embedding space. Finally, we conducted extensive experimental evaluations with real-world data to demonstrate the effectiveness of our framework: it achieves an accuracy of 91.65%, with average improvement ratios of 82.78% and 22.96% with respect to F1 and AUC, compared with baseline methods.
Hyperbolic-SVD-Based Square-Root Unscented Kalman Filters in Continuous-Discrete Target Tracking Scenarios

This article aims at presenting novel square-root unscented Kalman filters (UKFs) for treating various continuous-discrete nonlinear stochastic systems, including target tracking scenarios. These new methods are grounded in the commonly used singular value decomposition (SVD), that is, they propagate not the covariance matrix itself but its SVD factors instead. The SVD based on orthogonal transforms is applicable to any UKF with only nonnegative weights, whereas the remaining ones, which can enjoy negative weights as well, are treated by means of the hyperbolic SVD based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$J$</tex-math></inline-formula> -orthogonal transforms. The filters constructed are presented in a concise algorithmic form, which is convenient for practical utilization. Their two particular versions grounded in the classical and cubature UKF parameterizations and derived with use of the It <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\hat{\rm o}$</tex-math></inline-formula> -Taylor discretization are examined in severe conditions of tackling a seven-dimensional radar tracking problem, where an aircraft executes a coordinated turn, in the presence of ill-conditioned measurements.