Reviewerly/exHarmony huggingface dataset:
Real-Time Multi-view Face Mask Detector on Edge Device for Supporting Service Robots in the COVID-19 Pandemic

The COVID-19 pandemic requires everyone to wear a face mask in public areas. This situation expands the ability of a service robot to have a masked face recognition system. The challenge is detecting multi-view faces. Previous works encountered this problem and tended to be slow when implemented in practical applications. This paper proposes a real-time multi-view face mask detector with two main modules: face detection and face mask classification. The proposed architecture emphasizes light and robust feature extraction. The two-stage network makes it easy to focus on discriminating features on the facial area. The detector filters non-faces at the face detection stage and then classifies the facial regions into two categories. Both models were trained and tested on the benchmark datasets. As a result, the proposed detector obtains high performance with competitive accuracy from competitors. It can run 20.60 frames per second when working in real-time on Jetson Nano.
Cascade correlation neural network with deterministic weight modification for predicting stock market price

Abstract A new weight updating approach is proposed for cascade correlation neural network (CCNN) in this paper. The deterministic weight modification (DWM) algorithm is used to adjust the connection weights of CCNN. The introduced new method can improve the global convergence capability of the conventional CCNN and optimally reduced the system error. The proposed DWM+CCNN prediction algorithm is applied well-known stock market dataset in order to evaluate the robustness and efficiency. The experimental results are confirmed that the proposed DWM+CCNN algorithm is achieves higher performance in terms of convergence rate and the capability of global converges.
Distributed Nonlinear Fusion Estimation Without Knowledge of Noise Statistical Information: A Robust Design Approach

This article is concerned with the distributed fusion estimation problem for nonlinear systems without knowledge of noise statistical information. By using the Taylor expansion, the linearization errors are modeled by the state-dependent terms with uncertain parameters when constructing nonlinear estimation error systems. Then, based on the idea of bounded recursive optimization, a robust design approach is developed to obtain local nonlinear estimators and distributed fusion criterion such that the designed local/fusion estimators are stable. Notice that, by establishing different convex optimization problems, the gains of stable nonlinear local/fusion estimators can be directly obtained using the standard softwares. Finally, two illustrative examples are exploited to show the advantages and effectiveness of the proposed methods.
Perceptual Image Hashing Based on Three-Dimensional Global Features and Image Energy

In order to improve classification performance and operating efficiency of the hash algorithm, this paper proposes a novel hash algorithm that combines three-dimensional global features and local energy features. During the stage of three-dimensional features extraction, the image is firstly compressed by SVD decomposition to form a secondary image. Then the statistical features of the secondary image at the three-dimensional visual angle are extracted as the global features. Finally, the global feature hash is generated by using the relationship between the statistical features of the image layers from different three-dimensional visual angles. In the energy feature extraction stage, the luminance image is divided into blocks, and then the energy value of each image sub-block is obtained. The multi-directional energy change features are taken as the local features of the image. Subsequent experimental results prove the effectiveness of the proposed algorithm. The algorithm not only has good robustness to the conventional content-preserving operations, but also achieves a good balance between discrimination capability and robustness. In addition, compared with several state-of-the-art schemes, this algorithm has the best ROC curve, the shortest running time and the best local tamper detection ability.
Black Box Testing on ukmbantul.com Page with Boundary Value Analysis and Equivalence Partitioning Methods

Abstract One of the most important aspects in information system development is system testing. System testing aims to evaluate the capabilities of the application and determine whether the program developed is in accordance with the expected results. It also aims to ensure that the application is of the best quality and is maintained. In this study, system testing was conducted on ukmbantul.com application. ukmbantul.com is a website that is used to promote the potential of craftsmen / home-based businesses in Bantul. The testing is done by black box testing and all data input is tested with various data that are not in accordance with the rules. Black Box Testing is done by testing the application without seeing / knowing the internal structure of the code or program. The research was carried out using boundary value analysis and equivalence partitioning methods. The results show that the development of ukmbantul.com has considered the limitations in data entry. The form used for the data entry process has been validated in accordance with the applicable limitations.
Semisupervised Feature Selection via Generalized Uncorrelated Constraint and Manifold Embedding

Ridge regression is frequently utilized by both supervised learning and semisupervised learning. However, the results cannot obtain the closed-form solution and perform manifold structure when ridge regression is directly applied to semisupervised learning. To address this issue, we propose a novel semisupervised feature selection method under generalized uncorrelated constraint, namely SFS. The generalized uncorrelated constraint equips the framework with the elegant closed-form solution and is introduced to the ridge regression with embedding the manifold structure. The manifold structure and closed-form solution can better save data's topology information compared to the deep network with gradient descent. Furthermore, the full rank constraint of the projection matrix also avoids the occurrence of excessive row sparsity. The scale factor of the constraint that can be adaptively obtained also provides the subspace constraint more flexibility. Experimental results on data sets validate the superiority of our method to the state-of-the-art semisupervised feature selection methods.
Protoda: Efficient Transfer Learning for Few-Shot Intent Classification

Practical sequence classification tasks in natural language processing often suffer from low training data availability for target classes. Recent works towards mitigating this problem have focused on transfer learning using embeddings pre-trained on often unrelated tasks, for instance, language modeling. We adopt an alternative approach by transfer learning on an ensemble of related tasks using prototypical networks under the meta-learning paradigm. Using intent classification as a case study, we demonstrate that increasing variability in training tasks can significantly improve classification performance. Further, we apply data augmentation in conjunction with meta-learning to reduce sampling bias. We make use of a conditional generator for data augmentation that is trained directly using the meta-learning objective and simultaneously with prototypical networks, hence ensuring that data augmentation is customized to the task. We explore augmentation in the sentence embedding space as well as prototypical embedding space. Combining meta-learning with augmentation provides upto 6.49% and 8.53% relative F1-score improvements over the best performing systems in the 5-shot and 10-shot learning, respectively.
Cross-modal context-gated convolution for multi-modal sentiment analysis

When inferring sentiments, using verbal clues only is problematic because of the ambiguity. Adding related vocal and visual contexts as complements for verbal clues can be helpful. To infer sentiments from multi-modal temporal sequences, we need to identify both sentiment-related clues and their cross-modal interactions. However, sentiment-related behaviors of different modalities may not occur at the same time. These behaviors and their interactions are also sparse in time, making it hard to infer the correct sentiments. Besides, unaligned sequences from sensors also have varying sampling rates, which amplify the misalignment and sparsity mentioned above. While most previous multi-modal sentiment analysis works only focus on word-aligned sequences, we propose cross-modal context-gated convolution for unaligned sequences. Cross-modal context-gated convolution captures the local cross-modal interactions, dealing with the misalignment while reducing the effect of unrelated information. Cross-modal context-gated convolution introduces the concept of cross-modal context gate, enabling itself to catch useful cross-modal interactions more effectively. Cross-modal context-gated convolution also brings more possibilities to the layer design for multi-modal sequential modeling. Experiments on multi-modal sentiment analysis datasets under both word-aligned and unaligned conditions show the validity of our approach.
SafeDrop: Detecting Memory Deallocation Bugs of Rust Programs via Static Data-flow Analysis

Rust is an emerging programming language that aims to prevent memory-safety bugs. However, the current design of Rust also brings side effects, which may increase the risk of memory-safety issues. In particular, it employs ownership-based resource management and enforces automatic deallocation of unused resources without using the garbage collector. It may therefore falsely deallocate reclaimed memory and lead to use-after-free or double-free issues. In this article, we study the problem of invalid memory deallocation and propose SafeDrop , a static path-sensitive data-flow analysis approach to detect such bugs. Our approach analyzes each function of a Rust crate iteratively in a flow-sensitive and field-sensitive way. It leverages a modified Tarjan algorithm to achieve scalable path-sensitive analysis and a cache-based strategy for efficient inter-procedural analysis. We have implemented our approach and integrated it into the Rust compiler. Experiment results show that the approach can successfully detect all such bugs in our experiments with a limited number of false positives and incurs a very small overhead compared to the original compilation time.
UPDResNN: A Deep Light-Weight Image Upsampling and Deblurring Residual Neural Network

The physical process used in CCD cameras for image formation makes it imperative to simultaneously upsample and deblur the captured images. In this paper, we provide an efficient scheme to solve this problem through a nonlinear end-to-end mapping carried out by a novel deep light-weight residual neural network. The proposed network is designed based on two main modules, namely, image upsampling and image deblurring, aimed for carrying out simultaneously the two tasks involved with the problem. The proposed network employs a residual block with a capacity of generating features in multiple receptive fields and enhancing the network's representational capability. The proposed network is extensively experimented using benchmark datasets for image restoration.
Multi-Task Convolution Operators With Object Detection for Visual Tracking

Recently, multi-task correlation filters has drawn much attention in the object tracking field, which utilizes the multi-task learning (MTL) approach to explore the interdependencies among deep features for object tracking. However, the existing multi-task correlation filters based method fails to consider the relations between the correlation filters. To address this problem, a novel correlation filters based visual tracking method is proposed in this paper, with the integration of multi-task convolution operators and object detection. In our method, convolution and correction filters are jointly learnt through using the MTL technique, with the purpose of exploring not only the interdependencies of deep features but also the internal relevance of the convolution filters. In addition, object detection is introduced into our algorithm to handle the problem of object missing to ensure a better performance of our tracking method. Experiments on five benchmark datasets demonstrate that the proposed visual tracking method outperforms existing state-of-the-art approaches.
Adaptive multiscale feature for object detection

In object detection, multiscale features are necessary to deal with objects with different size. Using Feature Pyramid Network (FPN) as the backbone network is a very popular paradigm in existing object detectors, we call this paradigm FPN+. However, feature fusion of FPN is insufficient to express object of similar size but different appearance due to the unidirectional feature fusion. We motivate and present Adaptive Multiscale Feature (AMF), a new multiscale feature fusion method with bidirectional feature fusion, using to solve the one-direction fusion of FPN. AMF module fuses multiscale features from two aspects: (1) shattering features by the way of CLSM; (2) fusing features by the way of channel-wise attention. The proposed AMF improves the expression ability of multiscale features of the backbone network, and effectively improves the performance of the object detector. The proposed feature fusion method can be added to all object detector, such as the one-stage detector, the two-stage detector, anchor-based detector and anchor-free based detector. Experimental results on the COCO 2014 dataset show that the proposed AMF module performs the popular FPN based detector. Whether anchored-free based detectors or anchored based detectors, performance of detector can be improved through AMF. And the resulting best model can achieve a very competitive mAP on COCO 2014 dataset.
Enhancing Security of Data in Cloud Storage using Decentralized Blockchain

Nowadays, large amount of data is stored on the cloud which is required to be protected from the unauthorized users. To maintain the privacy and security of data various algorithms are used. The objective of every system is to achieve confidentiality, integrity, availability (CIA). However, the existing centralized cloud storage lacks to provide these CIA properties. So, to enhance the security of data and storing techniques, decentralized cloud storage is used along with blockchain technology. It effectively helps to protect data from tampering or deleting a part of data. The data stored in blockchain is linked to each other by a chain of blocks. Each block has its hash value, which is stored in next block. Thus it reduces the chances of data altering. For this purpose, SHA-512 Hashing algorithm is used. Hashing algorithm is used in many aspects, where the security of data is required such as message digest, password verification, digital certificates and in blockchain. By the combination of these methods and algorithms, data becomes more secure and reliable. However, with the help of various algorithms, the security of the data can be enhanced. Also, Advance Encryption Standard (AES) is used to encrypt and decrypt the data due to the significant features of this algorithm.
Soft target and functional complexity reduction: A hybrid regularization method for genetic programming

Regularization is frequently used in supervised machine learning to prevent models from overfitting.This paper tackles the problem of regularization in genetic programming.We apply, for the first time, soft target regularization, a method recently defined for artificial neural networks, to genetic programming.Also, we introduce a novel measure of functional complexity of the genetic programming individuals, aimed at quantifying their degree of curvature.We experimentally demonstrate that both the use of soft target regularization, and the minimization of the complexity during learning, are often able to reduce overfitting, but they are never able to eliminate it.On the other hand, we demonstrate that the integration of these two strategies into a novel hybrid genetic programming system can completely eliminate overfitting, for all the studied test cases.Last but not least, consistently with what found in the literature, we offer experimental evidence of the fact that the size of the genetic programming models has no correlation with their generalization ability.
STA3D: Spatiotemporally attentive 3D network for video saliency prediction

3D fully convolutional networks (FCN), which jointly leverage the spatial and temporal cues, have achieved great success in video saliency prediction. However, they still have limitations in some challenging cases, e.g. fixation shift. To address this issue, we propose a SpatioTemporally Attentive 3D Network (STA3D) to selectively propagate the significant temporal features and refine the spatial features in 3D FCN for video saliency prediction. Extensive experiments on three standard datasets demonstrate the superiority of the proposed model against the state-of-the-art.
A stochastic subspace approach to gradient-free optimization in high dimensions

We present a stochastic descent algorithm for unconstrained optimization that is particularly efficient when the objective function is slow to evaluate and gradients are not easily obtained, as in some PDE-constrained optimization and machine learning problems. The algorithm maps the gradient onto a low-dimensional random subspace of dimension $$\ell$$ at each iteration, similar to coordinate descent but without restricting directional derivatives to be along the axes. Without requiring a full gradient, this mapping can be performed by computing $$\ell$$ directional derivatives (e.g., via forward-mode automatic differentiation). We give proofs for convergence in expectation under various convexity assumptions as well as probabilistic convergence results under strong-convexity. Our method provides a novel extension to the well-known Gaussian smoothing technique to descent in subspaces of dimension greater than one, opening the doors to new analysis of Gaussian smoothing when more than one directional derivative is used at each iteration. We also provide a finite-dimensional variant of a special case of the Johnson–Lindenstrauss lemma. Experimentally, we show that our method compares favorably to coordinate descent, Gaussian smoothing, gradient descent and BFGS (when gradients are calculated via forward-mode automatic differentiation) on problems from the machine learning and shape optimization literature.
Sentiment Classification in Bangla Textual Content: A Comparative Study

Sentiment analysis has been widely used to understand our views on social and political agendas or user experiences over a product. It is one of the cores and well-researched areas in NLP. However, for low-resource languages, like Bangla, one of the prominent challenge is the lack of resources. Another important limitation, in the current literature for Bangla, is the absence of comparable results due to the lack of a well-defined train/test split. In this study, we explore several publicly available sentiment labeled datasets and designed classifiers using both classical and deep learning algorithms. In our study, the classical algorithms include SVM and Random Forest, and deep learning algorithms include CNN, FastText, and transformer-based models. We compare these models in terms of model performance and time-resource complexity. Our finding suggests transformer-based models, which have not been explored earlier for Bangla, outperform all other models. Furthermore, we created a weighted list of lexicon content based on the valence score per class. We then analyzed the content for high significance entries per class, in the datasets. For reproducibility, we make publicly available data splits and the ranked lexicon list. The presented results can be used for future studies as a benchmark.
Efficient local stereo matching algorithm based on fast gradient domain guided image filtering

Guided image filtering (GIF) based cost aggregation or disparity refinement stereo matching algorithms are studied extensively owing to the edge-aware preserved smoothing property. However, GIF suffers from halo artifacts in sharp edges and shows high computational costs on high-resolution images. The performance of GIF in stereo matching would be limited by the above two defects. To solve these problems, a novel fast gradient domain guided image filtering (F-GDGIF) is proposed. To be specific, halo artifacts are effectively alleviated by incorporating an efficient multi-scale edge-aware weighting into GIF. With this multi-scale weighting, edges can be preserved much better. In addition, high computational costs are cut down by sub-sampling strategy, which decreases the computational complexity from O(N) to O(N/s2) (s: sub-sampling ratio) To verify the effectiveness of the algorithm, F-GDGIF is applied to cost aggregation and disparity refinement in stereo matching algorithms respectively. Experiments on the Middlebury evaluation benchmark demonstrate that F-GDGIF based stereo matching method can generate more accuracy disparity maps with low computational cost compared to other GIF based methods.
Privacy-Preserving Crowd-Monitoring Using Bloom Filters and Homomorphic Encryption

This paper introduces an architecture for crowd-monitoring which allows statistical counting for pedestrian dynamics while considering privacy-preservation for the individuals being sensed. Monitoring crowds of pedestrians has been an interesting area of study for many years. The recent prevalence of mobile devices paved the way for wide-scale deployments of infrastructures which perform automated sensing. Suddenly, people could be discreetly monitored by leveraging radio signals such as Wi-Fi probe requests periodically sent by their devices. However, this monitoring process implies dealing with sensitive data which is prone to privacy infringement by nature. While routinely performing their tasks, parties involved in this process can try to infer private information about individuals from the data they handle. Following privacy by design principles, we envision a construction which protects the short-term storage and processing of the collected privacy-sensitive sensor readings with strong cryptographic guarantees such that only the end-result (i.e. a statistical count) becomes available in the clear. We combine Bloom filters, to facilitate set membership testing for counting, with homomorphic encryption, to allow the oblivious performance of operations under encryption. We carry out an implementation of our solution using a resource-constrained device as a sensor and perform experiments which demonstrate its feasibility in practice.