Reviewerly/exHarmony huggingface dataset:
Denoising Autoencoder Aided Spectrum Reconstruction for Colloidal Quantum Dot Spectrometers

Recently, the colloidal quantum dot spectrometer has received much attention due to its advantages in cost, size, and operation. Yet, just like many other filter-based miniature spectrometers, spectrum reconstruction for the colloidal quantum dot spectrometer is typically prone to the measurement noise due to the correlation of the filters. In this paper, we propose an effective spectrum reconstruction method for the colloidal quantum dot spectrometer, which can recover high-quality spectra in noisy environments. Specifically, we employ a denoising autoencoder, a machine-learning approach, to reduce noise in the filters' raw measurements before performing the reconstruction. After that, we reconstruct the spectra with the denoised data by a sparse recovery algorithm. We investigate the feasibility of the proposed reconstruction approach on a synthetic dataset and an experimental dataset collected by the colloidal quantum dot spectrometer. The results demonstrate that the proposed approach could deliver accurate reconstruction results even when data are corrupted with the measurement noise.
Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation

Conventional unsupervised domain adaptation (UDA) studies the knowledge transfer between a limited number of domains. This neglects the more practical scenario where data are distributed in numerous different domains in the real world. A technique to measure domain similarity is critical for domain adaptation performance. To describe and learn relations between different domains, we propose a novel Domain2Vec model to provide vectorial representations of visual domains based on joint learning of feature disentanglement and Gram matrix. To evaluate the effectiveness of our Domain2Vec model, we create two large-scale cross-domain benchmarks. The first one is TinyDA, which contains 54 domains and about one million MNIST-style images. The second benchmark is DomainBank, which is collected from 56 existing vision datasets. We demonstrate that our embedding is capable of predicting domain similarities that match our intuition about visual relations between different domains. Extensive experiments are conducted to demonstrate the power of our new datasets in benchmarking state-of-the-art multi-source domain adaptation methods, as well as the advantage of our proposed model (Data and code are available at https://github.com/VisionLearningGroup/Domain2Vec ).
An Automated System in ATM Booth Using Face Encoding and Emotion Recognition Process

Nowadays, the banking transaction system is more flexible than the previous one. When the banking sector introduces the ATM booth to us, it was a step ahead to ease the human effort. Here, ATM booth is an automated teller machine that gives out money to the consumer by inserting a card in it. All ATM booths support both credit and debit cards for the transaction, and this has saved everyone's time. But still, there are some certain situations, i.e., forgetting the card authentication details for a transaction can ruin a consumer's day. For this reason, this paper has tried to propose a system that will help everyone regarding this situation. This proposed system is about face encoding process with an emotion recognition test for making transactions faster and accurate, based on Convolutional Neural Network (CNN). However, normal card transactions can still be possible besides using the proposed system. FER2013 dataset was used for training and then tested the model using our own sample images. The result shows that the proposed system can correctly separate ‘Happy’ faces from other emotional faces and allow the transaction to proceed.
A Toolkit for the Automatic Analysis of Human Behavior in HCI Applications in the Wild

Nowadays, smartphones and laptops equipped with cameras have become an integral part of our daily lives.The pervasive use of cameras enables the collection of an enormous amount of data, which can be easily extracted through video images processing.This opens up the possibility of using technologies that until now had been restricted to laboratories, such as eye-tracking and emotion analysis systems, to analyze users' behavior in the wild, during the interaction with websites.In this context, this paper introduces a toolkit that takes advantage of deep learning algorithms to monitor user's behavior and emotions, through the acquisition of facial expression and eye gaze from the video captured by the webcam of the device used to navigate the web, in compliance with the EU General data protection regulation (GDPR).Collected data are potentially useful to support user experience assessment of web-based applications in the wild and to improve the effectiveness of e-commerce recommendation systems.
Evaluation of Approaches for Designing and Developing Decentralized Applications on Blockchain

Blockchain as a novel technology has shown the capacity to transform multiple sectors. Due to opportunities such as data security, transparency, privacy and interoperability presented by decentralized applications on blockchain networks, organizations are now moving their inter-organizational functions to blockchain-based systems. Traditional system-design methods are not suitable for developing blockchain systems since they are not capable of capturing new functions that are associated with decentralized applications. This paper seeks to address this gap by exploring existing design methods and frameworks that support the development of decentralized application on a blockchain. To achieve this, case-study based research is used in identifying current software-development techniques used in designing decentralized applications (DApp). Experts in this field are interviewed to identify their chosen techniques for designing and developing block-chain applications. Furthermore, related literature is selected to identify current techniques for DApp-designs based on the state of the art. A systematic analysis of the identified techniques is conducted using the current limitations of blockchain technology as criteria for the analyses. Finally, based on the findings of this study, we propose a new model-driven framework for designing blockchain applications.
Exploring multi-scale deformable context and channel-wise attention for salient object detection

Contextual information has played an important role in salient object detection. However, due to the fixed geometric structures of convolution kernels employed by existing Convolutional Neural Networks (CNNs) based methods, it is difficult to extract meaningfully visual contexts for those salient objects with varying sizes and non-rigid shapes. To address this problem, in this paper, we propose a Multi-Scale Deformation Module (MSDM) to capture multi-scale visual cues and varying shapes of salient objects. Moreover, most existing CNNs based methods treat all channels of feature maps equally, which tends to differ from the fact that different channels actually contribute differently to saliency prediction. For that, we involve a novel Channel-Wise Attention Mechanism (CWAM) after MSDM to highlight those informative channels while suppressing those confusing ones. Experimental results on five benchmark datasets demonstrate the superiority of the proposed method over the state-of-the-art approaches.
MSB-FCN: Multi-Scale Bidirectional FCN for Object Skeleton Extraction

The performance of state-of-the-art object skeleton detection (OSD) methods have been greatly boosted by Convolutional Neural Networks (CNNs). However, the most existing CNN-based OSD methods rely on a `skip-layer' structure where low-level and high-level features are combined to gather multi-level contextual information. Unfortunately, as shallow features tend to be noisy and lack semantic knowledge, they will cause errors and inaccuracy. Therefore, in order to improve the accuracy of object skeleton detection, we propose a novel network architecture, the Multi-Scale Bidirectional Fully Convolutional Network (MSB-FCN), to better gather and enhance multi-scale high-level contextual information. The advantage is that only deep features are used to construct multi-scale feature representations along with a bidirectional structure for better capturing contextual knowledge. This enables the proposed MSB-FCN to learn semantic-level information from different sub-regions. Moreover, we introduce dense connections into the bidirectional structure to ensure that the learning process at each scale can directly encode information from all other scales. An attention pyramid is also integrated into our MSB-FCN to dynamically control information propagation and reduce unreliable features. Extensive experiments on various benchmarks demonstrate that the proposed MSB-FCN achieves significant improvements over the state-of-the-art algorithms.
An improved algorithm for deep learning YOLO network based on Xilinx ZYNQ FPGA

With the development of artificial intelligence, convolutional neural networks (CNN) have been widely used in image processing and other aspects due to their excellent performance. However, as a computationally intensive algorithm, CNN face huge challenges in the realization of mobile devices. FPGA have the advantages of high performance, reprogramming, and low power consumption, and have becoming suitable choices for CNN deployment. Compared with various CNN algorithms, the YOLO algorithm regards target detection as a regression problem. It is a one-step algorithm with fast execution speed and small amount of calculation. It is suitable for implementation on FPGA hardware platforms. This paper proposes an improved algorithm for deep learning YOLO network based on Xilinx ZYNQ FPGA. By optimizing the YOLO network model and fixed-point, etc., the problem of large computational of CNN and limited resources on FPGA chips is solved, and the parallelism of FPGA is used to accelerate the CNN. Experimental results show that the method proposed in this paper has greatly improved the operation rate while maintaining accuracy, and has important practical value in the realization of mobile terminals of CNN and real-time computing.
Stochastic Optimization with Decision-Dependent Distributions

Stochastic optimization problems often involve data distributions that change in reaction to the decision variables. This is the case, for example, when members of the population respond to a deployed classifier by manipulating their features so as to improve the likelihood of being positively labeled. Recent works on performative prediction identify an intriguing solution concept for such problems: find the decision that is optimal with respect to the static distribution that the decision induces. Continuing this line of work, we show that, in the strongly convex setting, typical stochastic algorithms—originally designed for static problems—can be applied directly for finding such equilibria with little loss in efficiency. The reason is simple to explain: the main consequence of the distributional shift is that it corrupts algorithms with a bias that decays linearly with the distance to the solution. Using this perspective, we obtain convergence guarantees for popular algorithms, such as stochastic gradient, clipped gradient, prox-point, and dual averaging methods, along with their accelerated and proximal variants. In realistic applications, deployment of a decision rule is often much more expensive than sampling. We show how to modify the aforementioned algorithms so as to maintain their sample efficiency when performing only logarithmically many deployments. Funding: This work was supported by the Division of Computing and Communication Foundations [Grant 1740551] and Division of Mathematical Sciences [Grant 1651851].
Identity-Based Provable Data Possession From RSA Assumption for Secure Cloud Storage

As cloud storage services have become popular nowadays, the integrity of outsourced data stored at untrusted servers received increased attention. Provable data possession (PDP) provides an effective and efficient solution for cloud data integrity by asking the cloud server to prove that the stored data are not tampered with or maliciously discarded without returning the actual data to users. In this article, we propose an efficient identity-based privacy-preserving provable data possession scheme (ID-P <inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> DP) based on the RSA assumption for secure cloud storage. In ID-P <inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> DP, a cloud user takes the outsourcing file and a global parameter in a time period as inputs to generate identity-based homomorphic authenticators, and any third-party auditor (TPA) can check the integrity of the outsourced file by verifying the validity of homomorphic authenticators. The distinguished feature of ID-P <inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> DP is to support the aggregation of identity-based homomorphic authenticators generated by different users under the RSA assumption, which is an open problem in provable data possession. Specifically, we transfer the identity-based homomorphic authenticators generated in distinct time periods into those with the same period parameter, and the cloud can compress the homomorphic authenticators of different users to generate a data possession proof for integrity verification. Besides, by exploiting zero-knowledge proof, the leakage of outsourced data to TPA can be prevented. The soundness of ID-P <inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> DP is proved based on the RSA assumption, and the privacy against TPA is perfectly preserved. Finally, we demonstrate ID-P <inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> DP is more efficient on integrity verification than the existing BLS-based schemes, and cross-user aggregate verification can significantly reduce computational and communication overhead for TPA.
Deep multi-level feature pyramids: Application for non-canonical firearm detection in video surveillance

The epidemic of gun violence worldwide necessitates the need for an active-based video surveillance network to combat this crime. In this context, autonomously detecting handguns is crucial in capturing firearm-related crimes. However, current object detectors using deep learning are unable to capture handguns at different scales in an unconstrained environment. Hence, this paper puts forward an enhanced deep multi-level feature pyramid network that addresses the difficulty in inferring handguns from a non-canonical perspective. We first construct a dataset containing handguns in an unconstrained environment for representation learning. The dataset is constructed from a set of 250 recorded videos and with over 2500 distinct labeled frames. Crucially, these labeled frames account for the absence of a proper video surveillance-based handgun dataset. We then train the dataset on a multi-level multi-scale object detector, i.e., M2Det. We further improve the performance of M2Det by: (1) Enhancing the base features by concatenating shallow, medium and deep features from the backbone according to its relative receptive field; (2) Implementing generalized intersection-over-union as its localization loss; and (3) Integrating Focal Loss as its classification loss to improve detection of small-scale handguns. Experiments on a challenging video surveillance test dataset demonstrate that the proposed model achieves 87.42% accuracy. In addition, we implement adaptive surveillance image partitioning to redetect handguns at specific regions. This method potentially solves the challenge of sporadically poor real-world handgun classifications. This model is capable of pioneering non-canonical handgun detection for active-based video surveillance systems. The dataset and trained models are available at: https://github.com/MarcusLimJunYi/Monash-Guns-Dataset.
Unsupervised neural domain adaptation for document image binarization

Binarization is a well-known image processing task, whose objective is to separate the foreground of an image from the background. One of the many tasks for which it is useful is that of preprocessing document images in order to identify relevant information, such as text or symbols. The wide variety of document types, alphabets, and formats makes binarization challenging. There are multiple proposals with which to solve this problem, from classical manually-adjusted methods, to more recent approaches based on machine learning. The latter techniques require a large amount of training data in order to obtain good results; however, labeling a portion of each existing collection of documents is not feasible in practice. This is a common problem in supervised learning, which can be addressed by using the so-called Domain Adaptation (DA) techniques. These techniques take advantage of the knowledge learned in one domain, for which labeled data are available, to apply it to other domains for which there are no labeled data. This paper proposes a method that combines neural networks and DA in order to carry out unsupervised document binarization. However, when both the source and target domains are very similar, this adaptation could be detrimental. Our methodology, therefore, first measures the similarity between domains in an innovative manner in order to determine whether or not it is appropriate to apply the adaptation process. The results reported in the experimentation, when evaluating up to 20 possible combinations among five different domains, show that our proposal successfully deals with the binarization of new document domains without the need for labeled data.
Soft-Information Assisted Modulation Recognition for Reconfigurable Radios

Modulation recognition plays a critical role in the development of reconfigurable radios which intelligently interact with their environments to maximize resources utilization. This research topic has been intensively investigated in the literature for different communication systems during the last decades. However, to the best of the authors' knowledge, no work has been reported to take advantage of channel decoders, which are typically implemented in most practical systems, in designing modulation recognition algorithms. In this contribution, for the first time in the literature, we show how the output of a channel decoder can be exploited to iteratively estimate the maximum-likelihood value of the transmit modulation format. The proposed algorithm is implemented via an expectation-maximization procedure. The proposed algorithm operates with any channel code as long as its decoder can provide the a posteriori probabilities of the transmitted bits. Monte Carlo simulations indicate that the proposed algorithm achieves a high recognition performance over a wide range of signal-to-noise ratios compared to the traditional ones.
Philosophy of education in a new key: Constraints and possibilities in present times with regard to dignity

Human beings as imperfect rational beings face continuous challenges, one of them has to do with the lack of recognizing and respecting our inner dignity in present times. In this collective paper,...
Supervised Machine Learning Approach For The Prediction of Breast Cancer

In the present situation it has seen that malignant growth is ordered illness as a heterogeneous ailment comprising of various subcategories. It is known from late explores that the second most driving malignant growth turning out in ladies is bosom disease contrast with every other malignant growth. It turned into the significant wellspring of mortality between ladies. Bosom malignant growth is turning into the purpose behind a ton of passings at present henceforth its initial finding is fundamental. So as to all the more likely get it and to help decrease its happening rate in future different advances are being done. Grouping is an ordinary impulse just as an undeniable logical order. Characterization of disease and the way toward classifying malignant growth sub types is talked about dependent on their watched clinical and organic highlights. We utilized five mainstream ML calculations (K Nearest-Neighbor(KNN), Logistic Regression(LR), Random Forest(RF), Support Vector Machine(SVM), Decision Tree(DT)) to build up the expectation models utilizing a huge dataset (699 Breast Cancer Cases), bringing about productive and precise dynamic. We have utilized 10-Fold cross-approval strategies to gauge the impartial gauge of the five expectation models for the examination of execution. The significant explanation for checking with different models is that, at most precise calculation is required to work with so as to guarantee immaculate outcomes. The outcomes showed that Logistic Regression and K closest neighbor are the best indicators with the most elevated effectiveness of 96.52 % and 98 %.
Data Augmentation For Chinese Text Classification Using Back-Translation

Abstract Text classification is a basic task in natural language processing. When the amount of data is insufficient, the classification accuracy will be greatly affected. We propose to use the back-translation method to expand three Chinese data sets used for text classification, and then train and predict the data sets through deep learning classification model. The results prove that using back-translation to expand the data is particularly helpful on a smaller dataset, it also can reduce the unbalanced distribution of samples and improve the classification performance.
Multilabel Classification With Group-Based Mapping: A Framework With Local Feature Selection and Local Label Correlation

Multilabel learning, which handles instances associated with multiple labels, has attracted much attention in recent years. Many extant multilabel feature selection methods target global feature selection, which means feature selection weights for each label are shared by all instances. Also, many extant multilabel classification methods exploit global label selection, which means labels correlations are shared by all instances. In real-world objects, however, different subsets of instances may share different feature selection weights and different label correlations. In this article, we propose a novel framework with local feature selection and local label correlation, where we assume instances can be clustered into different groups, and the feature selection weights and label correlations can only be shared by instances in the same group. The proposed framework includes a group-specific feature selection process and a label-specific group selection process. The former process projects instances into different groups by extracting the instance-group correlation. The latter process selects labels for each instance based on its related groups by extracting the group-label correlation. In addition, we also exploit the intergroup correlation. These three kinds of group-based correlations are combined to perform effective multilabel classification. The experimental results on various datasets validate the effectiveness of our approach.