Reviewerly/exHarmony huggingface dataset:
VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization

Quantization has been proven to be an effective method for reducing the computing and/or storage cost of DNNs. However, the trade-off between the quantization bitwidth and final accuracy is complex and non-convex, which makes it difficult to be optimized directly. Minimizing direct quantization loss (DQL) of the coefficient data is an effective local optimization method, but previous works often neglect the accurate control of the DQL, resulting in a higher loss of the final DNN model accuracy. In this paper, we propose a novel metric called Vector Loss. Based on this new metric, we develop a new quantization solution called VecQ, which can guarantee minimal direct quantization loss and better model accuracy. In addition, in order to speed up the proposed quantization process during model training, we accelerate the quantization process with a parameterized probability estimation method and template-based derivation calculation. We evaluate our proposed algorithm on MNIST, CIFAR, ImageNet, IMDB movie review and THUCNews text data sets with numerical DNN models. The results demonstrate that our proposed quantization solution is more accurate and effective than the state-of-the-art approaches yet with more flexible bitwidth support. Moreover, the evaluation of our quantized models on Saliency Object Detection (SOD) tasks maintains comparable feature extraction quality with up to 16$\times$ weight size reduction.
Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty

Weakly-supervised learning (WSL) has recently triggered substantial interest as it mitigates the lack of pixel-wise annotations. Given global image labels, WSL methods yield pixel-level predictions (segmentations), which enable to interpret class predictions. Despite their recent success, mostly with natural images, such methods can face important challenges when the foreground and background regions have similar visual cues, yielding high false-positive rates in segmentations, as is the case in challenging histology images. WSL training is commonly driven by standard classification losses, which implicitly maximize model confidence, and locate the discriminative regions linked to classification decisions. Therefore, they lack mechanisms for modeling explicitly non-discriminative regions and reducing false-positive rates. We propose novel regularization terms, which enable the model to seek both non-discriminative and discriminative regions, while discouraging unbalanced segmentations. We introduce high uncertainty as a criterion to localize non-discriminative regions that do not affect classifier decision, and describe it with original Kullback-Leibler (KL) divergence losses evaluating the deviation of posterior predictions from the uniform distribution. Our KL terms encourage high uncertainty of the model when the latter inputs the latent non-discriminative regions. Our loss integrates: (i) a cross-entropy seeking a foreground, where model confidence about class prediction is high; (ii) a KL regularizer seeking a background, where model uncertainty is high; and (iii) log-barrier terms discouraging unbalanced segmentations. Comprehensive experiments and ablation studies over the public GlaS colon cancer data and a Camelyon16 patch-based benchmark for breast cancer show substantial improvements over state-of-the-art WSL methods, and confirm the effect of our new regularizers (our code is publicly available at https://github.com/sbelharbi/deep-wsl-histo-min-max-uncertainty).
Predictability, distinguishability, and entanglement

Recent times have seen a spurt of research activity focused on “completing” certain wave-particle duality relations using entanglement or polarization. These studies use a duality relation involving path-predictability, and not path-distinguishability. Quantum origins of these results are explored here, in the more general framework of multipath quantum interference. Multipath interference with a path-detector is theoretically analyzed to find the connection between predictability and distinguishability. It is shown that entanglement is what quantitatively connects distinguishability with predictability. Thus, a duality relation between distinguishability and coherence can also be viewed as a triality between predictability, entanglement, and coherence. There exist two different kinds of duality relations in the literature, which pertain to two different kinds of interference experiments, with or without a path-detector. Results of this study show that the two duality relations are quantitatively connected via entanglement. The roots of the new, to the best of our knowledge, results in the classical optical domain, including the polarization coherence theorem, can be understood in the light of this work.
Implementation of online and offline product selection system using FCNN deep learning: Product analysis

Now a day’s Artificial intelligence and deep learning techniques recommended critical E-commerce applications. The human computing, computer aided designs cannot understand the alternative offline and online products. Therefore, customers are critical to find out the products such as groceries, fashion and health. However, it is a major task to overcome this limitation for human perceptions. In this research work an advanced FCNN deep learning model is proposed with global thresholding technique. For this work selecting the digital images and online images for pre-processing and classification. At primary stage segmentation is applied then classification is performed through FCNN, at final calculating the performance measures such as accuracy 98.7%, sensitivity 98.7% and throughput 99.23% has been achieved and outcomes are challenging the present technology.
A Comparative Measurement Study of Deep Learning as a Service Framework

Big data powered Deep Learning (DL) and its applications have blossomed in recent years, fueled by three technological trends: a large amount of digitized data openly accessible, a growing number of DL software frameworks in open source and commercial markets, and a selection of affordable parallel computing hardware devices. However, no single DL framework, to date, dominates in terms of performance and accuracy even for baseline classification tasks on standard datasets, making the selection of a DL framework an overwhelming task. This paper takes a holistic approach to conduct empirical comparison and analysis of four representative DL frameworks with three unique contributions. First, given a selection of CPU-GPU configurations, we show that for a specific DL framework, different configurations of its hyper-parameters may have a significant impact on both performance and accuracy of DL applications. Second, to the best of our knowledge, this study is the first to identify the opportunities for improving the training time performance and the accuracy of DL frameworks by configuring parallel computing libraries and tuning individual and multiple hyper-parameters. Third, we also conduct a comparative measurement study on the resource consumption patterns of four DL frameworks and their performance and accuracy implications, including CPU and memory usage, and their correlations to varying settings of hyper-parameters under different configuration combinations of hardware, parallel computing libraries. We argue that this measurement study provides in-depth empirical comparison and analysis of four representative DL frameworks, and offers practical guidance for service providers to deploying and delivering DL as a Service (DLaaS) and for application developers and DLaaS consumers to select the right DL frameworks for the right DL workloads.
Blockchain-Based Data Management and Edge-Assisted Trusted Cloaking Area Construction for Location Privacy Protection in Vehicular Networks

With the tremendous pervasion of location-based services (LBSs) in vehicular networks, the location privacy of vehicles has become an utmost concern. K-anonymity is one of the most popular privacy protection solutions, in which the real location of the request vehicle (RV) can be covered by a cloaking area including the location of k -1 cooperative vehicles (CVs). However, K-anonymity assumes all CVs are always honest and thus offering opportunities for dishonest CVs to provide false location information. To combat such threats, we propose a trusted cloaking area construction (TCAC) scheme based on the trust mechanism to protect the location privacy of vehicles. In this article, the trust value is not only used to identify dishonest CVs, but also utilized to decide the LBS request of RV. A low trust value will make the LBS request be rejected due to dishonest and selfish cooperative behaviors when the RV has played the role of CV. To deal with the massive trust requirements caused by frequent vehicle movement, edge computing is employed to assist trust value evaluation. Moreover, traditional central and distributed trust data management may be unsuitable for vehicular networks. We also propose a blockchain-based trust data management method by combining vehicular regions partition, so as to rapidly evaluate trust value during the cloaking area construction. The security analysis and simulation results indicate that our proposed scheme is resilient to suppress dishonest CVs, inspire selfish CVs, and protect the location privacy of vehicles effectively, whereas the required computation time and communication cost are both limited.
Active Visual Information Gathering for Vision-Language Navigation

Vision-language navigation (VLN) is the task of entailing an agent to carry out navigational instructions inside photo-realistic environments. One of the key challenges in VLN is how to conduct a robust navigation by mitigating the uncertainty caused by ambiguous instructions and insufficient observation of the environment. Agents trained by current approaches typically suffer from this and would consequently struggle to avoid random and inefficient actions at every step. In contrast, when humans face such a challenge, they can still maintain robust navigation by actively exploring the surroundings to gather more information and thus make more confident navigation decisions. This work draws inspiration from human navigation behavior and endows an agent with an active information gathering ability for a more intelligent vision-language navigation policy. To achieve this, we propose an end-to-end framework for learning an exploration policy that decides i) when and where to explore, ii) what information is worth gathering during exploration, and iii) how to adjust the navigation decision after the exploration. The experimental results show promising exploration strategies emerged from training, which leads to significant boost in navigation performance. On the R2R challenge leaderboard, our agent gets promising results all three VLN settings, i.e., single run, pre-exploration, and beam search.
Simulating quantum chemistry in the seniority-zero space on qubit-based quantum computers

Accurate quantum chemistry simulations remain challenging on classical computers for problems of industrially relevant sizes and there is reason for hope that quantum computing may help push the boundaries of what is technically feasible. While variational quantum eigensolver (VQE) algorithms may already turn noisy intermediate scale quantum (NISQ) devices into useful machines, one has to make all efforts to use the scarce quantum resources as efficiently as possible. We combine the so-called seniority-zero, or paired-electron, approximation of computational quantum chemistry with techniques for simulating molecular chemistry on gate-based quantum computers and obtain a very resource efficient quantum simulation algorithm. While some accuracy is lost through the paired-electron approximation, we show that using the freed-up quantum resources for increasing the basis set can lead to more accurate results and reductions in the necessary number of quantum computing runs by several orders of magnitude, already for a simple system like lithium hydride. We also discuss an error mitigation scheme based on post-selection which shows an attractive scaling when the given Hamiltonian format is considered, increasing the viability of its NISQ implementation.
Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks

Given the ubiquity of mobile devices, understanding the context of human activity with non-intrusive solutions is of great value. A novel deep neural network model is proposed, which combines feature extraction and convolutional layers, able to recognize human physical activity in real-time from tri-axial accelerometer data when run on a mobile device. It uses a two-layer convolutional neural network to extract local features, which are combined with 40 statistical features and are fed to a fully-connected layer. It improves the classification performance, while it takes up 5–8 times less storage space and outputs more than double the throughput of the current state-of-the-art user-independent implementation on the Wireless Sensor Data Mining (WISDM) dataset. It achieves 94.18% classification accuracy on a 10-fold user-independent cross-validation of the WISDM dataset. The model is further tested on the Actitracker dataset, achieving 79.12% accuracy, while the size and throughput of the model are evaluated on a mobile device.
Improved Mosaic: Algorithms for more Complex Images

Abstract Data augmentation plays a vital role in deep learning, and image augmentation, as an important part of target detection and image classification, significantly improves the performance of the algorithm. The Mosaic data augmentation algorithm in YOLOv4 randomly selects 4 pictures from the train set and puts the contents of the 4 pictures into a synthetic picture that is directly used for training. This data augmentation method can improve the model’s recognition ability in complex backgrounds. In this paper, we improve the Mosaic data augmentation algorithm. After analyzing the synthesized picture area, it is divided into irregular grids, and a certain number of training set pictures are filled randomly, which further improves the synthesis ability and achieves a synthesis picture that can accommodate 6 and 9 training set pictures. After basic image processing methods such as zooming, flipping, and color gamut transformation, the model’s recognition ability under complex backgrounds is improved, and the accuracy of identifying small targets is improved. During the batch normalization operation, the data of 6 or 9 pictures can be calculated at the same time, which makes the model hyperparameter mini-batch need not be set very large, which reduces the GPU memory requirements.
Adaptive Object Detection with Dual Multi-label Prediction

In this paper, we propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection by exploiting multi-label object recognition as a dual auxiliary task. The model exploits multi-label prediction to reveal the object category information in each image and then uses the prediction results to perform conditional adversarial global feature alignment, such that the multimodal structure of image features can be tackled to bridge the domain divergence at the global feature level while preserving the discriminability of the features. Moreover, we introduce a prediction consistency regularization mechanism to assist object detection, which uses the multi-label prediction results as an auxiliary regularization information to ensure consistent object category discoveries between the object recognition task and the object detection task. Experiments are conducted on a few benchmark datasets and the results show the proposed model outperforms the state-of-the-art comparison methods .
Cancer image classification based on DenseNet model

Abstract Computer-aided diagnosis establishes methods for robust assessment of medical image-based examination. Image processing introduced a promising strategy to facilitate disease classification and detection while diminishing unnecessary expenses. In this paper, we propose a novel metastatic cancer image classification model based on DenseNet Block, which can effectively identify metastatic cancer in small image patches taken from larger digital pathology scans. We evaluate the proposed approach to the slightly modified version of the PatchCamelyon (PCam) benchmark dataset. The dataset is the slightly modified version of the PatchCamelyon (PCam) benchmark dataset provided by Kaggle competition, which packs the clinically-relevant task of metastasis detection into a straight-forward binary image classification task. The experiments indicated that our model outperformed other classical methods like Resnet34, Vgg19. Moreover, we also conducted data augmentation experiment and study the relationship between Batches processed and loss value during the training and validation process.
Self-Supervised Learning to Detect Key Frames in Videos

Detecting key frames in videos is a common problem in many applications such as video classification, action recognition and video summarization. These tasks can be performed more efficiently using only a handful of key frames rather than the full video. Existing key frame detection approaches are mostly designed for supervised learning and require manual labelling of key frames in a large corpus of training data to train the models. Labelling requires human annotators from different backgrounds to annotate key frames in videos which is not only expensive and time consuming but also prone to subjective errors and inconsistencies between the labelers. To overcome these problems, we propose an automatic self-supervised method for detecting key frames in a video. Our method comprises a two-stream ConvNet and a novel automatic annotation architecture able to reliably annotate key frames in a video for self-supervised learning of the ConvNet. The proposed ConvNet learns deep appearance and motion features to detect frames that are unique. The trained network is then able to detect key frames in test videos. Extensive experiments on UCF101 human action and video summarization VSUMM datasets demonstrates the effectiveness of our proposed method.
Semantic Curiosity for Active Visual Learning

In this paper, we study the task of embodied interactive learning for object detection. Given a set of environments (and some labeling budget), our goal is to learn an object detector by having an agent select what data to obtain labels for. How should an exploration policy decide which trajectory should be labeled? One possibility is to use a trained object detector’s failure cases as an external reward. However, this will require labeling millions of frames required for training RL policies, which is infeasible. Instead, we explore a self-supervised approach for training our exploration policy by introducing a notion of semantic curiosity. Our semantic curiosity policy is based on a simple observation – the detection outputs should be consistent. Therefore, our semantic curiosity rewards trajectories with inconsistent labeling behavior and encourages the exploration policy to explore such areas. The exploration policy trained via semantic curiosity generalizes to novel scenes and helps train an object detector that outperforms baselines trained with other possible alternatives such as random exploration, prediction-error curiosity, and coverage-maximizing exploration.
Highlighting keyphrases using senti-scoring and fuzzy entropy for unsupervised sentiment analysis

Sentiment Analysis is a process that aids in assessing the performance of products or services from user generated online posts. In present time, there are various websites that allow customers to post reviews about movies, products, events or services, etc. This has led to cumulative aggregation of a lot of reviews written in natural language. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the evolution of opinion mining systems that can automatically classify customers' reviews. It is observed that in Sentiment Analysis (SA), to highlight the significant keyphrases which contribute towards correct sentiment cognition is a tedious task. In this paper, we have proposed an unsupervised sentiment classification system that comprehensively formulates phrases, computes their senti-scores (sentiment scores) and polarity using the SentiWordNet lexicon and fuzzy linguistic hedges. Further it extracts the keyphrases significant for SA using fuzzy entropy filter and k-means clustering. We have deployed document level SA on online reviews using n-gram techniques, specifically combination of unigram, bigram and trigram. Experiments on two benchmark movie review datasets- polarity dataset by Pang and Lee and IMDB dataset, achieve high accuracy for our approach as compared to the other state-of-the-art-methods for phrase-level SA.
Toward quantum simulations of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi mathvariant="double-struck">Z</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math>gauge theory without state preparation

Preparing strongly-coupled particle states on quantum computers requires large resources. In this work, we show how classical sampling coupled with projection operators can be used to compute Minkowski matrix elements without explicitly preparing these states on the quantum computer. We demonstrate this for the 2+1d $\mathbb{Z}_2$ lattice gauge theory on small lattices with a quantum simulator.
How emotional are emoji?: Exploring the effect of emotional valence on the processing of emoji stimuli

Emoji are vastly becoming an integral part of everyday communication, yet little is understood about the extent to which these are processed emotionally. Previous research shows that there is a processing advantage for emotionally-valenced words over neutral ones, therefore if emoji are indeed emotional, one could expect an equivalent processing advantage. In the Pilot Study, participants (N = 44) completed a lexical decision task to explore accuracy and response latency of word, face and emoji stimuli. This stimuli varied in emotional valence (positive vs. neutral). Main effects were found for stimuli type and valence on both accuracy and latency, although the interaction for accuracy was not significant. That is, there were processing advantages of positively-valenced stimuli over neutral ones, across all stimuli types. Also, faces and emoji were processed significantly more quickly than words, and latencies between face and emoji stimuli, irrespective of valence were largely equivalent. The Main Study recruited 33 participants to undertake a modified and extended version of the lexical decision task, which included three valence conditions (positive, negative and neutral) per stimuli type. Although no main effects were found for accuracy, there was a significant main effect found for stimuli but not for valence on latency. Namely, that word stimuli irrespective of valence were processed significantly more slowly than face or emoji stimuli. There was not a significant interaction between stimuli and valence, however. Therefore, overall although there was partial support for a processing advantage of emoji stimuli, this was not replicated across the studies reported here, suggesting additional work may be needed to corroborate further evidence.