Reviewerly/exHarmony huggingface dataset:
Video Person Re-Identification Using Attribute-Enhanced Features

In this work we propose to boost video-based person re-identification (Re-ID) by using attribute-enhanced feature presentation. To this end, we not only try to use the ID-relevant attributes more effectively, but also for the first time in literature harness the ID-irrelevant attributes to help model training. The former mainly include gender, age, clothing characteristics, etc., which contain rich and supplementary information about the pedestrian; the latter include viewpoint, action, etc., which are seldom used for identification previously. In particular, we use the attributes to enhance the significant areas of the image with a novel Attribute Salient Region Enhance (ASRE) module that can attend more accurately to the body of the pedestrian, so as to better separate the target from the background. Furthermore, we find that many ID-irrelevant but subject-relevant factors, like the view angle and movement of the target pedestrian, have great impact on the two-dimensional appearance of a pedestrian. We then propose to exploit both the ID-relevant and the ID-irrelevant attributes via a novel triplet loss called the Viewpoint and Action-Invariant (VAI) triplet loss. Based on the above, we design an Attribute Salience Assisted Network (ASA-Net) to perform attribute recognition along with identity recognition, and use the attributes for feature enhancement and hard sample mining. Extensive experiments on MARS and DukeMTMC-VideoReID datasets show that our method outperforms the state-of-the-arts. Also, the visualizations of learning results further prove the effectiveness of the proposed method.
Estimating distinguishability measures on quantum computers

The performance of a quantum information processing protocol is ultimately judged by distinguishability measures that quantify how distinguishable the actual result of the protocol is from the ideal case. The most prominent distinguishability measures are those based on the fidelity and trace distance, due to their physical interpretations. In this paper, we propose and review several algorithms for estimating distinguishability measures based on trace distance and fidelity. The algorithms can be used for distinguishing quantum states, channels, and strategies (the last also known in the literature as "quantum combs"). The fidelity-based algorithms offer novel physical interpretations of these distinguishability measures in terms of the maximum probability with which a single prover (or competing provers) can convince a verifier to accept the outcome of an associated computation. We simulate many of these algorithms by using a variational approach with parameterized quantum circuits. We find that the simulations converge well in both the noiseless and noisy scenarios, for all examples considered. Furthermore, the noisy simulations exhibit a parameter noise resilience. Finally, we establish a strong relationship between various quantum computational complexity classes and distance estimation problems.
Security Threats of a Blockchain-Based Platform for Industry Ecosystems in the Cloud

In modern industrial production lines, the integration and interconnection of various different manufacturing components, like robots, laser cutting machines, milling machines, CNC-machines, etc. allows for a higher degree of autonomous production on the shop floor. Manufacturers of these increasingly complex machines are beginning to equip their business models with bidirectional data flows to other factories. This is creating a digital, cross-company shop floor infrastructure where the transfer of information is controlled by digital contracts. To establish a trusted ecosystem, the new technology "blockchain" and a variety of technology stacks must be combined while ensuring security. Such blockchain-based frameworks enable bidirectional trust across all contract partners. Essential data flows are defined by specific technical representation of contract agreements and executed through smart contracts.This work describes a platform for rapid cross-company business model instantiation based on blockchain for establishing trust between the enterprises. It focuses on selected security aspects of the deployment- and configuration processes applied by the industrial ecosystem. A threat analysis of the platform shows the critical security risks. Based on an industrial dynamic machine leasing use case, a risk assessment and security analysis of the key platform components is carried out.
MB-ZZLBP: Multiscale Block ZigZag Local Binary Pattern for Face Recognition

This work presents the LBP variant so-called MB-ZZLBP under illumination and expression variations. In MB-ZZLBP, initially mean is computed for all the square sub-blocks (size 2 × 2) of the 6 × 6 pixel window. After the mean computation, 3 × 3 window (pixel) is produced. Then ZigZag pixels are compared with each other, or in other words, the higher-order pixels are subtracted from the lower-order pixels. Differences of those which produce value ≥0 are allocated the label 1, else 0. After encoding each pixel position (binary pattern) eventually results in MB-ZZLBP transformed image. The MB-ZZLBP image is further divided into 3 × 3 sub-regions for extraction of histograms. The fused histogram is the feature size of MB-ZZLBP. The proposed FR approach attains remarkable results on EYB and Faces94 databases.
An Analysis of the Influence of Noneffective Instructions in Linear Genetic Programming

Linear Genetic Programming (LGP) represents programs as sequences of instructions and has a Directed Acyclic Graph (DAG) dataflow. The results of instructions are stored in registers that can be used as arguments by other instructions. Instructions that are disconnected from the main part of the program are called noneffective instructions, or structural introns. They also appear in other DAG-based GP approaches like Cartesian Genetic Programming (CGP). This article studies four hypotheses on the role of structural introns: noneffective instructions (1) serve as evolutionary memory, where evolved information is stored and later used in search, (2) preserve population diversity, (3) allow neutral search, where structural introns increase the number of neutral mutations and improve performance, and (4) serve as genetic material to enable program growth. We study different variants of LGP controlling the influence of introns for symbolic regression, classification, and digital circuits problems. We find that there is (1) evolved information in the noneffective instructions that can be reactivated and that (2) structural introns can promote programs with higher effective diversity. However, both effects have no influence on LGP search performance. On the other hand, allowing mutations to not only be applied to effective but also to noneffective instructions (3) increases the rate of neutral mutations and (4) contributes to program growth by making use of the genetic material available as structural introns. This comes along with a significant increase of LGP performance, which makes structural introns important for LGP.
Feature Extraction Network with Attention Mechanism for Data Enhancement and Recombination Fusion for Multimodal Sentiment Analysis

Multimodal sentiment analysis and emotion recognition represent a major research direction in natural language processing (NLP). With the rapid development of online media, people often express their emotions on a topic in the form of video, and the signals it transmits are multimodal, including language, visual, and audio. Therefore, the traditional unimodal sentiment analysis method is no longer applicable, which requires the establishment of a fusion model of multimodal information to obtain sentiment understanding. In previous studies, scholars used the feature vector cascade method when fusing multimodal data at each time step in the middle layer. This method puts each modal information in the same position and does not distinguish between strong modal information and weak modal information among multiple modalities. At the same time, this method does not pay attention to the embedding characteristics of multimodal signals across the time dimension. In response to the above problems, this paper proposes a new method and model for processing multimodal signals, which takes into account the delay and hysteresis characteristics of multimodal signals across the time dimension. The purpose is to obtain a multimodal fusion feature emotion analysis representation. We evaluate our method on the multimodal sentiment analysis benchmark dataset CMU Multimodal Opinion Sentiment and Emotion Intensity Corpus (CMU-MOSEI). We compare our proposed method with the state-of-the-art model and show excellent results.
Exploiting Translation Model for Parallel Corpus Mining

Parallel corpus mining (PCM) is beneficial for many corpus-based natural language processing tasks, e.g., machine translation and bilingual dictionary induction, especially in low-resource languages and domains. It relies heavily on cross-lingual representations to model the interdependencies between different languages and determine whether sentences are parallel or not. In this paper, we take the first step towards exploiting the multilingual Transformer translation model to produce expressive sentence representations for PCM. Since the traditional Transformer lacks an immediate sentence representation, we pool the output representation of the encoder as the sentence representation, which is further optimized as a part of the training flow of the translation model. Experiments conducted on the BUCC PCM task show that the proposed method improves mining performance over the existing methods with the assistance of the pre-trained multilingual BERT. To further test the usability of the proposed method, we mine parallel sentences from public resources and find that the mined sentences can indeed enhance low-resource machine translation.
Learn-Explain-Reinforce: Counterfactual Reasoning and Its Guidance to Reinforce an Alzheimer's Disease Diagnosis Model

Existing studies on disease diagnostic models focus either on diagnostic model learning for performance improvement or on the visual explanation of a trained diagnostic model. We propose a novel learn-explain-reinforce (LEAR) framework that unifies diagnostic model learning, visual explanation generation (explanation unit), and trained diagnostic model reinforcement (reinforcement unit) guided by the visual explanation. For the visual explanation, we generate a counterfactual map that transforms an input sample to be identified as an intended target label. For example, a counterfactual map can localize hypothetical abnormalities within a normal brain image that may cause it to be diagnosed with Alzheimer's disease (AD). We believe that the generated counterfactual maps represent data-driven knowledge about a target task, i.e., AD diagnosis using structural MRI, which can be a vital source of information to reinforce the generalization of the trained diagnostic model. To this end, we devise an attention-based feature refinement module with the guidance of the counterfactual maps. The explanation and reinforcement units are reciprocal and can be operated iteratively. Our proposed approach was validated via qualitative and quantitative analysis on the ADNI dataset. Its comprehensibility and fidelity were demonstrated through ablation studies and comparisons with existing methods.
Towards Robust Classification Model by Counterfactual and Invariant Data Generation

Despite the success of machine learning applications in science, industry, and society in general, many approaches are known to be non-robust, often relying on spurious correlations to make predictions. Spuriousness occurs when some features correlate with labels but are not causal; relying on such features prevents models from generalizing to unseen environments where such correlations break. In this work, we focus on image classification and propose two data generation processes to reduce spuriousness. Given human annotations of the subset of the features responsible (causal) for the labels (e.g. bounding boxes), we modify this causal set to generate a surrogate image that no longer has the same label (i.e. a counterfactual image). We also alter non-causal features to generate images still recognized as the original labels, which helps to learn a model invariant to these features. In several challenging datasets, our data generations outperform state-of-the-art methods in accuracy when spurious correlations break, and increase the saliency focus on causal features providing better explanations.
Implications of bundled offerings for business development and competitive strategy in digital insurance

The ubiquitous availability of interconnected digital devices does not only change the kinds of risks that can be insured and the way insurance companies work, it also facilitates the formation of bundled offerings across different industrial sectors, including insurance as well as other products and services. We identify two distinct patterns in which such bundles change value creation in the insurance industry, which we describe as sales bundles and value bundles. In the former, the objects of insurance become more specific and risks can be estimated more precisely, but insurance becomes an add-on to other offerings at the point of sale. In the latter, insurance is merged with other products and services in comprehensive value bundles in which the exact distribution of responsibilities remains flexible. Although huge effort is required to establish these value bundles and maintain their systemic infrastructures, they create many new opportunities for business development in the insurance industry.
Deep Learning for Age Estimation Using EfficientNet

The human face constitutes various biometric features that could be used to estimate important details from humans, such as age. The automation of age estimation has been further limited by variations in facial landmarks and appearances, together with the lack of enormous databases. These have also limited the efficiencies of conventional approaches such as the handcrafted method for adequate age estimation. More recently, Convolutional Neural Network (CNN) methods have been applied to age estimation and image classification with recorded improvements. In this work, we utilise the CNN-based EfficientNet architecture for age estimation, which, so far, has not been employed in any current study to the best of our knowledge. This research focused on applying the EfficientNet architecture to classify an individual’s age in the appropriate age group using the UTKface and Adience datasets. Seven EfficientNet variants (B0–B6) were presented herein, which were fine-tuned and used to evaluate age classification efficiency. Experimentation showed that the EfficientNet-B4 variant had the best performance on both datasets with accuracy of 73.5% and 81.1% on UTKFace and Adience, respectively. The models showed a promising pathway in solving problems related to learning global features, reducing training time and computational resources.
DVREI: Dynamic Verifiable Retrieval over Encrypted Images

The increasing awareness in privacy has partly contributed to the renewed interest in privacy-preserving encrypted image retrieval, and designing for outsourced images stored on cloud servers, etc. However, there are some limitations in these existing schemes such as low retrieval accuracy, low retrieval efficiency, and less efficient result verification in the dynamic setting. Therefore, in this paper we present a novel Dynamic Verifiable Retrieval over Encrypted Images (DVREI) scheme. First, a pre-trained Convolutional Neural Network (CNN) model is utilized to extract image features to improve retrieval accuracy. Then, an encrypted index based on the K-means clustering algorithm is designed to improve retrieval efficiency. Finally, a dynamic verification tree based on the chameleon hash is used to verify the correctness of the retrieval results and support dynamic updates. We theoretically and experimentally evaluate the security and performance of DVREI to demonstrate its practicability.
Detection of Double JPEG Compression With the Same Quantization Matrix via Convergence Analysis

Detecting double JPEG compression with the same quantization matrix is a challenging task in image forensics. To address this problem, in this paper, a novel method is proposed by leveraging the component convergence during repeated JPEG compressions. Firstly, an in-depth analysis of the pipeline in successive JPEG compressions is conducted, and it reveals that the rounding/truncation errors as well as JPEG coefficients tend to converge after multiple recompressions. Based on this fact, the backward quantization error (BQE) is defined, and we find that the ratio of non-zero BQE for single compression is larger than that for double compression. Moreover, to exploit the convergence property of JPEG coefficients, a multi-threshold strategy is designed for capturing the statistics of the number of different JPEG coefficients between two sequential compressions. Finally, the statistical features of the dual components are concatenated into a 15-D vector to detect double JPEG compression. Experimental results demonstrate the efficiency of the proposed method, which outperforms some state-of-the-art schemes.
Driving With Distraction: Measuring Brain Activity and Oculomotor Behavior Using fMRI and Eye-Tracking

Driving motor vehicles is a complex task that depends heavily on how visual stimuli are received and subsequently processed by the brain. The potential impact of distraction on driving performance is well known and poses a safety concern - especially for individuals with cognitive impairments who may be clinically unfit to drive. The present study is the first to combine functional magnetic resonance imaging (fMRI) and eye-tracking during simulated driving with distraction, providing oculomotor metrics to enhance scientific understanding of the brain activity that supports driving performance.As initial work, twelve healthy young, right-handed participants performed turns ranging in complexity, including simple right and left turns without oncoming traffic, and left turns with oncoming traffic. Distraction was introduced as an auditory task during straight driving, and during left turns with oncoming traffic. Eye-tracking data were recorded during fMRI to characterize fixations, saccades, pupil diameter and blink rate.Brain activation maps for right turns, left turns without oncoming traffic, left turns with oncoming traffic, and the distraction conditions were largely consistent with previous literature reporting the neural correlates of simulated driving. When the effects of distraction were evaluated for left turns with oncoming traffic, increased activation was observed in areas involved in executive function (e.g., middle and inferior frontal gyri) as well as decreased activation in the posterior brain (e.g., middle and superior occipital gyri). Whereas driving performance remained mostly unchanged (e.g., turn speed, time to turn, collisions), the oculomotor measures showed that distraction resulted in more consistent gaze at oncoming traffic in a small area of the visual scene; less time spent gazing at off-road targets (e.g., speedometer, rear-view mirror); more time spent performing saccadic eye movements; and decreased blink rate.Oculomotor behavior modulated with driving task complexity and distraction in a manner consistent with the brain activation features revealed by fMRI. The results suggest that eye-tracking technology should be included in future fMRI studies of simulated driving behavior in targeted populations, such as the elderly and individuals with cognitive complaints - ultimately toward developing better technology to assess and enhance fitness to drive.
3D Human Pose Regression Using Graph Convolutional Network

3D human pose estimation is a difficult task, due to challenges such as occluded body parts and ambiguous poses. Graph convolutional networks encode the structural information of the human skeleton in the form of an adjacency matrix, which is beneficial for better pose prediction. We propose one such graph convolutional network named PoseGraphNet for 3D human pose regression from 2D poses. Our network uses an adaptive adjacency matrix and kernels specific to neighbor groups. We evaluate our model on the Human3.6M dataset which is a standard dataset for 3D pose estimation. Our model’s performance is close to the state-of-theart, but with much fewer parameters. The model learns interesting adjacency relations between-joints that have no physical connections, but are behaviorally similar.
A Lightweight Relu-Based Feature Fusion For Aerial Scene Classification

In this paper, we propose a transfer-learning based model construction technique for the aerial scene classification problem. The core of our technique is a layer selection strategy, named ReLU-Based Feature Fusion (RBFF), that extracts feature maps from a pretrained CNN-based single-object image classification model, namely MobileNetV2, and constructs a model for the aerial scene classification task. RBFF stacks features extracted from the batch normalization layer of a few selected blocks of MobileNetV2, where the candidate blocks are selected based on the characteristics of the ReLU activation layers present in those blocks. The feature vector is then compressed into a low-dimensional feature space using dimension reduction algorithms on which we train a low-cost SVM classifier for the classification of the aerial images. We validate our choice of selected features based on the significance of the extracted features with respect to our classification pipeline. RBFF remarkably does not involve any training of the base CNN model except for a few parameters for the classifier, which makes the technique very cost-effective for practical deployments. The constructed model despite being lightweight outperforms several recently proposed models in terms of accuracy for a number of aerial scene datasets.
Verification mechanism to obtain an elaborate answer span in machine reading comprehension

Machine reading comprehension (MRC) is a challenging task in natural language processing (NLP), which requires machine to determine the corresponding answer to a given passage and question. Whereas, there always exist unanswerable questions in the real world, which poses a new challenge to MRC tasks. Abundant research work has been carried out on the verification mechanism for the answerability of passage-question pairs. However, these researches only focus on its design and implementation, which has limitations in real-world scenarios. Thus, the method proposed in this paper not only verifies the answerability, but also validates and adjusts the predicted answer to obtain an elaborate answer span. Using powerful pre-trained model as encoder block, this paper explores a more comprehensive verification mechanism. Similar to how humans read passages and give answers, we propose a three-stage mechanism called ”Verification for an Elaborate Span” (V4ES): 1) sketchy reading that the model briefly browses the overall information of the passage and question, and then generates an initial answer; 2) intensive reading that it reads the passage and question again, judges the answerability of the question and gives an answer at this stage; 3) verification that it verifies these two answers produced at the previous two stages, and then gives the final prediction. Moreover, the proposed model is evaluated on two MRC challenge datasets: SQuAD2.0 and CMRC2018, and the experiment results show that our model has achieved great improvement compared with the ALBERT and BERT baselines. In conclusion, our proposed verification mechanism has demonstrated its effectiveness through a series of experiments and analysis.