Reviewerly/exHarmony huggingface dataset:
Convolutional Feature Transfer via Camera-Specific Discriminative Pooling for Person Re-Identification

Modern Convolutional Neural Networks (CNNs) have been improving the accuracy of person re-identification (re-id) using a large number of training samples. Such a re-id system suffers from a lack of training samples for deployment to practical security applications. To address this problem, we focus on the approach that transfers features of a CNN pre-trained on a large-scale person re-id dataset to a small-scale dataset. Most of the existing CNN feature transfer methods use the features of fully connected layers that entangle locally pooled features of different spatial locations on an image. Unfortunately, due to the difference of view angles and the bias of walking directions of the persons, each camera view in a dataset has a unique spatial property in the person image, which reduces the generality of the local pooling for different cameras/datasets. To account for the camera- and dataset-specific spatial bias, we propose a method to learn camera and dataset-specific position weight maps for discriminative local pooling of convolutional features. Our experiments on four public datasets confirm the effectiveness of the proposed feature transfer with a small number of training samples in the target datasets.
IC-ChipNet: Deep Embedding Learning for Fine-grained Retrieval, Recognition, and Verification of Microelectronic Images

Modern electronic devices consist of a wide range of integrated circuits (ICs) from various manufacturers. Ensuring that an electronic device functions correctly requires verifying that its ICs and other component parts are correct and legitimate. Towards this goal, we investigate using machine learning and computer vision to identify and verify integrated circuit packages using visual features alone. We propose a deep metric learning approach to learn a feature embedding to capture important visual features of the external packages of ICs. We explore several variations of Siamese networks for this task, and learn an embedding using a joint loss function. To evaluate our approach, we collected and manually annotated a large dataset of 6,387 IC images, and tested our embedding on three challenging tasks: (1) fine-grained retrieval, (2) fine-grained IC recognition, and (3) verification. We believe this to be among the first papers targeting the novel application of fine-grained IC visual recognition and retrieval, and hope it establishes baselines to advance research in this area.
Defining IoT Orchestrations with Security and Privacy by Design: A Gap Analysis

Structured approaches to the definition of service orchestrations facilitate the composition of components and services and allow for more capable and powerful applications, while introducing many advantages such as service reusability, cost reduction, and simplification of organization alliances. A variety of such approaches exist in the literature, with more recent approcahes focusing on the IoT. Nevertheless, oftentimes security and privacy aspects are not adequately considered, which constitutes a significant barrier to the deployment of IoT applications and services with security and privacy built-in by design. The objective of this work is to provide an overview of service composition approaches, highlighting how the scientific community approaches the IoT service composition problem, while identifying the limitations of the current solutions in terms of catering to the security and privacy challenges. Furthermore, a novel approach for composing IoT systems with proven security and privacy properties is proposed.
Lightweight Dual-Task Networks For Crowd Counting In Aerial Images

As a research hotspot of computer vision, crowd counting methods have achieved success in natural images. But crowd counting in aerial images are rarely explored, and existing methods do not perform well because of the higher resolution, smaller object scale and more complex scene. Therefore, this paper proposes a lightweight dual-task network (LDNet) for crowd counting, which only uses bifurcated structure to overcome these new challenges in aerial images without complicated pipelines. To realize this, a complete but efficient Guidance Branch is proposed to assist Counting Branch in fitting crowd distribution. Furthermore, a scene attention mechanism is used to consider the complex scene information, which are never considered by existing methods. Our LD-Net outperforms existing methods on aerial crowd counting dataset (Visdrone), and gets better or comparable results on natural crowd counting datasets (UCF_CC_50, UCF_QNRF, ShanghaiTech Part A).
Contemporaneous notes for digital forensic examinations

Given the complexity of many examination-scenarios, practitioners in the field of digital forensics should consider the production and maintenance of contemporaneous notes a core part of any examination process. Not only do contemporaneous notes support the practitioner as they conduct their case work, through to the production of any accompanying report, but in some instances disclosure of this information may be required in order for all parties to evaluate any evidential findings fully. This work discusses the need for, and role of, contemporaneous notes in digital forensics, where a ‘skeleton’ of content outlining the required information practitioners should look to include in their contemporaneous notes is offered.
Neuron-based Network Pruning Based on Majority Voting

The achievement of neural networks in a variety of applications is accompanied by a dramatic increase in computational costs and memory requirements. In this paper, we propose an efficient method to simultaneously identify the critical neurons and prune the model during training without involving any pre-training or fine-tuning procedures. Unlike existing methods, which accomplish this task in a greedy fashion, we propose a majority voting technique to compare the activation values among neurons and assign a voting score to quantitatively evaluate their importance. This mechanism helps to effectively reduce model complexity by eliminating the less influential neurons and aims to determine a subset of the whole model that can represent the reference model with much fewer parameters within the training process. Experimental results show that majority voting efficiently compresses the network with no drop in model accuracy, pruning more than 79% of the original model parameters on CIFAR10 and more than 91% of the original parameters on MNIST. Moreover, we show that with our proposed method, sparse models can be further pruned into even smaller models by removing more than 60% of the parameters, whilst preserving the reference model accuracy.
A New Matching Algorithm Between Trees of Shapes and Its Application to Brain Tumor Segmentation

Many approaches exist to compute the distance between two trees in pattern recognition. These trees can be structures with or without values on their nodes or edges. However, none of these distances take into account the shapes possibly associated to the nodes of the tree. For this reason, we propose in this paper a new distance between two trees of shapes based on the Hausdorff distance. This distance allows us to make inexact tree matching and to compute what we call residual forests, representing where two trees differ. We will also see that thanks to these residual forests, we can obtain good preliminary results in matter of brain tumor segmentation. This segmentation not only provides a segmentation but also the tree of shapes corresponding to the segmentation and its depth map.
Research on Intelligent Analysis Technology of Power Monitoring Video Data Based on Convolutional Neural Network

With the rapid increase of on-site monitoring objects and technological development, the power on-site image monitoring data has increased geometrically. For security managers, it is necessary to efficiently and quickly identify valid data and conduct intelligent early warning. Therefore, this paper proposes an intelligent analysis method of power field surveillance video data based on convolutional neural network. Firstly, the original surveillance video material is extracted. Secondly, the video cloud storage platform is used for storage and data cleaning. At the same time, the hidden danger behaviors are classified and analyzed and marked. Finally, the proposed method is used for feature extraction, model training and field verification to realize intelligent early warning analysis of video big data. The field application effect shows that the method proposed in this paper can realize the functions of video image data feature extraction, model training, and improve the effect of image processing. Eventually, it can meet the needs of on-site video data intelligent analysis and has a certain promotion prospect.
Meter Recognition And Reading System Based On Machine Car

In the field of automatic inspection and factory quality inspection, it is often necessary to analyze the readings of many different types of meters to understand the working status of the factory. Therefore, it is particularly important to get an accurate reading. To solve this problem, an automatic meter reading system based on core technologies such as image recognition, deep learning, internet of things and intelligent machine car was designed. We adopted YOLOV3, SIFT, FLANN, Hough transform and other efficient algorithms to achieve the function of reading meter. Our system is competent for the reading of digital meter as well as pointer meter. Experiments show that our system has a good effect.
Logic program invertibility in cryptography problems

Abstract This work contains an analysis of problems those occur when the logic programs are used for cryptography problems. We explore the following problems: whether a logic program is invertible; whether one program is an inverse of the other. It was proved coNP-completeness for both problems. The size of logic programs also considered. We have found an instance of a program of linear length such that the length of the inverse program is exponential.
Long Short-Term Memory Based Query Auto-Completion

In this study, Long Short-Term Memory (LSTM) based Query Auto-Completion (QAC) has been proposed to generate a query completion list using input prefix. The performance of the QAC system has been evaluated by using the relevancy score, and the quality of the QAC generation system has been evaluated by using partial and complete matching strategies, success rate, normalized discounted cumulative gain, and mean average precision. The proposed LSTM based QAC system has been extensively tested using AOL and ORCAS datasets. According to experimental results, the performance of the proposed QAC system is more successful with the partial matching strategy. Also, the quality of the QAC generation list by the proposed QAC system is better on the complete matching strategy.
Attention-based Model with Attribute Classification for Cross-domain Person Re-identification

Person re-identification (re-ID) which aims to recognize a pedestrian observed by non-overlapping cameras is a challenging task due to high variance between images from different viewpoints. Although remarkable progresses on research of re-ID had been obtained via leveraging the merits of deep learning framework through sufficient quantity training on a large amount of well labeled data, whereas, in real scenarios, re-ID generally suffers from lacking of well labeled training data. In this paper, we propose an attention-based model with attribute classification (AMAC) to facilitate a well trained model transferring across different data domains, which further enables an efficient cross-domain video-based person re-ID. Specifically, an attention-based sub-network is proposed for deep insight into the quality variations of local parts, hence, different local parts are cooperated with different weights to avoid the heavy occlusions or the cluttered background in datasets. Moreover, we introduce a transferred attribute classification sub-network to extract attribute-semantic features of any new target datasets without the requirement for new training attribute labels which are costly to annotate. Attribute-semantic features can be considered as valuable complementary information for person reidentification since they are robust to illumination varieties and different viewpoints across cameras. Due to the large gap between different datasets, we finetune each sub-network with pseudo labels on the target datasets respectively to strengthen the original model trained on other labeled datasets. Extensive comparable evaluations demonstrate the superiority of our AMAC in solving cross-domain person re- ID task on two benchmarks including PRID-2011 and iLIDS-VID.
A motion based object detection method

To improve the performance of motion detection and object detection in security monitor, a motion based object detection method is proposed. Combining motion detection, target detection is proposed. This method first improved the accuracy of motion detection, then combined the result of motion detection and object detection. The experimental results show that proposed method can effectively improve the detection of small targets, reduce the false detection and negative detection rate, and decrease the computing cost.
Modulation of a Hand-held Object's Property through Proprioceptive Stimulation during Active Arm Movement

The purpose of this study was to investigate whether stimulation of the proprioceptors in the arm during active movement can affect not only the proprioception of the arm but also the perception of the hand-held object. If it is possible to control the perception of a hand-held object through stimulation to the body, it can be applied to virtual-reality interfaces and controllers, which can be used in a wide range of situations. In the experiment, proprioceptive stimulation was based on the kinesthetic illusion induced by vibratory stimulation of muscle spindles and skin stretch near the joint. Participants were given a context in which they grasped an object and actively moved. They were asked to evaluate the perception of the object and the arm as the phase between movement and stimulation, and the conditions of stimuli were changed. Consequently, it was found that the perception of not only the arm but also the hand-held object could be changed, although there were large individual differences.
Webly Supervised Image-Text Embedding with Noisy Tag Refinement

In this paper, we address the problem of utilizing web images in training robust joint embedding models for the image-text retrieval task. Prior webly supervised approaches directly leverage weakly annotated web images in the joint embedding learning framework. The objective of these approaches would suffer significantly when the ratio of noisy and missing tags associated with the web images is very high. In this regard, we propose a CP decomposition based tensor completion framework to refine the tags of web images by modeling observed ternary inter-relations between the sets of labeled images, tags, and web images as a tensor. To effectively deal with the high ratio of missing entries likely in our case, we incorporate intra-modal correlation as side information in the proposed framework. Our tag refinement approach combined with existing web supervised image-text embedding approaches provide a more principled way for learning the joint embedding models in the presence of significant noise from web data and limited clean labeled data. Experiments on benchmark datasets demonstrate that the proposed approach helps to achieve a significant performance gain in image-text retrieval.
Modeling Hierarchical Intents and Selective Current Interest for Session-Based Recommendation

Session-based recommendation is a challenging problem due to the limited session data. In the real scene, there are two insights in sessions: (1) Hierarchical intents: the implicit hierarchy in user preference is a common phenomenon, since users usually click a specific item with a general intent. (2) The influence of the current interest: the items that users click in order have sequence dependencies, and the next item is affected by the current operation. However, recent approaches are all inherently flat and neglect the hierarchical intents. Besides, they neglect the truly related subsequence for modeling the current interest. This can lead to inaccurate user intents, and fail when the user’s next click tendency falls into the more general intent. In this paper, we propose a method modeling from both Hierarchical Intents and Selective Sequential Interests (HISSI). Methodologically, we design a general intent abstractor to extract the common features and transmit general intents through the hierarchy to form fine-to-coarse grained intents. In addition, a selector-GRU is proposed to model the user’s subsequence behavior that is related to the last click without noises. Extensive experiments on three real-world datasets verify our model’s effectiveness.
Transfer Learning for End-to-End ASR to Deal with Low-Resource Problem in Persian Language

End-to-end models are state of the art for Automatic Speech Recognition (ASR) systems. Despite all their advantages, they suffer a significant problem: huge amounts of training data are required to achieve excellent performance. This problem is a serious challenge for low-resource languages such as Persian. Therefore, we need some methods and techniques to overcome this issue. One simple, yet effective method towards addressing this issue is transfer learning. We aim to explore the effect of transfer learning on a speech recognition system for the Persian language. To this end, we first train the network on 960 hours of English LibriSpeech corpus. Then, we transfer the trained network and fine-tune it on only about 3.5 hours of training data from the Persian FarsDat corpus. Transfer learning exhibits better performance while needing shorter training time than the model trained from scratch. Experimental results on FarsDat corpus indicate that transfer learning with a few hours of Persian training data can achieve 31.48% relative Phoneme Error Rate (PER) reduction compared to the model trained from scratch.
Data Preparation for Reducing Computational Time with Transpose Stack Matrix for Action Recognition

The following topics are dealt with: learning (artificial intelligence); neural nets; pattern classification; regression analysis; natural language processing; support vector machines; text analysis; social networking (online); decision trees; and image classification.
Improved multi-source domain adaptation by preservation of factors

Domain Adaptation (DA) is a highly relevant research topic when it comes to image classification with deep neural networks. Combining multiple source domains in a sophisticated way to optimize a classification model can improve the generalization to a target domain. Here, the difference in data distributions of source and target image datasets plays a major role. In this paper, we describe based on a theory of visual factors how real-world scenes appear in images in general and how recent DA datasets are composed of such. We show that different domains can be described by a set of so called domain factors, whose values are consistent within a domain, but can change across domains. Many DA approaches try to remove all domain factors from the feature representation to be domain invariant. In this paper we show that this can lead to negative transfer since task-informative factors can get lost as well. To address this, we propose Factor-Preserving DA (FP-DA), a method to train a deep adversarial unsupervised DA model, which is able to preserve specific task relevant factors in a multi-domain scenario. We demonstrate on CORe50 how such factors can be identified by standard one-to-one transfer experiments between single domains combined with PCA. By applying FP-DA, we show that the highest average and minimum performance can be achieved. We also report improved performance for an adapted version of the OpenLORIS object dataset.
Comparative Study of Common Density-based Clustering Algorithms

Clustering is one of the most important data analysis tasks. It is used to organize data points into groups or clusters. Each cluster has similar instances, which are dissimilar to instances belonging to other clusters. Clustering is used in multiple disciplines and has an integral role in a wide variety of applications. This paper presents a comparative study of three common density-based clustering algorithms namely, DBSCAN, OPTICS and Mean-shift. The results are supported by an experimental evaluation using twelve datasets.
A Survey on Moving Object Detection in Video Using a Moving Camera for Smart Surveillance System

In real-time scenarios, the video surveillance is a highly demanding area of computer vision related activities for day to day applications due to security and monitoring purpose. This manuscript presents study of various techniques of moving object detection and publicly available datasets along with its major challenges. In this paper, we also explored various object detection methods such as modelling of background subtraction model, frame differencing based methods, optical-flow-based method etc. This paper also depicts various parameters for performance analysis along with challenging issues. The overall study of this paper demonstrates various applications that provide a collective study for new researchers of the domain.
Fast and Compact Set Intersection through Recursive Universe Partitioning

We present a data structure that encodes a sorted integer sequence in small space allowing, at the same time, fast intersection operations. The data layout is carefully designed to exploit word-level parallelism and SIMD instructions, hence providing good practical performance. The core algorithmic idea is that of recursive partitioning the universe of representation: a markedly different paradigm than the widespread strategy of partitioning the sequence based on its length. Extensive experimentation and comparison against several competitive techniques shows that the proposed solution embodies an improved space/time trade-off for the set intersection problem.
Knowledge and Expertise Sharing – Designing an AR-Mediated Cyber-Physical Production System for Industrial Set-Up Processes

Cyber-Physical Production Systems (CPPS) are receiving a lot of attention in the context of the fourth industrial revolution, especially in terms of complex architectures for data exchange among intelligent machines. However, the present elaboration is rather based on the question of what support such systems can provide for the exchange and appropriation of knowledge-intensive human practices in industrial surroundings. This paper presents the method and conceptual foundations of an Augmented Reality (AR) and sensor technology based CPPS for manual set-up processes on modern production machines. In the present context, both the recording of physical movements and the representation of local knowledge are potentially relevant. The basis is formed by design implications identified in the course of an extensive ethnographic study through the implementation of which a new methodological approach to the capture and transfer of (technical) knowledge embedded in embodied actions could be realized.