Reviewerly/exHarmony huggingface dataset:
Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste Inspection

Surface mount technology (SMT) is a process for producing printed-circuit boards. The solder paste printer (SPP), package mounter, and solder reflow oven are used for SMT. The board on which the solder paste is deposited from the SPP is monitored by the solder paste inspector (SPI). If SPP malfunctions due to the printer defects, the SPP produces defective products, and then abnormal patterns are detected by SPI. In this article, we propose a convolutional recurrent reconstructive network (CRRN), which decomposes the anomaly patterns generated by the printer defects, from SPI data. CRRN learns only normal data and detects the anomaly pattern through the reconstruction error. CRRN consists of a spatial encoder (S-Encoder), a spatiotemporal encoder and decoder (ST-Encoder-Decoder), and a spatial decoder (S-Decoder). The ST-Encoder-Decoder consists of multiple convolutional spatiotemporal memories (CSTMs) with a spatiotemporal attention (ST-Attention) mechanism. CSTM is developed to extract spatiotemporal patterns efficiently. In addition, an ST-Attention mechanism is designed to facilitate transmitting information from the spatiotemporal encoder to the spatiotemporal decoder, which can solve the long-term dependency problem. We demonstrate that the proposed CRRN outperforms the other conventional models in anomaly detection. Moreover, we show the discriminative power of the anomaly map decomposed by the proposed CRRN through the printer defect classification.
Blockchain Technology to Support Employee Recruitment and Selection in Industrial Revolution 4.0

Blockchain will come to pervade the business environment, bringing huge implications for HR and the workforce. There was also a sense that technology has eroded trust in recent years, especially with the growth of cyber risks, blockchain presents a way to use technology to win back that lost trust. HR functions need to start to include blockchain alongside other emerging technologies in formulating their digital strategy. Assessing the potential blockchain to enhance efficiency and effectiveness should be considered alongside the broader implications for the future of work. Having identified and unpacked the problem to be solved, a good next step is to start to create prototypes that can develop into proofs of concept (POCs) that will target the most valuable case usage. This article develops the concept of blockchain technology application in the field of human resource management, especially in the employee recruitment and selection process. The benefit from HR blockchain is targeting productivity gains. The enhanced ability to match people’s skills and performance to jobs would provide an uplift to productivity. Small and medium-sized enterprises (SMEs) may benefit particularly. The burden of finding and recruiting the right talent is especially difficult for smaller businesses, and anything that can help them do this more effectively and efficiently will boost their productivity. Hence, blockchain will simplify the process of employee selection and recruitment by helping to identify skills, knowledge, and experiences of potential candidates that are validated accurately. In the e-HRM context, the implementation of the blockchain system has the potential to improve the process of human resource management (HRM), specifically in the selection and recruitment process through screening of best candidates.
Two-point FFT-based high capacity image steganography using calendar based message encoding

In this work, we show how to hide a maximum of 8 bits of secret data into the pixel difference value. Adjacent pixel pairs are chosen in a non-sequential manner in the spatial domain without using any seed or key. In spite of having high average embedding capacity and embedding efficiency, our algorithm can withstand visual, structural and statistical attacks. We compare our work with state-of-the-art works and also show that our work is capable of resisting the series of tests provided by the benchmark “StirMark”.
Self-paced active learning for deep CNNs via effective loss function

Deep convolutional neural networks have achieved great success on computer vision tasks, but the lack of labeled samples hinders the application of it in the real world. Active learning is one of the effective methods to address large volumes of unlabeled data. It interactively selects a few samples based on a certain criterion and queries their labels from annotators. In order to reduce the labor of annotation, we propose a novel framework “self-paced multi-criteria active learning” on the image classification task. Unlike previous work, we treat each iteration of active learning as the step of self-paced learning which considers the model should gradually proceed from simplicity to complexity in training. In the stage of selecting samples, we combine clustering with a multiple criteria selection method to reduce the negative effects of hard samples. In the training phase, we design a similarity classification loss function to mitigate the impact of the deficiency of labeled data. Experiments on multiple datasets demonstrate that our proposed method can achieve higher performance than current approaches.
An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation

Impulse noise is regarded as an outlier in the local window of an image. To detect noise, many proposed methods are based on aggregated distance, including spatially weighted aggregated distance, n nearest neighbour distance, local density, and angle-weighted quaternion aggregated distance. However, these methods ignore the weight of each pixel or have limited adaptability. This study introduces the concept of degree of aggregation and proposes a weighting method to obtain the weight vector of the pixels by minimizing the degree of aggregation. The weight vector obtained gives larger components on the signal pixels than on the noisy pixels. Then it is fused with the aggregated distance to form a weighted aggregated distance that can reasonably characterise the noise and signal. The weighted aggregated distance, along with an adaptive segmentation method, can effectively detect the noise. To further enhance the effect of noise detection and removal, an adaptive selection strategy is incorporated to reduce the noise density in the local window. At last, noisy pixels detected are replaced with the weighted channel combination optimization values. The experimental results exhibit the validity of the proposed method by showing better performance in terms of both objective criteria and visual effects.
Learning-Free Text Line Segmentation for Historical Handwritten Documents

We present a learning-free method for text line segmentation of historical handwritten document images. This method relies on automatic scale selection together with second derivative of anisotropic Gaussian filters to detect the blob lines that strike through the text lines. Detected blob lines guide an energy minimization procedure to extract the text lines. Historical handwritten documents contain noise, heterogeneous text line heights, skews and touching characters among text lines. Automatic scale selection allows for automatic adaption to the heterogeneous nature of handwritten text lines in case the character height range is correctly estimated. In the extraction phase, the method can accurately split the touching characters among the text lines. We provide results investigating various settings and compare the model with recent learning-free and learning-based methods on the cBAD competition dataset.
RTFM! Automatic Assumption Discovery and Verification Derivation from Library Document for API Misuse Detection

To use library APIs, a developer is supposed to follow guidance and respect some constraints, which we call integration assumptions (IAs). Violations of these assumptions can have serious consequences, introducing security-critical flaws such as use-after-free, NULL-dereference, and authentication errors. Analyzing a program for compliance with IAs involves significant effort and needs to be automated. A promising direction is to automatically recover IAs from a library document using Natural Language Processing (NLP) and then verify their consistency with the ways APIs are used in a program through code analysis. However, a practical solution along this line needs to overcome several key challenges, particularly the discovery of IAs from loosely formatted documents and interpretation of their informal descriptions to identify complicated constraints (e.g., data-/control-flow relations between different APIs).
Efficient Neural Image Decoding via Fixed-Point Inference

Recent learned image coding has emerged with superior efficiency to conventional methods. It, however, is criticized for its complexity-exhaustive deep neural network (DNN) architectures, especially on resource-constrained mobile platforms. Thus we devise a two-stage approach: first, a range pre-processing is applied to constrain the dynamic range of feature map activation by leveraging its sparsity nature with densely clustered distribution, then a layer-wise range-adaptive quantization for convolutional parameter (e.g., weight, bias), and simple yet efficient linear scaling and range-dependent normalization for activation are executed, leading to a fully fixed-point inference architecture. All arithmetic operations and associated data tensors are processed using low-bit-width fixed-point numbers, yielding significant reductions of the computational complexity, memory space, and the elimination of platform-dependent inconsistency induced by floating-point operations. We first exemplify such fixed-point inference in a DNN-based image decoder, showing the comparable coding efficiency with its native floating-point model, against the same anchor using the High-Efficiency Video Coding (HEVC)-based intra image coder. We also extend proposed approach to the super-resolution network for learned resolution scaling-based video streaming, and VGG network-based classification tasks, both of which present negligible performance loss. These evidence the generalization of our approach for efficient DNN processing of various tasks. All materials are made publicly accessible at http://njuvision.github.io/fixed-point/.
Investigation of Pig Activity Based on Video Data and Semi-Supervised Neural Networks

The activity level of pigs is an important stress indicator which can be associated to tail-biting, a major issue for animal welfare of domestic pigs in conventional housing systems. Although the consideration of the animal activity could be essential to detect tail-biting before an outbreak occurs, it is often manually assessed and therefore labor intense, cost intensive and impracticable on a commercial scale. Recent advances of semi- and unsupervised convolutional neural networks (CNNs) have made them to the state of art technology for detecting anomalous behavior patterns in a variety of complex scene environments. In this study we apply such a CNN for anomaly detection to identify varying levels of activity in a multi-pen problem setup. By applying a two-stage approach we first trained the CNN to detect anomalies in the form of extreme activity behavior. Second, we trained a classifier to categorize the detected anomaly scores by learning the potential activity range of each pen. We evaluated our framework by analyzing 82 manually rated videos and achieved a success rate of 91%. Furthermore, we compared our model with a motion history image (MHI) approach and a binary image approach using two benchmark data sets, i.e., the well established pedestrian data sets published by the University of California, San Diego (UCSD) and our pig data set. The results show the effectiveness of our framework, which can be applied without the need of a labor intense manual annotation process and can be utilized for the assessment of the pig activity in a variety of applications like early warning systems to detect changes in the state of health.
Multistage Segmentation of Prostate Cancer Tissues Using Sample Entropy Texture Analysis

In this study, a multistage segmentation technique is proposed that identifies cancerous cells in prostate tissue samples. The benign areas of the tissue are distinguished from the cancerous regions using the texture of glands. The texture is modeled based on wavelet packet features along with sample entropy values. In a multistage segmentation process, the mean-shift algorithm is applied on the pre-processed images to perform a coarse segmentation of the tissue. Wavelet packets are employed in the second stage to obtain fine details of the structured shape of glands. Finally, the texture of the gland is modeled by the sample entropy values, which identifies epithelial regions from stroma patches. Although there are three stages of the proposed algorithm, the computation is fast as wavelet packet features and sample entropy values perform robust modeling for the required regions of interest. A comparative analysis with other state-of-the-art texture segmentation techniques is presented and dice ratios are computed for the comparison. It has been observed that our algorithm not only outperforms other techniques, but, by introducing sample entropy features, identification of cancerous regions of tissues is achieved with 90% classification accuracy, which shows the robustness of the proposed algorithm.
K-Means Clustering Guided Generative Adversarial Networks for SAR-Optical Image Matching

Synthetic Aperture Radar and optical (SAR-optical) image matching is a technique of finding correspondences between SAR and optical images. SAR-optical image matching can be simplified to single-mode image matching through image synthesis. However, the existing SAR-optical image synthesis methods are unable to provide qualified images for SAR-optical image matching. In this work, we present a K-means Clustering Guide Generative Adversarial Networks (KCG-GAN) to improve the image quality of synthesizing by constraining spatial information synthesis. KCG-GAN uses k-means segmentations as one of the image generator's inputs and introduces feature matching loss, segmentation loss, and L1 loss to the objective function. Meanwhile, to provide repeatable k-means segmentations, we develop a straightforward 1D k-means algorithm. We compare KCG-GAN with a leading image synthesis method-pix2pixHD. Qualitative results illustrate that KCG-GAN preserves more spatial structures than pix2pixHD. Quantitative results show that, compared with pix2pixHD, images synthesized by KCG-GAN are more similar to original optical images, and SAR-optical image matching based on KCG-GAN obtains at most 3.15 times more qualified matchings. Robustness tests demonstrate that SAR-optical image matching based on KCG-GAN is robust to rotation and scale changing. We also test three SIFT-like algorithms on matching original SAR-optical image pairs and matching KCG-GAN synthesized optical-optical image pairs. Experimental results show that our KCG-GAN significantly improves the performances of the three algorithms on SAR-optical image matching.
Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference

This paper studies the fundamental problem of learning deep generative models that consist of multiple layers of latent variables organized in top-down architectures. Such models have high expressivity and allow for learning hierarchical representations. Learning such a generative model requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference typically requires Markov chain Monte Caro (MCMC) that can be time consuming. In this paper, we propose to use noise initialized non-persistent short run MCMC, such as finite step Langevin dynamics initialized from the prior distribution of the latent variables, as an approximate inference engine, where the step size of the Langevin dynamics is variationally optimized by minimizing the Kullback-Leibler divergence between the distribution produced by the short run MCMC and the posterior distribution. Our experiments show that the proposed method outperforms variational auto-encoder (VAE) in terms of reconstruction error and synthesis quality. The advantage of the proposed method is that it is simple and automatic without the need to design an inference model.
APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection

Physical adversarial attacks threaten to fool object detection systems, but reproducible research on the real-world effectiveness of physical patches and how to defend against them requires a publicly available benchmark dataset. We present APRICOT, a collection of over 1,000 annotated photographs of printed adversarial patches in public locations. The patches target several object categories for three COCO-trained detection models, and the photos represent natural variation in position, distance, lighting conditions, and viewing angle. Our analysis suggests that maintaining adversarial robustness in uncontrolled settings is highly challenging but that it is still possible to produce targeted detections under white-box and sometimes black-box settings. We establish baselines for defending against adversarial patches via several methods, including using a detector supervised with synthetic data and using unsupervised methods such as kernel density estimation, Bayesian uncertainty, and reconstruction error. Our results suggest that adversarial patches can be effectively flagged, both in a high-knowledge, attack-specific scenario and in an unsupervised setting where patches are detected as anomalies in natural images. This dataset and the described experiments provide a benchmark for future research on the effectiveness of and defenses against physical adversarial objects in the wild. The APRICOT project page and dataset are available at apricot.mitre.org .
Adversarial Self-Supervised Scene Flow Estimation

This work proposes a metric learning approach for self-supervised scene flow estimation. Scene flow estimation is the task of estimating 3D flow vectors for consecutive 3D point clouds. Such flow vectors are fruitful, e.g. for recognizing actions, or avoiding collisions. Training a neural network via supervised learning for scene flow is impractical, as this requires manual annotations for each 3D point at each new timestamp for each scene. To that end, we seek for a self-supervised approach, where a network learns a latent metric to distinguish between points translated by flow estimations and the target point cloud. Our adversarial metric learning includes a multi-scale triplet loss on sequences of two-point clouds as well as a cycle consistency loss. Furthermore, we outline a benchmark for self-supervised scene flow estimation: the Scene Flow Sandbox. The benchmark consists of five datasets designed to study individual aspects of flow estimation in progressive order of complexity, from a moving object to real-world scenes. Experimental evaluation on the benchmark shows that our approach obtains state-of-the-art self-supervised scene flow results, outperforming recent neighbor-based approaches. We use our proposed benchmark to expose shortcomings and draw insights on various training setups. We find that our setup captures motion coherence and preserves local geometries. Dealing with occlusions, on the other hand, is still an open challenge.
SRPTackle: A semi-automated requirements prioritisation technique for scalable requirements of software system projects

Requirement prioritisation (RP) is often used to select the most important system requirements as perceived by system stakeholders. RP plays a vital role in ensuring the development of a quality system with defined constraints. However, a closer look at existing RP techniques reveals that these techniques suffer from some key challenges, such as scalability, lack of quantification, insufficient prioritisation of participating stakeholders, overreliance on the participation of professional expertise, lack of automation and excessive time consumption. These key challenges serve as the motivation for the present research. This study aims to propose a new semiautomated scalable prioritisation technique called 'SRPTackle' to address the key challenges. SRPTackle provides a semiautomated process based on a combination of a constructed requirement priority value formulation function using a multi-criteria decision-making method (i.e. weighted sum model), clustering algorithms (K-means and K-means++) and a binary search tree to minimise the need for expert involvement and increase efficiency. The effectiveness of SRPTackle is assessed by conducting seven experiments using a benchmark dataset from a large actual software project. Experiment results reveal that SRPTackle can obtain 93.0% and 94.65% as minimum and maximum accuracy percentages, respectively. These values are better than those of alternative techniques. The findings also demonstrate the capability of SRPTackle to prioritise large-scale requirements with reduced time consumption and its effectiveness in addressing the key challenges in comparison with other techniques. With the time effectiveness, ability to scale well with numerous requirements, automation and clear implementation guidelines of SRPTackle, project managers can perform RP for large-scale requirements in a proper manner, without necessitating an extensive amount of effort (e.g. tedious manual processes, need for the involvement of experts and time workload).
Financial structures, political risk and economic growth

Using a panel of 113 countries over the period from 1990 to 2013, this paper provides new empirical evidence to the intensive debate of whether financial structure is relevant for economic growth. Specifically, we evaluate the role of political risk, development stage and their interactions with the structure of the financial system. We find that on average a more market-based financial system is associated with a higher level of economic growth. This impact varies with different levels of political risk and different stages of economic development. Specifically, the comparative development of equity markets compared with banks appear to promote more economic growth in countries with lower political risk and at a better stage of economic development. Moreover, banks are more important to economic growth in over-market-based financial systems, whilst equity markets are more sensitive to economic growth in over-bank-based financial systems. Our paper provides new insights into the real effects of the mixture of banks and markets on the economy.
Toward Achieving Fine-Grained Access Control of Data in Connected and Autonomous Vehicles

A connected and autonomous vehicle (CAV) is often fitted with a large number of onboard sensors and applications to support autonomous driving functions. Based on the current research, little work on applications' access to in-vehicle data has been done. Furthermore, most existing autonomous driving operating systems lack authentication and encryption units. As such, applications can excessively obtain confidential information, such as vehicle location and owner preferences and even upload it to the cloud, threatening the security of the vehicle and the privacy of the owner. In this study, we propose a fine-grained access control scheme to restrict applications' access to data in CAVs (FGAC-inCAVs). First, we present a system model composed of the following elements: a trusted third party (TTP), which is a fully trusted authority; perception components like sensors, which can capture the road information (pictures, videos, etc.); and multiple applications. Then, a fast attribute-based encryption (ABE) is presented, and security analysis also shows it is secure against selective and chosen-plaintext attacks. Furthermore, we propose a key update scheme based on the Chinese remainder theorem (CRT). Finally, the theoretical analysis and simulation experiments demonstrate its feasibility and efficiency.