Reviewerly/exHarmony huggingface dataset:
Wavefront-coded phase measuring deflectometry for the all-focused measurement

Phase measuring deflectometry is a powerful measuring method for complex optical surfaces, which captures the reflected fringe images encoded on the screen under the premise of focusing the measured specular surface. Due to the limited depth of field of the camera, the captured images and the measured surface cannot be focused at the same time. To solve the position-angle uncertainty issue, in this Letter, the wavefront coding technology is used to modulate the imaging wavefront of the deflectometry, thereby making the measuring system insensitive to the defocus and other low-order aberration including astigmatism, field curvature, and so on. To obtain the accurate phase, the captured fringe images are deconvoluted using the modulated point spread function to reduce the phase error. Demonstrated with a highly curved spherical surface, the measurement accuracy can be improved by four times. Experiments demonstrate that the proposed method can successfully reconstruct the complex surfaces defocusing the captured images, which can greatly release the focusing requirement and improve measurement accuracy.
Outsourced privacy-preserving anomaly detection in time series of multi-party

Anomaly detection has practical significance for finding unusual patterns in time series. However, most existing algorithms may lose some important information in time series presentation and have high time complexity. Another problem is that privacy-preserving was not taken into account in these algorithms. In this paper, we propose a new data structure named Interval Hash Table (IHTable) to capture more original information of time series and design a fast anomaly detection algorithm based on Interval Hash Table (ADIHT). The key insight of ADIHT is distributions of normal subsequences are always similar while distributions of anomaly subsequences are different and random by contrast. Furthermore, to make our proposed algorithm fit for anomaly detection under multiple participation, we propose a privacy-preserving anomaly detection scheme named OP-ADIHT based on ADIHT and homomorphic encryption. Compared with existing anomaly detection schemes with privacy-preserving, OP-ADIHT needs less communication cost and calculation cost. Security analysis of different circumstances also shows that OP-ADIHT will not leak the privacy information of participants. Extensive experiments results show that ADIHT can outperform most anomaly detection algorithms and perform close to the best results in terms of AUC-ROC, and ADIHT needs the least time.
A Linked Data Mosaic for Policy-Relevant Research on Science and Innovation: Value, Transparency, Rigor, and Community

This article presents a new framework for realizing the value of linked data understood as a strategic asset and increasingly necessary form of infrastructure for policy-making and research in many domains.We outline a framework, the 'data mosaic' approach, which combines socio-organizational and technical aspects.After demonstrating the value of linked data, we highlight key concepts and dangers for community-developed data infrastructures.We concretize the framework in the context of work on science and innovation generally.Next we consider how a new partnership to link federal survey data, university data, and a range of public and proprietary data represents a concrete step toward building and sustaining a valuable data mosaic.We discuss technical issues surrounding linked data but emphasize that linking data involves addressing the varied concerns of wide-ranging data holders, including privacy, confidentiality, and security, as well as ensuring that all parties receive value from participating.The core of successful data mosaic projects, we contend, is as much institutional and organizational as it is technical.As such, sustained efforts to fully engage and develop diverse, innovative communities are essential.
Metro Wheel Set Size Measurement Method Based on CDD Image Recognition

In the process of train operation, the contact friction between wheel and track will change the size of wheel set. In order to ensure the safe operation of train, it is of great significance to detect the change of wheel set size timely and accurately. On the basis of summarizing the research at home and abroad, the online dynamic detection technology of wheel set size is studied. Firstly, the overall architecture of the wheel set size online detection system is studied, the working principle of each detection module is explained, and the installation parameters of the laser displacement sensor are designed. Secondly, the machine learning algorithm of online dynamic detection technology of wheel set size is studied. The passing wheel sets are photographed by CCD camera, and then the three-dimensional model is reconstructed by point cloud data, and then the parameters of each part are measured and calculated according to the model. The wheel set size can be obtained.
Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priors

We consider the problem of realistic bokeh rendering from a single all-in-focus image. Bokeh rendering mimics aesthetic shallow depth-of-field (DoF) in professional photography, but these visual effects generated by existing methods suffer from simple flat background blur and blurred in-focus regions, giving rise to unrealistic rendered results. In this work, we argue that realistic bokeh rendering should (i) model depth relations and distinguish in-focus regions, (ii) sustain sharp in-focus regions, and (iii) render physically accurate Circle of Confusion (CoC). To this end, we present a Defocus to Focus (D2F) framework to learn realistic bokeh rendering by fusing defocus priors with the all-in-focus image and by implementing radiance priors in layered fusion. Since no depth map is provided, we introduce defocus hallucination to integrate depth by learning to focus. The predicted defocus map implies the blur amount of bokeh and is used to guide weighted layered rendering. In layered rendering, we fuse images blurred by different kernels based on the defocus map. To increase the reality of the bokeh, we adopt radiance virtualization to simulate scene radiance. The scene radiance used in weighted layered rendering reassigns weights in the soft disk kernel to produce the CoC. To ensure the sharpness of in-focus regions, we propose to fuse upsampled bokeh images and original images. We predict the initial fusion mask from our defocus map and refine the mask with a deep network. We evaluate our model on a large-scale bokeh dataset. Extensive experiments show that our approach is capable of rendering visually pleasing bokeh effects in complex scenes. In particular, our solution receives the runner-up award in the AIM 2020 Rendering Realistic Bokeh Challenge.
Back-compatible Color QR Codes for colorimetric applications

Color correction techniques in digital photography often rely on the use of color correction charts, which require including this relatively large object in the field of view. We propose here to use QR Codes to pack these color charts in a compact form factor, in a fully compatible manner with conventional black and white QR Codes; this is, without losing any of their easy location, sampling and digital data storage features. First, we present an algorithm to build these new colored QR Codes that preserves the original QR Code functionality - much more than other coloring proposals based on the random substitution of black and white pixels by colors - that relies on the ability of the native CRC code to correct and counteract these alterations. Second, we demonstrate that, as a result, these QR Codes can allocate far many more colors than the conventional color correction charts, enabling much more accurate color correction schemes in a more convenient and usable format.
Predicting interaction effort in web interface widgets

The product of good design should render a tool invisible for a user who is executing a task. Unfortunately, web applications are often far from invisible to users, who struggle with poor design of websites and processes in them. We are particularly interested in web processes that involve form filling, so we have been studying how people interact with web forms. Besides cataloguing user interaction problems that are common in web forms, we have noticed that, in many cases, there is a single form element or widget to blame for a certain interaction problem, because such widget is not the most appropriate one for the required input in that particular context. This unfitness of the widget causes an extra burden to the user, which we call interaction effort. In this work we propose measuring the interaction effort of a widget with a unified score based on micro-measures automatically captured from interaction logs. We present the micro-measures that were found relevant to predict the interaction effort in 6 different types of web forms widgets. We describe a large data collection process and prediction models, showing that it is indeed possible to automatically predict a widget interaction effort score by learning from expert human ratings. We consequently believe that the interaction effort could be used as an effective metric to compare small variations in a design in terms of user experience.
Blockchain-based mobile crowdsourcing model with task security and task assignment

Crowdsourcing is a task assignment technology that has emerged in recent years, enabling companies to reduce costs and increase work efficiency. Most of the current crowdsourcing systems (BCSs) built on blockchain focus on solving privacy issues and use rigorous proof protocols for task and solution security. Such systems cannot be directly applied in mobile environments because it would lead to excessive computational overhead for the endpoint. To achieve confidentiality of crowdsourcing tasks during transmission while reducing the energy consumption of mobile devices and optimizing task assignment results, in this paper, we develop a blockchain-based crowdsourcing model. We first build a model structure consisting of basic components, such as users, smart contracts, and blockchain, and design contracts to implement various crowdsourcing operations, such as registering, publishing tasks, and acquiring solutions. Unlike before, the ciphertext of the task and the encrypted secret key are embedded into the contract. This, together with the three operations we designed for users to interact with the smart contracts, can effectively protect the crowdsourcing data from being stolen while using the address anonymity of Ethereum to ensure the privacy of users. During task assignment, we use the DBSCAN algorithm to delineate each valid service area and implement the established multiobjective planning model within each area to achieve as many benefit goals as possible in the best way. Finally, we implement our prototype on the Ethereum test network Ropsten. The experimental results show that the cost overhead of the designed model is acceptable to mobile users, and the security analysis establishes the confidentiality of the designed operations.
RM-NTT: An RRAM-Based Compute-in-Memory Number Theoretic Transform Accelerator

As more cloud computing resources are used for machine learning training and inference processes, privacy-preserving techniques that protect data from revealing at the cloud platforms attract increasing interest.Homomorphic encryption (HE) is one of the most promising techniques that enable privacy-preserving machine learning because HE allows data to be evaluated under encrypted forms.However, deep neural network (DNN) implementations using HE are orders of magnitude slower than plaintext implementations.The use of very long polynomials and associated number theoretic transform (NTT) operations for polynomial multiplications is the main bottlenecks of HE implementation for practical uses.This article introduces RRAM number theoretic transform (RM-NTT): a resistive random access memory (RRAM)-based compute-in-memory (CIM) system to accelerate NTT and inverse NTT (INTT) operations.Instead of running fast Fourier transform (FFT)-like algorithms, RM-NTT uses a vector-matrix multiplication (VMM) approach to achieve maximal parallelism during NTT and INTT operations.To improve the efficiency, RM-NTT stores modified forms of the twiddle factors in the RRAM arrays to process NTT/INTT in the same RRAM array and employs a Montgomery reduction algorithm to convert the VMM results.The proposed optimization methods allow RM-NTT to significantly reduce NTT operation latency compared with other NTT accelerators, including both CIM and non-CIM-based designs.The effects of different RM-NTT design parameters and device nonidealities are also discussed.
Reversible Data Hiding Scheme Based on Coefficient Pair Mapping for Videos H.264/AVC without Distortion Drift

Reversible data hiding is a technique for embedding secret data into a cover media. Such technique has the ability to recover marked cover media to its original version after extracting the secret data. In this paper, a new reversible data hiding algorithm for videos H.264/AVC is proposed to improve the embedding capacity while the distortion drift is prevented. To embed the secret data into videos H.264/AVC without any intra-frame distortion drift, in our proposed scheme, the relationship of QDCT coefficients is explored. Then, a coefficient pair mapping mechanism in 2D histogram is introduced for embedding data. The experimental results demonstrated that the proposed scheme obtains reversibility. The proposed scheme prevents intra-frame distortion drift on the marked videos. In addition, the embedding capacity of the proposed scheme is superior to that of existing schemes while guaranteeing the high visual quality of marked videos.
Customer relationship management and customer loyalty: Examining the mediating role of business process management

The purpose of this study is to investigate the impact of customer relationship management on customer loyalty (CL) at Jordan Telecommunication Company through business process management. The researcher uses the descriptive analytical method to scan members’ opinions. The random sample includes (367) workers from different sectors all over the country. The results show that customer relation management (CRM) has a dramatic impact on customer loyalty and business process management (BPM). Besides, BPM plays a vital role between CRM and CL. This study appears to be the first study in Jordan which empirically examines the indirect relationship between customer relation management and customer loyalty via business process management.
SoK: Network-Level Attacks on the Bitcoin P2P Network

Over the last decade, Bitcoin has revolutionized the global economic and technological landscape, inspiring a new generation of blockchain-based technologies. Its protocol is today among the most influential for cryptocurrencies and distributed networks. In particular, the P2P layer represents a reference point for all permissionless blockchains, which often implement its solutions in their network layer. Unfortunately, the Bitcoin network protocol lacks a strong security model, leaving it exposed to several threats. Attacks at this level can affect the reliability and trustworthiness of the consensus layer, mining the credibility of the whole system. It is therefore of utmost importance to properly understand and address the security of the Bitcoin P2P protocol. In this paper, we give a comprehensive and detailed overview of known network-level attacks in Bitcoin, as well as the countermeasures that have been implemented in the protocol. We propose a generic network adversary model, and propose an objective-based taxonomy of the attacks. Finally, we identify the core weaknesses of the protocol and study the relationship between different types of attack. We believe our contribution can help both new and experienced researchers have a broader and deeper understanding of the Bitcoin P2P network and its threats, and allow for a better modeling of its security properties.
A novel stochastic configuration network with iterative learning using privileged information and its application

In the real-world applications, some auxiliary information is commonly available in addition to the standard data, which is often ignored by the traditional learning algorithms. To effectively utilize the auxiliary information for assisting in building learner models with improved performance, this paper presents a novel stochastic configuration network (SCN) incorporating the iterative learning using privileged information (LUPI) paradigm, termed ISCN+. Meanwhile, to make the comments generated by the privileged information more closely match the ISCN+, these comments should be iteratively updated as the number of hidden nodes of the SCN increases. To this end, the training of SCN and the learning of comments generated by the privileged information is integrated into a new objective function, and the alternating optimization strategy is adopted to optimize the parameters of ISCN + and update the comments generated by the privileged information. Moreover, the L1/2-norm-regularization-based sparse ISCN+ (S-ISCN+) is proposed to further improve the generalization capacity and reduce the complexity of ISCN+. Furthermore, the convergence analysis of the optimization process is provided. The experimental results on two benchmark data sets and a real-world data set demonstrate the effectiveness of the proposed method.
BlockCrime: Blockchain and Deep Learning-Based Collaborative Intelligence Framework to Detect Malicious Activities for Public Safety

Detecting malicious activity in advance has become increasingly important for public safety, economic stability, and national security. However, the disparity in living standards incites the minds of certain undesirable members of society to commit crimes, which may disrupt society’s stability and mental calm. Breakthroughs in deep learning (DL) make it feasible to address such challenges and construct a complete intelligent framework that automatically detects such malicious behaviors. Motivated by this, we propose a convolutional neural network (CNN)-based Xception model, i.e., BlockCrime, to detect crimes and improve public safety. Furthermore, we integrate blockchain technology to securely store the detected crime scene locations and alert the nearest law enforcement authorities. Due to the scarcity of the dataset, transfer learning has been preferred, in which a CNN-based Xception model is used. The redesigned Xception architecture is evaluated against various assessment measures, including accuracy, F1 score, precision, and recall, where it outperforms existing CNN architectures in terms of train accuracy, i.e., 96.57%.
TA-BiDet: Task-aligned binary object detector

Binary CNN-based object detector, largely saving storage and computation costs, has received high attention recently for the potention of efficient deployment on resource-constrained devices. However, previous designs often suffer from significant accuracy loss when compared to their real-value counterparts. This study demonstrates that the primary reason lies in the mis-alignment of the classification and regression tasks, which is generally caused by the limited representational capacity of the binary neural network and biased training procedures used in traditional detection frameworks. Based on this observation, we propose TA-BiDet (Task-Alignment Binary object Detection) that can guarantee aligned training of the two tasks by adopting a task-aware feature disentanglement (TFD) network architecture with an alignment-oriented learning (AOL) approach. The proposed approaches can ensure more informative and tailored task-specific features to be learned jointly for each task and select the most accurate detected boxes with both high confidence scores and precise locations. Experiments on the PASCAL VOC and COCO datasets have shown that TA-BiDet outperformed state-of-the-art binary object detectors by a considerable margin. Moreover, TA-BiDet has successfully narrowed the performance gap with the real-valued SSD300 detector to only 0.7% in terms of mAP, and reduced the model size by 4.86 × and the total OPs by 9.89 × , respectively.
INVESTIGATING THE NEXUS BETWEEN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNOLOGIES IN THE CASE OF INDIAN SERVICES INDUSTRY

The future in the services industry belongs to Artificial Intelligence (AI) driven machines, which is a major source of worry for the job market in India. Over 50% of India’s GDP constitutes services, and it is a major source of employment for the skilled manpower of India. The research measures the impact of AI on service jobs in India based on qualitative parameters such as logical, natural, physical, and compassion; and finds which aspect serves the jobs better between machines and humans. The jobs taken over by AI are primarily at the task level more than the job level and for the basic tasks predominantly. The replacement starts with the basic tasks involved in providing service and then it grows to perform all the tasks involved in services. The research finds out that the logical aspects of the service will slowly reduce in the coming 5–10 years as AI will perform all the logic-related tasks leaving more emotional tasks such as compassion for humans. Finally, even these emotional-related tasks will be taken over by the AI which provides us with a very interesting combination of man and machine in the Indian scenario still threatening human employment.
On Exploiting Rating Prediction Accuracy Features in Dense Collaborative Filtering Datasets

One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item. Afterward, the items having the largest rating prediction values will be recommended to the users by the recommender system. Collaborative filtering algorithms can be applied to both sparse and dense datasets, and each of these dataset categories involves different kinds of risks. As far as the dense collaborative filtering datasets are concerned, where the rating prediction coverage is, most of the time, very high, we usually face large rating prediction times, issues concerning the selection of a user’s near neighbours, etc. Although collaborative filtering algorithms usually achieve better results when applied to dense datasets, there is still room for improvement, since in many cases, the rating prediction error is relatively high, which leads to unsuccessful recommendations and hence to recommender system unreliability. In this work, we explore rating prediction accuracy features, although in a broader context, in dense collaborative filtering datasets. We conduct an extensive evaluation, using dense datasets, widely used in collaborative filtering research, in order to find the associations between these features and the rating prediction accuracy.