Reviewerly/exHarmony huggingface dataset:
An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering

At present, traditional visual-based surveillance systems are becoming impractical, inefficient, and time-consuming. Automation-based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted resampling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.
Introducing Layers of Meaning (LoM): A Framework to Reduce Semantic Distance of Visualization In Humanistic Research

Information visualization (infovis) is a powerful tool for exploring rich datasets. Within humanistic research, rich qualitative data and domain culture make traditional infovis approaches appear reductive and disconnected, leading to low adoption. In this paper, we use a multi-step approach to scrutinize the relationship between infovis and the humanities and suggest new directions for it. We first look into infovis from the humanistic perspective by exploring the humanistic literature around infovis. We validate and expand those findings though a co-design workshop with humanist and infovis experts. Then, we translate our findings into guidelines for designers and conduct a design critique exercise to explore their effect on the perception of humanist researchers. Based on these steps, we introduce Layers of Meaning, a framework to reduce the semantic distance between humanist researchers and visualizations of their research material, by grounding infovis tools in time and space, physicality, terminology, nuance, and provenance.
I Know If the Journey Changes: Flexible Source and Path Validation

No matter from the perspective of detection or defense, source and path validations are fundamentally primitive in constructing security mechanisms to greatly enhance network immunity in the face of malicious attacks, such as injection, traffic hijacking and hidden threats. However, existing works for source and path verification still impose a non-trivial operational overhead and lack adjustment capability for path dynamic changes. In this paper, we propose a flexible and convenient source and path validation protocol called PSVM, which uses an authentication structure PIC composed of ordered pieces to carry out packet verification. Specifically, in the basic PSVM protocol, PIC (related to cryptographic computation) in the packet header does not require any update during packet verification, which thus enables a lower processing overhead in routers. To cope with the challenge of path policy changes in the running protocol, the dynamic PSVM protocol supports controllable adjustment and migration, especially in the case of avoiding a malicious node or region. Our evaluation of a prototype experiment on Click demonstrates that the verification efficiency of PSVM is barely influenced by payload size or path length. Compared to the baseline of normal IP routing, the throughput reduction ratio of the basic PSVM is about 13%, which is much better than 28% of existing best solution Origin and Path Trace (OPT). In addition, for a 35-hop path with 30 pieces of PIC needed to be adjusted in dynamic PSVM, the throughput reduction ratio of routing cross node performing the adjustment operation after normal verification is only 2.4 %.
Improved dispersion bounds for modified Fibonacci lattices

We study the dispersion of point sets in the unit square; i.e. the size of the largest axes-parallel box amidst such point sets. It is known that lim infN→∞Ndisp(N,2)∈54,2, where disp(N,2) is the minimal possible dispersion for an N-element point set in the unit square. The upper bound 2 is obtained by an explicit point construction—the well-known Fibonacci lattice. In this paper we find a modification of this point set such that its dispersion is significantly lower than the dispersion of the Fibonacci lattice. Our main result will imply that lim infN→∞Ndisp(N,2)≤φ3∕5=1.894427...
Consortium blockchain-driven decentralized organization and operation for manufacturing community in social manufacturing

Recently, social manufacturing emerges to help micro-and-small-scale manufacturing enterprises (MSMEs) to cope with service-oriented, personalized, diverse and dynamic market demand. In social manufacturing, MSMEs are usually peer-to-peer and geographically distributed, and they self-organize into manufacturing communities (MCs) to share their manufacturing resources, order and business benefit. Therefore, the decentralized organization and operation of MCs become an emergency. To solve this problem, a consortium block chain based decentralized mechanism is established for the organization and operation of manufacturing community in social manufacturing, and a corresponding reference framework is proposed to guide MCs to implement decentralized organization and operation. On this basis, three key enabled technologies are elaborated, which are mainly about the determination of verification dataset, the design of smart contract, and the definition of endorsement policy. It is expected that the reference framework proposed in this paper will provide a possible way for MSMEs to organize and operate MC without core enterprise in social manufacturing.
Vacancies in graphene: an application of adiabatic quantum optimization

Quantum annealers have grown in complexity to the point that quantum computations involving a few thousand qubits are now possible. In this paper, with the intentions to show the feasibility of quantum annealing to tackle problems of physical relevance, we used a simple model, compatible with the capability of current quantum annealers, to study the relative stability of graphene vacancy defects. By mapping the crucial interactions that dominate carbon-vacancy interchange onto a quadratic unconstrained binary optimization problem, our approach exploits the ground state as well as the excited states found by the quantum annealer to extract all the possible arrangements of multiple defects on the graphene sheet together with their relative formation energies. This approach reproduces known results and provides a stepping stone towards applications of quantum annealing to problems of physical-chemical interest.
The Highway Robber’s Road to Knowledge Socialism: A Collective Work on Collective Work

This chapter presents a collective response to the propositions and provocations raised in Michael Peters’ essay ‘Knowledge socialism: The rise of peer production—collegiality, collaboration, and collective intelligence’. For Peters, knowledge socialism pushes back against, deconstructs, or otherwise critically engages with, the individualization and proprietization of knowledge. As such, there’s something important to explore regarding the role of scholars, academics and their individual contributions towards a collective project. This chapter emerges within a wider project, on the road to knowledge socialism, of peer review, peer production, academic publishing and the life of the scholar. Peters regards this wider project as a movement aimed at amplifying collegiality, collaboration and collective intelligence, in which knowledge socialism is explored and enacted philosophically. In this chapter, the movement is explored in themes of liberating social knowledges, criminal relationships with the university, the professionalization of knowledge and the burden of openness. The chapter concludes with a response from Michael A. Peters.
A novel statistical approach for multiplicative speckle removal using t-locations scale and non-sub sampled shearlet transform

One of the most interesting problems in denoising of images includes despeckling of multiplicative noise. This paper proposes a novel statistical processor in the framework of Non-sub Sampled Shearlet Transform (NSST) to reduce the effect of the multiplicative noise on images given preserving of structural and visual quality of image. First, we indicate that NSST coefficients of logarithmically transformed images can be statistically meaningful modeled by t-location scale (TLS). For designing our processor, we employ Minimum Mean Squared Error (MMSE) estimator to reduce noise distortion. We show by using TLS as the prior distribution, non-linear noise suppression behavior is obtained in test images. Finally, we compare our method by the state-of-the-art algorithms like soft and hard thresholding and also with well-known adaptive filters in this area like Wiener, Frost, Lee and one recent method in shearlet denoising framework.
Analysis of Adversarial Attacks on Skin Cancer Recognition

Cancer is one of the most detrimental diseases having the highest death rates in recent years. There are various types of cancers; among them, skin cancer is the most familiar one. Early detection and treatment of it can lead to full recovery for the patients. Deep learning based image classification models have been proven to perform exclusively well to classify images. This is also true for images in the domain of medical image analysis. However, in recent years researchers have shown that adding small calculated noises which are not noticeable by human eyes can induce these models to generate wrong answers. These adversarial examples have proven to be harmful with regards to security measures and hamper launching of deep learning based models in to the real world. In this regard, here we performed experiments to improve the robustness of deep learning based models from these type of attacks for skin cancer recognition. We performed adversarial training based on Projected Gradient Descent (PGD) to increase the robustness of two popular deep learning models, namely MobileNet and VGG16, against white-box attacks of PGD and FGSM attacks. We performed our experiments on a dataset of 10015 images and have shown that our models are much robust than standard training ones and achieved almost similar results as them.
Evaluation of the Economic Efficiency of Blockchain for Customer Identification by Financial Institutions

Abstract Today, digital technologies are developed and used in all economic sectors without any exception. The financial sector is one of the drivers for the digitalization of the economy, which caused the emergence of fintech, an innovational industry that is based on the fusion of state-of-the-art digital and financial technologies. Blockchain is one of the most promising technologies in the financial sector. It has become widely used to create cryptocurrencies. This technology is used in many areas of the financial sector, for example, for customer identification by financial institutions. This research involved building of a model using the net present value method to evaluate the efficiency of the implementation and use of the automated decentralized database using the blockchain technology for bank customer identification. The paper presents the results of the modeling for three cases: for the most favorable economic conditions with minimum costs and maximum benefits, for average conditions and the least favorable economic conditions. The analysis of the modeling results identified financial institutions with a certain number of customers for which it will be economically viable to implement blockchain.
Self-calibration for linear structured light 3D measurement system based on quantum genetic algorithm and feature matching

In this paper, a self-calibration method for a linear structured light 3D measurement system and its advantages are presented. According to the mathematical model of the linear-structured light 3D measurement system, the calibration problem is a highdimensional optimization problem. The principle of this self-calibration method can be drawn from a traditional calibration method. In this method, quantum genetic algorithm and feature matching are applied to self-calibration. Feature matching is used to derive two points with fixed spatial relationships, and an optimal solution of system parameters can be obtained by quantum genetic algorithm. Finally, experimental results are given. The measurement error is 0.05 mm and the ratio of the error to scanning distance is 1.54e-4.In this paper, a self-calibration method for a linear structured light 3D measurement system and its advantages are presented. According to the mathematical model of the linear-structured light 3D measurement system, the calibration problem is a high-dimensional optimization problem. The principle of this self-calibration method can be drawn from a traditional calibration method. In this method, quantum genetic algorithm and feature matching are applied to selfcalibration. Feature matching is used to derive two points with fixed spatial relationships, and an optimal solution of system parameters can be obtained by quantum genetic algorithm. Finally, experimental results are given. The measurement error is 0.05 mm and the ratio of the error to scanning distance is 1.54e-4.
Abnormal Trajectory Detection Based on Geospatial Consistent Modeling

Anomalous trajectory detection plays a significant role in fraud detection and adverse events monitoring for ride-hailing services. The spatial and temporal dynamics of road networks and the sparsity of trajectories make anomalous trajectory detection a challenging task. Most existing methods are based on density and isolation approaches, which ignore geographical information. Motivated by these challenges and shortcomings, we propose a novel method, which considers geospatial constraints of the trajectories and avoids sparsity issues. In our method, the geographical information and topological constraints of trajectories are embedded into structured vector space. Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are used to model common trajectory features. Our method could identify anomalous trajectories and determine which parts are responsible for anomalies by using these features. Experiments on two real-world datasets have been conducted, and results demonstrate the effectiveness and feasibility of the proposed method.
Towards compression-resistant privacy-preserving photo sharing on social networks

The massive photos shared through the social networks nowadays, e.g., Facebook and Instagram, have aided malicious entities to snoop private information, especially by utilizing deep neural networks (DNNs) to learn from those personal photos. To protect photo privacy against DNNs, recent advances adopting adversarial examples could successfully fool DNNs. However, they are sensitive to those image compression methods that are commonly used on social networks to reduce transmission bandwidth or storage space. A recent work proposed to resist JPEG compression, while the compression methods adopted in social networks are black boxes, and variation of compression methods would significantly degrade the resistance.
The effect of internet services on child education outcomes: evidence from <i>poa! Internet</i> in Kenya

In 2016-2018 poa! Internet, a private internet service provider, distributed free internet services across schools in Nairobi, Kenya. Using inverse probability weighting (IPW) method, the study finds that the intervention significantly increases students’ internet use and training, receipt of information on educational content, and participation in information and communication technologies (ICTs) education programmes. However, the study does not find evidence that internet services increase school attendance. The results suggest that facilitating access to internet services in schools can significantly increase child computer and internet skills and training in developing countries, even if those investments have limited gains on school attendance.
Domain-specific compilers for dynamic simulations of quantum materials on quantum computers

Abstract Simulation of the dynamics of quantum materials is emerging as a promising scientific application for noisy intermediate-scale quantum (NISQ) computers. Due to their high gate-error rates and short decoherence times, however, NISQ computers can only produce high-fidelity results for those quantum circuits smaller than some given circuit size. Dynamic simulations, therefore, pose a challenge as current algorithms produce circuits that grow in size with each subsequent time-step of the simulation. This underscores the crucial role of quantum circuit compilers to produce executable quantum circuits of minimal size, thereby maximizing the range of physical phenomena that can be studied within the NISQ fidelity budget. Here, we present two domain-specific (DS) quantum circuit compilers for the Rigetti and IBM quantum computers, specifically designed to compile circuits simulating dynamics under a special class of time-dependent Hamiltonians. The compilers outperform state-of-the-art general-purpose compilers in terms of circuit size reduction by around 25%–30% as well as wall-clock compilation time by around 40% (dependent on system size and simulation time-step). Drawing on heuristic techniques commonly used in artificial intelligence, both compilers scale well with simulation time-step and system size. Code for both compilers is open-source and packaged into a full-stack quantum simulation software with tutorials included for ease of use for future researchers wishing to perform dynamic simulations of quantum materials on quantum computers. As our DS compilers provide significant improvements in both compilation time and simulation fidelity, they provide a building block for accelerating progress toward physical quantum supremacy.
Comparison of Personality Recognition Algorithms

Automation of document circulation is becoming more and more relevant every day. Today, it is difficult to imagine life without the Internet, as a result of which enterprises develop and the need for personal identification grows without leaving home. Personal identification is used in many areas (banking, electronic payment systems, forensics, etc.). To simplify the verification process, many businesses are switching to identity verification by providing personal information and photographs of identity documents. The relevance of the study is due to the growing need to simplify and improve the accuracy of personal identification by providing photographs of documents and personal data. The aim of the work is to analyze existing algorithms for recognizing text and faces in an image.
Empirical study on code smells in iOS applications

Code smells are recurring patterns in code that have been identified as bad practices. They have been analysed extensively in Java desktop applications. For mobile applications most of the research has been done for Android with very little research done for iOS. Although Android has the largest market share, iOS is a very popular platform. Our goal is to understand the distribution of code smells in iOS applications. For this analysis we used a collaborative list of open source iOS applications from GitHub. We combined code smells defined by Fowler and object oriented code smells studied on Android. We developed a tool that can detect these code smells in Swift applications. We discovered that iOS applications are most often affected by Lazy Class, Long Method and Message Chain code smells. Most often occurring code smells are Internal Duplication, Lazy Class and Long Method.
Sampling methods and feature selection for mortality prediction with neural networks

Along with digitization, automatic data-driven decision support systems become increasingly popular. Mortality prediction is a vital part of that decision process. With more data available, sophisticated machine learning models like (Artificial) Neural Networks (NNs) can be applied and promise favorable performance. We evaluate the reproducibility of a published mortality prediction approach using NNs along with the possibility to generalize it to a bigger and more generic dataset. We describe an extensive preprocessing pipeline, as well as the evaluation of different sampling techniques and NN architectures. Through training on a loss function that optimizes both, precision and recall, in combination with a good set of hyperparameters and a set of new features, we use a NN to predict in-hospital mortality with accuracy, sensitivity, and area under the receiver operating characteristic score of greater than 0.8.
Generating an industrial process graph from 3D pipe routing information

The automatic generation of digital twins of industrial processes requires the integration of several sources of information. If the twin is expected to accurately capture thermo-hydraulic phenomena, dimensions of tanks and other process components as well as detailed pipe routing information is relevant. Such information is not comprehensively captured in P&IDs (Piping & Instrumentation Diagrams), but it is available from 3D CAD models. However, information about control loops is not available from 3D CAD models, but is available from P&IDs. Previous research has demonstrated the extraction of such information from machine-readable P&IDs and 3D CAD models and converting this information to graphs. Further research is expected on applying graph matching methods for integrating these separate graphs to a common graph-based data structure that captures all of the desired information. This common model could support further work to develop digital twins. A major obstacle to this is that the graphs that have currently been generated from P&IDs and 3D CAD models are at very different abstraction levels, so graph matching methods are not feasible. This article address this obstacle by building on previous work, in which graphs were generated from P&IDs and 3D CAD models. The contribution of this paper is several novel algorithms for preprocessing a 3D CAD generated graph, until it is at the same level of abstraction as a P&ID generated graph of the same industrial process. The algorithms are demonstrated in the context of a laboratory process.
Comparing Position- and Image-Based Visual Servoing for Robotic Assembly of Large Structures

This paper considers image-guided assembly for large composite panels. By using fiducial markers on the panels and robot gripper mounted cameras, we are able to use an industrial robot to align the panels to sub-millimeter accuracy. We considered two commonly used visual servoing schemes: position-based visual servoing (PBVS) and image-based visual servoing (IBVS). It has been noted that IBVS possesses superior robustness with respect to the camera calibration accuracy. However, we have found that in our case, PBVS is both faster and slightly more accurate than IBVS. This result is due to the fact that the visual servoing target in the image plane is derived from a reference target, which depends on the accuracy of the camera model. This additional dependency essentially nullifies the robustness advantage of IBVS. We also implemented a simple scheme to combine inputs from multiple cameras to improve the visual servoing accuracy. Both simulation and experimental results are included to show the effectiveness of visual servoing in an industrial setting.