Reviewerly/exHarmony huggingface dataset:
Supply Chain Risks in Cybersecurity

Covid-19 has highlighted the fragility of supply chains in a range of critical infrastructure: food, medicines, health care, information technology, communications, and more. This paper focuses on an under-appreciated supply chain risk—network cybersecurity—that was present before the pandemic and which the pandemic brings into sharper focus. While the proliferation of digital services has created significant value and employment opportunities, it has also created a wide array of new cybersecurity vulnerabilities. Vulnerabilities of DVRs, CCTVs, voting machines, and municipal systems, leading to denial of service attacks and ransomware hold ups are known. But, these examples miss a problem. Although these examples give the impression that only certain hardware and specific entities are affected, taking networked cybersecurity into account changes yields different conclusions. For example, given that enterprise software, which is common for work at home situations, is rapidly becoming a cybersecurity vulnerability, anyone connected by this software necessarily becomes a target too. Malicious cyber incidents, like data breaches, can have ripple effects across a network of businesses and sectors. Current definitions and regulations of Critical Infrastructure (CI) overlook this point. We argue that the network dimension of cybersecurity supply chain risk is an important, under-studied aspect of the problem. Legal definitions of CI and the voluntary nature of cybersecurity governance leave gaps in the classification of CI and how to identify cybersecurity risk, particularly in the professional services sector. In addition, the voluntary nature of cybersecurity governance demands risk-based and objective measures to aid in identifying when to take steps on improving cybersecurity, but exactly what such metrics are is, at best, evolving. We address both these problems. By drawing on a new dataset, we develop metrics that measure productivity effects and that captures network cybersecurity risk. This approach allows us to show that a major sector, professional services, is missed by current definitions of critical infrastructure, but could be captured if CI definitions accounted for networked cybersecurity risk. In addition, the approach aids voluntary participation in mitigating cybersecurity risk because it provides a way for any firm or sector to identify and assess the nature of its networked cybersecurity risk. In short, networked cybersecurity vulnerabilities can adversely affect aggregate growth and national security objectives because of connectivity across firms and sectors. This work seeks to provide a path forward for understanding, defining, and protecting networked cybersecurity.
A Caption Is Worth A Thousand Images: Investigating Image Captions for Multimodal Named Entity Recognition.

Multimodal named entity recognition (MNER) requires to bridge the gap between language understanding and visual context. Due to advances in natural language processing (NLP) and computer vision (CV), many neural techniques have been proposed to incorporate images into the NER task. In this work, we conduct a detailed analysis of current state-of-the-art fusion techniques for MNER and describe scenarios where adding information from the image does not always result in boosts in performance. We also study the use of captions as a way to enrich the context for MNER. We provide extensive empirical analysis and an ablation study on three datasets from popular social platforms to expose the situations where the approach is beneficial.
Optimized Quantization for Convolutional Deep Neural Networks in Federated Learning

Federated learning is a distributed learning method that trains a deep network on user devices without collecting data from central server. It is useful when the central server can't collect data. However, the absence of data on central server means that deep network compression using data is not possible. Deep network compression is very important because it enables inference even on device with low capacity. In this paper, we proposed a new quantization method that significantly reduces FPROPS(floating-point operations per second) in deep networks without leaking user data in federated learning. Quantization parameters are trained by general learning loss, and updated simultaneously with weight. We call this method as OQFL(Optimized Quantization in Federated Learning). OQFL is a method of learning deep networks and quantization while maintaining security in a distributed network environment including edge computing. We introduce the OQFL method and simulate it in various Convolutional deep neural networks. We shows that OQFL is possible in most representative convolutional deep neural network. Surprisingly, OQFL(4bits) can preserve the accuracy of conventional federated learning(32bits) in test dataset.
Design of an Interactive Map Teaching Aid Based on Arduino and Processing

In allusion to the rapid development of interactive design products, an interactive map teaching aid board based on Arduino and Processing is designed. The article describes the software and hardware selection, circuit design, and program design of the interactive map teaching aid in detail. It mainly uses some sensor technologies such as multi touch and voice recognition to receive instructions, and send information to the upper computer through the single chip microcomputer serial port. After the program receives and processes the data, the upper computer transforms the content displayed in the window to achieve interactive feedback. The technology and ideas of this design can be used to make more interactive art and educational products, and has certain application prospects.
General Sets of Bell-Sheffer and Log-Sheffer Polynomials

The introduction of iterated exponential and logarithmic functions allows to construct new sets of Bell-Sheffer and logarithmic Sheffer (shortly log-Sheffer) polynomials, whose shift operators and differential equations exhibit an iterative character. In this context it is possible to define, for every integers r and s, polynomials of higher order. They give back, in particular cases, the Bell-exponential and logarithmic polynomials and numbers introduced in preceding papers. Connections with integer sequences appearing in Combinatorial analysis are also mentioned.
Contextual Linear Types for Differential Privacy

Language support for differentially private programming is both crucial and delicate. While elaborate program logics can be very expressive, type-system-based approaches using linear types tend to be more lightweight and amenable to automatic checking and inference, and in particular in the presence of higher-order programming. Since the seminal design of Fuzz , which is restricted to ϵ-differential privacy in its original design, significant progress has been made to support more advanced variants of differential privacy, like (ϵ, δ )-differential privacy. However, supporting these advanced privacy variants while also supporting higher-order programming in full has proven to be challenging. We present Jazz , a language and type system that uses linear types and latent contextual effects to support both advanced variants of differential privacy and higher-order programming. Latent contextual effects allow delaying the payment of effects for connectives such as products, sums, and functions, yielding advantages in terms of precision of the analysis and annotation burden upon elimination, as well as modularity. We formalize the core of Jazz , prove it sound for privacy via a logical relation for metric preservation, and illustrate its expressive power through a number of case studies drawn from the recent differential privacy literature.
Training Sensitivity in Graph Isomorphism Network

Graph neural network (GNN) is a popular tool to learn the lower-dimensional representation of a graph. It facilitates the applicability of machine learning tasks on graphs by incorporating domain-specific features. There are various options for underlying procedures (such as optimization functions, activation functions, etc.) that can be considered in the implementation of GNN. However, most of the existing tools are confined to one approach without any analysis. Thus, this emerging field lacks a robust implementation ignoring the highly irregular structure of the real-world graphs. In this paper, we attempt to fill this gap by studying various alternative functions for a respective module using a diverse set of benchmark datasets. Our empirical results suggest that the generally used underlying techniques do not always perform well to capture the overall structure from a set of graphs.
Research on Forward-looking Ranging Algorithm of Missile-borne Phased Array Detector Based on Pulse Compression

Aiming at the forward-looking ranging problem of phased array detectors, this paper proposes a forward-looking ranging algorithm suitable for missile-borne phased array detectors, it uses stepped pulse compression to extract the distance information from the echo signal, and it achieves high-precision and high-efficiency distance the target area for a missile-borne platform with high real-time requirements. In this paper, the forward-looking ranging model of the missile-borne detector is established, and the linear frequency modulation sub-pulse frequency stepping signal system is used as the ranging signal. Then the ranging principle of the missile-borne detector is derived from the ranging theory. Finally, the ranging algorithm was verified through simulation experiments.
Designing an Effective Information Security Policy for Public Organizations

This chapter aims to study the success factors of the ISO 27001 framework related to the implementation of information security in organizations, with particular emphasis on the different maturity controls of ISO 27001 in the implementation of information security policies in organizations. The purpose of this paper is to investigate what controls are commonly used and how they are selected to the implementation of information security in large public organizations in Middle East and North Africa MENA through ISO 27001, with a specific focus on practical framework for the implementation of an effective information security policy through ISO27001. The finding will help organizations to assess organizations to implement an effective information security policy.
Linked data education and training: Past, present, and future

Abstract Studying, designing, and implementing linked data is becoming an increasingly important part of many information settings. Whether taking part in high‐profile grant funded collaborations, local exploratory projects, or adopting a “wait and see” approach, institutions such as libraries, archives, and enterprise organizations will need professionals versed in linked data technologies and practices. In recent years, many advances have been made toward the establishment of an educational foundation for information professionals in this area, through both the incorporation of linked data topics into formal course curricula as well as the establishment of community‐driven, open access learning resources online. However, questions and challenges remain. In this panel, presenters will explore the past, current, and future states of linked data education and training while addressing ongoing challenges and opportunities for preparing metadata professionals in this area.
Text Mining for Industrial Machine Predictive Maintenance with Multiple Data Sources

This paper presents an innovative methodology, from which an efficient system prototype is derived, for the algorithmic prediction of malfunctions of a generic industrial machine tool. It integrates physical devices and machinery with Text Mining technologies and allows the identification of anomalous behaviors, even of minimal entity, rarely perceived by other strategies in a machine tool. The system works without waiting for the end of the shift or the planned stop of the machine. Operationally, the system analyzes the log messages emitted by multiple data sources associated with a machine tool (such as different types of sensors and log files produced by part programs running on CNC or PLC) and deduces whether they can be inferred from them future machine malfunctions. In a preliminary offline phase, the system associates an alert level with each message and stores it in a data structure. At runtime, three algorithms guide the system: pre-processing, matching and analysis: Preprocessing, performed only once, builds the data structure; Matching, in which the system issues the alert level associated with the message; Analysis, which identifies possible future criticalities. It can also analyze an entire historical series of stored messages The algorithms have a linear execution time and are independent of the size of the data structure, which does not need to be sorted and therefore can be updated without any computational effort.
AFT-Anon: A scaling method for online trace anonymization based on anonymous flow tables

Aiming at the problem of trace anonymization performance of backbone networks, we propose a real-time anonymization method for the IP address of backbone network packets based on flow tables (named AFT-Anon). This method can dynamically build an anonymous flow table based on the captured data packets. The first data packet of a network flow is encrypted according to a specific encryption algorithm, and the encrypted fields are stored in the flow record. Subsequent data packets can obtain the encrypted fields by searching flow records and replace the corresponding fields of the original data packets to achieve anonymization of data packets. Based on the proposed method, a high-speed network anonymization system is developed and deployed on the backbone links of an Internet service provider. Experimental results show that the proposed method can improve the anonymization performance by more than 20 times, compared with the existing methods such as Crypto-Pan, and it can meet the requirements for online anonymization of 10G link.
Supervised Method for Congestion Detection at Entry and Exit Corridors of Public Places

Continuous monitoring and automatic detection of crowd activities is extremely helpful for management at public places to avoid any possible disaster. Analysis of crowded scene is a critical task as it typically involves poor resolution of objects, occlusions and complex dynamics. In this paper, we propose a novel, systematic and generalized method based on global motion analysis of people to detect Congestion situation in crowded scenes at entry/exit corridors. Our approach is tested on video footages acquired from surveillance cameras installed at exit corridors of public places. The results show the expediency of our approach.
Data Sharing Over the Cloud Based on Ring Signature

Cloud computing, which provides on demand delivery of services, wherever and whenever, mainly focuses on data sharing over the Internet. So, when data sharing among group of participants from same or dissimilar environment is involved, it may face compromising situation in terms with integrity, confidentiality and privacy of content/data as well as of data owner. One of the features of ring signature is to construct confidential what is more, unknown information sharing framework among the participants group. The ring signature can be used to provide signer’s privacy as well as user’s secrecy. But, for ring mark or signature, the certificate validity verification processing remains a pinch point which does not permit any framework to be flexible. So, to defeat this, the ID-based ring mark or signature can be used. Further, forward security is being appended to make ID-based ring mark or signature increasingly certain, on the grounds that regardless of whether single key used in one transaction have been undermined, still all recently created marks or signatures remain uncompromised and legitimate with the forward security theory use. In this, data sharing over the cloud based on ring signature system, ID-based ring mark or signature added with forward security is moreover improvised by appending Weil pairing, which keeps any key secure irrespective of its size, and it also has less time and space multifaceted nature and hence saves power. All these combined together is essential to construct scalable information sharing framework. So, this paper provides proficient and substantial application of the above-mentioned framework.
Superpixel Segmentation by Forgetting Geodesic Distance

Superpixel segmentation could be of benefit to computer vision tasks due to its perceptually meaningful results with similar appearance and location. To obtain the accurate superpixel segmentation, existing methods introduce geodesic distance to fit the object boundaries. However, conventional geodesic distance easily suffers from error accumulation and excessive time consumption. This paper proposes a fast superpixel segmentation method based on a new geodesic distance, called forgetting geodesic distance. In contrast to the conventional geodesic distance, the forgetting geodesic distance utilizes a forgetting factor to gradually reduce the influence of previous path cost and focuses on the latest pixels' difference. Intuitively, a pixel with lower difference with respect to the latest path contextual distance will be more similar with the corresponding region. In the new path, the path cost devotes much greater attention to the latest pixels' difference and could significantly relieve error accumulation. The pixels are also aggregated with less dependence on seeds as the path extends, which avoids the seed updating. The experimental results validate that the proposed method obtains 2 percent and 1 percent gain on average compared with most of the state-of-the-art methods in terms of BSD500 and VOC2012 datasets, respectively.
A Holistic Study on Approaches to Prevent Sexual Harassment on Twitter

Sexual harassment is a serious social illness that has seeped into our digital societies as well. Research on sexual harassment in social media has been gaining popularity of late, due to the genuine long-term effects such harassment can have on an individual like depression, withdrawal, loss of self-confidence, self-destructive tendencies, and propagation of a conducive environment for sexual assault. The approaches to tackle this issue using technology range from simple lexicon-based models to sophisticated machine learning models. Lexicon-based systems utilize dictionaries that contain offensive words that are characteristic of sexual harassment. Rule-based approaches need to be supplemented with logical reasoning in order to define rules which can be matched with the text to distinguish sexual harassment. Machine learning approaches develop predictive models that can detect sexually offensive messages. This paper holistically studies approaches used to solve the problem of classification of sexual harassment in cyberspace and aims to determine the best directions for future research in this field. It also studies the methods of analyzing the social network in terms of the structure of ties between users that can be used to study specific user patterns and characteristics.
Recommending Inferior Results: A General and Feature-Free Model for Spam Detection

Spam activities on multifarious online platforms, such as the opinion spam and fake following relationships have been extensively studied for years. Existing works separately employ hand-crafted features --- mainly extracted from user behavior, text information, and relational network, to detect the specific spamming phenomenon on a certain kind of online platform. Although these attempts have made some headway, rapidly emerging spamming categories and frequently changing cheating strategies lead detection models to be subject to circumscribed usability and fragile effectiveness.
Enhancing Teaching Effectiveness in Mobile Application Development with Structured Practice

Mobile application development is gaining increasing attention due to the rapid growth of the mobile technologies and mobile application market. Many universities are putting effort on integrating mobile application development into curriculum. The complexity and consistent changing in mobile development environment brings barriers and difficulties in course development. This paper describes how to enhance teaching effectiveness and engage students actively in mobile application development class through structured practice. The proposed structured practice features two levels of hands-on practice: first, prepare students with foundational knowledge and skills by having them follow the instructor on developing base applications; second, the philosophy of challenge-based learning is adopted in practice, where students are encouraged to independently work on developing implementations for applications with gradually increased complexity. Student feedbacks and performance have indicated the structured practice is an effective way of teaching mobile development which not only enhances the teaching effectiveness but also provides students a positive learning experience. Details of course design and project samples are provided to assist audiences in understanding the usage of structured practice and help with the developing of mobile application development courses.
Unsupervised Deep Learning for Text Steganalysis

Text steganography aims to embed hidden messages in text information while the goal of text steganalysis is to identify the existence of hidden information or further uncover the embedded message from the text. Steganalysis has received significant attention recently for the security and privacy purpose. In this paper, we develop unsupervised learning approaches for text steganalysis. In particular, two detection models based on deep learning have been proposed to detect hidden information that may be embedded in text from a global and a local perspective. Extensive studies have been carried out on the Chinese poetry text steganography datasets. It is seen that the proposed models show strong empirical performance in steganographic text detection.
Decision-Making Systems Based on Semantic Image Analysis

In this paper principles of decision-making systems construction are considered. An approach to image analysis based on semantic model is proposed and studied. The results show an improvement in processing speed and image captioning quality based on Visual Genome dataset.
Robust hand tracking method by synchronized high-speed cameras with orthogonal geometry

In this paper, we suggest an intuitive hand motion tracking method using synchronized high-speed cameras with orthogonal geometry. Three synchronized high-speed cameras that capture and process images at the speed of 1,000 frames-persecond concurrently are intentionally arranged so that any pair of optical axes of the cameras cross each other orthogonally. The combination of the high speed with the orthogonal arrangement contributes to robust tracking during dynamic motion with the high-speed three-dimensional reconstruction under occlusion. We present a preliminary hand tracking system using three cameras adopting a minimalist design and also study its feasibility and effectiveness both mathematically and experimentally.