Reviewerly/exHarmony huggingface dataset:
Decentralized Group Signature Scheme Based on Blockchain

The group signature scheme is widely used in military and financial fields due to its efficient anonymity and revocability. However, in an untrusted environment, previous group signature schemes often suffer joint attacks from management centers or revocation centers, which reduces the security of the scheme. At the same time, the malicious group center and the signature recipient can identify the signer identity and also threaten the anonymity of the program. It is proved by calculation that the scheme can maintain the security and anonymity of the group signature algorithm in an untrusted environment.
The Ethical Dilemma When (Not) Setting up Cost-Based Decision Rules in Semantic Segmentation

Neural networks for semantic segmentation can be seen as statistical models that provide for each pixel of one image a probability distribution on predefined classes. The predicted class is then usually obtained by the maximum a-posteriori probability (MAP) which is known as Bayes rule in decision theory. From decision theory we also know that the Bayes rule is optimal regarding the simple symmetric cost function. Therefore, it weights each type of confusion between two different classes equally, e.g., given images of urban street scenes there is no distinction in the cost function if the network confuses a person with a street or a building with a tree. Intuitively, there might be confusions of classes that are more important to avoid than others. In this work, we want to raise awareness of the possibility of explicitly defining confusion costs and the associated ethical difficulties if it comes down to providing numbers. We define two cost functions from different extreme perspectives, an egoistic and an altruistic one, and show how safety relevant quantities like precision / recall and (segment-wise) false positive / negative rate change when interpolating between MAP, egoistic and altruistic decision rules.
GUIRO: User-Guided Matrix Reordering

Matrix representations are one of the main established and empirically proven to be effective visualization techniques for relational (or network) data. However, matrices-similar to node-link diagrams-are most effective if their layout reveals the underlying data topology. Given the many developed algorithms, a practical problem arises: “Which matrix reordering algorithm should I choose for my dataset at hand?” To make matters worse, different reordering algorithms applied to the same dataset may let significantly different visual matrix patterns emerge. This leads to the question of trustworthiness and explainability of these fully automated, often heuristic, black-box processes. We present GUIRO, a Visual Analytics system that helps novices, network analysts, and algorithm designers to open the black-box. Users can investigate the usefulness and expressiveness of 70 accessible matrix reordering algorithms. For network analysts, we introduce a novel model space representation and two interaction techniques for a user-guided reordering of rows or columns, and especially groups thereof (submatrix reordering). These novel techniques contribute to the understanding of the global and local dataset topology. We support algorithm designers by giving them access to 16 reordering quality metrics and visual exploration means for comparing reordering implementations on a row/column permutation level. We evaluated GUIRO in a guided explorative user study with 12 subjects, a case study demonstrating its usefulness in a real-world scenario, and through an expert study gathering feedback on our design decisions. We found that our proposed methods help even inexperienced users to understand matrix patterns and allow a user-guided steering of reordering algorithms. GUIRO helps to increase the transparency of matrix reordering algorithms, thus helping a broad range of users to get a better insight into the complex reordering process, in turn supporting data and reordering algorithm insights.
MVPointNet: Multi-View Network for 3D Object Based on Point Cloud

The research of hand pose estimation is a hot topic in computer vision, robotics and virtual reality. Compared with using data glove, vision based methods show great advantage for its contactless property, low-cost and convenience. With the commercial depth cameras became widely available and the great success of Convolution Neural Network (CNN) on images, various works focused on hand pose estimation have achieved promising performance. This research is inspired by the recent work that directly perform 3D classification and segmentation tasks on point cloud. In this paper, Multi-View PointNet (MVPointNet) is proposed which takes several views of point cloud as input source. Then, they fed into the well-performing point cloud-based architecture. In addition, to better capture the hand context structure and improve the performance, more features between centroid and local neighborhood points (norm, edge, angle) are extracted and fed into a deep CNN architecture. To our knowledge, our proposed method achieved good performance on the ModelNet40 dataset for 3D shape classification. Besides, it achieved superior performance over other deep learning methods for 3D hand pose estimation based on point cloud, which is evaluated on MSRA dataset.
“The internet is not pleased”: twitter and the 2017 Equifax data breach

The 2017 Equifax data breach left 144.5 million users digitally vulnerable to identity theft and future hacks. The organization’s failure to provide ongoing communication and information regarding the attack, motivated users to form crisis communities within the Twitter platform. This study examines the discourses present within the platform as users discussed the data breach. Digital crisis communities provide researchers opportunities to study how public users respond and react to a digital threat, particularly one that impacts online security and privacy. Through a qualitative analysis of tweets from the 3 weeks after the breach was announced, three discourses emerged that represented user frustrations and reactions to the security violation. These include breaking news, anger and outrage, and blame attribution. The findings of this study are relevant for those studying crisis communication, the Twitter platform, and online communities.
Implementing Babylonian/S by Putting Examples Into Contexts

Based on context-oriented programming (COP), we implemented source code instrumentation for example-based live programming in a modular way. These tools provide programmers with feedback on the dynamic program behavior by showing traced values of example invocations of a program. For that, we have to trace intermediate, expression-level runtime states during the execution of an example. As the instrumentation is only intended to improve tool support, the default behavior of the system must not be altered. In this paper, we demonstrate how context-oriented programming can be used to keep the execution of examples separate from the default behavior even in the presence of expression-level behavior variations. We illustrate our approach by implementing Babylonian Programming using ContextS2 in Squeak/Smalltalk. Based on the implementation, we compare our COP-based Smalltalk implementation to the module-rewriting-based implementation for JavaScript.
Two-Stage Entity Alignment: Combining Hybrid Knowledge Graph Embedding with Similarity-Based Relation Alignment

Entity alignment aims to automatically determine whether an entity pair in different knowledge graphs refers to the same entity in reality. Existing entity alignment methods can be classified into two categories: string-similarity-based methods and embedding-based methods. String-similarity-based methods have higher accuracy, however, they might have difficulty in dealing with literal heterogeneity, i.e., an entity pair in diverse forms. Though embedding-based entity alignment can deal with literal heterogeneity, they also suffer the shortcomings of higher time complexity and lower accuracy. Moreover, there remain limitations and challenges due to only using the structure information of triples for existing embedding methods. Therefore, in this study, we propose a two-stage entity alignment framework, which can combine the advantages of both methods. In addition, to enhance the embedding performance, a hybrid knowledge graph embedding model with both fact triples and logical rules is introduced for entity alignment. Experimental results on two real-world datasets show that the proposed method is significantly better than the state-of-the-art embedding-based entity alignment methods.
Novel Hand Gesture Alert System

. Sexual assault can cause great societal damage, with negative socio-economic, mental, sexual, physical and reproductive consequences. According to the Eurostat, the number of crimes increased in the European Union between 2008 and 2016. However, despite the increase in security tools such as cameras, it is usually difficult to know if an individual is subject to an assault based on his or her posture. Hand gestures are seen by many as the natural means of nonverbal communication when interacting with a computer, and a considerable amount of research has been performed. In addition, the identifiable hand placement characteristics provided by modern inexpensive commercial depth cameras can be used in a variety of gesture recognition-based systems, particularly for human-machine interactions. This paper introduces a novel gesture alert system that uses a combination of Convolution Neural Networks (CNNs). The overall system can be subdivided into three main parts: firstly, the human detection in the image using a pretrained “You Only Look Once (YOLO)” method, which extracts the related bounding boxes containing his/her hands; secondly, the gesture detection/classification stage, which processes the bounding box images; and thirdly, we introduced a module called “counterGesture”, which triggers the alert.
Comparative Analysis of Three Language Spheres: Are Linguistic and Cultural Differences Reflected in Password Selection Habits?

This work aims to determine the propensity of password creation through the lens of language spheres. To this end, we consider three different countries/languages, each with a different culture: China/Chinese, United Kingdom (UK)/English, and Japan/Japanese. We first employ a user study to verify whether language and culture are reflected in password creation. We found that users in all three countries prefer to create their passwords from base words, and the kinds of words they are incorporated into passwords vary between countries. We then test whether the findings obtained through the user study are reflected in a corpus of leaked passwords. We found that users in China and Japan prefer dates, while users in Japan and the UK prefer names. We also found that cultural words(e.g., "sakura" in Japan and "football" in the UK) are frequently used to create passwords. Finally, we demonstrate that the knowledge on the linguistic background of targeted users can be exploited to increase the speed of the password guessing process.
Bitcoin in Troubled Economies: The Potential of Cryptocurrencies in Argentina and Venezuela

The rise of cryptocurrencies in Argentina and Venezuela in recent years shows how a highly distressed economy could become fertile ground for decentralized digital currency. This article analyzes similar traits between these two nations and examines why a collateral result of high inflation, tangled monetary regulation, and political instability could be the rapid growth of cryptocurrencies that are not linked to a central bank. Mistrust in central government authorities and national currency volatility that surpasses that of traded cryptocurrencies open a window for intangible ways of storing the falling value of local fiat currency. This article sets a general framework in order to have a better understanding of the growth of decentralized digital currencies in developing economies and continues to explain the rise of such technology in recent times in Argentina and Venezuela.
2 Fast 2 Secure: A Case Study of Post-Breach Security Changes

A security breach often makes companies react by changing their attitude and approach to security within the organization. This paper presents an in-depth case study of post-breach security changes made by a company and the consequences of those changes. We employ the principles of participatory action research and humble inquiry to conduct a long-term study with employee interviews while embedded in the organization's security division. Despite an extremely high level of financial investment in security, and consistent attention and involvement from the board, the interviews indicate a significant level of friction between employees and security. In the main themes that emerged from our data analysis, a number of factors shed light on the friction: fear of another breach leading to zero risk appetite, impossible security controls making non-compliance a norm, security theatre underminining the purpose of security policies, employees often trading-off security with productivity, and as such being treated as children in detention rather than employees trying to finish their paid jobs. This paper shows that post-breach security changes can be complex and sometimes risky due to emotions often being involved. Without an approach considerate of how humans and security interact, even with high financial investment, attempts to change an organization's security behaviour may be ineffective.
AttoNets: Compact and Efficient Deep Neural Networks for the Edge via Human-Machine Collaborative Design

While deep neural networks have achieved state-of-the-art performance across a large number of complex tasks, it remains a big challenge to deploy such networks for practical, on-device edge scenarios such as on mobile devices, consumer devices, drones, and vehicles. There has been significant recent effort in designing small, low-footprint deep neural networks catered for low-power edge devices, with much of the focus on two extremes: hand-crafting via design principles or fully automated network architecture search. In this study, we take a deeper exploration into a human-machine collaborative design approach for creating highly efficient deep neural networks through a synergy between principled network design prototyping and machine-driven design exploration. The efficacy of human-machine collaborative design is demonstrated through the creation of AttoNets, a family of highly efficient deep neural networks for on-device edge deep learning. Each AttoNet possesses a human-specified network-level macro-architecture comprising of custom modules with unique machine-designed module-level macro-architecture and micro-architecture designs, all driven by human-specified design requirements. Experimental results for the task of object recognition showed that the AttoNets created via human-machine collaborative design has significantly fewer parameters and computational costs than state-of-the-art networks designed for efficiency while achieving noticeably higher accuracy (with the smallest AttoNet achieving ~1.8% higher accuracy while requiring ~10x fewer multiply-add operations and parameters than MobileNet-V1). Furthermore, the efficacy of the AttoNets is demonstrated for the task of instance segmentation and object detection, where an AttoNet-based Mask R-CNN network was constructed with significantly fewer parameters and computational costs (~5x fewer multiply-add operations and ~2x fewer parameters) than a ResNet-50 based Mask R-CNN network.
Gender Differences in Cognitive Loads, Attitudes, and Academic Achievements in Mobile English Learning

Studies on gender differences in mobile English learning are in their initial stages. College English IV, designed by a number of professors and engineers in a university in China, serves as a mobile English learning platform. This study aims to determine gender differences in cognitive loads, attitudes, and academic achievements in English language learning assisted with this mobile English learning platform. Through a mixed research design, 79 randomly selected participants join the research, together with interviews and posts to collect qualitative data. Cognitive loads, attitudes, and academic achievements are measured through related scales to collect quantitative data. It is concluded that there are significant gender differences in cognitive loads, attitudes, and academic achievements in English language learning assisted with the mobile learning platform. Future research on gender differences in mobile English learning should also examine relationships between gender differences, learner motivation, achievement goals, and learning outcomes.
Sticky Policies: A Survey

In the digital age, where the Internet connects things across the globe and individuals are constantly online, data security and privacy are becoming key drivers (and barriers) of change for adoption of innovative solutions. Traditional approaches, whereby communication links are secured by means of encryption, and access control is run in a static way by a centralized authority, are showing their limits when applied to massive-scale, interconnected and distributed systems. Regulations, while still fragmented, are moving to adapt to changes in technology and society, with the aim to protect confidential information by governments, businesses, and individual citizens. In this landscape, proper mechanisms should be defined to allow a strict control over the data life-cycle and to guarantee the privacy and the application of specific regulations on personal information's disclosure, usage and access. Sticky policies represent one approach to improve owners' control over their data. In such an approach, machine-readable policies are attached to data. They are called 'sticky' in that they travel together with data, as data travels across multiple administrative domains. In this article we survey the state-of-the-art in sticky policies, discussing limitations, open issues, applications and research challenges, with a specific focus on their applicability to Internet of Things, cloud computing, and Content Centric Networking.
CLAD: A corpus-derived Chinese Lexical Association Database

The application of word associations has become increasingly widespread. However, the association norms produced by traditional free association tests tend not to exceed 10,000 stimulus words, making the number of associated words too small to be representative of the overall language. In this study we used text corpora totaling over 400 million Chinese words, along with a multitude of association measures, to automatically construct a Chinese Lexical Association Database (CLAD) comprising the lexical association of over 80,000 words. Comparison of the CLAD with a database of traditional Chinese word association norms shows that word associations extracted from large text corpora are similar in strength to those elicited from free association tests but contain a much greater number of associative word pairs. Additionally, the relatively small numbers of participants involved in the creation of traditional norms result in relatively coarse scales of association measurement, whereas the differentiation of association strengths is greatly enhanced in the CLAD. The CLAD provides researchers with a great supplement to traditional word association norms. A query website at www.chinesereadability.net/LexicalAssociation/CLAD/ affords access to the database.
Adversarial Out-domain Examples for Generative Models

Deep generative models are rapidly becoming a common tool for researchers and developers. However, as exhaustively shown for the family of discriminative models, the test-time inference of deep neural networks cannot be fully controlled and erroneous behaviors can be induced by an attacker. In the present work, we show how a malicious user can force a pre-trained generator to reproduce arbitrary data instances by feeding it suitable adversarial inputs. Moreover, we show that these adversarial latent vectors can be shaped so as to be statistically indistinguishable from the set of genuine inputs. The proposed attack technique is evaluated with respect to various GAN images generators using different architectures, training processes and for both conditional and not-conditional setups.
A random‐effects model for clustered circular data

Abstract This article considers a circular regression model for clustered data, where both the cluster effects and the regression errors have von Mises distributions. It involves β , a vector of parameters for the fixed effects, and two concentration parameters for the error distribution. A measure of intra‐cluster circular correlation and a predictor for an unobserved cluster random effect are studied. Preliminary estimators for the vector β and the two concentration parameters are proposed, and their performance is compared with that of the maximum likelihood estimators in a simulation study. A numerical example investigating the factors impacting the orientation taken by a sand hopper when released is presented. The Canadian Journal of Statistics 47: 712–728; 2019 © 2019 Statistical Society of Canada
Non-linear MPC Motion Planner for Autonomous Vehicles Based on Accelerated Particle Swarm Optimization Algorithm

Automotive field is about to face a deep revolution: autonomous driving. This paper presents an algorithm of motion planning that can be considered as a part of a hierarchical scheme describing the complex task of driving. The algorithm proposed consists in an optimization problem by means of a MPC formulation, numerically solved through Accelerated Particle Swarm Optimization (APSO). It can properly handle with urban environment considering the presence of moving obstacles as well as constraints (e.g. vehicle dynamics, road boundaries,:.). Simulation results confirm the performances of the algorithm.
Development of a multiparametric characterisation protocol for chert investigation and application on the Gargano Promontory mines

A nondestructive multiparametric protocol for chert investigations (NM-PCI) was developed to provide objective criteria for raw material procurements and provenance investigations of archaeological cherts. For this purpose, macroscopic and mesoscopic characteristics (structures, textures and paleontological contents) were used to describe the main visible chert features as well as colourimetric and geochemical data acquired with portable devices. Data handling was performed by means of cluster analysis multivariate statistics, with an appropriate selection of algorithms to process and visualize mixed data matrices (partitioning around medoids (PAM), t-distributed stochastic neighbor embedding (t-SNE)). The protocol was applied to chert samples from the Gargano Promontory (Italy, Apulia) mining complex located in the districts of Peschici, Vieste and Mattinata. These mines, exploited for approximately four millennia, constitute an exceptional prehistoric record of mining archaeology and, above all, a key tool to understand the Neolithisation process throughout the Adriatic region. A map of the primary and secondary chert sources of the Gargano Promontory was produced to manage field sampling of mine structures. Waste materials were selected for the geologic samples of the five mines. Due to their inaccessibility, the samples from two mines consisted exclusively of waste materials. The NM-PCI proved able to discriminate most of the analysed mines and is well suited to provenance investigations of archaeological lithic industries. Compared to previous geochemical studies, the nondestructive method proposed here improves geological resolution, allowing the discrimination of cherts within the same formation.