Reviewerly/exHarmony huggingface dataset:
DistriTrust: Distributed and low-latency access validation in zero-trust architecture

Enterprise networks typically use perimeter-based security to protect their IT resources from security threats originating from outside the enterprise perimeter. In such networks, connection requests from devices residing inside the perimeter are implicitly assumed to be trusted. However, in practice, a considerable fraction of cyberattacks emanate from inside the enterprise network making the perimeter-based trust assumption unrealistic. Zero-trust architecture (ZTA) is an emerging alternative to perimeter-based security, where each connection request for accessing an enterprise resource goes through stringent security checks and validation irrespective of the location of the requesting device. In ZTA, every connection request is authenticated and authorized by a trusted centralized component called the policy decision point (PDP) and subsequently granted (or denied) access to the requested resource. However, the centralized nature of the PDP often makes it vulnerable to various attacks such as compromise of secret keys, impersonation and denial of service. In this article, we propose DistriTrust which distributes trust across multiple PDPs using the notion of threshold signatures. However, involving multiple PDPs also increases latency. In order to keep latency as low as possible, we study different threshold signature schemes and identify a suitable scheme for DistriTrust. We also discuss the security properties achieved by DistriTrust. Finally, we analyze the asymptotic performance of DistriTrust and report the experimental results as well.
Blockchain-Based Identity Management for Personal Data: A Survey

This research was conducted to analyse the domain of blockchain-based identity management for personal data. The study primarily adopted the research methodology of a systematic literature review, in which relevant and contemporary scientific literature related to the topic were identified and included, and irrelevant papers or those with repetitive information, were excluded. An analysis of the literature indicates that the common public perception of blockchain is positive overall. However, there is a substantial difference between public perception and reality in relation to the maturity of the technology. Proponents of blockchain often overlook and minimise the possibility of the damage that blockchain adoption could cause. This study found that main problem is not blockchain itself, but the surrounding infrastructure which has to be transformed at the same pace and direction. Furthermore, concerning identity management, it was found that the technology provides considerable improvement over database-based systems of identity and user authentication, however, it still contains systemic avenues of centralisation, through which a denial-of-service attack could increase.
Understanding the Challenges and Needs of Programmers Writing Web Automation Scripts

For web scraping and task automation purposes, programmers write scripts to interact with websites. This is similar to writing end-to-end user interface (UI) test automation suites for software, but on third-party websites that the programmer does not own, introducing new challenges. A programmer might know what semantic operations they want their script to perform, but translating this to code can be difficult. The programmer must investigate the website's internal structure, content, and how UI elements behave, and then write code to click, type, and otherwise interact with UI elements. Many tools and frameworks for creating web automation scripts exist but the challenges programmers face in using them remains understudied. We conducted two studies to study how programmers write web automation scripts. The first study focuses on understanding general challenges. The second focuses on the ways website UI context and script feedback can be helpful. We also provide a set of design findings that detail the kinds of context and feedback developers need while writing web automation scripts.
Source-Code Similarity Measurement: Syntax Tree Fingerprinting for Automated Evaluation

A majority of the current automated evaluation tools focus on grading a program based only on functionally testing the outputs. This approach suffers both false positives (i.e. finding errors where there are not any) and false negatives (missing out on actual errors). In this paper, we present a novel system which emulates manual evaluation of programming assignments based on the structure and not the functional output of the program using structural similarity between the given program and a reference solution. We propose an evaluation rubric for scoring structural similarity with respect to a reference solution. We present an ML based approach to map the system predicted scores to the scores computed using the rubric. Empirical evaluation of the system is done on a corpus of Python programs extracted from the popular programming platform, HackerRank, in combination with programming assignments submitted by students undertaking an undergraduate Python programming course. The preliminary results have been encouraging with the errors reported being as low as 12 percent with a deviation of about 3 percent, showing that the automatically generated scores are in high correlation with the instructor assigned scores.
Graph relation network for person counting in construction site using UAV

Due to its portability and maneuverability, Unmanned Aerial Vehicles (UAVs) are increasingly used in industrial fields. Our work aims to develop a framework of person counting executed by an UAV, which can count the total number of persons accurately. To solve the problem of multiple counts for the same person, this work proposes a novel Graph Similarity-based Person Counting Network (GSPCN) which consists of three modules: person detection network, person re-identification module, and person counting module. To begin with, we detect the bounding boxes and the corresponding images for each object in image sequence. Secondly, we calculate the visual similarity between persons. And then, each person is taken as the root node to construct a graph, then we calculate the similarity between different graphs as the graph similarity. After that, we comprehensively consider the visual similarity and graph similarity to determine whether the person is repeated. Finally, by removing all duplicate persons, we can get the total number of persons. The proposed framework is tested in a real-world scenario and it empirically outperforms the existing state-of-the-art methods.
Research Theme Map of Ubiquitous Learning: A 27-Years Overview

Research on ubiquitous learning is various although it is limited to selected country. This study purposes mapping the research trend in ubiquitous learning. Bibliometric techniques used to gather data from Scopus as the secondary. VOSviewer program and the analyses search results function on Scopus are used to visualize and analyze data. This study analyzed 1,566 scientific articles published from 1993 to 2020. Based on the result, Monash University turn into the most dynamic institutions, and Hiroaki Ogata became the enthusiastic individual scientist in ubiquitous learning research. Lecture Notes in Computer Science were the most studied and published outlets of ubiquitous learning research. Five category maps of collaborative researchers were found in this research. Based on the result, over twenty-seven years of publication, this research recommends a grouping research themes of ubiquitous learning: Knowledge management, Application of Social networking, Learning process, Information technology, Ubiquitous technology, and Mobile learning process, shortened as KALIUM research themes.
Keep Your Stakeholders Engaged: Interactive Vision Videos in Requirements Engineering

One of the most important issues in requirements engineering (RE) is the alignment of stakeholders’ mental models. Making sure that all stakeholders share the same vision of a changing system is crucial to the success of any project. Misaligned mental models of stakeholders can lead to conflicting requirements. A promising approach to this problem is the use of video showing a system vision, so-called vision videos, which help stakeholders to disclose, discuss, and align their mental models of the future system. However, videos have the drawback of allowing viewers to adopt a passive role, as has been shown in research on e-learning. In this role, viewers tend to be inactive, unfocused and bored while watching a video. In this paper, we learn and adopt findings from scientific literature in the field of e-learning on how to mitigate this passive role while watching vision videos in requirements engineering. In this way, we developed concepts that incorporate interactive elements into vision videos to help viewers stay focused. These elements include questions that are asked during the video and ways for viewers to decide what happens next in the video. In a preliminary evaluation with twelve participants, we found statistically significant differences when comparing the interactive vision videos with their traditional form. Using an interactive vision videos, viewers are noticeably more engaged and gather more information on the shown system.
Multi-Domain Semantic-Segmentation using Multi-Head Model

Semantic segmentation is a pixel-wise class identification problem, which is important for automatic driving support such as recognizing the driving area. However, segmentation accuracy significantly degrades in scenes that differ from the training domain. Therefore, it is necessary to prepare multiple models for each domain, which increases the memory cost. When training multiple datasets with a single-head model, it is also necessary to redefine a different object class for each dataset. We propose a semantic-segmentation method that involves using a multi-head model for supporting multiple domains. The proposed method also involves using a shared network for sharing all domains for training datasets. This makes it possible to train multiple datasets with different object classes in a single network. To train all datasets equally, we also introduce mix loss, which simultaneously back-propagates the loss of each dataset. From experiments evaluating the proposed method, we confirmed that the method achieves higher or equivalent recognition accuracy with fewer parameters than using a single-head model for each dataset when training datasets with the same class, training different datasets at the same time, and training datasets individually.
Theory of Constructed Emotion Meets RE

This article proposes to employ one of the most up to date theories of emotion - the theory of constructed emotion for engineering and validating requirements. We first provide an overview of different theories of emotion and indicate where the theory of constructed emotion lies in relation to these theories. After that, we describe possible advantages in applying theory of constructed emotion to requirements engineering. Thereafter, we postulate how the theory of constructed emotion could be applied in requirements engineering. We then hypothesize how the theory of constructed could be supported by appropriate methods and tools. Finally, we draw conclusions, and sketch the research agenda in applying the theory of constructed emotion in requirements engineering.
Design of Automatic Scoring System for Oral English Test Based on Sequence Matching and Big Data Analysis

With the application of an automatic scoring system to all kinds of oral English tests at all levels, the efficiency of test implementation has been greatly improved. The traditional speech signal processing method only focuses on the extraction of scoring features, which could not ensure the accuracy of the scoring algorithm. Aiming at the reliability of the automatic scoring system, based on the principle of sequence matching, this paper adopts the spoken speech feature extraction method to extract the features of spoken English test pronunciation and establishes a dynamic optimized spoken English pronunciation signal model based on sequence matching, which could maintain good dynamic selection and clustering ability in a strong interference environment. According to the comprehensive experiment, the automatic scoring result of the system is much higher than that of the traditional method, which greatly improves the recognition ability of oral pronunciation, solves the difference between the automatic scoring of the system and the manual scoring, and promotes the computer automatic scoring system to replace or partially replace the manual marking.
A Natural Language Processing Technique for Formalization of Systems Requirement Specifications

Natural language processing techniques have proven to be useful for analysis of technical specifications documents. One such technique, information extraction (IE), can help automate the analysis of software systems requirement specifications (SysRS) by extracting structured information from unstructured or semi-structured natural language data, allowing for requirements to be converted into formal logic. Current IE techniques are not designed for SysRS data, and often do not extract the information needed for requirements formalization. In this work, we introduce an IE method specifically designed for SysRS data. We provide a description of our approach, analysis of the technique on a set of real requirements, example of how information obtained using our technique can be converted into a formal logic representation, and discussion of our technique and its benefits in automated SysRS analysis tasks.
Comparing MaxEnt and Noisy Harmonic Grammar

MaxEnt grammar is a probabilistic version of Harmonic Grammar in which the harmony scores of candidates are mapped onto probabilities. It has become the tool of choice for analyzing phonological phenomena involving probabilistic variation or gradient acceptability, but there is a competing proposal for making Harmonic Grammar probabilistic, Noisy Harmonic Grammar, in which variation is derived by adding random ‘noise’ to constraint weights. In this paper these grammar frameworks, and variants of them, are analyzed by reformulating them all in a format where noise is added to candidate harmonies, and the differences between frameworks lie in the distribution of this noise. This analysis reveals a basic difference between the models: in MaxEnt the relative probabilities of two candidates depend only on the difference in their harmony scores, whereas in Noisy Harmonic Grammar it also depends on the differences in the constraint violations incurred by the two candidates. This difference leads to testable predictions which are evaluated against data on variable realization of schwa in French (Smith &amp;amp; Pater 2020). The results support MaxEnt over Noisy Harmonic Grammar.
AutoTrans: Automating Transformer Design via Reinforced Architecture Search

Though the transformer architectures have shown dominance in many natural language understanding tasks, there are still unsolved issues for the training of transformer models, especially the need for a principled way of warm-up which has shown importance for stable training of a transformer, as well as whether the task at hand prefer to scale the attention product or not. In this paper, we empirically explore automating the design choices in the transformer model, i.e., how to set layer-norm, whether to scale, number of layers, number of heads, activation function, etc., so that one can obtain a transformer architecture that better suits the tasks at hand. RL is employed to navigate along search space, and special parameter sharing strategies are designed to accelerate the search. It is shown that sampling a proportion of training data per epoch during search help to improve the search quality. Experiments on the CoNLL03, Multi-30k and WMT-14 shows that the searched transformer model can outperform the standard transformers. In particular, we show that our learned model can be trained more robustly with large learning rates without warm-up.
Tessera: A Practical System for Extended WaveFunctionCollapse

Constraint-based procedural generation has recently had a lot of interest following the publication of the WaveFunctionCollapse (WFC) algorithm, but usability issues have restricted the number of games and projects that have resulted. We present Tessera, a library and tool for WFC specifically designed to address the practical issues that arise from constraint based generation. We discuss the user interface of the tool and useful extensions to the base algorithm of WFC.
Classifying In-Place Gestures with End-to-End Point Cloud Learning

Walking in place for moving through virtual environments has attracted noticeable attention recently. Recent attempts focused on training a classifier to recognize certain patterns of gestures (e.g., standing, walking, etc) with the use of neural networks like CNN or LSTM. Nevertheless, they often consider very few types of gestures and/or induce less desired latency in virtual environments. In this paper, we propose a novel framework for accurate and efficient classification of in-place gestures. Our key idea is to treat several consecutive frames as a “point cloud”. The HMD and two VIVE trackers provide three points in each frame, with each point consisting of 12-dimensional features (i.e., three-dimensional position coordinates, velocity, rotation, angular velocity). We create a dataset consisting of 9 gesture classes for virtual in-place locomotion. In addition to the supervised point-based network, we also take unsupervised domain adaptation into account due to inter-person variations. To this end, we develop an end-to-end joint framework involving both a supervised loss for supervised point learning and an unsupervised loss for unsupervised domain adaptation. Experiments demonstrate that our approach generates very promising outcomes, in terms of high overall classification accuracy (95.0%) and real-time performance (192ms latency). We will release our dataset and source code to the community.
Citizens’ Cybersecurity Behavior: Some Major Challenges

Citizens’ cybersecurity behaviors are an important concern in the modern age. This work discusses the challenges of studying citizen cybersecurity behaviors and the directions for future research.
OTCMR: Bridging Heterogeneity Gap with Optimal Transport for Cross-modal Retrieval

Cross-modal retrieval is a classic task in the multimedia community, which aims to search for semantically similar results from different modalities. The core of cross-modal retrieval is to learn the most correlated features in a common feature space for the multi-modal data so that the similarity can be directly measured. In this paper, we propose a novel model using optimal transport for bridging the heterogeneity gap in cross-modal retrieval tasks. Specifically, we calculate the optimal transport plans between feature distributions of different modalities and then minimize the transport cost by optimizing the feature embedding functions. In this way, the feature distributions of multi-modal data can be well aligned in the common feature space. In addition, our model combines the complementary losses in different levels: 1) semantic level, 2) distributional level, and 3) pairwise level for improving cross-modal retrieval performance. In extensive experiments, our method outperforms many other cross-modal retrieval methods, which proves the efficacy of using optimal transport in cross-modal retrieval tasks.
Multi actor hierarchical attention critic with RNN-based feature extraction

Deep reinforcement learning has made significant progress in multi-agent tasks in recent years. However, most previous studies focus on solving fully cooperative tasks, which do not perform well in mixed tasks. In mixed tasks, the agent needs to comprehensively consider the information provided by its friends and enemies to learn its strategy, and its strategy is sensitive to the received information. Additionally, the input space of the critic network increases rapidly with the number of agents in the actor-critic framework. It's of great necessity to efficiently learn information representation to obtain important features. To this end, we present an approach that conducts information representation with attention mechanism. Our approach adopts the framework of centralized training and decentralized execution. We apply the multi-head hierarchical attention mechanism to centrally computed critics, so critics can process the received information more accurately and assist actors in choosing better actions. The hierarchical attention critic adopts a bi-level attention structure which is composed of the agent-level and the group-level. They are designed to assign different weights to friends' and enemies' information and then summarize them at each timestep. It achieves high efficiency and scalability in mixed tasks. Furthermore, we use the feature extraction based on the recurrent neural network to encode the state-action sequence information of each agent. Experimental results show that our approach is not only applicable to cooperative environments but also better in mixed environments, especially in the predator-prey task, the reward obtained by our method is twice that of the baselines.
Cooperative Federated Learning-Based Task Offloading Scheme for Tactical Edge Networks

In this study, we focus on the federated learning (FL) based tactical edge network platform to cooperatively operate computation-hungry tasks as efficiently as possible.Based on the incentive of FL model training, each individual device makes offloading decisions for their tactical tasks in resource-constrained network environments.According to the ideas of two different bargaining solutions -weighted average solution and constant elasticity substitution solution -edge server and IoT devices work together in a coordinated manner to approximate a well-balanced system performance.Therefore, we can reach an agreement while exploring the mutual benefits to leverage a reciprocal consensus between different viewpoints.The main novelty of our approach is to investigate the dual-interactive bargaining process based on the interdependent relationship between IoT devices and the tactical edge server.To the best of our knowledge, this is the first work that jointly considers different bargaining solutions to handle tactical edge-assisted task offloading services.Based on the numerical simulation, it is demonstrated that the proposed approach can increase the system throughput, device payoff and device fairness up to 10%, 15% and 20%, respectively, in comparison with existing protocols.
Visual Analytics and Similarity Search - Interest-based Similarity Search in Scientific Data

Visual Analytics enables solving complex analytical tasks by coupling interactive visualizations and machine learning approaches. Besides the analytical reasoning enabled through Visual Analytics, the exploration of data plays an essential role. The exploration process can be supported through similarity-based approaches that enable finding similar data to those annotated in the context of visual exploration. We propose in this paper a process of annotation in the context of exploration that leads to labeled vectors-of-interest and enables finding similar publications based on interest vectors. The generation and labeling of the interest vectors are performed automatically by the Visual Analytics system and lead to finding similar papers and categorizing the annotated papers. With this approach, we provide a categorized similarity search based on an automatically labeled interest matrix in Visual Analytics.