Reviewerly/exHarmony huggingface dataset:
Using large-scale augmented floor surfaces for industrial applications and evaluation on perceived sizes

Abstract Large high-resolution displays (LHRDs) provide an enabling technology to achieve immersive, isometrically registered, virtual environments. It has been shown that LHRDs allow better size judgments, higher collaboration performance, and shorter task completion times. This paper presents novel insights into human size perception using large-scale floor displays, in particular in-depth evaluations of size judgment accuracy, precision, and task completion time. These investigations have been performed in the context of six, novel applications in the domain of automotive production planning. In our studies, we used a 54-sqm sized LED floor and a standard tablet visualizing relatively scaled and true to scale 2D content, which users had to estimate using different aids. The study involved 22 participants and three different conditions. Results indicate that true to scale floor visualizations reduce the mean absolute percentage error of spatial estimations. In all three conditions, we did not find the typical overestimation or underestimation of size judgments.
Sub-Matrix Factorization for Real-Time Vote Prediction

We address the problem of predicting aggregate vote outcomes (e.g., national) from partial outcomes (e.g., regional) that are revealed sequentially. We combine matrix factorization techniques and generalized linear models (GLMs) to obtain a flexible, efficient, and accurate algorithm. This algorithm works in two stages: First, it learns representations of the regions from high-dimensional historical data. Second, it uses these representations to fit a GLM to the partially observed results and to predict unobserved results. We show experimentally that our algorithm is able to accurately predict the outcomes of Swiss referenda, U.S. presidential elections, and German legislative elections. We also explore the regional representations in terms of ideological and cultural patterns. Finally, we deploy an online Web platform (www.predikon.ch) to provide real-time vote predictions in Switzerland and a data visualization tool to explore voting behavior. A by-product is a dataset of sequential vote results for 330 referenda and 2196 Swiss municipalities.
Benchmarks and goals

Abstract Benchmarks can be a useful step toward the goals of the field (when the benchmark is on the critical path), as demonstrated by the GLUE benchmark, and deep nets such as BERT and ERNIE. The case for other benchmarks such as MUSE and WN18RR is less well established. Hopefully, these benchmarks are on a critical path toward progress on bilingual lexicon induction (BLI) and knowledge graph completion (KGC). Many KGC algorithms have been proposed such as Trans[DEHRM], but it remains to be seen how this work improves WordNet coverage. Given how much work is based on these benchmarks, the literature should have more to say than it does about the connection between benchmarks and goals. Is optimizing P@10 on WN18RR likely to produce more complete knowledge graphs? Is MUSE likely to improve Machine Translation?
Hybrid Fusion Learning

Federated and fusion learning methods are state-of-the-art distributed learning approaches which enable model training without collecting private data from users. While federated learning involves lower computation cost as compared to fusion learning, the overall communication cost is higher due to a large number of communication rounds between the clients and the server. On the other hand, fusion learning reduces the overall communication cost by sending distributions of features and model parameters using only one communication round but suffers from high computation cost as it needs to find the distributions of features at the client. This paper presents hybrid fusion learning, a system that leverages hierarchical client-edge-cloud architecture and builds a deep learning model by integrating both fusion and federated learning methods. Our proposed approach uses fusion learning between the client and the edge layer to minimise the communication cost whereas it uses federated learning between the edge and the cloud layer to minimise the computation cost. Our results show that the proposed hybrid fusion learning can significantly reduce the total time taken to train the model with a small drop of around 2% in accuracies as compared to the other two algorithms. Specifically, our results show that fusion and federated learning algorithms take up to 26.28% and 9.74% higher average total time to build the model, respectively, than the proposed hybrid fusion learning approach.
ON ANNOUNCEMENT FOR UNIVERSITY WHITEBOARD USING MOBILE APPLICATION

&lt;p&gt;The university students who use traditional announcement boards nowadays are exposed to various problems. The most severe problem is that the instructors may want to publish an announcement, but they may not exist in university, or maybe they are after a work time. So, they cannot or can inform to the announcement board moment by moment. Moreover, the students should be near the announcement board all the time. The scope of this research is to build a unique mobile application that acts as a digital university announcement board. Where this application mainly consists of two sides; the server-side (PHP with MySQL) and client-side Android Applications. Through using the proposed application, the university staff can send announcements to students directly, and the announcement will be delivered to student devices in real-time with push notification. Instructors will be able to announce one student or a group of students or all the students from different departments and stages. The suggested application was built and tested in the android operating system and the real mobile device.&lt;/p&gt;
Binary Code Optimized for Partial Encryption

A common technique for the partial encryption of compressed images and videos encrypts only the sign bits of some syntax elements such as the quantized transform coefficients or the motion vector differences. The sign bit can be interpreted as the most significant bit (MSB) in the binary representation of the syntax element. Our work is motivated by the key observation that the binary code used for this representation has an impact on the quality of the reconstruction at the eavesdropper and on the size of the stream to be encrypted. Therefore, we address the problem of optimal binary code design for partial encryption. Ideally, the goal is to simultaneously maximize the eavesdropper's distortion and minimize the length of the compressed MSB stream. Since these two objectives are conflicting in general, we formulate the problem as the maximization of a weighted sum of the eavesdropper's distortion and of the probability of the MSB being 0. We cast the problem as a binary integer linear program equivalent to a maximum weight matching problem, which has a polynomial-time solution algorithm. We show that when the source to be quantized and the quantizer are symmetric, the problem can be converted to a linear program of a smaller size, for a family of distortion metrics. Extensive experiments assess the performance of the optimized binary code in comparison with existing approaches. The results reveal that certain existing partial encryption schemes could benefit from the proposed design.
Construction of Multimodal Chinese Tourism Knowledge Graph

With the development of tourism knowledge graphs (KGs), recommendation, question answering (QA) and other functions under its support enable various applications to better understand users and provide services. Existing Chinese tourism KGs do not contain enough entity information and relations. Besides, the knowledge storage usually contains only the text modality but lacks other modalities such as images. In this paper, a multi-modal Chinese tourism knowledge graph (MCTKG) is proposed based on Beijing tourist attractions to support QA and help tourists plan tourism routes. An MCTKG ontology was constructed to maintain the semantic consistency of heterogeneous data sources. To increase the number of entities and relations related to the tourist attractions in MCTKG, entities were automatically expanded belonging to the concepts of building, organization, relic, and person based on Baidu Encyclopedia. In addition, based on the types of tourist attractions and the styles of tourism route, a tourism route generation algorithm was proposed, which can automatically schedule the tourism routes by incorporating tourist attractions and the route style. Experimental results show that the generated tourist routes have similar satisfaction compared with the tourism routes crawled from specific travel websites.
Robust digital signature revisited

In CT-RSA 2019, Geraud, Naccache and Rosie introduced the notion of robustness (ROB) for digital signature schemes to guarantee that the same signature and message pair cannot be valid under two different public keys. Their definition of complete ROB (CROB) can even support the ROB when the keys are malignantly generated. Motivated by the fact that the signature and the key could be illegally produced in some circumstances, we extended the ROB security one step further to guarantee that one valid signature cannot be modified to another valid signature under a different public key, which we call extreme robustness (EXROB). After analysing the relations between the EXROB security and existing ROB related definitions, we describe generic constructions to convert any digital signature scheme that is unforgeable into an EXROB secure one. Our hash-then-sign construction is very efficient, which only adds one hash calculation to the underlying digital signature scheme and does not increase the size of the signature generated by the underlying digital signature scheme.
A Stream Algebra for Performance Optimization of Large Scale Computer Vision Pipelines

There is a large growth in hardware and software systems capable of producing vast amounts of image and video data. These systems are rich sources of continuous image and video streams. This motivates researchers to build scalable computer vision systems that utilize data-streaming concepts for processing of visual data streams. However, several challenges exist in building large-scale computer vision systems. For example, computer vision algorithms have different accuracy and speed profiles depending on the content, type, and speed of incoming data. Also, it is not clear how to adaptively tune these algorithms in large-scale systems. These challenges exist because we lack formal frameworks for building and optimizing large-scale visual processing. This paper presents formal methods and algorithms that aim to overcome these challenges and improve building and optimizing large-scale computer vision systems. We describe a formal algebra framework for the mathematical description of computer vision pipelines for processing image and video streams. The algebra naturally describes feedback control and provides a formal and abstract method for optimizing computer vision pipelines. We then show that a general optimizer can be used with the feedback-control mechanisms of our stream algebra to provide a common online parameter optimization method for computer vision pipelines.
The SOCIAL Platform and the Integration of Internet of Things Devices to Monitor Activities and Behaviors of Older Adults

Current developments of healthcare technologies made possible innovative ehealth applications that provide the opportunity for personalized care in residential environments. The research study reported by the present chapter aimed to develop for the Social Cooperation for Integrated Assisted Living (SOCIAL) platform, a platform of services to support the care and assistance of community-dwelling older adults, four types of Internet of Things (IoT) monitoring devices (i.e., contact-, motion-, pressure-, and power-monitoring devices) together with analytical procedures to infer important events that might characterize the health status of the care receivers. Moreover, the existence of platform mechanisms such as interoperability, authentication, authorization, logging, and auditing allows an interoperable and secure access to the processed information.
ViBaR: VR Platform Using Kinesthetic Illusions to Enhance Movement Experience

We propose a virtual reality (VR) experience platform based on kinesthetic illusions. While presenting sensation of movement is important for enhancing immersion and presence in VR experience, large devices and complicated systems are typically required. We found the kinesthetic illusion could create the motion of a bar that we hold, and the whole-body movement illusion was also induced with the illusion of the bar. We implemented a VR experience platform using these phenomena, which does not require physical movement. This platform is suitable for home use since it does not require large-scale and complicated devices, and it can provide an experience without body restrictions.
Multiple Hand Gestures for Cursor Movement Using Convolution Neural Networks

Developing a multi-hand gesture to create a magical mouse movement by which we can tab, swipe, zoom-in, zoom-out by using one or more fingers. The machine learning technique can be used to perform all the operations as a state of art. The objective of the proposed algorithm is to use the hand gesture in front of the built-in webcam in the computers or the cellphones which can control the mouse movement. A multilayer convolution neural network is proposed in this paper which considers the pixel-level segmentation to identify the hand region, in the second stage, it identifies the number of fingers used, and in the last stage, the movement detection of the fingers to identify the operations is to be performed.
Fidelity Leakages: Applying Membership Inference Attacks to Preference Data

We apply membership inference attacks in the context of preference data exploited by recommendation systems and show that they can lead to “fidelity leakages”. These leakages allow one service provider to determine whether or not its users are faithful. We first provide experimental results based on real-world data made available by Spotify that confirm the feasibility of such attacks and allow us to put forward their influencing parameters. We then discuss the challenges for interpreting and mitigating fidelity leakages.
ADAL System: Aspect Detection for Arabic Language

Sentiment analysis can be done at different levels of granularity: document, sentence, and aspect. In our case, we are interested in the aspect which presents the finest level of granularity. This level is named Aspect Based Sentiment Analysis. In fact, Aspect Based Sentiment Analysis (ABSA) requires two primordial steps: (i) extract entity aspects and (ii) determine the sentiments from all the aspects. Aspect extraction is an important step for the ABSA. It aims at detecting all the existing aspects in a sentence. The extraction of these aspects is complicated considering the presence of several challenges especially when the Aspect extraction is done in the Arabic language. In this paper, we propose a supervised system ADAL for aspects detection in the Arabic language. The obtained results indicate that our proposed method outperforms previous works to achieve 96% in terms of f-measure when applied to the same dataset provided by The International Workshop on Semantic Evaluation 2016 (SemEval-2016).
Reducing Consumer Uncertainty: Towards an Ontology for Geospatial User-Centric Metadata

With the increased use of geospatial datasets across heterogeneous user groups and domains, assessing fitness-for-use is emerging as an essential task. Users are presented with an increasing choice of data from various portals, repositories, and clearinghouses. Consequently, comparing the quality and evaluating fitness-for-use of different datasets presents major challenges for spatial data users. While standardization efforts have significantly improved metadata interoperability, the increasing choice of metadata standards and their focus on data production rather than potential data use and application, renders typical metadata documents insufficient for effectively communicating fitness-for-use. Thus, research has focused on the challenge of communicating fitness-for-use of geospatial data, proposing a more “user-centric” approach to geospatial metadata. We present the Geospatial User-Centric Metadata ontology (GUCM) for communicating fitness-for-use of spatial datasets to users in the spatial and other domains, to enable them to make informed data source selection decisions. GUCM enables metadata description for various components of a dataset in the context of different application domains. It captures producer-supplied and user-described metadata in structured format using concepts from domain-independent ontologies. This facilitates interoperability between spatial and nonspatial metadata on open data platforms and provides the means for searching/discovering spatial data based on user-specified quality and fitness-for-use criteria.
Estimating the effect of democracy, governance and militarisation on peace in Africa

Peace has been deemed paramount to socioeconomic progress and economic development across nations. It is for this reason nations strive to improve the peaceful coexistence of citizens. This study investigates the effect of democracy, governance and militarisation on peace in 43 African countries for the year 2018 in a cross sectional framework. The ordinary least square (OLS), the tobit regression and the quantile regression (QR) where employed as estimation strategies. The empirical result firstly reveal that democracy increases peace in Africa, particularly in countries where the initial level of peace is at its highest level. Secondly, militarisation of Africa reduces peace in the region only in countries where the initial level of peace is at its highest level. Thirdly, the influence of governance on peace in Africa depends majorly on the measure of governance utilized. The control of corruption, government effectiveness and regulatory quality increases peace where the initial level of peace is at its lowest level. Political stability increases peace across the entire quantiles utilized while rule of law increases peace in countries where the initial level of peace is low. In conclusion, governance in general increases peace in the countries where initial level of peace is very low. Policy recommendations based on these findings are discussed.
Demo: Benchmarking Live Migration Performance of Two Trendy Virtualization Technologies

Live migration is a technology that seamlessly moves a virtualized running service between hosts, which allows services to rapidly adapt to changes. The principal of this technology is that the application downtime is small enough so the end user is not aware of the migration. The two most used off-the-shelf technologies for virtualization are docker for containers and KVM for virtual machines. However, there is still a lack of understanding of its performance in live migration. In this paper, we present a demonstrator that migrates different applications using docker and KVM under different conditions that can be configured manually. The audience will be able to select an application from a pool of applications and the conditions of the migration. Then, the migration will be triggered and the audience will observe different statistics such as the migration time, the downtime, and the resources consumed. Finally, they will be able to compare it to previous runs and observe the impact of the conditions selected.
Prediction of Twitter Traffic Based on Machine Learning and Data Analytics

With the rapidly increasing number of online social network (OSN) users, the study of OSN application-specific mobile network traffic has attracted many research efforts in recent years. In this work, we study the temporal characteristics of Twitter traffic and propose a Twitter traffic prediction framework which combines statistical analytics and machine learning techniques. Experimental results based on real-world Twitter traffic dataset collected in central London have shown that the proposed framework has a high prediction accuracy with low computation complexity and low demand for the size of the dataset.
Sperax: An Approach To Defeat Long Range Attacks In Blockchains

Since Bitcoin's seminal work of achieving consensus under the assumption that more than 51% computational power is honest, many researchers have proposed various kinds of consensus protocols. In recent years, there has been a trend in designing proof of stake (PoS) based consensus protocols. Unfortunately, PoS based consensus protocols are inherently not secure and are vulnerable to “long range attacks”. Thus PoS based blockchains have the potential risk of being forked with a minimal cost for double spending. In this paper, we propose a PoS protocol that is secure against long range attack based double spending. The security is achieved using secure randomness beacons generated by tamper proof hardware modules.
“Dude, that Sucks”: Examining Scrum’s Influence on Empathy in Student Teams

The role of empathy in student team collaborations in technical and professional communication has been understudied. In this mixed methods study, we assess how Scrum affects both student perceptions of empathy and student use of empathetic strategies. We found that students who used Scrum considered themselves to be no more empathetic than students who did not use Scrum. However, a discourse analysis revealed that students who used Scrum deployed significantly more empathetic strategies than students who did not use Scrum.
Quaternion-Based Trajectory Optimization of Human Postures for Inducing Target Muscle Activation Patterns

In exercise and rehabilitation, to effectively train the human body, human motion trajectory is essential because it induces muscle activity patterns. In this letter, we develop a novel framework for the trajectory optimization of human postures, including the head, the limbs, and the body to induce patterns of target muscle activities. Our framework has the following features: 1) a data-driven muscle-skeleton model for managing user-specific features; 2) quaternion-based state representation amenable for IMU sensors in human posture measurement; 3) joint optimization of human postures to replicate therapists who adjust not only paralyzed limbs but also patient's other limbs and body postures. We experimentally investigated the effectiveness of our framework with a shoulder joint assistive exoskeleton robot for rehabilitation.
A Binary Superior Tracking Artificial Bee Colony for Feature Selection

Feature selection is a NP-hard combinatorial problem of selecting the effective features from a given set of original features to reduce the dimension of dataset. This paper aims to propose an improved variant of learning algorithm for feature selection, termed as Binary Superior Tracking Artificial Bee Colony (BST-ABC) algorithm. In BST-ABC, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension for exploitation capacity enhancement. Ten datasets from UCI repository are adopted as test problems, and the results of BST-ABC are compared with particle swarm optimization (PSO) and original ABC. Experimental results demonstrate that BST-ABC could obtain the optimal classification accuracy and the minimum number of features.