Reviewerly/exHarmony huggingface dataset:
A Method for Automated Detection of Cultural Difference Based on Image Similarity

In intercultural collaboration, the lack of a common ground, typically evidenced by language differences, can result in misunderstandings. Many times, team members do not realize that a misunderstanding exists during the collaboration. One solution is to identifying the words that have a high probability of causing misunderstanding. However, it is difficult for people to identify those words, especially for monolingual and monocultural people, as they have never experienced the language and culture of the other party. Many researchers have been trying to identify cultural differences using survey studies but the resulting coverage is limited, requires excessive effort, and can yield bias. In this paper, we propose a novel method that applies an image comparison technique to an image database to automatically detect words that might cause misunderstanding. We test our method on 2,500 words in a Japanese-English concept dictionary called Japanese WordNet. This paper provides explains the results gained. We also discuss the use of the proposal and visualization as a support tool to enhance intercultural workshops.
Investigating Attack Propagation in a SoS via a Service Decomposition

A term systems of systems (SoS) refers to a setup in which a number of independent systems collaborate to create a value that each of them is unable to achieve independently. Complexity of a SoS structure is higher compared to its constitute systems that brings challenges in analyzing its critical properties such as security. An SoS can be seen as a set of connected systems or services that needs to be adequately protected. Communication between such systems or services can be considered as a service itself, and it is the paramount for establishment of a SoS as it enables connections, dependencies, and a cooperation. Given that reliable and predictable communication contributes directly to a correct functioning of an SoS, communication as a service is one of the main assets to consider. Protecting it from malicious adversaries should be one of the highest priorities within SoS design and operation. This study aims to investigate the attack propagation problem in terms of service-guarantees through the decomposition into sub-services enriched with preconditions and postconditions at the service levels. Such analysis is required as a prerequisite for an efficient SoS risk assessment at the design stage of the SoS development life cycle to protect it from possibly high impact attacks capable of affecting safety of systems and humans using the system.
BioID: A Privacy-Friendly Identity Document

We design a suite of protocols so that a small tamper-resistant device can be used as a biometric identity document which can be scanned by authorized terminals. We target both strongly secure identification and strong privacy. Unlike biometric passports, our protocols leak no digital evidence and are essentially deniable. Besides, getting the identity information from the device requires going through access control. Access control can follow either a strong PKI-based path or a weak password-based path which offer different functionalities. We implemented our protocols on JavaCard using finger-vein recognition as a proof of concept.
On the Use of Persistent Spatial Points for Deploying Path Navigation in Augmented Reality: An Evaluation Study

People use various techniques and tools to perform spatial navigation tasks, such as asking a local person for instructions in order to reach a destination or creating a detailed route plan for their trip through technology-mediated tools. Novel technologies, such as augmented reality, have been introduced to improve the performance and enhance the experience of the end-users. To provide navigation experiences with improved accuracy and decent stability, the developers of such tools can use persistent spatial points, which are stationary points in the real world that an augmented-reality system should keep track of over time. However, the use of persistent spatial points can dramatically increase the development effort, as it requires additional and time-consuming actions to be made by the developers. In this paper, we investigate the use of persistent spatial points for navigation in an AR environment from a developer and an end-user perspective, aiming to understand the trade-off between the development effort, the user performance, and the user experience. We report an empirical study in which a software engineer developed two versions of an augmented-reality navigation application (one with persistent spatial points and one without) which were used by twenty-eight individuals to navigate. Our study results revealed a trade-off between the development effort, the user performance, and the user experience, which depends on the length of the navigation path. The shorter the path is, the less the need for persistent spatial points is, while, on the other hand, the longer the path is, the more critical it is to use persisting spatial points. Based on the results, we discuss ways of mitigating the development effort while maintaining high user performance and experience.
Exploring the Challenges of Integrating Data Science Roles in Agile Autonomous Teams

The notion of autonomous teams is core to agile software development. However, autonomy in agile teams is challenged by increasingly complex software projects, large-scale agile and perhaps increasingly multidisciplinary teams. At the same time, data science roles are making their way into agile teams as companies seek to reap the potential advantages of using data to develop better and more competitive services and products. The consequences of implementing such roles in traditional agile teams are largely unknown. In this paper, we take an exploratory approach to the topic of data science roles in agile teams by a set of interviews with five data scientists as well as three members of an agile software development team. Based on the interviews we identify a set of challenges associated with incorporating the role in agile autonomous teams. Based on these challenges we discuss preliminary recommendations for companies seeking to integrate data science roles in agile teams.
ViDA: A Visual System of DFA Process for Interactive Surface

Interactive surface integrates geometry, structure, materials and electronic subsystems that requires innovative design methods and processes. Additionally, with the development of information technology and smart materials, these surfaces require tailor-made constructive process (in both fabrication and assembly stages) in order to testify the prototype proposed. Along with design, a prototyping framework called DFA is defined and a visual system called ViDA (VIsual system of DfA) for enhancing the experimental process in proposed in this paper. Five experiments are conducted and two cases are described in details for ViDA.
Comparing Traceability through Information Retrieval, Commits, Interaction Logs, and Tags

Context and motivation: Traceability is used to follow and understand the relationships between various software engineering artifacts such as requirements and source code. Comprehensive traceability of software engineering artifacts is important to ensure that a software can be further developed or maintained. Traceability links are often created manually by using commit ids or retrospectively by using information retrieval (IR). Question/Problem: However, creating traceability links manually is costly and it is error-prone in retrospect. As part of our ongoing research on feature management where traceability is also of interest, we use a lightweight tagging approach to relate artifacts. We want to investigate how such a tag-based approach compares to approaches using commit ids, interaction logs (IL), and IR for creating traceability links. Principal ideas/results: We conducted a case study in which students applied the tag-based, the IL-based, and the commit-based approach. We transformed the tags to traceability links and compared them with the commit-based and IL-based approach as well as with IR-based approaches, using a gold standard. We applied different improvements. Contribution: This is the first study comparing four completely different traceability approaches in one project. The combination of IL and commit ids shows the best precision and recall but is intrusive. The other approaches differ less in precision and recall and both need improvement for practical application. We discuss the benefits and drawbacks of the different approaches and state implications for research and practice.
Novel Approach for Image Text Recognition and Translation

One of the most concerned problems of today is to exactly translate the text present in an image to a human readable text. This has been gaining attention these days because of the immense work done by the Computer Vision Community. The main important concept behind this technology is something called as OCR - Optical Character Recognition. With the help of the OCR, we can search and recognize the text in electronic documents and can easily convert them into human readable text. It converts electronic documents' text into related ASCII character and if the document is a handwritten one, then the OCR uses database to recognize what character it is and try to solve it to its highest accuracy.
Once Upon a Time in a Land Far Away: Guidelines for Spatio-Temporal Narrative Visualization

Creating a visualization that conveys a narrative requires choosing the dimensions and features that help tell the story. Time and space are two of these storytelling attributes which are commonly present in the story's structure. Thus, these should be considered in the creation process. Narrative Visualization is still a new field in Information Visualization research, and while there are guidelines for designing visualizations, specific ones for this new area are still lacking. Therefore, supported by previous research on broad recommendations for designing visualizations, we propose a specific set of guidelines to structure effective visual narratives divided into four decision categories: Intent, Spatio-temporal, Interaction, and Narrative Elements.
Anomaly Detection in Vehicle Traffic Data Using Batch and Stream Supervised Learning

Anomaly detection in the execution time of vehicular traffic routes is important in smart cities context. This is due to identified anomaly situations allow replanning traffic in large cities. However, this is a problem of difficult treatment, since it is difficult to label such data. This fact leads to some works in literature tackle the problem using unsupervised or semi-supervised learning algorithms. Techniques for labeling data allow using supervised learning. On the other hand, data streams raises questioning the performance of batch and stream learning algorithms. The purpose of this work is to present a process of anomaly detection in the execution time of vehicle traffic routes. Part of the proposed process was evaluated, using data collected in real scenarios. The results were promising for two of the evaluated routes.
SafeDB: Spark Acceleration on FPGA Clouds with Enclaved Data Processing and Bitstream Protection

This paper proposes SafeDB: Spark Acceleration on FPGA Clouds with Enclaved Data Processing and Bitstream Protection. SafeDB provides a comprehensive and systematic hardware-based security framework from the bitstream protection to data confidentiality, especially for the cloud environment. The AES key shared between FPGA and client for the bitstream encryption is generated in hard-wired logic using PKI and ECC. The data security is assured by the enclaved processing with encrypted data, meaning that the encrypted data is processed inside the FPGA fabric. Thus, no one in the system is able to look into clients' data because plaintext data are not exposed to memory and/or memory-mapped space. SafeDB is resistant not only to the side channel attack but to the attacks from malicious insiders. We have constructed an 8-node cluster prototype with Zynq UltraScale+ FPGAs to demonstrate the security, performance, and practicability.
Power and Time Aware VM Migration for Multi-Tier Applications over Geo-Distributed Clouds

This paper proposes a virtual machine (VM) migration model to reduce the power consumption while migrating a set of VMs over geo-distributed clouds. We develop an approach to find out the migration path across different Internet Service Providers (ISPs) leading to the most feasible destination. For this, we make use of the variation in the electricity price at the ISPs for deciding the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. Hence, we propose an Ant Colony Optimization (ACO) based bi-objective optimization technique to strike a balance between the power consumption and the migration time to make the implementation realistic. Thorough simulation analysis of the proposed approach shows that it can achieve low power consumption cost with acceptable migration time.
Face Alignment by Discriminative Feature Learning

In this paper, we study the effect of discriminative feature learning for face alignment. We claim that features of the same facial landmarks on different images should share similarities at the feature-level. Thus, we propose the Discriminative Feature Learning method (DFL) for face alignment based on the Fully Convolutional Network (FCN). First, the face image is aligned frontal to eliminate the effect of pose and scale. Second, landmark-specific features are extracted from feature maps of the FCN. A distance constraint on landmark features is added to learn discriminative landmark features. Our experiment results show that DFL can effectively improve the performance of face alignment.
Edge Detection Algorithm based on Morphology and Grey Relation Analysis

Aiming at the problem that the traditional edge detection operator are sensitive to noise and the positioning accuracy is not high, an edge detection algorithm based on morphology and grey relation analysis is designed, Grey relation analysis is used to denoise pre-processing of images, Ostu is used to adaptively generate the binarized image, a computing model based on mathematical morphology is designed to detect edges. The simulation results show that the edge detection algorithm proposed in this paper has stronger anti-noise ability and edge location ability than the traditional edge detection operators, which can provide reference for edge detection in the field of artificial intelligence.
Road Machinery Fault Prediction Based on Big Data and Machine Learning

Road machinery is a significant essential equipment for earthwork, base course, surface layer and maintenance in road construction, such as paver, roller and so on. Machinery fault is an important factor influencing the construction efficiency. This is a time of abundant informatization infrastructure facilities, based on this background, massive data regarding multi-dimensional operation of the construction machinery industry can be acquired by the Internet of Things (hereafter referred to as IOT) or cloud processing technology, at the same time, the data of fault service module belong to CRM system is used as sample data to establish neural network model for machine learning. In so doing, we can get the rule on fault prediction of each subsystem of the road machinery, and establishing a diagnose system to synchronize with the cloud server; this diagnose system will conduct real-time fault prediction on the road machinery served by the IOT cloud server, which can dramatically decreasing the idle loss caused by machinery fault. It is an efficient way to transfer from fault dealing to fault prediction and prevention of road construction machinery.
Advanced Fuzzy Set: An Application to Flat Electroencephalography Image

Epileptic seizures refer to temporary disturbance in the electrical activity of the brain. The real time electrical activities of the cortical and subcortical neuronal activity are recorded by using Electroencephalogram (EEG) whereby few specific electrodes are placed on the scalp. EEG measures the differential voltage fluctuations resulting from ionic current flows within the neurons of the brain and can detect the changes over milliseconds. In this study, the image form of the EEG signals known as Flat EEG image is carried out. The advanced fuzzy techniques namely intuitionistic fuzzy set (IFS) and type-2 fuzzy set are explored to enhance the image of Flat EEG. The parameter in intuitionistic fuzzy image is optimized using intuitionistic fuzzy entropy. Whereas Hamacher t-conorm is applied for type-2 fuzzy enhancement. Experimental results on Flat EEG input images at two different time show that type-2 produced better output images compared to intuitionistic fuzzy methods.
Explorative Visualization of Food Data to Raise Awareness of Nutritional Value

The following research proposes a web based, interactive data visualization of a large food dataset, with an aim to raise awareness of their nutritional value. The system consists of an automatic dimensionality reduction scheme, based on t-SNE algorithm, and an exploratory user interface based on dynamic views of the data. Our approach allows users to effectively compare and relate visual representations of nutritional value in both micro and macro levels. The communicativeness of the application was evaluated regarding user experience and the storytelling functionality. Results show that the participants experienced a shift in perception from one mainly focused on taste to one aware of nutritional aspects. Additionally, the results suggest that a narrative experience can be generated, along with an effective long-term engagement.
A New Framework for Studying Tubes Rearrangement Strategies in Surveillance Video Synopsis

The manual review of raw surveillance video is a time consuming task which can be optimized by using a Video Synopsis (VS) algorithm. The aim of such approaches is to condense a long video into shorter one to allow a quicker review of surveillance data. However, VS is a complex problem. A typical object-based VS algorithm requires three main modules to perform the following tasks: object detection and tracking, tubes rearrangement, condensed video generation. Although the aforementioned three steps are equally critical, we realized that the core of Video Synopsis lies in the tubes rearrangement. This led us to propose an original approach to tackle the problem of tubes rearrangement. To this aim, we first introduce a new toolbox to generate a proper testing dataset, which allows to bypass the lack of public databases including proper annotated videos for testing synopsis approaches. Additionally, we propose an improvement of a tubes arrangement algorithm based on graph colouring and we prove its validity on our generated dataset. For a proper comparison, we show that our algorithm also outperforms the original one on UA-DETRAC public dataset.
A Practical Planning Framework and Its Implementation for Autonomous Navigation in Off-road Environment

In this paper, we introduce a practical two-layer planning framework for autonomous vehicles operating in unknown off-road environment. The first layer refers to a global path planning layer, searching a shortest global path from road network according to given task points. We build up road network through pre-processing on Google earth and refactoring network in terms of vehicle real historical trajectory on real-time. The second layer refers to a local planning layer, solving a real-time planning problem to generate a collision-free and kinematic-feasible local path by a hybrid trajectory planning method. Our method has been verified in real off-road environment. Experimental results show that the proposed planning method performs well in off-road environment.
Relative Privacy Threats and Learning From Anonymized Data

We consider group-based anonymization schemes, a popular approach to data publishing. This approach aims at protecting privacy of the individuals involved in a dataset, by releasing an obfuscated version of the original data, where the exact correspondence between individuals and attribute values is hidden. When publishing data about individuals, one must typically balance the learner's utility against the risk posed by an attacker, potentially targeting individuals in the dataset. Accordingly, we propose a unified Bayesian model of group-based schemes and a related MCMC methodology to learn the population parameters from an anonymized table. This allows one to analyze the risk for any individual in the dataset to be linked to a specific sensitive value, when the attacker knows the individual's nonsensitive attributes, beyond what is implied for the general population. We call this relative threat analysis. Finally, we illustrate the results obtained with the proposed methodology on a real-world dataset.
A New Data Selection Strategy for One-Shot Video-Based Person Re-Identification

Person re-identification under one-shot setting is an under-researched semi-supervised task in essence, in which label estimation and data selection are two key modules. In this paper, we propose an effective strategy named Collaboration between the Labeled and Unlabeled(CLU) to estimate pseudo labels and select reliable data progressively. In CLU, label estimation is an iterative process where class center representations are estimated by high-scored unlabeled data and corrected by corresponding labeled data. Then reliable data are selected based on confidence scores decided by both labeled and unlabeled data actually. Moreover, a stepwise learning framework for one-shot video-based person re-identification named SCLU is implemented to validate the effectiveness of CLU. Extensive experiments conducted on MARS and DukeMTMC-VideoReID illustrate that CLU generates pseudo labels of high quality and facilitates the subsequent model training. Meanwhile, the developed scheme SCLU yields the state-of-the-art results on the two datasets.
Thermal Face Recognition Based on Physiological Information

In this paper, we propose a novel thermal face recognition based on physiological information. The training phase includes preprocessing, feature extraction and classification. In the beginning, the human face can be depicted from the background of thermal image using the Bayesian framework and normalized to uniform size. A grid of 22 thermal points is extracted as a feature vector. These 22 extracted points are used to train Linear Support Vector Machine Classifier (linear SVC). The classifier calculates the support vectors and uses them to find the hyperplane for classification. A feature vector of testing image is inputted to the classifier for face recognition. Our contribution is that the proposed method firstly applies temperature information in face recognition. Experimental results prove the effectiveness of the proposed method.