Reviewerly/exHarmony huggingface dataset:
Methodological Considerations Concerning Motion Sickness Investigations during Automated Driving

Automated driving vehicles will allow all occupants to spend their time with various non-driving related tasks like relaxing, working, or reading during the journey. However, a significant percentage of people is susceptible to motion sickness, which limits the comfort of engaging in those tasks during automated driving. Therefore, it is necessary to investigate the phenomenon of motion sickness during automated driving and to develop countermeasures. As most existing studies concerning motion sickness are fundamental research studies, a methodology for driving studies is yet missing. This paper discusses methodological aspects for investigating motion sickness in the context of driving including measurement tools, test environments, sample, and ethical restrictions. Additionally, methodological considerations guided by different underlying research questions and hypotheses are provided. Selected results from own studies concerning motion sickness during automated driving which were conducted in a motion-based driving simulation and a real vehicle are used to support the discussion.
Comparing the Quality and Speed of Sentence Classification with Modern Language Models

After the advent of Glove and Word2vec, the dynamic development of language models (LMs) used to generate word embeddings has enabled the creation of better text classifier frameworks. With the vector representations of words generated by newer LMs, embeddings are no longer static but are context-aware. However, the quality of results provided by state-of-the-art LMs comes at the price of speed. Our goal was to present a benchmark to provide insight into the speed–quality trade-off of a sentence classifier framework based on word embeddings provided by selected LMs. We used a recurrent neural network with gated recurrent units to create sentence-level vector representations from word embeddings provided by an LM and a single fully connected layer for classification. Benchmarking was performed on two sentence classification data sets: The Sixth Text REtrieval Conference (TREC6)set and a 1000-sentence data set of our design. Our Monte Carlo cross-validated results based on these two data sources demonstrated that the newest deep learning LMs provided improvements over Glove and FastText in terms of weighted Matthews correlation coefficient (MCC) scores. We postulate that progress in LMs is more apparent when more difficult classification tasks are addressed.
Collective Perception Using UAVs: Autonomous Aerial Reconnaissance in a Complex Urban Environment

This article examines autonomous reconnaissance in a complex urban environment using unmanned aerial vehicles (UAVs). Environments with many buildings and other types of obstacles and/or an uneven terrain are harder to be explored as occlusion of objects of interest may often occur. First, in this article, the problem of autonomous reconnaissance in a complex urban environment via a swarm of UAVs is formulated. Then, the algorithm based on the metaheuristic approach is proposed for a solution. This solution lies in deploying a number of waypoints in the area of interest to be explored, from which the monitoring is performed, and planning the routes for available UAVs among these waypoints so that the monitored area is as large as possible and the operation as short as possible. In the last part of this article, two types of main experiments based on computer simulations are designed to verify the proposed algorithms. The first type focuses on comparing the results achieved on the benchmark instances with the optimal solutions. The second one presents and discusses the results obtained from a number of scenarios, which are based on typical reconnaissance operations in real environments.
Webcam controlled robotic arm for persons with SSMI

Background: People with severe speech and motor impairment (SSMI) often uses a technique called eye pointing to communicate with outside world. One of their parents, caretakers or teachers hold a printed board in front of them and by analyzing their eye gaze manually, their intentions are interpreted. This technique is often error prone and time consuming and depends on a single caretaker. Objective: We aimed to automate the eye tracking process electronically by using commercially available tablet, computer or laptop and without requiring any dedicated hardware for eye gaze tracking. The eye gaze tracker is used to develop a video see through based AR (augmented reality) display that controls a robotic device with eye gaze and deployed for a fabric printing task. Methodology: We undertook a user centred design process and separately evaluated the web cam based gaze tracker and the video see through based human robot interaction involving users with SSMI. We also reported a user study on manipulating a robotic arm with webcam based eye gaze tracker. Results: Using our bespoke eye gaze controlled interface, able bodied users can select one of nine regions of screen at a median of less than 2 secs and users with SSMI can do so at a median of 4 secs. Using the eye gaze controlled human-robot AR display, users with SSMI could undertake representative pick and drop task at an average duration less than 15 secs and reach a randomly designated target within 60 secs using a COTS eye tracker and at an average time of 2 mins using the webcam based eye gaze tracker.
Enhanced Discrete Multi-Modal Hashing: More Constraints Yet Less Time to Learn

Due to the exponential growth of multimedia data, multi-modal hashing as a promising technique to make cross-view retrieval scalable is attracting more and more attention. However, most of the existing multi-modal hashing methods either divide the learning process unnaturally into two separate stages or treat the discrete optimization problem simplistically as a continuous one, which leads to suboptimal results. Recently, a few discrete multi-modal hashing methods that try to address such issues have emerged, but they still ignore several important discrete constraints (such as the balance and decorrelation of hash bits). In this paper, we overcome those limitations by proposing a novel method named &#x201C;Enhanced Discrete Multi-modal Hashing (EDMH)&#x201D; which learns binary codes and hashing functions simultaneously from the pairwise similarity matrix of data, under the aforementioned discrete constraints. Although the model of EDMH looks a lot more complex than the other models for multi-modal hashing, we are actually able to develop a fast iterative learning algorithm for it, since the subproblems of its optimization all have closed-form solutions after introducing a couple of auxiliary variables. Our experimental results on three real-world datasets have revealed the usefulness of those previously ignored discrete constraints and demonstrated that EDMH not only performs much better than state-of-the-art competitors according to several retrieval metrics but also runs much faster than most of them.
Blockchain Based Digital Evidence Chain of Custody

With the development of technology, the preservation of digital evidence becomes increasingly important in case investigations. To maintain the authenticity of an evidence, its entire lifecycle has to be recorded. In addition, traditional database technologies are not able to maintain the integrity and authenticity of digital evidence. In order to achieve authentication and integrity, as well as confidentiality, of digital evidence, we propose a protocol for digital evidence chain of custody based on revocable ciphertextpolicy attribute-based encryption, BLS signature, and blockchain technology. In our protocol, attribute-based encryption is used to achieve fine-grained access control and BLS signature is used to verify digital evidence. Besides, we use blockchain technology to ensure the integrity and traceability of digital evidence. Analysis and experimental results show that the proposed protocol, which well balances the privacy and the traceability, guarantees the integrity and validity of evidence.
Mouillé: Exploring Wetness Illusion on Fingertips to Enhance Immersive Experience in VR

Providing users with rich sensations is beneficial to enhance their immersion in Virtual Reality (VR) environments. Wetness is one such imperative sensation that affects users' sense of comfort and helps users adjust grip force when interacting with objects. Researchers have recently begun to explore ways to create wetness illusions, primarily on a user's face or body skin. In this work, we extended this line of research by creating wetness illusion on users' fingertips. We first conducted a user study to understand the effect of thermal and tactile feedback on users' perceived wetness sensation. Informed by the findings, we designed and evaluated a prototype---Mouillé---that provides various levels of wetness illusions on fingertips for both hard and soft items when users squeeze, lift, or scratch it. Study results indicated that users were able to feel wetness with different levels of temperature changes and they were able to distinguish three levels of wetness for simulated VR objects. We further presented applications that simulated an ice cube, an iced cola bottle, and a wet sponge, etc, to demonstrate its use in VR.
Free viewpoint image generation system using fisheye cameras and a laser rangefinder for indoor robot teleoperation

Abstract In robot teleoperation, a lack of depth information often results in collisions between the robots and obstacles in its path or surroundings. To address this issue, free viewpoint images can greatly benefit the operators in terms of collision avoidance as the operators are able to view the robot’s surrounding from the images at arbitrary points, giving them a better depth information. In this paper, a novel free viewpoint image generation system is proposed. One approach to generate free viewpoint images is to use multiple cameras and Light Detection and Ranging (LiDAR). Instead of using the expensive LiDAR, this study utilizes a cost-effective laser rangefinder (LRF) and a characteristic of man-made environments. In other words, we install multiple fisheye cameras and an LRF on a robot. Free viewpoint images are generated under the assumption that walls are perpendicular to the floor. Furthermore, an easy calibration for estimating the poses of the multiple fisheye cameras, the LRF, and the robot model is proposed. Experimental results show that the proposed method can generate free viewpoint images using cameras and an LRF. Finally, the proposed method is primarily implemented using OpenGL Shading Language to utilize a graphics processing unit computation to achieve a real-time processing of the multiple high-resolution images. Supplementary videos and our source code are available at our project page ( https://matsuren.github.io/fvp ).
How We Type: Eye and Finger Movement Strategies in Mobile Typing

Relatively little is known about eye and finger movement in typing with mobile devices. Most prior studies of mobile typing rely on log data, while data on finger and eye movements in typing come from studies with physical keyboards. This paper presents new findings from a transcription task with mobile touchscreen devices. Movement strategies were found to emerge in response to sharing of visual attention: attention is needed for guiding finger movements and detecting typing errors. In contrast to typing on physical keyboards, visual attention is kept mostly on the virtual keyboard, and glances at the text display are associated with performance. When typing with two fingers, although users make more errors, they manage to detect and correct them more quickly. This explains part of the known superiority of two-thumb typing over one-finger typing. We release the extensive dataset on everyday typing on smartphones.
HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks

The unsupervised detection of anomalies in time series data has important applications in user behavioral modeling, fraud detection, and cybersecurity. Anomaly detection has, in fact, been extensively studied in categorical sequences. However, we often have access to time series data that represent paths through networks. Examples include transaction sequences in financial networks, click streams of users in networks of cross-referenced documents, or travel itineraries in transportation networks. To reliably detect anomalies, we must account for the fact that such data contain a large number of independent observations of paths constrained by a graph topology. Moreover, the heterogeneity of real systems rules out frequency-based anomaly detection techniques, which do not account for highly skewed edge and degree statistics. To address this problem, we introduce HYPA, a novel framework for the unsupervised detection of anomalies in large corpora of variable-length temporal paths in a graph. HYPA provides an efficient analytical method to detect paths with anomalous frequencies that result from nodes being traversed in unexpected chronological order.
Learning Programming Language in Higher Education for Sustainable Development: Point-Earning Bidding Method

Promoting learning passion for students in higher education is a vital and challenging issue. Due to the swift changes in society and technology development, many convenient and interesting technologies increasingly interfere with students’ learning performances and reduce students’ motivation for attending classes. For example, mobile games and social networking have led students to lose their passion for learning. MAPS (Mind Mapping, Asking Questions, Presentation, Scaffolding Instruction) is a flipped teaching model which has been proved as an effective approach to increasing students’ reflective learning, allowing students to devote to self-learning and recovering their passion for learning. Thus, this study employed a MAPS teaching strategy that adopts a point-earning approach to encourage students to learn from peer feedback while the teacher can understand students’ learning from the process. When the class is over, students can exchange the points for other rewards through a bidding mechanism which encourages students to regain passion for learning. The concept of Scaffolding Instruction points out that allowing students to sense positive peer pressure, which is caused by their peers’ improvements, and the atmosphere where their peers are studying hard can enhance their learning motivation and reflection. The empirical results of this study found that peer assessment and feedback can improve the learning effectiveness of students with poor performance.
De-Health: All Your Online Health Information Are Belong to Us

In this paper, we study the privacy of online health data. We present a novel online health data De-Anonymization (DA) framework, named De-Health. Leveraging two real world online health datasets WebMD and HealthBoards, we validate the DA efficacy of De-Health. We also present a linkage attack framework which can link online health/medical information to real world people. Through a proof-of-concept attack, we link 347 out of 2805 WebMD users to real world people, and find the full names, medical/health information, birthdates, phone numbers, and other sensitive information for most of the re-identified users. This clearly illustrates the fragility of the privacy of those who use online health forums.
Attention Network for Non-Uniform Deblurring

Recently, image deblurring task is valuable and challenging in computer vision. However, existing learning-based methods can not produce satisfactory results, such as lacking of salient structures and fine details. In this paper, we propose a solution to transform spatially variant blurry images into the photo-realistic sharp manifold. In this paper, we investigate an attention network for image deblurring. Instead of relying on local receptive fields to construct features by previous state-of-the-art methods, the non-local features for capturing long-range dependencies and the local features rely on receptive fields should be jointly considered. Therefore, we propose a novel dense feature fusion block that consists of a channel attention module and a pixel attention module. In addition, we further densely connected multiple dense feature fusion blocks to acquire high-order feature representation. Moreover, a scale attention module is further introduced for removing unfavorable features while retaining features that facilitate image recovery. Comprehensive experimental results show that our method is able to generate photo-realistic sharp images from real-world blurring images and outperforms state-of-the-art methods.
Menumbuhkan minat baca anak MI/SD dengan media buku bergambar seri

Interest in reading Indonesian people, especially the level of MI/SD children is still relatively low because they prefer playing online games, watching television etc. This factor causes their ability to read is still lacking. In addition, there are reading books (both storybooks and textbooks) in school institutions that are still monotonous with colourless pictures so that children are easily bored and are not interested in reading them. Based on the above phenomenon, the reading interest of MI/SD children must be increased by the teacher because "books are a repository of knowledge and reading can open the horizons of the world". The use of learning media to foster children's interest in reading is with a picture book series. This series of picture books is more developed if the teacher dares to be creative by making colourful pictures equipped with simple sentences that can interest children to read them. This series picture book is effectively used in learning elementary school children because it contains games and also education. In the picture book series, there are several learning activities including observing, telling, reading and sorting pictures so that it becomes a complete story. Furthermore, teachers can add/collaborate on strategies or methods that can support the use of series picture books to be more innovative in each lesson.
Evidence-based investment selection: Prioritizing agricultural development investments under climatic and socio-political risk using Bayesian networks

Agricultural development projects have a poor track record of success mainly due to risks and uncertainty involved in implementation. Cost-benefit analysis can help allocate resources more effectively, but scarcity of data and high uncertainty makes it difficult to use standard approaches. Bayesian Networks (BN) offer a suitable modelling technology for this domain as they can combine expert knowledge and data. This paper proposes a systematic methodology for creating a general BN model for evaluating agricultural development projects. Our approach adapts the BN model to specific projects by using systematic review of published evidence and relevant data repositories under the guidance of domain experts. We evaluate a large-scale agricultural investment in Africa to provide a proof of concept for this approach. The BN model provides decision support for project evaluation by predicting the value-measured as net present value and return on investment-of the project under different risk scenarios.
Discovering Image Manipulation History by Pairwise Relation and Forensics Tools

Given a potentially manipulated probe image, provenance analysis aims to find all images derived from the probe (offsprings) and all images from which the probe is derived (ancestors) in a large dataset (provenance filtering), and reconstruct the manipulation history with the retrieved images (provenance graph building). In this paper, we address two major challenges in provenance analysis, retrieving the source image of the small regions that are spliced into the probe image, and, detecting source images within the search results. For the former challenge, we propose to detect spliced regions by pairwise image comparison and only use local features extracted from the spliced region to perform an additional search. This removes the influence of the background and greatly improves the recall. For the latter, we propose to learn a pairwise ancestor-offspring detector and use it jointly with a holistic image manipulation detector to identify the source image. The proposed provenance analysis system has performed remarkably in evaluations using comprehensive provenance datasets. It's the winning solution for NIST Media Forensics Challenge (MFC) in 2018, 2019 and 2020. In MFC 2019, our provenance results achieved a 12% improvement in filtering and a 20% gain in oracle provenance graphs building over the alternative methods. In the real-world Reddit dataset, the edge overlap between our reconstructed provenance graphs and the ground-truth graphs is 5 times better than the state-of-the-art system.
Visual Relations Augmented Cross-modal Retrieval

Retrieving relevant samples across multiple-modalities is a primary topic that receives consistently research interests in multimedia communities, and has benefited various real-world multimedia applications (e.g., text-based image searching). Current models mainly focus on learning a unified visual semantic embedding space to bridge visual contents & text query, targeting at aligning relevant samples from different modalities as neighbors in the embedding space. However, these models did not consider relations between visual components in learning visual representations, resulting in their incapability of distinguishing images with the same visual components but different relations (i.e., Figure 1). To precisely modeling visual contents, we introduce a novel framework that enhanced visual representation with relations between components. Specifically, visual relations are represented by the scene graph extracted from an image, then encoded by the graph convolutional neural networks for learning visual relational features. We combine the relational and compositional representation together for image-text retrieval. Empirical results conducted on the challenging MS-COCO and Flicker 30K datasets demonstrate the effectiveness of our proposed model for cross-modal retrieval task.
Sparse Regularization-Based Approach for Point Cloud Denoising and Sharp Features Enhancement

Denoising the point cloud is fundamental for reconstructing high quality surfaces with details in order to eliminate noise and outliers in the 3D scanning process. The challenges for a denoising algorithm are noise reduction and sharp features preservation. In this paper, we present a new model to reconstruct and smooth point clouds that combine L1-median filtering with sparse L1 regularization for both denoising the normal vectors and updating the position of the points to preserve sharp features in the point cloud. The L1-median filter is robust to outliers and noise compared to the mean. The L1 norm is a way to measure the sparsity of a solution, and applying an L1 optimization to the point cloud can measure the sparsity of sharp features, producing clean point set surfaces with sharp features. We optimize the L1 minimization problem by using the proximal gradient descent algorithm. Experimental results show that our approach is comparable to the state-of-the-art methods, as it filters out 3D models with a high level of noise, but keeps their geometric features.