Reviewerly/exHarmony huggingface dataset:
A Gradient heatmap based Table Structure Recognition

Most methods to recognize the structure of a table are to use the object detection approach to directly locate each cell in the table or to segment the table line based on the fully convolutional network (FCN). The problem of the former is that it is laborious to recognize the distorted table, while the problem of the latter is that the sample imbalance makes it difficult to train the model. In this paper, a gradient heatmap based table structure recognition method is proposed, by exploring the gradient heatmaps of the vertical lines and horizontal lines in the table. Specifically, the pixels of the vertical lines of the table are obtained according to the gradient heatmap, then the pixels of the horizontal lines are obtained using the same method, and finally the table structure is restored by using the connected domain search method. Compared with the Single Shot MultiBox Detector (SSD) and Faster RCNN that directly detects cells, our Average Precision (AP) value reached up to 99.5%, which is much higher than the above models. Additionally, we demonstrate that the AP values of the proposed models are reduced almost negligibly when the IoU threshold increased from 0.5 to 0.75, while the AP value of the fast RCNN and SSD model decreased significantly.
Adaptation to Unknown Situations as the Holy Grail of Learning-Based Self-Adaptive Systems: Research Directions

Self-adaptive systems continuously adapt to changes in their execution environment. Capturing all possible changes to define suitable behaviour beforehand is unfeasible, or even impossible in the case of unknown changes, hence human intervention may be required. We argue that adapting to unknown situations is the ultimate challenge for self-adaptive systems. Learning-based approaches are used to learn the suitable behaviour to exhibit in the case of unknown situations, to minimize or fully remove human intervention. While such approaches can, to a certain extent, generalize existing adaptations to new situations, there is a number of breakthroughs that need to be achieved before systems can adapt to general unknown and unforeseen situations. We posit the research directions that need to be explored to achieve unanticipated adaptation from the perspective of learning-based self-adaptive systems. At minimum, systems need to define internal representations of previously unseen situations on-the-fly, extrapolate the relationship to the previously encountered situations to evolve existing adaptations, and reason about the feasibility of achieving their intrinsic goals in the new set of conditions. We close discussing whether, even when we can, we should indeed build systems that define their own behaviour and adapt their goals, without involving a human supervisor.
From Limitations to “Superpowers”: A Design Approach to Better Focus on the Possibilities of Virtual Reality to Augment Human Capabilities

Serious applications in virtual reality (VR) often imitate real environments, tasks, and interaction techniques. VR however, allows to transcend reality and augment the physical, cognitive, and perceptual capabilities of users through, for example, telekinesis. In this sense, VR offers “superpowers”, which are not always considered as central and unique elements of interaction design. In a workshop with interaction designers, we emphasized the notion of “superpowers” to think less about VR limitations to imitate reality, but more about its possibilities to augment human capabilities. Consequently, we developed a design method that explicitly introduced the concept of superpowers (e.g., X-Ray-Vision, super-memory, telekinesis) and a VR application to support exploring each superpower. We evaluated this approach with designers. They responded positively. The notion of superpowers combined with the possibility to easily experience them changed and widened their perspective: away from limitations and reality, towards possibilities and the unique experience of being in VR.
SUSTAINING BEHAVIOUR CHANGE THROUGH IMMERSIVE TECHNOLOGIES: TRENDS, PERSPECTIVES, AND APPROACHES

Abstract Long-term behaviour changes are critical to answering societal and individual challenges surrounding areas such as sustainability and health. Current understanding of how to bring about sustained behaviour is focused on the identification of Behaviour Change Techniques (BCTs) without explicit guidance on how these should be matched with technological solutions. Based on this gap we set out to answer the research question: What is the relationship between BCTs and interactive immersive technologies with respect to long-term, sustainable behaviour? To this end, we report a literature review on technology trends in the fields of human computer interaction, human robot interaction, and game design. Based on this review we develop three main contributions with implications for design theory and practice. First, we propose a number of characteristics and mechanisms in emerging immersive technologies. Second, we highlight technological pathways connected to specific BCT clusters likely to be disrupted: technology as a conveyor of information, an augmenter of feedback, and as an embodiment of empathy. Third, we explore these connections between BCT clusters and the actual technological interventions.
Conversation Analysis for Facilitation in Children’s Intercultural Collaboration

In order to find solutions to various international problems, Global Citizenship Education (GCED) for children must consider the diversity of societies beyond basic issues of language and culture. For GCED, machine translation can be used as a tool to allow children to collaborate without a common language. However, low-resource language speakers often cannot enter the conversation and have difficulties in participating in a collaboration, because existing machine translations have poor translation quality in the low-resource languages. Messages from facilitators play an important role in encouraging children’s responses and participation. We, therefore, have analyzed the role of the facilitator in a real-world intercultural children workshop. Specifically, we examine actual conversation log data that links the facilitator’s utterances with children’s utterances in adjacency pairing. We annotate the paired data with tags and then statistically analyze the tagged data. The analysis results show that some types of facilitator messages can significantly impact the responses of low-resource language speaking children. For example, ”request” type utterances tended to encourage responses.
Vision-based Drones Tracking Using Correlation Filters and Linear Integrated Multiple Model

This paper presents the use of Correlation Filters and Integrated Multiple Model (IMM) for filtering the position measurement of fast moving drones acquired by computer vision, with probability for model selection. The maneuvering movement of the drones are often non-linear making it hard to be estimated by a simple Kalman filter. Instead of using a non-linear filter which are more complex and non-universal, this paper attempt to integrate multiple filters to estimate the drone position using low computational cost. The IMM switches between the Constant Velocity (CV), Constant Acceleration (CA) and a Constant Turn (CT) model with a Markov Chain in different flight scenario depending on the drone movement. Other filters i.e. Kernelized Correlation Filter (KCF), Particle Filter (PF) and Discriminative Correlation Filter (DCF) models are also presented for direct comparison.
A study on artificial intelligence for monitoring smart environments

Wireless networking has made enormous improvements. These developments have brought about new paradigms of wireless networking and communications Environmental protection has been in recent years a more intelligent and linked system for all facets of a global city. With the rise in data gathering, machine learning (ML) approaches can be used to boost the knowledge and the skill of an application. As the numbers increase and technology develops, the number of available data increases. Smart collection and interpretation of these Big Data is the underground to the rising of smart Internet of Things IoT apps. This study discovers the diverse methods of machine learning that resolve data difficulties in smart cities. The discussion takes place on applications such as air quality, water pollution, radiation pollution, smart buildings, smart transport, etc., which pose genuine environmental challenges. Adequate monitoring is needed to ensure sustainable growth in the world by safeguarding a healthy society. The potential and challenges in particular the role of machine learning technology for the Internet of Things and Big Data Analytics.
Establishment of the rational economic and analytical basis for projects in different sectors for their integration into the targeted diversified program for sustainable energy development

The article presents mathematic algorithms allowing to do comprehensive ranging of investment projects by various parameters, as well as prepare the long-term investment strategy for their implementation. In addition, the article provides the description of algorithms used for calculations formation applied to assess the financial, economic, social, and environmental effects of certain investment projects implementation.
Dual wavelength optical system for multiple quantum communication transmitters in Cubesat platform

Quantum Key Distribution (QKD) systems will play an important role in future networks for secure data communication. In order to provide a global coverage of a future QKD service, satellites are needed to bridge large distances. These satellite systems must be cost efficient to facilitate deployment since many network nodes will be needed. The CubeSat standard is frequently used for New Space projects as a versatile platform for satellite payloads. It is also chosen as a baseline for the construction of the system described in this paper. The DLR Institute of Communications and Navigation develops optical free space communication systems for scientific research in classical and quantum communications. A previous development of a 1/3 U CubeSat communication terminal for up to 100 Mbit/s downlinks is adapted to be used for quantum communications tests with multiple transmitters. Since the original laser terminal was designed for C-band wavelengths, a redesign of the optical system is needed to achieve polychromatic performance for the three sources. The optical system consists of a fiber collimator, a beam steering system and an afocal telescope. Most important requirement of the latter is a similar magnification for all wavelengths to ensure concentric beams pointing to the OGS. Scenarios in which the system is optimized either for lowest divergences or a similar magnification of the telescope are demonstrated in Figure 1. As the afocal telescope is used bidirectionally, it also needs to be optimized for the incident wave-front of the beacon from the OGS with respect to the beam shape at the four quadrant detector. These parameters are important for a correct pointing control. A fiber-based wavelength division multiplexer (WDM) is used for combining signals from three sources into one output fiber. As shown in the block diagram in Figure 2, it is based on cascaded thin film interference filters which are coupled to the fibers. Especially the propagation of 850 nm and 1550 nm signals in one single mode fiber is critical. Therefore the types of optical fibers were selected with respect to the bend loss, single mode propagation behavior, polarization integrity and optimal cladding diameter for the production.
Revisiting Perturbed Quantization

In this work, we revisit Perturbed Quantization steganography with modern tools available to the steganographer today, including near-optimal ternary coding and content-adaptive embedding with side-information. In PQ, side-information in the form of rounding errors is manufactured by recompressing a JPEG image with a judiciously selected quality factor. This side-information, however, cannot be used in the same fashion as in conventional side-informed schemes nowadays as this leads to highly detectable embedding. As a remedy, we utilize the steganographic Fisher information to allocate the payload among DCT modes. In particular, we show that the embedding should not be constrained to contributing coefficients only as in the original PQ but should be expanded to the so-called "contributing DCT modes." This approach is extended to color images by slightly modifying the SI-UNIWARD algorithm. Using the best detectors currently available, it is shown that by manufacturing side information with double compression, one can embed the same amount of information into the doubly-compressed cover image with a significantly better security than applying J-UNIWARD directly in the single-compressed image. At the end of the paper, we show that double compression with the same quality makes side-informed steganography extremely detectable and should be avoided.
Breast Cancer Classification Using Convolution Neural Network (CNN)

Machine learning and AI plays a crucial role in the recent advancements of multiple science disciplines. The medical field has seen new developments and achievements with the boost of technology. Breast cancer is one of the most dominant types of cancer in women. Recent studies indicate that there is an increase in the numbers globally. Many researchers started using the power of deep learning and created a model which helps doctors to diagnose and treat this cancer effectively. In this paper, we have used Kaggle dataset of histopathology images which contains 2,77,524 images. A deep learning CNN model is created and used with 80% training and 20% testing split. Without tuning hyperparameters, 61.01% accuracy is achieved. However, with parameters tuning 81% accuracy is achieved.
The 7th international workshop on news recommendation and analytics (INRA 2019)

Publishing news represents a vital function for societal health. News recommender systems, which support readers finding relevant content, face challenges beyond those encountered by other types of recommender systems. They have to deal with a dynamic flow of unstructured, fragmentary, and potentially unreliable news stories. The International Workshop on News Recommendation and Analytics (INRA) focuses on the challenges of news recommender systems and aims to connect researchers, practitioners and journalists. The seventh edition of INRA takes place as a half-day workshop in conjunction with thirteenth ACM Conference on Recommender Systems (RecSys '19) on September 16--20, 2019 in Copenhagen, Denmark. INRA 2019 focuses on the news recommender systems under three main categories: News recommendation, news analytics, and ethical aspects of news recommendation.
Adaptive and Robust Tracking for Radar Maneuvering Targets

The robustness and stability of maneuvering target tracking algorithms based on Kalman filtering are not satisfactory. The state estimation results are relatively ineffective when there exists model uncertainties or turning maneuvers during the target moving. In extreme cases, this may lead to filter divergence. According to this issue, a novel adaptive robust tracking algorithm for radar targets has been proposed in this paper. By using strong tracking filtering strategy, an adaptive fading factor is introduced to adjust the matrix gain in real time for dealing with the model uncertainties and ensuring the tracking effect. The simulation results show that this algorithm has better stability and tracking effect for maneuvering targets.
Fully Convolutional DenseNet with Attention Mechanism for Liver Lesion Segmentation in MRI Images

The goal of this research is to achieve accurate segmentation of liver tumors in noncontrast T2-weighted magnetic resonance imaging. As liver tumors and adjacent organs are represented by pixels of very similar gray intensity, segmentation is challenging, and the presence of different sizes of liver tumor makes segmentation more difficult. Differing from previous work to capture contextual information using multiscale feature fusion with concatenation, attention mechanism is added to our segmentation model to extract precise global contextual information for pixel labeling without requiring complex dilated convolution. This study describe a liver lesion segmentation model derived from FC-DenseNet with attention mechanism. Specifically, a global attention module (GAM) is added to up-sampling path, and high-level features are processed by the GAM to generating weighting information for guiding high resolution detail features recovery. High-level features are very effective for accurate category classification, but relatively weak at pixel classification and predicting restoration of the original resolution, so the fusion of high-level semantic features and low-level detail features can improve segmentation accuracy. A weighted focal loss function is used to solve the problem of lesion area occupying a relatively low proportion of the whole image, and to deal with the disequilibrium of foreground and background in the training liver lesion images. Experimental results show our segmentation model can automatically segment liver tumors from complete MRI images, and the addition of the GAM model can effectively improve liver tumor segmentation. Our algorithms have obvious advantages over other CNN algorithms and traditional manual methods of feature extraction.
BBEMA

In order to increase the efficiency of the power system balancing process, the Baltic countries launched a common balancing market in 2018. While the new market does have a data dashboard, its visual functionalities and interactivity are limited, which hinders its use for deeper analysis. In this study, a dedicated dashboard for visual analysis has been developed utilizing data from the first three years of the market's operation. The tool can be useful to various market stakeholders such as operators, participants, policy makers and researchers. The paper provides examples on the insights that can be gained from the dashboard.
Experience in Teaching Design of Human-Computer Interaction Through Evaluation and Research

This work aims to analyze and summarize the teaching work of design postgraduate students. The article demonstrates a design method based on evaluation and research, and applies this method to teaching. Use research methods such as literature research, case research, teaching practice, and analogy analysis. Firstly, related concepts such as design evaluation and design research are combed out through literature research, and relationships between them. Then, teaching plan of human factors and interaction related courses of the School of Design and Art of Beijing Institute of Technology from 2015 to 2019 are summarized, including three parts of theoretical study, design evaluation and design research, design practice. Lastly, the teaching experience and models are obtained through the comparison of various types of design courses. Evaluation and research are indispensable parts of design. Design teaching with evaluation and research as the starting point is sufficient to make students pay attention to design improvements based on current products.
Joint Waveform and Guidance Control Optimization by Statistical Linearisation for Target Rendezvous

The algorithm proposed in this paper jointly selects the transmitted waveform and the control input so that a radar sensor on a moving platform can prosecute a target by minimising a predefined cost that accounts for the energy of the transmitted radar signal, the energy of a platform control input and the relative position error between the platform and the target. The cost is a function of the waveform design and control input. The algorithm extends the existing Joint Waveform Guidance and Control Optimization (JWGCO) solution to nonlinear equations to account for the dependency of the radar measurement accuracies on Signal to Noise Ratio (SNR) and, as a consequence, on the target position. The performance of the proposed solution based on statistical linearisation is assessed with a set of simulations for a pulsed Doppler radar transmitting linearly frequency modulated chirps.
Survey Paper on Blockchain Based Cloud Computing Applications

Evolution of Blockchain since the past few years has been simply breathtaking, it has drawn attention as the next generation technology. There have been several use cases of block chain other than in crypto-currencies. In recent studies it has been estimated that the market of block chain is expected to grow by a huge amount in upcoming years. Cloud computing is currently being adopted in numerous different IT environments due to its efficiency and availability. The article aims to highlight various cloud computing applications which are based on blockchain.
GE526: A Dataset of Open-Source Game Engines

Game engines, are frameworks that provide a platform for developers to build games with an interface tailored to handle the complexity of game development. Though there is extensive empirical research on software frameworks, there is a need for empirical studies on game engines, as they differ from traditional software frameworks. Thus, to aid research and development in the area of game engines, we present GE526, a curated dataset of 526 game engine repositories mined from GitHub, which can help researchers to analyze game engines in terms of the release cycles, code quality, API usability and so on. To the best of our knowledge, we are not aware of any curated dataset that caters to game engines in the literature. The dataset contains metadata of all the mined repositories, including 582,079 commits, 20,138 pull requests, 30,287 issues reports and 2,111 releases. The dataset along with its documentation is made available at - https://bit.ly/3pyexnc.
Mixed Reality Simulation for Medical Training: How It Affects Learners' Cognitive State

A mixed reality (MR) system, by providing visual, auditory, and haptic feedback to the learner, can offer a high level of immersion and realism, especially in the healthcare context. In medical training through MR simulations, it is particularly important to avoid mental overload, discomfort, fatigue, and stress, to guarantee productive learning. The present work proposes a systematic assessment of stress, cognitive load, and performance (through subjective and objective measures) of students during an MR simulation for the rachicentesis procedure. A specific application has been developed to enhance the sense of realism, by showing, over the skill trainer, a digital patient that responds with auditory and visual feedback, based on the learner’s interaction. A sample of 18 students has been enrolled in the pilot study. Preliminary results suggest the effectiveness of the proposed MR application using Hololens: high performances are achieved, and the cognitive conditions are well balanced.
Can We Characterize Tasks Without Labels or Features?

The problem of expert model selection deals with choosing the appropriate pretrained network ("expert") to transfer to a target task. Methods, however, generally depend on two separate assumptions: the presence of labeled images and access to powerful "probe" networks that yield useful features. In this work, we demonstrate the current reliance on both of these aspects and develop algorithms to operate when either of these assumptions fail. In the unlabeled case, we show that pseudolabels from the probe network provide discriminative enough gradients to perform nearly-equal task selection even when the probe network is trained on imagery unrelated to the tasks. To compute the embedding with no probe network at all, we introduce the Task Tangent Kernel (TTK) which uses a kernelized distance across multiple random networks to achieve performance over double that of other methods with randomly initialized models. Code is available at https://github.com/BramSW/task_characterization_cvpr_2021/.