Reviewerly/exHarmony huggingface dataset:
Scrum Higher Education – The Scrum Master Supports as Solution-focused Coach

The article proposes to determine, discuss and qualify how the Framework Scrum Higher Education - based on the original Framework Scrum Version 2017 - supports teachers and students to recognize the difficulties and challenges related to the intended learning outcome and the missing motivation of students' learning process. The study aims to answer several research questions, e.g. what is the effect of a value-based framework and the intervention by a Scrum Master on students’ agile and critical performance in teamwork? The team performance was diagnosed by applying a set of tools to measure the capacity for teamwork. The evaluation confirms that agile and collaborative adaption of Scrum for Higher Education improves the self-learning process, self-motivation and self-emotion for the learner. Especially the event Sprint-Retrospective with the tailor-made framework for the Definition of Flow plays a key role and the Scrum Master gets a chance for solution-focused coaching in a convenient phase of reflection. The paper shows empirical results of a quantitative survey, which analyses quality criteria of teamwork during the sprints of the Scrum Framework. As an agile method of learning, students figured out their own learning and team working obstacles by using the solution-focused coaching.
Augmented Reality and Matlab® for Visuospatial Competence Development

During the learning process of multivariable calculus, the spatial perception of 3D surfaces represents a major challenge for the students. Since it requires the ability to sketch the graph in a 3D system, a handmade sketch can be confusing and lead to misinterpretations of other concepts. In the specific case of triple integration, the task of setting the limits of a solid requires the combination of a good visuospatial competency plus a correct management of symbolic language of functions. Nowadays, one of the most relevant objectives for using mathematical visualization programs and/or technologies is to improve the spatial perception of 3D surfaces. In this work, a comparison between the use of tailor-made Augmented Reality software and Matlab® for visuospatial skills development in multivariable calculus course, is presented.
Revisit Retinex Theory: Towards a Lightness-Aware Restorer for Underexposed Images

We investigate how to correct exposure of underexposed images. The bottleneck of previous methods mainly lies in their naturalness and robustness when dealing with images with various exposure levels. When facing well-exposed or extremely underexposed images, they may produce over- or underenhanced outputs. In this paper, we propose a novel retinex-based approach, namely, LiAR (short for lightness-aware restorer). The word “lightness-aware” refers to that the estimated illumination not only is a component to be adjusted but also serves as a measure that reflects the brightness of the scene, determining the degree of adjustment. In this way, underexposed images can be restored adaptively according to their own brightness. Given an image, LiAR first estimates its illumination map using a specially designed loss function which can ensure the result’s color consistency and texture richness. Then adaptive correction is performed to get properly exposed output. LiAR is based on internal optimization of the single test image and does not need any prior training, implying that it can adapt itself to different settings per image. Additionally, LiAR can be easily extended to the video case due to its simplicity and stability. Experiments demonstrate that facing images/videos with various exposure levels, LiAR can achieve robust and real-time correction with high contrast and naturalness. The relevant code and collected data are publicly available at https://cslinzhang.github.io/LiAR-Homepage/ .
Analyzing Hate Speech with Incel-Hunters’ Critiques

The ubiquity of online media services helps to promote free speech but also provides opportunities for the spread of problematic content such as hate speech. A group of individuals known as “incels” (involuntary celibates) sometimes use online media services to publish hateful content. To expose and condemn incel hate speech, other individuals, sometimes called “incel hunters” create online communities where they critique screenshots of content posted by incels. In this paper, using 18,187 posts collected from a subreddit named r/IncelTears, we explore the potential for transforming screenshots of incel hate speech and oppositional statements into training data that could be used as the basis for automated or semi-automated content moderation tools.
Designing smart home controls for elderly

Technology is evolving by the day and with it the devices to control it. Sophisticated systems, like Smart Homes, are currently controlled in most cases via a smartphone app. While this may be acceptable for younger and middle-aged people, elders, however, have trouble keeping up with new devices and might not want to use a smartphone. Most modern-day control schemes like touch screens and menus are regarded as too complicated. However, Smart Homes provide many opportunities to reduce the every-day burden on elderly and people with special needs. Providing elderly people easy access to advanced and helpful technology via familiar interface types immensely improves their quality of life.
Don't Blush

HCI research is increasingly addressing how technology and design can support the ageing process; from everyday activities, to social life and civic participation, to experiences with ill health - particularly as age-related illness leads to problems with independent living. Despite this increasing interest in ageing, issues of sexuality and intimacy are rarely addressed, with elderly bodies often marginalised, infantilised and rarely considered as desired, desirable or sexual. Older age also brings an increased risk of dementia, which raises issues of informed consent in sexual relationships. Given a similarly recent increase in interest in sexual health in HCI, we bring together these converging interests in this one-day workshop, where we raise these and related topics with the DIS community to bring together researchers from a wide variety of fields to explore future avenues for research and design in this understudied but potentially rich field.
Express multi‐element determination in lake sediments by laser ablation mass‐spectrometry (<scp>LA‐ICP‐MS</scp>)

Abstract Analytical data on major and trace elements concentrations in lake sediments are frequently applied in a wide range of limnological, paleolimnological, and environmental studies. Conventionally applied methods are characterized by time‐consuming, expensive, and (or) hazardous sample preparation. The proposed express method of simultaneous multi‐element determination in lake sediments by laser ablation inductively coupled plasma mass‐spectrometry (LA‐ICP‐MS) of pressed powdered pellets comprises a minimal sample preparation, special laser sampling, and calibration strategy aimed at overcoming the problems of the sample heterogeneity and application of non‐matrix‐matched external standards. The optimal sampling strategy, providing the best precision results for our simply pressed powdered pellets, was line scanning ablation with a rate of 10 μ m s −1 , a laser spot size of 110 μ m and a fluence of 8–9 J cm −2 . Calibration strategy including external and internal standardization ensured the best precision (&lt;5–10% RSD) and accuracy (within 1–20% of reference values) even with non‐matrix‐matched external standards (GSD‐1g, NIST SRM 612). In comparison with solution ICP‐MS, the method recovered significantly higher range of analytes including 10 major and 49 trace elements and ensured a better accuracy of Zr, Hf, Ti, Nb, Ta, Sn, and Sb measurements. Application of the method to the Urals lake sediments ( n = 32) showed that LA‐ICP‐MS results for most trace elements where within 1–30% of the solution ICP‐MS data.
On Using The First Variant of Dependent RSA Encryption Scheme to Secure Text: A Tutorial

Abstract The Rivest-Shamir-Adleman (RSA) public key cryptosystem has been extensively used to secure digital communication since more than forty years. The security of RSA relies on the hardness of factoring a big integer into its two big prime factors: the bigger the integer, the securer the cryptosystem. However, the RSA cryptosystem is proven to be not semantically secure. Some variants of Dependent RSA encryption schemes have been introduced by Pointcheval to overcome this problem. This study shows how the first variant of Dependent RSA (DRSA-1) encryption scheme is used to secure text. All algorithms have been implemented in the Python programming language version 2.7.15.
QoS Enhancement In VoD Systems: Load Management And Replication Policy Optimization Perspectives

Abstract The amount of online video content is exponentially increasing, which spurs its access demands. Providing optimal quality of service (QoS) for this ever-increasing video data is a challenging task due to the number of QoS constraints. The system resources, the distributed system platform and the transport protocol thus all need to collaborate to guarantee an acceptable level of QoS for the optimal video streaming process. In this paper, we present a comprehensive survey on QoS management for the video-on-demand systems. First, we focus on load management and replication algorithms in content delivery networks and peer-to-peer (P2P) networks for their shortcomings. We also address the problem of admission control and resource allocation with the objectives of congestion avoidance and frame-loss reduction. Besides, we introduce and discuss various replication schemes. For both the client–server architecture and P2P networks, we highlight the need for a specific storage management policy to preserve system reliability and content availability. We also focus on content distribution and streaming protocols scaling. We deduce that content availability is linked to the characteristics and the performance of the streaming protocols. Finally, we create a comparison table that presents the different contributions of the discussed approaches as well as their limitations. We believe that such a comprehensive survey provides useful insights and contributes to the related domains.
Recent Advances in Object Detection and Tracking for High Resolution Video: Overview and State-of-the-Art

Object detection and tracking at real time is important and challenging tasks in many computer vision applications such as video robot navigation, surveillance, vehicle navigation, security applications, military applications, patient monitoring system and traffic monitoring system. Object detection includes detecting the object in sequence of videos. In this paper we reviewed the different methods/algorithms for object detection and tracking at real time for high resolution video. Now day's high resolution imaging sensors/cameras is being used in different areas of applications such as security system, in military applications etc. For object detection and tracking in high resolution, greater frame rate requires more time to process a single frame, so it is an extreme challenges for researchers to detect and track target at real time. This sets a demand for fast computational algorithms for real time processing of high resolution videos. Moving object detection and tracking is one of the decisive active areas of research since last decade. In this paper we address and highlight a brief survey or review of various real time object detection and tracking algorithms for high resolution video available in the literature.
A deep learning approach to predicting solutions in streaming optimisation domains

In the field of combinatorial optimisation, per-instance algorithm selection still remains a challenging problem, particularly with respect to streaming problems such as packing or scheduling. Typical approaches involve training a model to predict the best algorithm based on features extracted from the data, which is well known to be a difficult task and even more challenging with streaming data. We propose a radical approach that bypasses algorithm-selection altogether by training a Deep-Learning model using solutions obtained from a set of heuristic algorithms to directly predict a solution from the instance-data. To validate the concept, we conduct experiments using a packing problem in which items arrive in batches. Experiments conducted on six large datasets using batches of varying size show the model is able to accurately predict solutions, particularly with small batch sizes, and surprisingly in a small number of cases produces better solutions than any of the algorithms used to train the model.
Enabling Paratransit and TNC Services with Blockchain Based Smart Contracts

Paratransit services provide mobility solutions for disabled travelers and older adults. In the United States, the requirements for efficient and affordable provision of these services significantly increased in 1990 with the passage of the Americans with Disabilities Act. These transportation services are essential to the well-being of these populations, however, nearly thirty years later, they remain notoriously expensive to provide and inconvenient for its passengers. The issues related to paratransit are apparent from passenger feedback and complaint forms. In this work, we explore a potential solution for improving paratransit services under consideration by transit agencies around the world, the integration of TNC’s (Transportation Network Companies) such as Uber, Lyft, Didi or Grab and taxi services with paratransit. The contribution of this work is to develop privacy preserving secure smart contracts to enable these extended paratransit systems. We examine the use of blockchain and simple IoT devices to host these contracts. Through proof of concept prototype development using open source blockchain resources, we examined the proposed architecture and system design.
Sentiment Analysis of Telugu data and comparing advanced ensemble techniques using different text processing methods

Predicting or classifying a particular sentence or review is very important for companies to launch or upgrade their products. As customers comment, review or express their views via social media. Sentiment analysis plays a crucial role in this process by analyzing reviews, comments etc . The two classes under binary classification are positive and negative. The positive class shows a good note and the negative class shows a bad note. A valid method for predicting sentiments could enable us to pull out opinions from the internet and predict customers taste. In this paper, we compare advanced ensemble techniques and we brief where we can improve our algorithm.
Evaluation of Position RMS Error from Magnetic Field Gradient for Surgical EM Tracking Systems

Electromagnetic tracking systems (EMTSs) are widely used in surgical operations, supporting the surgeon for the localization of tools during intervention. In this paper we describe a novel EMTS prototype for surgical navigation, based on a magnetic field generator (FG) composed of five transmitting coils, to advance the state-of-the-art of current commercial systems.The root mean square error (or mean radial spherical error) of position estimation for the tracking system is characterized through propagation of the voltage measurement error of the magnetic sensor, for different orientations and positions, and taking into account magnetic field gradient. This method does not require a magnetic field model, resulting useful for fast and not expensive assessment of different EMTSs with different transmitting coils’ arrangement. In this paper a measurement volume of 400 mm x 500 mm x 400 mm has been considered.The results show good performance with a standard deviation of position lower than 1 mm in almost the entire measurement volume.
Hybrid Segmentation Algorithm for Medical Image Segmentation Based on Generating Adversarial Networks, Mutual Information and Multi-Scale Information

This paper proposes 3D-MedGAN, MLU-Net and Info-Max-Net models for overcoming the lack of labeled data and extracting the multi-level feature of images in medical image segmentation. 3D-MedGAN is aimed at dealing with the lack of labeled data in medical images. It uses a generative adversarial network to simulate data and then draws newly generated samples from the distribution learned by the model. Training the segmentation model by mixing generated samples with real samples can effectively improve the effect of the segmentation model. MLU-Net uses multiple layers of different levels of convolutional angles to extract feature information from multiple angles in medical images. By adopting the attention mechanism to fuse the multi-level feature information, MLU-Net is able to improve the feature expressions and segmentation effect. Info-Max-Net is aimed at handling the noise problem in medical images. When the information in the images is complex, it is difficult to extract features. Using mutual information to measure the dependency between the image and the extracted features can effectively reduce noise in the image and improve the effect of segmentation. At the same time, for solving the problem that the high dimension of the image makes it difficult to measure mutual information, this paper uses a lower bound BL-estimator to measure the mutual information between the optimized image and the extracted features. Therefore, the model can maintain a high convergence speed as it approaches the true value of mutual information. Considering that the quality of images generated by 3D-MedGAN are not as good as the original images, we combine the 3D-MedGAN, MLU-Net, and Info-Max-Net to improve the sensitivity and the power of feature extraction of the hybird model. The effectiveness of our model is verified through experiments of the 3D-MedGAN, MLU-Net, Info-Max-Net, and the hybrid model over the LIVER100 dataset.
Aspect Level Sentiment Analysis Approaches

The branch of sentiment classification is catching researchers' consideration in a couple of years, as social media is emerging rapidly on the internet. In sentiment classification or opinion mining reviews are gathered, analyzed and sentiments are determined. Reviews given by customers are helpful to other customers and manufacturers to formulate superior decisions and decide business strategies. There are various subareas in the branch of sentiment classification which has attracted the attention of academicians and researchers. In this review, the focus is on aspect-oriented sentiment classification; it also covers major work done in the sentiment classification area. This survey denotes major contribution of researchers in aspect-oriented sentiment classification using different approaches. Comparatively higher performances are reported for this referenced literature.
Night Time Vehicle Detection and Tracking by Fusing Sensor Cues from Autonomous Vehicles

Night time vehicle detection and tracking has been a challenging task in recent years. This paper presents a novel context-aware traffic surveillance system that integrates sensor information from autonomous vehicles to improve performance of night time vehicle detection and tracking. The key elements of the proposed method include a novel vehicle pairing framework that represents vehicles based on the fused sensor contexts and vehicle taillights. These detected vehicles are then tracked in real-time night time traffic videos. Experiments are conducted on real traffic videos and the proposed system attains 0.6319 in multiple object tracking accuracy (MOTA), which represents a 26.1% increase compared with the baseline performance.
Automatic Database Troubleshooting of Azure SQL Databases

Exponentially growing number of workloads has been transferred from on-premises to the cloud environment during last decade. It incurs a constantly increasing load on the monitoring and troubleshooting capabilities of the platforms that host those applications nowadays. Data mining techniques and use of telemetry data have recently become an unavoidable means for tracking the behavior of cloud services. The research effort elaborated in this paper is focused on exploiting real-world data to build an automatic database troubleshooting system that exploits the combination of the more comprehensive statistical data science models and an expert system to perform the root cause analysis. An extensive evaluation study was conducted during the eight-month period with a plethora of Azure SQL production workloads for a varying number of databases. The obtained results confirmed the viability and cost-effectiveness of such an approach at the scale of the cloud.
A Hybrid Character Recognition Approach Using Fuzzy Logic and Stroke Bayesian Program Learning With Naïve Bayes in Industrial Environments

Automated character recognition is critical for reading and tracking data in a variety of fields. It is particularly challenging in industrial settings since information may be printed on the surface of various materials with complex and uneven shapes, causing overlapping, obstructing, and distorting characters. We propose a hybrid character recognition approach using fuzzy logic and stroke Bayesian program learning with naïve Bayes. During character segmentation, touching characters are separated using support vector machines and a three-feature fuzzy segmentation strategy that uses particle swarm optimization. This approach includes a new methodology for stroke presentation and extraction using a prebuilt primitive-stroke library containing prior knowledge. During character recognition, a conceptual character model is constructed using stroke Bayesian program learning. Monte Carlo Markov chain sampling is used to produce a fitting model for each character. This model predicts character classification by calculating the probability that a target sample belongs to a training set. To this end, naïve Bayes effectively discerns extremely similar characters. We evaluate our method experimentally using a database of industrial images and the NIST dataset. Our method outperforms existing state-of-the art methods.
Together in Shape

We designed Together in Shape, a Co-responsibility system that supports bariatric patients with changing their lifestyle by keeping track of their lifestyle, and sending coaching content based on users' actions. The actions of bariatric patients are tracked with sensors and questions are asked based on these actions to contribute to the situated design research. This demo of the system showcases scenarios for interacting with sensor equipped everyday objects which triggers messages being sent by the system and visualising the data on a dashboard. In this way, demo visitors will experience the system both from a bariatric patient's, healthcare professional's and a design researcher's point of view.
WORM-E

How can children develop a healthy level of body-awareness and social sensitivity in a technology-mediated life full of computers and digital devices? With WORM-E, an interactive soft toy for children, we explore haptic sensations and invite for enhanced bodily and social play experiences through pausing the smartphone usage. To realize the WORM-E prototype, we used e-textile techniques and technologies, including a microcontroller for controlling actuators and NFC (Near Field Communication) tags for communication with a smartphone app. Our project contributes a design exemplar on how the future of play could look like when body movements and haptic material qualities of tangible artefacts are combined with interactions through digital technologies.