Reviewerly/exHarmony huggingface dataset:
Domain Identification of Scientific Articles Using Transfer Learning and Ensembles

This paper describes our transfer learning-based approach for domain identification of scientific articles as a part of the SDPRA-2021 Shared Task. We experiment with transfer learning using pre-trained language models (BERT, RoBERTa, SciBERT), and these are then fine-tuned for this task. The result shows that the ensemble approach performs best as the weights are being taken into consideration. We propose improvements for future work. The codes for the best system are published here: https://github.com/SDPRA-2021/shared-task/tree/main/IIITT.
Probabilistic Solar Power Forecasting Based on Bivariate Conditional Solar Irradiation Distributions

We propose a two-stage probabilistic solar power (SP) forecasting algorithm to utilize the solar irradiation (SI) observations measured from different locations. In the first stage, we predict the SI based on the numerical weather prediction (NWP) after interpolating SI observations. Since the SI on the target location is not measured, we interpolate it using the spatio-temporal Kriging technique based on the SI observed from nearby weather stations. In the second stage, we forecast the SP based on the SI predictions after training the SI and SP observations. The model is trained by observations, but it forecasts based on predictions. Furthermore, in the two-stage model, forecasting errors can propagate across stages. We overcome these problems by using probabilistic forecasting. We design distributions of SI predictions through the probabilistic graphical model. Then, we extract SI scenarios from the distributions and predict SP scenarios based on these SI scenarios. We also group the NWP with respect to its prediction time, and we subdivide these groups as subgroups with respect to weather conditions. Furthermore, we propose a changeable ensemble model, where we have different weights for each weather condition. We verify our algorithm based on the data from the Korea power exchange renewable energy forecasting competition 2019. We finished the competition in 2nd place among a few hundred participants.
Fingerprinting Deep Neural Networks - a DeepFool Approach

A well-trained deep learning classifier is an expensive intellectual property of the model owner. However, recently proposed model extraction attacks and reverse engineering techniques make model theft possible and similar quality deep learning solution reproducible at a low cost. To protect the interest and revenue of the model owner, watermarking on Deep Neural Network (DNN) has been proposed. However, the extra components and computations due to the embedded watermark tend to interfere with the model training process and result in inevitable degradation in classification accuracy. In this paper, we utilize the geometry characteristics inherited in the DeepFool algorithm to extract data points near the classification boundary of the target model for ownership verification. As the fingerprint is extracted after the training process has been completed, the original achievable classification accuracy will not be compromised. This countermeasure is founded on the hypothesis that different models possess different classification boundaries determined solely by the hyperparameters of the DNN and the training it has undergone. Therefore, given a set of fingerprint data points, a pirated model or its post-processed version will produce similar prediction but another originally designed and trained DNN for the same task will produce very different prediction even if they have similar or better classification accuracy. The effectiveness of the proposed Intellectual Property (IP) protection method is validated on the CIFAR-10, CIFAR-100 and ImageNet datasets. The results show a detection rate of 100% and a false positive rate of 0% for each dataset. More importantly, the fingerprint extraction and its run time are both dataset independent. It is on average ~130× faster than two state-of-the-art fingerprinting methods.
Open-Set Support Vector Machines

Often, when dealing with real-world recognition problems, we do not need, and often cannot have, knowledge of the entire set of possible classes that might appear during operational testing. In such cases, we need to think of robust classification methods able to deal with the "unknown" and properly reject samples belonging to classes never seen during training. Notwithstanding, existing classifiers to date were mostly developed for the closed-set scenario, i.e., the classification setup in which it is assumed that all test samples belong to one of the classes with which the classifier was trained. In the open-set scenario, however, a test sample can belong to none of the known classes and the classifier must properly reject it by classifying it as unknown. In this work, we extend upon the well-known Support Vector Machines (SVM) classifier and introduce the Open-Set Support Vector Machines (OSSVM), which is suitable for recognition in open-set setups. OSSVM balances the empirical risk and the risk of the unknown and ensures that the region of the feature space in which a test sample would be classified as known (one of the known classes) is always bounded, ensuring a finite risk of the unknown. In this work, we also highlight the properties of the SVM classifier related to the open-set scenario, and provide necessary and sufficient conditions for an RBF SVM to have bounded open-space risk.
Enhancing continuous control of mobile robots for end-to-end visual active tracking

In the last decades, visual target tracking has been one of the primary research interests of the Robotics research community. The recent advances in Deep Learning technologies have made the exploitation of visual tracking approaches effective and possible in a wide variety of applications, ranging from automotive to surveillance and human assistance. However, the majority of the existing works focus exclusively on passive visual tracking, i.e., tracking elements in sequences of images by assuming that no actions can be taken to adapt the camera position to the motion of the tracked entity. On the contrary, in this work, we address visual active tracking, in which the tracker has to actively search for and track a specified target. Current State-of-the-Art approaches use Deep Reinforcement Learning (DRL) techniques to address the problem in an end-to-end manner. However, two main problems arise: (i) most of the contributions focus only on discrete action spaces, and the ones that consider continuous control do not achieve the same level of performance; and (ii) if not properly tuned, DRL models can be challenging to train, resulting in considerably slow learning progress and poor final performance. To address these challenges, we propose a novel DRL-based visual active tracking system that provides continuous action policies. To accelerate training and improve the overall performance, we introduce additional objective functions and a Heuristic Trajectory Generator (HTG) to facilitate learning. Through extensive experimentation, we show that our method can reach and surpass other State-of-the-Art approaches performances, and demonstrate that, even if trained exclusively in simulation, it can successfully perform visual active tracking even in real scenarios.
Design (Not) Lost in Translation: A Case Study of an Intimate-Space Socially Assistive “Robot” for Emotion Regulation

We present a Research-through-Design case study of the design and development of an intimate-space tangible device perhaps best understood as a socially assistive robot, aimed at scaffolding children’s efforts at emotional regulation. This case study covers the initial research device development, as well as knowledge transfer to a product development company toward translating the research into a workable commercial product that could also serve as a robust “research product” for field trials. Key contributions to the literature include: (1) sharing of lessons learned from the knowledge transfer process that can be useful to others interested in developing robust products (whether commercial or research) that preserve design values, while allowing for large scale deployment and research; (2) articulation of a design space in HCI/HRI (Human Robot Interaction) of intimate space socially assistive robots , with the current artifact as a central exemplar, contextualized alongside other related HRI artifacts.
Religion and Women’s Intimate Health: Towards an Inclusive Approach to Healthcare

We present findings from a three country study exploring the intersection between female intimate health and religious beliefs. Through a qualitative study with Muslim female populations in Pakistan, Bangladesh and Malaysia, three different Muslim majority contexts, we examine the deep impact Islamic beliefs have on female intimate health and well-being. Our study investigates the perceptions, attitudes and behaviours of Muslim women to their own intimate and sexual bodies through their experiences of menarche, marriage and reproduction and menopause. The intersection of religion and female sexual bodies and health is a neglected area within HCI and we highlight how inextricably specific Islamic values are linked with women’s reproductive health in Muslim communities. We further discuss the opportunities and challenges of designing technologies for religious, non-secular beliefs and values with the aim to improve intimate health practices amongst Muslim women and to broaden the scope of health design within HCI.
Towards the Next Generation of Gaming Wearables

Recent studies on gaming wearables show that wearables can contribute to the gaming experience by bolstering performativity, facilitating social interaction, and accommodating distinct interaction modalities. Still, these studies focused on contexts such as role-playing, casual, or festival games. Stakeholder-oriented research that explores the integration of wearables for mainstream gaming platforms such as game consoles is scarce. To fill this gap, we have conducted an exploratory study through 6 participatory design workshops focusing on different aspects of wearables with 33 participants from different stakeholders. As a result, we have created fifteen design themes and three gaming wearable concepts that led to seven actionable design implications which can be adopted by designers and researchers for designing gaming wearables.
A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.
From FOMO to JOMO: Examining the Fear and Joy of Missing Out and Presence in a 360° Video Viewing Experience

Cinematic Virtual Reality (CVR), or 360° video, engages users in immersive viewing experiences. However, as users watch one part of the 360° view, they will necessarily miss out on events happening in other parts of the sphere. Consequently, fear of missing out (FOMO) is unavoidable. However, users can also experience the joy of missing out (JOMO). In a repeated measures, mixed methods design, we examined the fear and joy of missing out (FOMO and JOMO) and sense of presence in two repeat viewings of a 360° film using a head-mounted display. We found that users experienced both FOMO and JOMO. FOMO was caused by the users’ awareness of parallel events in the spherical view, but users also experienced JOMO. FOMO did not compromise viewers’ sense of presence, and FOMO also decreased in the second viewing session, while JOMO remained constant. The findings suggest that FOMO and JOMO can be two integral qualities in an immersive video viewing experience and that FOMO may not be as negative a factor as previously thought.
Voice-Activated Smart Home Controller Using Machine Learning

The emergence of the Internet of Things concept has provided a great vision for the technological future, intending to enable the extraction and comprehension of information from the environment around us, making use of the interaction and cooperation between several technological devices.The example of Smart Homes, in particular, aims to integrate these devices into households, enabling the automation of tasks previously performed by humans, to simplify their daily lives and create a more comfortable environment.However, many of these devices fail to keep their promise, since they were not developed taking into account the frequent change of habits and tastes of the user, being necessary reprogramming of the device to follow the new behaviors.Taking this problem into account, this article presents the design and end-to-end implementation of a voice-activated smart home controller for intelligent devices, deployed in a real environment and validated in an experimental setup of motorized blinds.The architecture of the proposed solution integrates evolvable intelligence with the use of an Online Learning framework, enabling it to automatically adapt to the user's habits and behavioral patterns.The results obtained from the various evaluation tests provide a validation of the operation and usefulness of the developed system.The main contributions of this work are: I) design of a smart home controller's architecture; II) endto-end implementation of a smart home controller and respective guidelines; III) open-source dataset of user behavior from the smart blinds scenario; IV) comparison between Online and Offline Learning approaches.
BNNpriors: A library for Bayesian neural network inference with different prior distributions

Bayesian neural networks have shown great promise in many applications where calibrated uncertainty estimates are crucial and can often also lead to a higher predictive performance. However, it remains challenging to choose a good prior distribution over their weights. While isotropic Gaussian priors are often chosen in practice due to their simplicity, they do not reflect our true prior beliefs well and can lead to suboptimal performance. Our new library, BNNpriors, enables state-of-the-art Markov Chain Monte Carlo inference on Bayesian neural networks with a wide range of predefined priors, including heavy-tailed ones, hierarchical ones, and mixture priors. Moreover, it follows a modular approach that eases the design and implementation of new custom priors. It has facilitated foundational discoveries on the nature of the cold posterior effect in Bayesian neural networks and will hopefully catalyze future research as well as practical applications in this area.
Interpretation-Enabled Software Reuse Detection Based on a Multi-level Birthmark Model

Software reuse, especially partial reuse, poses legal and security threats to software development. Since its source codes are usually unavailable, software reuse is hard to be detected with interpretation. On the other hand, current approaches suffer from poor detection accuracy and efficiency, far from satisfying practical demands. To tackle these problems, in this paper, we propose ISRD, an interpretation-enabled software reuse detection approach based on a multi-level birthmark model that contains function level, basic block level, and instruction level. To overcome obfuscation caused by cross-compilation, we represent function semantics with Minimum Branch Path (MBP) and perform normalization to extract core semantics of instructions. For efficiently detecting reused functions, a process for "intent search based on anchor recognition" is designed to speed up reuse detection. It uses strict instruction match and identical library call invocation check to find anchor functions (in short anchors) and then traverses neighbors of the anchors to explore potentially matched function pairs. Extensive experiments based on two real-world binary datasets reveal that ISRD is interpretable, effective, and efficient, which achieves 97.2% precision and 94.8% recall. Moreover, it is resilient to cross-compilation, outperforming state-of-the-art approaches.
Dynamic Point Cloud Texture Video Compression using the Edge Position Difference Oriented Motion Model

Immersive media representation format based on point clouds has underpinned significant opportunities for extended reality applications. Point cloud in its uncompressed format require very high data rate for storage and transmission. The video based point cloud compression (V-PCC) technique projects a dynamic point cloud into geometry and texture video sequences. The projected texture video is then coded using modern video coding standard like HEVC. Since the properties of projected texture video frames are different from traditional video frames, HEVC-based commonality modeling can be inefficient. An improved commonality modeling technique is proposed that employs edge position difference oriented motion model. Experimental results show that the proposed commonality modeling technique can yield savings in bit rate of up to 3.15% over the V-PCC HEVC reference encoder.
What do Teens Make of Personal Informatics?

Personal informatics (PI) technologies allow users to collect data about aspects of their lifestyle like mood or step count. Though teens increasingly encounter and use such technologies, little is known about how they ascribe meaning to their own PI activities. We report a qualitative study of the PI experiences of eighteen teens (aged 14 – 17). Following a learning phase focused on interpreting PI data, participants chose a personal goal that interested them and a PI tool to track it for 4-8 weeks in everyday contexts. Participants proved to be competent, flexible users of PI tools, tracking a range of meaningful life factors, from 'worries' to 'exercise'; they valued learning about 'natural patterns' in their lives and were motivated to manage their emotions and evaluate whether they were doing the right thing. Our findings contribute to understanding how young people can engage in appropriation and interpretation of PI data – suggesting opportunities for educational interventions and design.
Natural language processing and deep learning chatbot using long short term memory algorithm

Chatbots are becoming increasingly popular as virtual assistants, many businesses are launching If-This-Then-That programs to help them get started. Such systems, on the other hand, often generate chatbots that are stagnant and difficult to handle. The market for chatbots is increasing. Computer systems are often required, and online connections are improved by allowing users to express their needs, desires, or questions naturally and clearly by speaking, tapping, and talking. They're easy to use, perfect for people of all ages, and have the most detailed responses to questions. The majority of today's education is distributed through e-learning. A chatbot is one of the most powerful ways for students to read, as it can answer questions at any time without the need for human interaction. This chatbot is highly capable of overcoming student uncertainty without the need for human interaction. The IF THIS Then THAT paradigm is used by the bulk of chatbots. Natural language processing and deep learning technologies were used to build this chatbot. As a consequence, the chatbot will comprehend questions at a higher level.
COVID-19 vs Social Media Apps: Does Privacy Really Matter?

Many people around the world are worried about using or even downloading COVID-19 contact tracing mobile apps. The main reported concerns are centered around privacy and ethical issues. At the same time, people are voluntarily using Social Media apps at a significantly higher rate during the pandemic without similar privacy concerns compared with COVID-19 apps. To better understand these seemingly anomalous behaviours, we analysed the privacy policies, terms & conditions and data use agreements of the most commonly used COVID-19, Social Media & Productivity apps. We also developed a tool to extract and analyse nearly 2 million user reviews for these apps. Our results show that Social Media & Productivity apps actually have substantially higher privacy and ethical issues compared with the majority of COVID-19 apps. Surprisingly, lots of people indicated in their user reviews that they feel more secure as their privacy are better handled in COVID-19 apps than in Social Media apps. On the other hand, most of the COVID-19 apps are less accessible and stable compared to most Social Media apps, which negatively impacted their store ratings and led users to uninstall COVID-19 apps more frequently. Our findings suggest that in order to effectively fight this pandemic, health officials and technologists will need to better raise awareness among people about COVID-19 app behaviour and trustworthiness. This will allow people to better understand COVID-19 apps and encourage them to download and use these apps. Moreover, COVID-19 apps need many accessibility enhancements to allow a wider range of users from different societies and cultures to access to these apps.
The Effects of Cognitive Load on Engagement in a Virtual Reality Learning Environment

Engagement has been traditionally linked to presence in desktop-based virtual reality learning environments. Although several studies have been performed to determine other factors affecting cognitive engagement, the role of cognitive load as a factor of student's engagement in desktop-based virtual reality (VR) has received little attention in the literature. The main purpose of this study was to explain if individual dimensions of cognitive load (mental demand, effort, and frustration level) can be used in addition to factors like presence and self-efficacy to predict student's cognitive engagement. The results of the study confirmed presence and self-efficacy as significant predictors of student's engagement. Also, a three-step hierarchical regression analysis revealed that two of the three individual dimensions of cognitive load (effort and frustration level) were also significant predictors of student's engagement.
Improving Results on Russian Sentiment Datasets

In this study, we test standard neural network architectures (CNN, LSTM, BiLSTM) and recently appeared BERT architectures on previous Russian sentiment evaluation datasets. We compare two variants of Russian BERT and show that for all sentiment tasks in this study the conversational variant of Russian BERT performs better. The best results were achieved by BERT-NLI model, which treats sentiment classification tasks as a natural language inference task. On one of the datasets, this model practically achieves the human level .