Reviewerly/exHarmony huggingface dataset:
RingText: Dwell-free and hands-free Text Entry for Mobile Head-Mounted Displays using Head Motions

In this paper, we present a case for text entry using a circular keyboard layout for mobile head-mounted displays (HMDs) that is dwell-free and does not require users to hold a dedicated input device for letter selection. To support the case, we have implemented RingText whose design is based on a circular layout with two concentric circles. The outer circle is subdivided into regions containing letters. Selection is made by using a virtual cursor controlled by the user's head movements-entering a letter region triggers a selection and moving back into the inner circle resets the selection. The design of RingText follows an iterative process, where we initially conduct one first study to investigate the optimal number of letters per region, inner circle size, and alphabet starting location. We then optimize its design by selecting the most suitable features from the first study: one letter per region, narrowing the trigger area to lower error rates, and creating candidate regions that incorporate two suggested words to appear next to the current letter region (close to the cursor) using a dynamic (rather than fixed) approach. Our second study compares text entry performance of RingText with four other hands-free techniques and the results show that RingText outperforms them. Finally, we run a third study lasting four consecutive days with 10 participants (5 novice users and 5 expert users) doing two daily sessions and the results show that RingText is quite efficient and yields a low error rate. At the end of the eighth session, the novice users can achieve a text entry speed of 11.30 WPM after 60 minutes of training while the expert (more experienced) users can reach an average text entry speed of 13.24 WPM after 90 minutes of training.
Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation

Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy and stagnation into local optimal solutions. Therefore, to alleviate these shortcomings, in this paper, we present a modified remora optimization algorithm (MROA) for global optimization and image segmentation tasks. We used Brownian motion to promote the exploration ability of ROA and provide a greater opportunity to find the optimal solution. Second, lens opposition-based learning is introduced to enhance the ability of search agents to jump out of the local optimal solution. To substantiate the performance of MROA, we first used 23 benchmark functions to evaluate the performance. We compared it with seven well-known algorithms regarding optimization accuracy, convergence speed, and significant difference. Subsequently, we tested the segmentation quality of MORA on eight grayscale images with cross-entropy as the objective function. The experimental metrics include peak signal-to-noise ratio (PSNR), structure similarity (SSIM), and feature similarity (FSIM). A series of experimental results have proved that the MROA has significant advantages among the compared algorithms. Consequently, the proposed MROA is a promising method for global optimization problems and image segmentation.
Genetic Algorithms: Theory And Application

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 2220 Genetic Algorithms: Theory and Application Dennis I. Merino, Edgar N. Reyes/Carl Steidley Southeastern Louisiana University/Texas A&M University - Corpus Christi Hammond, LA 70402/ Corpus Christi, TX 78412 1 Introduction Genetic algorithms, a class of robust and efficient search techniques that can be randomly sample large spaces, have applications in the field of optimization and in a wide range of computer science problems in pattern recognition, search, scheduling, and machine learning. Genetic algorithms are motivated by characteristics found in natural population genetics, among them robustness and efficiency. Features of biological systems found in genetic algorithms include reproduction, self- guidance, self-repair, the nature of survival of the fittest, and variation through mutation. Genetic algorithms were developed by John Holland of the University of Michigan in the 1970's. Many of the essential properties of genetic algorithms discussed in this paper can be found in [1, 2]. When a genetic algorithm is used to find an optimal solution in the space of all feasible solutions, the algorithm maintains a population (or set) of feasible solutions which evolve through random process based on principles found in the mechanics of natural selection and genetics. Each time this set of solution evolves (or as we say iterated), a new set of solutions is generated. The iterations are repeated several times as necessary. An important goal from the standpoint of genetic algorithms is to improve the values of the objective function in an efficient, practical, or quick method. Ideally, as it is true for many optimization techniques, one would like to generate after several iterations a set of solutions that includes an optimal global solution; but a genetic algorithm has its priority set on tying improvement of an objective function with performance-practicality of the method. In the literature, one finds several applications of genetic algorithms in routing and scheduling problems, machine learning, setting weights in neural nets, and testing expert systems that control complex systems, amongst others [2]. In the next section, we focus on the algorithm itself by solving an elementary optimization problem. 2 Genetic Algorithm: An example We present the following to illustrate the effectiveness of genetic algorithms. We note that although the problem can be solved easily using a different method, it is the process involved with genetic algorithms that is put to focus. We wish to
MotionTransformer: Transferring Neural Inertial Tracking between Domains

Inertial information processing plays a pivotal role in egomotion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. However, they are affected greatly by changes in sensor placement/orientation or motion dynamics, and it is infeasible to collect labelled data from every domain. To overcome the challenges of domain adaptation on long sensory sequences, we propose MotionTransformer - a novel framework that extracts domain-invariant features of raw sequences from arbitrary domains, and transforms to new domains without any paired data. Through the experiments, we demonstrate that it is able to efficiently and effectively convert the raw sequence from a new unlabelled target domain into an accurate inertial trajectory, benefiting from the motion knowledge transferred from the labelled source domain. We also conduct real-world experiments to show our framework can reconstruct physically meaningful trajectories from raw IMU measurements obtained with a standard mobile phone in various attachments.
Real-Time Detection of Fall From Bed Using a Single Depth Camera

Toward the medical and living healthcare for the elderly and patients, fall from bed is a critical accident that may lead to serious injuries. To alleviate this, an essential problem is to detect this event in time for earning the rescue time. Although some efforts that resort to the wearable devices and smart healthcare room have already been paid to address this problem, the performance is still not satisfactory enough for the practical applications. In this paper, a novel fall from a bed detection method is proposed. In particular, the depth camera is used as the visual sensor due to its insensitivity to illumination variation and capacity of privacy protection. To characterize the human activity well, an effective human upper body detection approach able to extract human head and upper body center is proposed using random forest. Compared with the existing widely used human body parsing methods (e.g., Microsoft Kinect SDK or OpenNI SDK), our proposition can still work reliably when human-bed interaction happens. According to the motion information of human upper body, the fall from bed detection task is formulated as a two-class classification problem. Then, it is solved using the large margin nearest neighbor classification approach. Our method can meet the real-time running requirement with the normal computer. In experiments, we construct a fall from bed detection data set that contains the samples from 42 volunteers (26 males and 16 females) for test. The experimental results demonstrate the effectiveness and efficiency of our proposition.
Vision-Based Control and Stability Analysis of a Cable-Driven Parallel Robot

In cable-driven parallel robots (CDPRs), rigid links are substituted by flexible cables. This change in actuation allows for a large workspace with a high payload to weight ratio, among other appealing characteristics. However, the accuracy for such systems needs to be improved to truly outperform classical parallel robots. A possible and not yet well studied solution is the use of vision-based control for CDPRs. This letter deals with the stability analysis of such a control scheme with regard to uncertainties lying both in the analytical models and the experimental setup. Two CDPRs are analyzed as illustrative examples. The results obtained show the system's robustness with respect to uncertainties.
Recommendation strategies in personalization applications

While the initial goal of recommender systems (RSes) was to reduce the information overload for Internet users and make the information retrieval more efficient, they have become a crucial strategic tool for companies in the online markets. According to this evolution, research on RSes has produced a wide variety of approaches and algorithms. As a consequence, the companies deploying RSes in their business applications face the decision of how to generate and deliver personalized recommendations to their users by choosing among many options. The problem has been largely treated from the machine learning performance perspective because there is relatively little research done from the business perspective. The decision of what kind of recommender engines should be used in a personalization application, given certain business conditions, has a strategic value because it affects the way customers perceive the company with respect to its competitors. Choosing the wrong way to personalize recommendations may not only require the redesign of the information systems but also to rebuild the relationships with customers and even the entire brand strategic positioning. The research issues addressed by this paper are (i) which recommendation strategies a company can deploy to generate and deliver recommendations to users, and (ii) which specific strategies should be used depending on the current business conditions. We propose taxonomy based on a literature analysis and a framework to associate each strategy with a certain setting. The proposed framework is empirically supported by four case studies.
Multi-UAV Reconnaissance Task Assignment for Heterogeneous Targets Based on Modified Symbiotic Organisms Search Algorithm

This paper considers a reconnaissance task assignment problem for multiple unmanned aerial vehicles (UAVs) with different sensor capacities. A modified Multi-Objective Symbiotic Organisms Search algorithm (MOSOS) is adopted to optimize UAVs’ task sequence. A time-window based task model is built for heterogeneous targets. Then, the basic task assignment problem is formulated as a Multiple Time-Window based Dubins Travelling Salesmen Problem (MTWDTSP). Double-chain encoding rules and several criteria are established for the task assignment problem under logical and physical constraints. Pareto dominance determination and global adaptive scaling factors is introduced to improve the performance of original MOSOS. Numerical simulation and Monte-Carlo simulation results for the task assignment problem are also presented in this paper, whereas comparisons with non-dominated sorting genetic algorithm (NSGA-II) and original MOSOS are made to verify the superiority of the proposed method. The simulation results demonstrate that modified SOS outperforms the original MOSOS and NSGA-II in terms of optimality and efficiency of the assignment results in MTWDTSP.
Research on 3D Painting in Virtual Reality to Improve Students’ Motivation of 3D Animation Learning

The purpose of this study was to investigate the use of 6-DoF high immersive virtual reality for stereoscopic spatial mapping to assess the impact of perceived spatial capabilities on 3D software learning motivation. This study wasn’t a bound course with mandatory participation, and students were free to participate in the trial, and employed HTC VIVE, which provides highly immersive experiences, to elicit strong emotional responses. A total of 111 students from a university digital media department were invited to participate in a 3D VR painting experiment in which students created paintings using Google Tilt Brush. A 5-point scale based on the ARCS learning motivation model was adopted to collect student data. Perform a factor analysis of the data twice to select the appropriate factor (p = 0.000 &lt; 0.05). Specifically, exploratory factor analysis was used to classify factors based on four constructs. The Cronbach alpha values of ARCS were 0.920, 0.929, 0.693 and 0.664, respectively, both &gt;0.6, which still indicate favorable reliability. The results show that immersive VR can promote students’ motivation and interest in learning 3D animation. However, the practical application of this technology requires solving problems related to hardware and space.
Is It Human? The Role of Anthropomorphism as a Driver for the Successful Acceptance of Digital Voice Assistants

The market of digital voice has grown significantly over the recent years.Big players like Amazon, Google, Apple, Microsoft and Samsung are focusing on the development and expansion of their assistants.Especially smart speakers are on the rise but also in smartphone integrated voice applications are getting more popular.The main characteristics of this new technology are both elements of human-computerinteraction and especially the attribution of human characteristics.Although there is an increase of the number of current users as well as of consumers intending to use digital voice assistants in the future, drivers and barriers of digital voice assistants have not yet been sufficiently empirically investigated, especially concerning the phenomenon of anthropomorphism.This study points to additional key factors that are important to foster broader acceptance.Our empirical study is based on the UTAUT2 and highlights the importance of anthropomorphism in relation to other determinants known from the literature.
Blockchain-enabled technology: the emerging technology set to reshape and decentralise many industries

The past few years have seen an explosion in the business use of blockchain. The technology has great potential to drive simplicity and efficiency in financial services, and it is poised to drive the next wave of financial service innovation. Interest in exploiting blockchain in other industries, such as manufacturing and healthcare, is increasing, and deployments are gaining momentum. Yet, the adoption rate is slow, and organisations are only beginning to scratch the surface in regards to the potential applications of this technology. Implemented properly, the business benefits can be substantial. This article explores the changing dimensions of blockchain, highlights the importance of this technology, reviews its timeline from inception to maturity, identifies determinants of implementation success, and covers some of the potential benefits of this technology. Finally, this study highlights the successful implementation of blockchain solutions in the manufacturing and service industries.
The Political Economy of Facebook Advertising: Election Spending, Regulation and Targeting Online

Abstract Political advertising on Facebook is the latest in a long line of developments in campaign practice, and is a tool that has been mobilised extensively in elections around the world. In this article, we explore what we know about Facebook advertising at elections and ask what existing data from the UK Electoral Commission can reveal about current usage. Highlighting the principles behind Facebook advertising, we argue that existing metrics offer little insight into current campaign trends—posing analytical, methodological and normative challenges for academics and electoral regulators alike. Moreover, we argue that these challenges strike at the heart of debates about democratic responsibility and the degree to which governments should cede responsibility to commercial actors who may have differing understandings of fundamental democratic norms.
Compositional Semantic Parsing across Graphbanks

Most semantic parsers that map sentences to graph-based meaning representations are handdesigned for specific graphbanks.We present a compositional neural semantic parser which achieves, for the first time, competitive accuracies across a diverse range of graphbanks.Incorporating BERT embeddings and multi-task learning improves the accuracy further, setting new states of the art on DM, PAS, PSD, AMR 2015 and EDS.
Retinex-Based Laplacian Pyramid Method for Image Defogging

To address the problem that the retinex algorithm cannot effectively enhance color and detail simultaneously, we propose a retinex-based laplacian pyramid method for image defogging. The method is implemented via MSRCR and laplacian pyramid, and it doesn't require additional hardware devices. The overall defogging process is composed of three vital parts: illumination color enhancement, detail of reflection component enhancement, and linear weighted fusion. Firstly, we add the gamma correction illumination back to reflection to achieve color enhancement. And then, the detail enhancement is achieved by the laplacian pyramid to process the reflection component. Finally, the detail enhanced image and color corrected image are used to reconstruct the clear image. Comparing with the state-of-the-art image defogging methods, the experimental results have shown that the contrast of defogged images can be effectively improved by the proposed method, and with better effects on both subjective and objective results. The contrast of images can be enhanced by the proposed method. Meanwhile, sufficient details can be preserved. Therefore, the proposed method is an effective image defogging method.
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks

This paper addresses the problem of inferring the fine-grained type of an entity from a knowledge base.We convert this problem into the task of graph-based semi-supervised classification, and propose Hierarchical Multi Graph Convolutional Network (HMGCN), a novel Deep Learning architecture to tackle this problem.We construct three kinds of connectivity matrices to capture different kinds of semantic correlations between entities.A recursive regularization is proposed to model the subClassOf relations between types in given type hierarchy.Extensive experiments with two large-scale public datasets show that our proposed method significantly outperforms four state-of-the-art methods.
RFID based real-time recognition of ongoing gesture with adversarial learning

At present, wireless sensing based gesture recognition is becoming a rising star due to its convenience and non-invasiveness without privacy issues, while the strict requirement of the deployment and surrounding environment is still an unavoidable issue which limits its development and generalization. Although there are some works involving the environmental variance, the changes of relative positions between devices and users are ignored. As one of the most popular wireless sensing methods, RFID is widely used in activity recognition with its stable low-level physical characters such as phase and RSS. Besides, the signals reflected from RFID tags intuitively delineate its movements. On the other hand, many interactive gesture-driven applications, such as gesture input for video games, have a paramount and unavoidable issue about the latency between completion of a gesture and its recognition. Inspired by deep learning, this paper presents a real-time ongoing gesture recognition system EUIGR, which efficiently integrates phase and RSS data streams, and extracts both environment and user invariant features. The proposed system seamlessly integrates CNNs (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) to fuse RFID low-level physical characters and extract space-temporal information. Furthermore, with adversarial learning, EUIGR suppresses environment-related factors and the user-specific features, and obtains strong robustness to individual diversity and decreases the environmental dependence. We also implement the system with COTS RFID devices, and extensive experimental results show the effectiveness and accuracy of EUIGR.
Neural Snowball for Few-Shot Relation Learning

Knowledge graphs typically undergo open-ended growth of new relations. This cannot be well handled by relation extraction that focuses on pre-defined relations with sufficient training data. To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations. More specifically, we use Relational Siamese Networks (RSN) to learn the metric of relational similarities between instances based on existing relations and their labeled data. Afterwards, given a new relation and its few-shot instances, we use RSN to accumulate reliable instances from unlabeled corpora; these instances are used to train a relation classifier, which can further identify new facts of the new relation. The process is conducted iteratively like a snowball. Experiments show that our model can gather high-quality instances for better few-shot relation learning and achieves significant improvement compared to baselines. Codes and datasets are released on https://github.com/thunlp/Neural-Snowball.
Measure-Rewind-Measure: Tighter Quantum Random Oracle Model Proofs for One-Way to Hiding and CCA Security

We introduce a new technique called ‘Measure-Rewind-Measure’ (MRM) to achieve tighter security proofs in the quantum random oracle model (QROM). We first apply our MRM technique to derive a new security proof for a variant of the ‘double-sided’ quantum One-Way to Hiding Lemma (O2H) of Bindel et al. [TCC 2019] which, for the first time, avoids the square-root advantage loss in the security proof. In particular, it bypasses a previous ‘impossibility result’ of Jiang, Zhang and Ma [IACR eprint 2019]. We then apply our new O2H Lemma to give a new tighter security proof for the Fujisaki-Okamoto transform for constructing a strong ($${\mathsf {IND}}{\text {-}}{\mathsf {CCA}}$$) Key Encapsulation Mechanism (KEM) from a weak ($${\mathsf {IND}}{\text {-}}{\mathsf {CPA}}$$) public-key encryption scheme satisfying a mild injectivity assumption.