Reviewerly/exHarmony huggingface dataset:
Examining Fitts' and FFitts' Law Models for Children's Pointing Tasks on Touchscreens

Fitts' law has accurately modeled both children's and adults' pointing movements, but it is not as precise for modeling movement to small targets. To address this issue, prior work presented FFitts' law, which is more exact than Fitts' law for modeling adults' finger input on touchscreens. Since children's touch interactions are more variable than adults, it is unclear if FFitts' law should be applied to children. We conducted a 2D target acquisition task with 54 children (ages 5-10) to examine if FFitts' law can accurately model children's touchscreen movement time. We found that Fitts' law using nominal target widths is more accurate, with a R2 value of 0.93, than FFitts' law for modeling children's finger input on touchscreens. Our work contributes new understanding of how to accurately predict children's finger touch performance on touchscreens.
Safe Mission Planning under Dynamical Uncertainties

This paper considers safe robot mission planning in uncertain dynamical environments. This problem arises in applications such as surveillance, emergency rescue, and autonomous driving. It is a challenging problem due to mod-eling and integrating dynamical uncertainties into a safe planning framework, and finding a solution in a computationally tractable way. In this work, we first develop a probabilistic model for dynamical uncertainties. Then, we provide a framework to generate a path that maximizes safety for complex missions by incorporating the uncertainty model. We also devise a Monte Carlo method to obtain a safe path efficiently. Finally, we evaluate the performance of our approach and compare it to potential alternatives in several case studies.
A histogram equalization algorithm based on building a grey level binary tree dynamically

Histogram equalization algorithms may produce artifacts when the image is enhanced. Complex filtering algorithms are used for good results, or end-to-end deep learning networks are implemented for image enhancement. The competitive algorithm proposed in this paper uses a binary tree structure to remap grayscale and suppress artifacts. The algorithm can get different degrees of image enhancement results by changing unique variables. The proposed competitive algorithm can also be used to expand the dataset of deep learning tasks. Code and figures are available at https://github.com/F-Quasimo/DEBTHE.
Cooperative Privacy-Preserving Data Collection Protocol Based on Delocalized-Record Chains

This paper aims to advance the field of data anonymization within the context of Internet of Things (IoT), an environment where data collected may contain sensitive information about users. Specifically, we propose a privacy-preserving data publishing alternative that extends the privacy requirement to the data collection phase. Because our proposal offers privacy-preserving conditions in both the data collecting and publishing, it is suitable for scenarios where a central node collects personal data supplied by a set of devices, typically associated with individuals, without these having to assume trust in the collector. In particular, to limit the risk of individuals' re-identification, the probabilistic k-anonymity property is satisfied during the data collection process and the k-anonymity property is satisfied by the data set derived from the anonymization process. To carry out the anonymous sending of personal data during the collection process, we introduce the delocalized-record chain, a new mechanism of anonymous communication aimed at multi-user environments to collaboratively protect information, which by not requiring third-party intermediaries makes it especially suitable for private IoT networks (besides public IoT networks).
Program web-based SFBC untuk mereduksi kecemasan akademik siswa saat pandemi COVID-19; sebuah pilot studi

&lt;p&gt;This research is a pilot study which aims to created the website and analyzed the effectiveness of a website-based solution focused-brief counseling (SFBC) program to reduce academic anxiety experienced by high school students. This study conducted by one group pretest-postest experimental design and involving 30 high school students in Singaraja, Bali as participants. The academic anxiety scale is used to measure the academic anxiety condition of students before attending counseling and also after. Student perception questionnaires were used to evaluate the feasibility of the program based on students' perceptions as users. Student academic anxiety data were analyzed through an independent sample t-test assisted by the JASP version 0.13 program. Data about the feasibility of web-based SFBC based on students' perceptions was analyzed descriptively and narrated. The results showed that: 1) the website-based SFBC program was effective in reducing academic anxiety experienced by high school students; 2) the website-based SFBC program has a good level of acceptance and feasibility based on the perceptions of students. The results of this study have implications for the use of website-based counseling methods for students.&lt;/p&gt;
A Parallel Algorithm Combining Improved-Connect-RRT and JPS with Closed-operation

How to plan a path in big and complex environments has always been a key issue. In this paper, we proposed Global Sampling and Local Search Trees(GSLST), a parallel planning algorithm. It uses Connect-RRT to grow two global sampling trees in the entire map while using Closed-Operation and Jump Point Search(JPS) to extract narrow paths and create Local Dynamic Link Trees. GSLST shows both the fastness of the sampling-based algorithm for planning in a wide range of environment and the completeness of the search based algorithm for planning in complex environment with narrow paths. The simulations demonstrate that GSLST is faster than that of sampling-based and search-based algorithms in big complex environments.
Eliminating Redundant Computation in Noisy Quantum Computing Simulation

Noisy Quantum Computing (QC) simulation on a classical machine is very time consuming since it requires Monte Carlo simulation with a large number of error-injection trials to model the effect of random noises. Orthogonal to existing QC simulation optimizations, we aim to accelerate the simulation by eliminating the redundant computation among those Monte Carlo simulation trials. We observe that the intermediate states of many trials can often be the same. Once these states are computed in one trial, they can be temporarily stored and reused in other trials. However, storing such states will consume significant memory space. To leverage the shared intermediate states without introducing too much storage overhead, we propose to statically generate and analyze the Monte Carlo simulation simulation trials before the actual simulation. Those trials are reordered to maximize the overlapped computation between two consecutive trials. The states that cannot be reused in follow-up simulation are dropped, so that we only need to store a few states. Experiment results show that the proposed optimization scheme can save on average 80% computation with only a small number of state vectors stored. In addition, the proposed simulation scheme demonstrates great scalability as more computation can be saved with more simulation trials or on future QC devices with reduced error rates.
Efficient extended cubature Kalman filtering for nonlinear target tracking

For states estimation problem of continuous–discrete systems, the numerical approximation methods with high order of accuracy are commonly used to build the continuous–discrete filtering algorithms. However, there is a common contradiction between the computational efficiency and the accuracy. In order to improve the efficiency of state estimation in the continuous–discrete filtering method, continuous–discrete extended cubature Kalman filtering based on Adams–Bashforth–Moulton (ABM) numerical approximation is proposed. ABM is the linear multi-step numerical method, which can achieve the fourth-order accuracy for solving the differential state equation, and its ‘predictor–corrector’ mathematic structure is relatively simple. The performances of ABM method are theoretically analysed; the mixed-type filtering method for continuous–discrete nonlinear states estimation is proposed to integrate the best features of extended Kalman filtering and cubature Kalman filtering. More precisely, the time updates are deduced by extended Kalman filtering whereas the measurement updates are conducted by the third-degree spherical-radial cubature rule. The superior performances of proposed method are illustrated in the simulations under the conditions of different step-sizes and sampling periods.
Quantum random number generation based on spontaneous Raman scattering in standard single-mode fiber

We investigate quantum random number generation based on backward spontaneous Raman scattering in standard single-mode fiber, where the randomness of photon wavelength superposition and arrival time is simultaneously utilized. The experiment uses four avalanche photodiodes working in gated Geiger mode to detect backward Raman scattering photons from four different wavelength channels and a time-to-digital converter placed behind the detectors to record their arrival time. Both information of the wavelength and arrival time interval of photons from different channels are applied to generate random bits. Due to the independence of these two entropy sources, the random number resource of the present system is fully utilized. Five-bit raw data can be obtained for every effective click, which contains 2.87-bit min-entropy. To obtain the optimal generation rate of random bits, appropriate pump power and fiber length are adopted. The post-processing method by the SHA-256 hashing algorithm is used to remove the bias of the raw data, after which the final random bit sequences pass the NIST statistical test.
Remote Atomic Extension (RAE) for Scalable High Performance Computing

Emerging data-intensive applications such as graph analytics, machine learning, and data-driven scientific computing are driving the evolution of high-performance computing (HPC) systems from monolithic to scaled-out, heterogeneous, and complex architectures. In these systems, enormous data sets are mapped to discrete nodes to improve the performance of the system by using distributed storage and computing resources. As such, these data distributions induce frequent cross-node data transactions which challenge the performance of large-scale systems. Global atomic operations are one emerging class of the remote data operations that enable lock-free remote shared data operations. However, the cross-node read-modify-write operations consist of multiple distinct data operations and specific atomicity management, which induces a large amount of overhead. As such, these global atomic operations require an efficient communication methodology Existing advanced compo-nents, such as network interface controllers, network fabrics, network-on-chip (NoC) interconnects, are architected together to improve the system performance. However, complex software infrastructures are needed to provide integration between each discrete component. As a result, the redundant software routines across distinct devices induce a large amount of overhead that causes performance degradationIn this paper, we propose a remote atomic extension (RAE) design that provides inherent ISA-level instructions and micro-architecture support for remote atomic operations based on the RISC-V instruction set architecture (ISA). We design a toolchain and evaluate the RAE infrastructure via simulation. Our experiment results show that RAE eliminates 89.71% of the redundant software instructions used for remote atomic accesses and improves the performance by 17.61% on average (up to 23.35%), compared with the OpenSHMEM.
Which App Features Are Being Used? Learning App Feature Usages from Interaction Data

In the dynamic and fast-growing app market, monitoring and understanding how past releases are actually being used is indispensable for successful app maintenance and evolution. Current app usage analytics tools either log execution events, e.g., in stack traces, or general usage information such as the app activation time, location, and device. In this paper, we focus on analyzing the usages of the single app features as described in release notes and app pages. We suggest monitoring nine app-independent, privacy-friendly interaction events for training a machine learning model to learn app feature usages. We conducted a crowdsourcing study with 55 participants who labeled 5,815 feature usages of 170 unique apps for 18 days. Our within-apps evaluation shows that we could achieve encouraging precision and recall values already with ten labeled feature usages. For certain popular features such as browse newsfeed or send an email, we achieved F1 values above 88%. Betweenapps feature learning seems feasible with F1 values of up to 86%.
Integration Model For Multimedia Education Resource Based On Internet Of Things

In order to overcome the problems of high fusion error rate, low stability and low operating efficiency of traditional multimedia educational resource fusion model, this paper puts forward a multimedia educational resource fusion model based on the internet of things. Using the internet architecture for reference, the multimedia education resource integration model is designed, which consists of three parts: perception layer, network layer and application layer. The process is: using the installed educational resource collection equipment to detect and collect multimedia educational resources. The collected video, audio, image and other multimedia forms are digitised. The data features of resources are extracted and the fusion similarity of multimedia educational resources is calculated. The experimental results show that, compared with the traditional resource integration model, the error rate of the proposed model is reduced by 0.0169%, the stability is improved by 5.32%, and the operation efficiency is improved by 4.5%.
Aspect-level Sentiment Analysis for Social Media Data in the Political Domain using Hierarchical Attention and Position Embeddings

In this paper we present our work on aspect-level sentiment analysis on social media data, specifically in the political domain. Aside from being linguistically irregular, political tweets are often ambiguous or contain sentiments of opposite polarity. To address this, we use a deep learning architecture with a hierarchical attention and position embeddings to enable a finer-grained analysis of sentiments at different positions in the text. Our dataset consists of3022 tweets on the politics domain in Bahasa Indonesia having 1514 unique aspects. We find that there are two important factors for model performance: first, the use of a gating mechanism of appropriate complexity - in our case, LSTM gives the best performance in terms of accuracy and outperforms GRU and RNN by almost 7% in average recall. Second, the use of a trainable embeddings pre-trained on data in similar domains - a trainable Word2Vec embeddings trained on social media data in Bahasa Indonesia gives more than 4% better accuracy than without trainable embeddings. Our analysis also shows that correctly predicted tweets have more variance in attention weights, in contrast to incorrectly predicted ones to which input tokens are often assigned similar weights. This indicates the usefulness of attention mechanism in an aspect-based sentiment analysis.
The Way it Makes you Feel Predicting Users’ Engagement during Interviews with Biofeedback and Supervised Learning

Capturing users' engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collect and analyze users' feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in bespoke software development, or in contexts in which one needs to gather information from specific users. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biofeedback to complement interviews with information about the engagement of the user on the discussed features and topics. We evaluate our approach by interviewing users while gathering their biometric data using an Empatica E4 wristband. Our results show that we can predict users' engagement by training supervised machine learning algorithms on the biometric data. The results of our work can be used to facilitate the prioritization of product features and to guide the interview based on users' engagement.
On Interpretations of tests and effect sizes in regression models with a compositional predictor

Compositional data analysis is concerned with the relative importance of positive variables, expressed through their log-ratios. The literature has proposed a range of manners to compute log-ratios, some of whose interrelationships have never been reported when used as explanatory variables in regression models. This article shows their similarities and differences in interpretation based on the notion that one log-ratio has to be interpreted keeping all others constant. The article shows that centred, additive, pivot, balance and pairwise log-ratios lead to simple reparametrizations of the same model which can be combined to provide useful tests and comparable effect size estimates.
Approaches to Constrained Quantum Approximate Optimization

We study the costs and benefits of different quantum approaches to finding approximate solutions of constrained combinatorial optimization problems with a focus on the maximum independent set. Using the Lagrange multiplier approach, we analyze the dependence of the output on graph density and circuit depth. The Quantum Alternating Operator Ansatz approach is then analyzed, and we examine the dependence on different choices of initial states. This approach, although powerful, is expensive in terms of quantum resources. We also introduce a new algorithm, the dynamic quantum variational ansatz (DQVA), that dynamically adapts to ensure the maximum utilization of a fixed allocation of quantum resources. Our analysis and the new proposed algorithm can also be generalized to other related constrained combinatorial optimization problems.
Not 3D Re-ID: Simple Single Stream 2D Convolution for Robust Video Re-identification

Video-based person re-identification has received increasing attention recently, as it plays an important role within surveillance video analysis. Video-based Re-Id is an expansion of earlier image-based re-identification methods by learning features from a video via multiple image frames for each person. Most contemporary video Re-ID methods utilise complex CNN-based network architectures using 3D convolution or multi-branch networks to extract spatial-temporal video features. By contrast, in this paper, we illustrate superior performance from a simple single stream 2D convolution network leveraging the ResNet50-IBN architecture to extract frame-level features followed by temporal attention for clip level features. These clip level features can be generalised to extract video level features by averaging without any significant additional cost. Our approach uses best video Re-Id practice and transfer learning between datasets to outperform existing state-of-the-art approaches on the MARS, PRID2011 and iLIDS-VID datasets with 89.62%, 97.75%, 97.33% rank-1 accuracy respectively and with 84.61% mAP for MARS, without reliance on complex and memory intensive 3D convolutions or multi-stream networks architectures as found in other contemporary work. Conversely, our work shows that global features extracted by the 2D convolution network are a sufficient representation for robust state of the art video Re-ID.
Issues to Consider When Implementing Digital Pathology for Primary Diagnosis

In the article, the authors discuss the things to consider when using digital pathology for primary diagnosis during the coronavirus disease 2019 (COVID-19) pandemic Topics include their use of digital pathology for primary diagnosis in subspecialty areas in Canada, the need to distinguish open and closed systems in the implementation, and the use of image management system (IMS) and laboratory information system (LSI)
Conditional Generative Adversarial Network for Generating Communicative Robot Gestures

Non-verbal behaviors have an indispensable role for social robots, which help them to interact with humans in a facile and transparent way. Especially, communicative gestures allow robots to have the capability of using bodily expressions for emphasizing the meaning of their speech, describing something, or showing clear intention. This paper presents an approach to learn the synthesis of human actions and natural language. The generative framework is inspired by Conditional Generative Adversarial Network (CGAN), and it makes use of the Convolutional Neural Network (CNN) with the Action Encoder/Decoder for action representation. The experimental and comparative results verified the efficiency of the proposed approach to produce human actions synthesized with text descriptions. Finally, through the Transformation model, the generated data were converted to a set of joint angles of the target robot, being the robot's communicative gestures. By employing the generated human-like actions for robots, it suggests that robots' social cues could be more understandable by humans.
Unsupervised Image Segmentation based Graph Clustering Methods

Image Segmentation by Graph Partitioning is the subject of several research areas, recently, in the ﬁeld of artiﬁcial intelligence and computer vision. In this context, we use graphs as models of images or representations, then we apply a criterion or methodology to divide it into sub-graphs where a graph section consists on systematically removing the edges to generate two sub-graphs. In this paper, we present Several image segmentation algorithms formulated from the graph partition. We test our algorithms on the dataset BRATS and standard test image lenna. Our result are promising.
QueDI: From Knowledge Graph Querying to Data Visualization

Abstract While Open Data (OD) publishers are spur in providing data as Linked Open Data (LOD) to boost innovation and knowledge creation, the complexity of RDF querying languages, such as SPARQL, threatens their exploitation. We aim to help lay users (by focusing on experts in table manipulation, such as OD experts) in querying and exploiting LOD by taking advantage of our target users’ expertise in table manipulation and chart creation. We propose QueDI (Query Data of Interest), a question-answering and visualization tool that implements a scaffold transitional approach to 1) query LOD without being aware of SPARQL and representing results by data tables; 2) once reached our target user comfort zone, users can manipulate and 3) visually represent data by exportable and dynamic visualizations. The main novelty of our approach is the split of the querying phase in SPARQL query building and data table manipulation. In this article, we present the QueDI operating mechanism, its interface supported by a guided use-case over DBpedia, and the evaluation of its accuracy and usability level.