Reviewerly/exHarmony huggingface dataset:
Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on Touchscreens

Suggesting multiple target candidates based on touch input is a possible option for high-accuracy target selection on small touchscreen devices. But it can become overwhelming if suggestions are triggered too often. To address this, we propose SATS, a Suggestion-based Accurate Target Selection method, where target selection is formulated as a sequential decision problem. The objective is to maximize the utility: the negative time cost for the entire target selection procedure. The SATS decision process is dictated by a policy generated using reinforcement learning. It automatically decides when to provide suggestions and when to directly select the target. Our user studies show that SATS reduced error rate and selection time over Shift [51], a magnification-based method, and MUCS, a suggestion-based alternative that optimizes the utility for the current selection. SATS also significantly reduced error rate over BayesianCommand [58], which directly selects targets based on posteriors, with only a minor increase in selection time.
Augmented Perception Through Spatial Scale Manipulation in Virtual Reality for Enhanced Empathy in Design-Related Tasks

This research explores augmented perception by investigating the effects of spatial scale manipulation in Virtual Reality (VR) to simulate multiple levels of virtual eye height (EH) and virtual interpupillary distance (IPD) of the VR users in the design context. We have developed a multiscale VR system for design applications, which supports a dynamic scaling of the VR user’s EH and IPD to simulate different perspectives of multiple user’s groups such as children or persons with disabilities. We strongly believe that VR can improve the empathy of VR users toward the individual sharing or simulating the experience. We conducted a user study comprising two within-subjects designed experiments for design-related tasks with seventeen participants who took on a designer’s role. In the first experiment, the participants performed hazards identification and risks assessment tasks in a virtual environment (VE) while experiencing four different end-user perspectives: a two-year-old child, an eight-year-old child, an adult, and an adult in a wheelchair. We hypothesized that experiencing different perspectives would lead to different design outcomes and found significant differences in the perceived level of risks, the number of identified hazards, and the average height of hazards found. The second experiment had the participants scale six virtual chairs to a suitable scale for different target end-user groups. The participants experienced three perspectives: a two-year-old child, an eight-year-old child, and an adult. We found that when the designer’s perspective matched that of the intended end-user of the product, it yielded significantly lower variance among the designs across participants and more precise scales suitable for the end-user. We also found that the EH and IPD positively correlate with the resulting scales. The key contribution of this work is the evidence to support that spatial scale manipulation of EH and IPD could be a critical tool in the design process to improve the designer’s empathy by allowing them to experience the end-user perspectives. This could influence their design, making a safer or functionally suitable design for various end-user groups with different needs.
Blockchain Token Model for Supply Chain Financing of SMMEs

Financing difficulties are common among the small-, medium- and micro-enterprises (SMMEs). Although supply chain financing alleviates the problems of SMMEs, such as narrow financing channels, intractable financing and expensive financing, however, due to the centralised storage and management of data, the authenticity of data cannot be guaranteed. The credit of the core enterprises in the supply chain cannot penetrate the SMMEs in upstream and downstream. This paper establishes a blockchain pass-through model for supply chain financing by improving the PBFT consensus algorithm based on blockchain’s decentralised and tamper-evident characteristics and the pass-through of SMMEs’ assets in the supply chain. The model improves the circulation efficiency of the supply chain; moreover, it enables the credit of core enterprises to the upstream and downstream, solving the financing dilemma of SMMEs.
Anomaly object detection in x-ray images with Gabor convolution and bigger discriminative RoI pooling

In airports, railway stations and other public places, security inspectors generally use the way of viewing x-ray images for security inspection, so false detection and missed detection often occur. In this paper, an automatic anomaly object detection method in x-ray images is proposed under a two-stage framework. At the first stage, a learnable Gabor convolution layer is introduced into ResNeXt to facilitate the network to capture the edge information of objects. Then, region proposal network (RPN) is used to determine the candidate regions of objects as well as perform coarse classification. At the second stage, bigger discriminative RoI pooling (BDRP) is proposed to classify the candidate boxes to improve the classification accuracy of objects. Furthermore, dense local regression (DLR) is applied to predict the offset of multiple dense boxes in region proposals to locate the objects accurately. Experimental results on the SIXray and OPIXray datasets show that, compared with the state-of-the-art methods, the proposed method can achieve a competitive detection performance.
Word Sense Disambiguation Model based on Bi-LSTM

Word sense disambiguation is a very important task in natural language processing, and it is also a basic work in this field. There are many polysemous words in Chinese vocabulary. Using the word sense disambiguation model can determine the correct meaning of polysemous words in different contexts according to the context of polysemous words, so as to eliminate the ambiguity caused by polysemous words in Chinese. This paper proposes a neural sequence learning model based on Bi-LSTM (bidirectional long short memory) to realize word sense disambiguation. The word vector of the sentence is input into the neural network to train the similarity model, and the different sense items are classified, and the ambiguity is determined by the similarity comparison. The correct sense item of the word thus realizes the disambiguation of the word sense. Experiments show that the disambiguation model proposed in this paper has higher disambiguation accuracy than traditional methods.
OCLAR: logistic regression optimisation for Arabic customers' reviews

The recognition and classification of sentiments in customer feedback are crucial for improving the service experience. Sentiment analysis (SA), as one of natural language processing (NLP) applications, evaluates customers' reviews by computing polarity text sequences. This paper extends the brute grid search methodology with pipeline architecture on OCLAR dataset. The architecture facilitates the search for inverse regularisation strength (IRS) parameter of logistic regression (LR). The experiments are evaluated on simultaneously different measures as like accuracy, area under the curve (AUC), etc. The experiments showed 0.10% improvement in AUC measure using bag-of-words (BoW) with n-gram levels of unigrams, bigrams, and trigrams, whereas 0.9% enhancement has been achieved by using term frequency and inverse document frequency (TF*IDF). In particular, the BoW classifier has achieved the largest improvement of 0.21% recall and 0.27% f-measure in negative predication, whereas it has achieved 0.03% precision and 0.01% f-measure in positive predication.
Automatic Street Lighting System with Vehicle Detection using Deep-Learning Based Remote Sensing

Automated street lightning is advantageous to society as it decreases the rate of accidents, vandalism and street crimes. The ability to detect vehicles and smartly manage the street light system is among the major duties of electrical distribution companies. To recognize an objects of interest in an image by a classical technique called object detection. In order to recover the enactment and reduce the complexity of object detection, numerous computer vision methods have been proposed over the past decade years. Object detection has a wide variety of applications including vehicle detection especially in remote sensing applications. Vigorous and accurate detection of vehicles for such solicitations is a moderately stimulating problem because of discrepancy of color, size, aspect ratio and alignment of vehicles and complex backgrounds of satellite images. Nevertheless, the modern deep learning-based object detection frameworks and convolutional neural networks have great potential for improving the performance of vehicle detection methods in terms of exactness, sturdiness and detection time. This paper will review both conventional and modern object detection systems. Assortment of the supreme appropriate set of object sensor, mainstay story abstraction system, train-test proportion and hyper-parameters for recognition of automobiles for nifty street light system are studied in detail and compared by deep learning based object detectors Single shot multibox detector (SSD) and You Only Look Once (YOLO) and feature abstraction systems counting Alexnet, VGG-16, Resnet-18, Resnet-50. As between accuracy and detection time these methods offer several trade-off options. On mean average precision (mAP), precision vs, recall curves, computational complexity and time complexity is established by relative investigation.
E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches

What makes speeches effective has long been a subject for debate, and until today there is broad controversy among public speaking experts about what factors make a speech effective as well as the roles of these factors in speeches. Moreover, there is a lack of quantitative analysis methods to help understand effective speaking strategies. In this paper, we propose E-ffective, a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. From interviews with domain experts and investigating existing literature, we identified important factors to consider in inspirational speeches. We obtained the generated factors from multi-modal data that were then related to effectiveness data. Our system supports rapid understanding of critical factors in inspirational speeches, including the influence of emotions by means of novel visualization methods and interaction. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information). In our evaluation we studied the influence of our system on experts' domain knowledge about speech factors. We further studied the usability of the system by speaking novices and experts on assisting analysis of inspirational speech effectiveness.
Real Image Denoising With a Locally-Adaptive Bitonic Filter

Image noise removal is a common problem with many proposed solutions. The current standard is set by learning-based approaches, however these are not appropriate in all scenarios, perhaps due to lack of training data or the need for predictability in novel circumstances. The bitonic filter is a non-learning-based filter for removing noise from signals, with a mathematical morphology (ranking) framework in which the signal is postulated to be locally bitonic (having only one minimum or maximum) over some domain of finite extent. A novel version of this filter is developed in this paper, with a domain that is locally-adaptive to the signal, and other adjustments to allow application to real image sensor noise. These lead to significant improvements in noise reduction performance at no cost to processing times. The new bitonic filter performs better than the block-matching 3D filter for high levels of additive white Gaussian noise. It also surpasses this and other more recent non-learning-based filters for two public data sets containing real image noise at various levels. This is despite an additional adjustment to the block-matching filter, which leads to significantly better performance than has previously been cited on these data sets. The new bitonic filter has a signal-to-noise ratio 2.4dB lower than the best learning-based techniques when they are optimally trained. However, the performance gap is closed completely when these techniques are trained on data sets not directly related to the benchmark data. This demonstrates what can be achieved with a predictable, explainable, entirely local technique, which makes no assumptions of repeating patterns either within an image or across images, and hence creates residual images which are well behaved even in very high noise. Since the filter does not require training, it can still be used in situations where training is either difficult or inappropriate.
Can 5G mmWave Support Multi-user AR?

Augmented Reality (AR) has been widely hailed as a representative of ultra-high bandwidth and ultra-low latency apps that will be enabled by 5G networks. While single-user AR can perform AR tasks locally on the mobile device, multi-user AR apps, which allow multiple users to interact within the same physical space, critically rely on the cellular network to support user interactions. However, a recent study showed that multi-user AR apps can experience very high end-to-end latency when running over LTE, rendering user interaction practically infeasible. In this paper, we study whether 5G mmWave, which promises significant bandwidth and latency improvements over LTE, can support multi-user AR by conducting an in-depth measurement study of the same popular multi-user AR app over both LTE and 5G mmWave. Our measurement and analysis show that: (1) The E2E AR latency over LTE is significantly lower compared to the values reported in the previous study. However, it still remains too high for practical user interaction. (2) 5G mmWave brings no benefits to multi-user AR apps. (3) While 5G mmWave reduces the latency of the uplink visual data transmission, there are other components of the AR app that are independent of the network technology and account for a significant fraction of the E2E latency. (4) The app drains 66% more network energy, which translates to 28% higher total energy over 5G mmWave compared to over LTE.
Cosine Model Watermarking against Ensemble Distillation

Many model watermarking methods have been developed to prevent valuable deployed commercial models from being stealthily stolen by model distillations. However, watermarks produced by most existing model watermarking methods can be easily evaded by ensemble distillation, because averaging the outputs of multiple ensembled models can significantly reduce or even erase the watermarks. In this paper, we focus on tackling the challenging task of defending against ensemble distillation. We propose a novel watermarking technique named CosWM to achieve outstanding model watermarking performance against ensemble distillation. CosWM is not only elegant in design, but also comes with desirable theoretical guarantees. Our extensive experiments on public data sets demonstrate the excellent performance of CosWM and its advantages over the state-of-the-art baselines.
Universal Unitary Transfer of Continuous-Variable Quantum States into a Few Qubits

We present a protocol for transferring arbitrary continuous-variable quantum states into a few discrete-variable qubits and back. The protocol is deterministic and utilizes only two-mode Rabi-type interactions that are readily available in trapped-ion and superconducting circuit platforms. The inevitable errors caused by transferring an infinite-dimensional state into a finite-dimensional register are suppressed exponentially with the number of qubits. Furthermore, the encoded states exhibit robustness against noise, such as dephasing and amplitude damping, acting on the qubits. Our protocol thus provides a powerful and flexible tool for discrete-continuous hybrid quantum systems.
Review of economic and mathematical modeling tools for the innovation diffusion process

The relevance of the study is due to the great significance and laboriousness of the problem of modeling the innovation diffusion in complex economic systems, since the analysis and management of the sustainability of the organization's innovative development is an important decision support tool in developing a competitive strategy. The purpose of this paper is to review the features of using approaches of analytical and simulation modeling in describing economic processes and systems, as well as to consider them from the point of view of studying the materiality of the spatio-temporal characteristics of the process of innovation economic diffusion, and to assess the possibility of modeling approaches used for these tasks. The features of using approaches of analytical and simulation modeling in describing innovative changes in economic systems are described. The research methods are descriptive and analytical. The results of the study allow to single out several areas of economic models for the diffusion of innovations and draw a conclusion about the advisability of using natural-scientific approaches to system dynamics.
Production and use of educational technologies in nursing post-graduation

To report, under the light of Gardner's Theory of Multiple Intelligences, the experience of students with advanced educational practices, involving the production and use of technologies in the postgraduation nursing course of the Universidade Federal de Pernambuco.Experience report of students from the post-graduation nursing program from the Universidade Federal de Pernambuco, about the production of knowledge using educational technology and the application of active methodologies in the teaching-learning process for face-to-face and distance teaching.The post-graduate students participated in the educational process in a creative way, both during in-person and distance teaching. They created: domino games, educational videos, parodies, conceptual maps, virtual role-playing, an interactive quiz, among others.The experience of the students with the advanced educational practices, including the production and use of technologies in nursing post-graduation showed that the teaching-learning process can be innovative and planned to go beyond traditional methods.
Segmentation of breast cancer using fuzzy C means and classification by SVM based on LBP features

Recent years many women are affected by breast cancer. Mammogram is one of the early breast cancer diagnosis techniques used to identify the abnormal regions of breast. The recommended research work uses Fuzzy C-means segmentation algorithm to locate the wound area of mammogram breast image. Further the features of abnormal regions were extracted using Local Binary pattern (LBP) techniques. The statistical features are Entropy, Mean, RMS (Root Mean Square), correlation helps to train the neural network. Finally SVM (Support Vector Machine) classifier is utilized to categorize the abnormal regions of mammogram images with the accuracy rates of 86%.
Home monitoring of vital signs and generation of alerts in a cohort of people living with dementia

People with dementia (PwD) are at increased risk of adverse medical events (e.g. infections and falls). These often cause clinical deterioration, and potentially preventable admissions. Remote home monitoring of vital signs using internet-of-things technology can identify risk factors for these events - something particular pertinent during the COVID-19 pandemic. We present data from an on-going UK Dementia Research Institute and Technology Integrated Health Management (DRI-TIHM) project. We aim to define algorithms that generate automated alerts, like the hospital-based NEWS system (Morgan, 1997, Clin Intensive Car), and provide more proactive care for PwD.PwD recorded their systolic/diastolic blood pressure (SBP/DBP), heart rate (HR), temperature (BTM), bodyweight (BW) and oxygen saturation (Sats) daily (figure 1) and data were collected centrally. A 'monitoring team' followed algorithms in response to alerts and advised medical attention if required. Events such as infections, were logged and correlated with the data. The dataset was then used to calculate the number of alerts that would have been raised if different thresholds were used.52 PwD living at home were included. Patients had a range of dementia diagnoses, most commonly Alzheimer's disease. In total, 89,894 measurements were collected over 556 days (figure 2). During the pandemic, a sub-group were given Sats probes and these produced a significant number of false positive results (figure 4f). On sub-group analysis, PwD with Parkinson's disease dementia had significantly lower SBP and DBP (p=0.004 and p=0.01 respectively, Mann-Whitney U) (figure 3a), one of whom suffered from repeated falls (figure 3b). Pilot data were used to set alert thresholds. Average values and number of alerts for each domain are shown (table 1). We then modelled the effect of different thresholds, based on NEWS, to the number of alerts generated (figure 4, table 2). This is informative in optimising sensitivity and specificity of alerts whilst considering the burden on the monitoring service.Remote monitoring can be implemented for PwD. These data can be used to generate clinical alerts that may signify adverse medical outcomes. The thresholds need to be adjusted to optimise efficiency in the context of varying reliability of home monitoring devices.
A novel filter for removing image noise and improving the quality of image

This paper proposed a hybrid wavelet double window median filter (HWDWM) which is made by blending decision-based coupled window median filter and discrete wavelet transform (DWT) and review is made to increase the filters which are widespread for removing noise. In the proposed filter there are double window such as row window and column window. This proposed method takes the noisy image for processing and it moves row window for indexing from the 1st pixel of the noisy image up to the last pixel of the noised image then indexing is made by column window then decompose the signal of the image to provide the localisation. The noisy image is decomposed by DWT and then coefficients are transformed to independent distributed variables. The coefficients are then analysed on the basis of thresholding. The image is reconstructed using wavelet transform's inverse after the threshold. Experiments were executed in order to show the effect of noise removal filters on soil image. Two metrics are used to measure the quality of image; they are: peak signal to noise ratio (PSNR) and root mean square error (RMSE). Experimental results show the superiority of this filter over other noise removal filters.