Reviewerly/exHarmony huggingface dataset:
Press-n-Paste: Copy-and-Paste Operations with Pressure-sensitive Caret Navigation for Miniaturized Surface in Mobile Augmented Reality

Copy-and-paste operations are the most popular features on computing devices such as desktop computers, smartphones and tablets. However, the copy-and-paste operations are not sufficiently addressed on the Augmented Reality (AR) smartglasses designated for real-time interaction with texts in physical environments. This paper proposes two system solutions, namely Granularity Scrolling (GS) and Two Ends (TE), for the copy-and-paste operations on AR smartglasses. By leveraging a thumb-size button on a touch-sensitive and pressure-sensitive surface, both the multi-step solutions can capture the target texts through indirect manipulation and subsequently enables the copy-and-paste operations. Based on the system solutions, we implemented an experimental prototype named Press-n-Paste (PnP). After the eight-session evaluation capturing 1,296 copy-and-paste operations, 18 participants with GS and TE achieve the peak performance of 17,574 ms and 13,951 ms per copy-and-paste operation, with 93.21% and 98.15% accuracy rates respectively, which are as good as the commercial solutions using direct manipulation on touchscreen devices. The user footprints also show that PnP has a distinctive feature of miniaturized interaction area within 12.65 mm * 14.48 mm. PnP not only proves the feasibility of copy-and-paste operations with the flexibility of various granularities on AR smartglasses, but also gives significant implications to the design space of pressure widgets as well as the input design on smart wearables.
Distributed Learning on Mobile Devices: A New Approach to Data Mining in the Internet of Things

It is well known that deep learning is one of the most important methods for data mining. With the development of the fifth-generation mobile networks (5G) and the Internet of Things (IoT), the large volume of data collected in IoTs provides a new way to improve the capability of deep learning. Due to privacy, bandwidth, and legal concerns, it is impractical to send the data to a server or the cloud. The computing power of mobile devices makes it possible to process the data. Therefore, this article focuses on training these models in mobile devices. To solve the challenges, including unreliable networks, constrained resources, and slow convergence, we let multiple mobile devices learn a shared model collaboratively. We propose a novel architecture, GREAT, where each node chooses partners to share local model parameters according to link reliability. To balance the constrained resources and learning effectiveness, an optimization problem is developed by taking the reliability threshold as the variable of controlling the resources’ overhead. To implement this architecture, a dynamic control algorithm called Alpha-GossipSGD has been proposed. Its performance is evaluated by extensive experiments, which show that Alpha-GossipSGD can realize stable learning effectiveness over unreliable networks with constrained resources.
Nonlinear color space coded by additive digital pulses

Additive color mixing modulated by digital pulses enables universal applications in lights and displays. Conventional methods map binarized signals to ordinary red–green–blue color spaces, loosely connected with the color perception of human eyes, causing the complexity of gamut mapping and inaccuracy of chromatic manipulations. Here we developed a complete theory that encodes and decodes digital signals directly to a perceptually nonuniform color space of the commission internationale de l’éclairage, featuring a strict bijection between the duty cycles of each binary pulse and color components of the mixed light and an analytic nonlinear gamut volume. Exemplary applications in biophotonic lighting, active full-color displaying, and metaphotonic sensing confirmed our theory. The method applies to the fields with accurate manipulations of chromaticity and luminosity of a light, opening an avenue toward the next generation of perceptual displays, cameras, and sensors.
Training Effective Neural Networks on Structured Data with Federated Learning

Federated Learning decreases privacy risks when training Machine Learning (ML) models on distributed data, as it removes the need for sharing and centralizing sensitive data. However, this learning paradigm can also influence the effectiveness of the obtained prediction models. In this paper, we specifically study Neural Networks, as a powerful and popular ML model, and contrast the impact of Federated Learning on the effectiveness compared to a centralized approach – when data is aggregated at one place before processing – to assess to what extent Federated Learning is suited as a replacement. We also analyze the effect of non-independent and identically distributed (non-iid) data on effectiveness and convergence speed (efficiency) of Federated Learning. Based on this, we show in which scenarios (depending on the dataset, the number of nodes in the setting and data distribution) Federated Learning can be successfully employed.
Hybrid Learning Method for Image Segmentation

Image segmentation by graph partitioning is popular in the field of artificial intelligence and computer vision, so it is the subject of several researches due to the good performance in a wide range of applications. Many image segmentation techniques employ classical machine learning processes and extract features according to machine-learning methods whereas classifying features via highly specialized training programs. It is becoming, an important branch of artificial intelligence and computer science. This paper put forward a new method based on traditional machine learning, which combines Random forest with the problem of Image Segmentation by Graph Partitioning. This paper introduced a novel clustering algorithm based on a Graph cut generated with a random forest. We test our method on the dataset BRATS, some lungs Images, and standard test image Lenna.
Improvement of entanglement via catalytic quantum scissors

We theoretically investigate the generation of a novel non-Gaussian entangled state by using two single-photon catalytic quantum scissors (CQS) on an input two-mode squeezed vacuum state, and derive the effective operator of the CQS which is closely associated with the transmittance of beam splitters. The output state of this setup composes of the zero-, single- and two-photon entangled states. The results show that both larger success probability of achieving such event and higher fidelity between the input and output states can be attained by taking appropriate parameters of the transmittance and the initial squeezing parameters. Moreover, the entanglement properties of the generated non-Gaussian entangled state are discussed in terms of the degree of entanglement, the Einstein–Podolsky–Rosen (EPR) correlation and the two-mode squeezing effect. Our simulation results indicate that, in the small initial squeezing levels, there are always two improved areas of entanglement at the low and high transmittance ranges, respectively. Comparing to the improved areas of the entanglement, we find that both EPR correlation and two-mode squeezing effect rather than the entanglement degree are more rigorous in the measurement of the improved entanglement. These results show that the CQS operation is able to efficiently enhance the entanglement, which may be useful for the continuous-variable quantum communication.
Future of Augmented Reality in Healthcare Department

In today’s world, augmented reality (AR) is a highly challenging and immerging technology which presents some additional information to the existing real world. This is done by using special glasses like Google glasses or with help of advanced devices. This technology is an advance version of virtual reality. As, in VR, we have to work in a completely virtual environment, but in this technology, we do not have to work in virtual world, but being in the real world, we are getting some additional information. This paper provides a brief description about the architecture of AR, possible solutions provided by several researchers, and academicians, their challenging issues and real-time application in medical or emergency department.
FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs

This paper presents the FinMatcher system and its results for the FinSim 2021 shared task which is co-located with the Workshop on Financial Technology on the Web (FinWeb) in conjunction with The Web Conference. The FinSim-2 shared task consists of a set of concept labels from the financial services domain. The goal is to find the most relevant top-level concept from a given set of concepts. The FinMatcher system exploits three publicly available knowledge graphs, namely WordNet, Wikidata, and WebIsALOD. The graphs are used to generate explicit features as well as latent features which are fed into a neural classifier to predict the closest hypernym.
A correlation-based approach to detecting wireless physical covert channels

With the rapid development of wireless communication technology, the openness of wireless communication makes it more likely to be threated by radio interception or even maliciously use. As a widely used way to exfiltrate information, the wireless covert channel brings a great threat to communication security. Compared with the wireless covert channel on the upper layer, the wireless covert channel based on the physical layer (WCC-P) has better concealment and greater capacity. However, due to the high variation in the wireless communication channel, detecting WCC-P is a challenging task. Existing detection schemes are ineffective at detecting most of WCC-P schemes. In this paper, we propose a new correlation-based scheme to detect WCC-P communication signal. The proposed detection scheme is based on the correlation coefficient of the constellation error vectors (CEV) for multiple antennas. The difference in the correlation coefficient of a detection process provides a critical clue for WCC-P signal detection. Exploiting this observation, we investigate the use of the correlation coefficient in detecting WCC-P communication signal. Simulations show that the proposed scheme can effectively detect WCC-P signal. When the signal-to-noise ratio (SNR) reaches 5 dB, the detection accuracy can reach more than 90%.
Readability of online sources of information regarding epilepsy surgery and its impact on decision-making processes

<h2>Abstract</h2><h3>Introduction</h3> People with epilepsy can have cognitive deficits, including difficulty with reading tasks. This can potentially impact on how written information is understood. Websites increasingly provide information about different medical interventions including epilepsy surgery. Our aim was to assess the readability of a sample of one hundred patient-oriented educational English language websites related to epilepsy surgery. <h3>Methods</h3> A Google search was carried out using the terms <i>epilepsy</i> and <i>surgery</i>, and a sample of forty-nine websites from both the UK and the US were chosen. These websites were uploaded to a freely available online readability scoring tool (readabilityformulas.com) and seven measures of readability generated were examined. Other data including use of figures/diagrams, patient narratives, and mention of the risks/benefits of surgery were noted. <h3>Results</h3> The majority of the websites analyzed in this study were found to be 'difficult' to read for the average reading level of the population (<i>n</i> = 1-impossible, 10-very difficult, 23-difficult, 14 fairly difficult). Only 1 website was considered suitable for average reading level. The use of infographics was variable – some webpages used them extensively to reiterate the textual information and other websites did not. <h3>Discussion and conclusion</h3> The available online patient information literature is likely to be too difficult for the average reader to understand. It is important that websites are accessible, reliable, and aid understanding related to epilepsy surgery, taking potential cognitive deficits into consideration.
Taking an Extended Embodied Perspective of Touch: Connection-Disconnection in iVR

Bringing touch into VR experiences through haptics is considered increasingly important for user engagement and fostering feelings of presence and immersion, yet few qualitative studies have explored users' iVR touch experiences. This paper takes an embodied approach–bringing attention to the tactile-kinaesthetic body–to explore users' wholistic experiences of touch in iVR, moving beyond the cutaneous and tactile elements of “feeling” to elaborate upon themes of movement and kinetics. Our findings show how both touch connections and disconnections emerged though material forms of tactility (the controller, body positioning, tactile expectations) and through “felt proximities” and the tactile-kinaesthetic experience thus shaping the sense of presence. The analysis shows three key factors that influence connection and disconnection, and how connection is re-navigated or sought at moments of experienced disconnection: a sense of control or agency; identity; and bridging between the material and virtual. This extended notion of touch deepens our understanding of its role in feelings of presence by providing insight into a range of factors related to notions of touch – both physical and virtual–that come into play in creating a sense of connection or presence (e.g., histories, expectations), and highlights the potential for iVR interaction to attend to the body beyond the hands in terms of touch.
A Mobile-Based Farm Machinery Hiring System

Agriculture as the oldest profession dates back to the Stone Age. Advances in technologies like diesel engine tractors and other tools with hydrostatic capability and control brought about agricultural mechanization which increased food productivity and industrialization. The aim of this research work is to design a mobile application for distributing or leasing agricultural machineries to farmers using locations-based services. The design also took into consideration the configuration of the various topologies and other factors that could enhance the flexibility of a mobile application of this nature. The user platform is categorized into three sections: the presentation layer, the business layer, and the data layer. The presentation layer is focused on the design logic and the navigational tools used in locating the right hiring type. It is also responsible for choosing the right data format using data validation technique to protect the app from invalid data entry. The business layer is responsible for logging in, authentication, exception handling, and security matters. While the data layer is focused on facilitating secure data transactions, the beauty of it is that it can be rescaled over time to meet the challenges of the time. The application was developed using JavaScript and MySQL with Phonegap/Cordova, XAMMP, and PHP for the backend. It was validated using formative evaluation which was conducted using interviews and open-ended questionnaires. The results of usability test obtained are promising.
Understanding Differential Privacy

Abstract Differential privacy promises to strike a balance between the protection of privacy for individuals and the generation of insights from data. But how exactly does it work? Christoph F. Kurz works through a simple hypothetical example
Reversible Sub-Feature Retrieval: Toward Robust Coverless Image Steganography for Geometric Attacks Resistance

Traditional image steganography hides secret information by embedding, which inevitably leaves modification traces and is easy to be detected by steganography analysis tools.Since coverless steganography can effectively resist steganalysis, it has become a hotspot in information hiding research recently.Most coverless image steganography (CIS) methods are based on mapping rules, which not only exposes the vulnerability to geometric attacks, but also are less secure due to the revelation of mapping rules.To address the above issues, we introduced camouflage images for steganography instead of directly sending stego-image, which further improves the security performance and information hiding ability of steganography scheme.In particular, based on the different sub-features of stego-image and potential camouflage images, we try to find a larger similarity between them so as to achieve the reversible steganography.Specifically, based on the existing CIS mapping algorithm, we first can establish the correlation between stego-image and secret information and then transmit the camouflage images, which are obtained by reversible sub-feature retrieval algorithm.The received camouflage image can be used to reverse retrieve the stego-image in a public image database.Finally, we can use the same mapping rules to restore secret information.Extensive experimental results demonstrate the better robustness and security of the proposed approach in comparison to state-of-art CIS methods, especially in the robustness of geometric attacks.
Partition-then-Overlap Method for Labeling Cyber Threat Intelligence Reports by Topics over Time

The Topics over Time (TOT) model allows users to be aware of changes in certain topics over time. The proposed method inputs the divided dataset of security blog posts based on a fixed period using an overlap period to the TOT. The results suggest the extraction of topics that include malware and attack campaign names that are appropriate for the multi-labeling of cyber threat intelligence reports.
A Survey on Recent Advances in Image Captioning

Abstract Image captioning, an interdisciplinary research field of computer vision and natural language processing, has attracted extensive attention. Image captioning aims to produce reasonable and accurate natural language sentences to describe images. It requires the captioning model to recognize objects and describe their relationships accurately. Intuitively, it is difficult for a machine to have the general image understanding ability like human beings. However, deep learning provides the basis for intelligent exploration. In this review, we will focus on recent advanced deep methods for image captioning. We classify existing methods into different categories and discuss these categories respectively; meanwhile, we discuss the related datasets and evaluation metrics. We also prospect the future research directions.
An image stitching algorithm for the mineralogical analysis

Mineralogical analysis performed by automatic techniques, it includes qualitative and quantitative data about composition, grain size and mineral liberation, is a very important process while being the first step in the mineral processing and is one of the most useful parameters for metallurgists. Currently, there are many mineralogical analysis methods, but the problem is that analysis equipment is expensive and in-situ analysis is not possible. Therefore, in this paper, we have proposed an in-situ mineralogical analysis method using a relatively inexpensive handheld microscope. At this time, image stitching was applied to solve the inconvenience of observation due to the small field of view of the microscope. Experiments using a ferruginous quartzite ore confirmed that this method is very useful for immediate mineralogical analysis and detection of interesting areas for more detailed analysis. The algorithm suggested in this paper can be applied to the development of a dedicated mineralogical analysis system in future.
Prediction and Classification

In this chapter, a complete overview of prediction and classification processes has been provided. The concept of prediction is explained with simple linear regression examples that are easily and manually solved; with this, in-depth idea of regression is understandable for readers. In continuation to this, multiple linear regression is also explained. After this, the concept of classification has been explained through logistic regression. Complete process of feature extraction and classification has been demonstrated by real ECG dataset. MatLab codes have also been given to get the real experience of predication and classification processes. After getting an idea of regression and logistic function used for classification, the concept of artificial neural networks becomes very easy to understand. Basic and necessary steps of ANN and its training and testing have been explained. After reading this chapter and following the given examples, readers will easily understand the complex models of ANN and deep neural networks for the classification and prediction problems.
The Price of Meltdown and Spectre

The Meltdown and Spectre hardware vulnerabilities shocked hardware manufacturers and system users upon discovery. Numerous attack vectors and mitigations have been developed and deployed. However, due to the deep entanglement in core CPU components they will be an important topic for years. Although the performance overhead of software mitigations has been examined closely, the energy overhead has experienced little attention---even though the energy demand is a critical cost factor in data centres.
Real-Time Evaluation Algorithm of Human Body Movement in Football Training Robot

As one of the most challenging topics in the field of artificial intelligence, soccer robots are currently an important platform for humanoid robotics research. Its fields cover a wide range of fields, including robotics, artificial intelligence, and automatic control. Kinematics analysis and action planning are the key technologies in the research of humanoid soccer robots and are the basis for realizing basic actions such as walking. This article mainly introduces the real-time evaluation algorithm of human motion in the football training robot. The football robot action evaluation algorithm proposed here designs the angle and wheel speed of the football robot movement through the evaluation of the angular velocity and linear velocity of the center of mass of the robot. The overall system of the imitation human football robot is studied, including the mechanical system design. The design of the leg structure, the decision-making system based on the finite state machine, the robot vision system, and the image segmentation technology are introduced. The experimental results in this article show that the action of the football training robot model is very stable, the static rotation movement time is about 220 ms, and the fixed-point movement error is less than 1 cm, which fully meets the accuracy requirements of the large-space football robot.