Reviewerly/exHarmony huggingface dataset:
On the Response of a Two-Level System to Two-Photon Inputs

The purpose of this paper is to study the interaction between a two-level system (qubit) and two continuous-mode photons. Two scenarios are investigated: Case 1, how a two-level system changes the pulse shapes of two input photons propagating in a single input channel; and Case 2, how a two-level system responds to two counterpropagating photons, one in each input channel. The steady-state output field states for both cases are derived analytically in both the time and frequency domains. Numerical simulations demonstrate rich and interesting two-photon scattering phenomena induced by a two-level system.
Kartta Labs

This paper introduces the Kartta Labs project, an ongoing open-source and open-data project aiming at organizing the world's historical maps and making them universally accessible and useful. Kartta Labs' framework is designed as a composition of multiple modules. Each module has a crowdsourcing implementation and an artificial intelligence based implementation. The framework takes images of historical maps, registers them in space and time, generates a vector version of the map content, and allows the users to query for the vector content and recreate the historical maps in various cartographic styles. We refer to this process as unrendering. The resulting machine readable map data can support a variety of scientific studies and applications that require long-term, detailed geographic information in the past, while opening up opportunities in other areas such as entertainment. This paper also presents the preliminary results from one automated module to geolocalize a given historical map.
A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation

Reinforcement learning is effective in optimizing policies for recommender systems. Current solutions mostly focus on model-free approaches, which require frequent interactions with a real environment, and thus are expensive in model learning. Offline evaluation methods, such as importance sampling, can alleviate such limitations, but usually request a large amount of logged data and do not work well when the action space is large. In this work, we propose a model-based reinforcement learning solution which models the user-agent interaction for offline policy learning via a generative adversarial network. To reduce bias in the learnt policy, we use the discriminator to evaluate the quality of generated sequences and rescale the generated rewards. Our theoretical analysis and empirical evaluations demonstrate the effectiveness of our solution in identifying patterns from given offline data and learning policies based on the offline and generated data.
On the Edge of a Digital Pathology Transformation: Views from a Cellular Pathology Laboratory Focus Group

Digital pathology has the potential to revolutionize the way clinical diagnoses are made while improving safety and quality. With a few notable exceptions in the UK, few National Health Service (NHS) departments have deployed digital pathology platforms. Thus, in the next few years, many departments are anticipated to undergo the transition to digital pathology. In this period of transition, capturing attitudes and experiences can elucidate issues to be addressed and foster collaboration between NHS Trusts. This study aims to qualitatively ascertain the benefits and challenges of transitioning to digital pathology from the perspectives of pathologists and biomedical scientists in a department about to undergo the transition from diagnostic reporting via traditional microscopy to digital pathology.A focus group discussion was held in the setting of a large NHS teaching hospital's cellular pathology department which was on the brink of transitioning to digital pathology. A set of open questions were developed and posed to a group of pathologists and biomedical scientists in a focus group setting. Notes of the discussion were made along with an audio recording with permission. The discussion was subsequently turned into a series of topic headings and analyzed using content analysis.Identified benefits of digital pathology included enhanced collaboration, teaching, cost savings, research, growth of specialty, multidisciplinary teams, and patient-centered care. Barriers to transitioning to digital pathology included standardization, validation, national implementation, storage and backups, training, logistical implementation, cost-effectiveness, privacy, and legality.Many benefits of digital pathology were identified, but key barriers need to be addressed in order to fully implement digital pathology on a trust and national level.
Away and (Dis)connection

We present a study of long-term outdoor activities, based on altogether 34 interviews with 19 participants. Our goal was not only to explore these enjoyable experiences, but more broadly to examine how technology use was recontextualized 'away' from the everyday. Outdoor activities are commonly presented as an escape from our technology-infused world. In contrast, our interviews reveal experiences that are heavily dependent on technology, both digital and not. However, digital technology - and in particular the mobile phone - is reconfigured when taken out of its ordinary, often urban and indoor, context. We first present a diversity of 'aways' during outdoor activities by depicting cherished freedoms and interpersonal preferences. We then describe how participants managed connection and disconnection while away and upon coming back. To conclude, we discuss how constructions of away can support more purposeful engagements with digital technology, and how pointed (dis)connection can be useful for technology design also in non-outdoor settings.
The high-level benefits of low-level sandboxing

Sandboxing is a common technique that allows low-level, untrusted components to safely interact with trusted code. However, previous work has only investigated the low-level memory isolation guarantees of sandboxing, leaving open the question of the end-to-end guarantees that sandboxing affords programmers. In this paper, we fill this gap by showing that sandboxing enables reasoning about the known concept of robust safety , i.e. , safety of the trusted code even in the presence of arbitrary untrusted code. To do this, we first present an idealized operational semantics for a language that combines trusted code with untrusted code. Sandboxing is built into our semantics. Then, we prove that safety properties of the trusted code (as enforced through a rich type system) are upheld in the presence of arbitrary untrusted code, so long as all interactions with untrusted code occur at the “any” type (a type inhabited by all values). Finally, to alleviate the burden of having to interact with untrusted code at only the “any” type, we formalize and prove safe several wrappers , which automatically convert values between the “any” type and much richer types. All our results are mechanized in the Coq proof assistant.
Four Things Everyone Should Know to Improve Batch Normalization

A key component of most neural network architectures is the use of normalization layers, such as Batch Normalization. Despite its common use and large utility in optimizing deep architectures that are otherwise intractable, it has been challenging both to generically improve upon Batch Normalization and to understand the circumstances that lend themselves to other enhancements. In this paper, we identify four improvements to the generic form of Batch Normalization and the circumstances under which they work, yielding performance gains across all batch sizes while requiring no additional computation during training. These contributions include proposing a method for reasoning about the current example in inference normalization statistics, fixing a training vs. inference discrepancy; recognizing and validating the powerful regularization effect of Ghost Batch Normalization for small and medium batch sizes; examining the effect of weight decay regularization on the scaling and shifting parameters gamma and beta; and identifying a new normalization algorithm for very small batch sizes by combining the strengths of Batch and Group Normalization. We validate our results empirically on five datasets: CIFAR-100, SVHN, Caltech-256, Oxford Flowers102, and ImageNet.
Deep Q-Learning and Particle Swarm Optimization for Bot Detection in Online Social Networks

Social bot is a software program which preforms malicious activities, such as distributing spam content, phishing URLs and creating fake accounts. Moreover, social bots dynamically change their behavior by manipulating their trust value and pretend like a legitimate user in order to avoid detection. Hence, detecting social bots more accurately is an important task. In traditional Q-learning methods, Q-values have to be computed for all learning actions. As a result, convergence of Q-learning will be slow. In this paper, a particle swarm optimization (PSO) method is adopted to improve Q-learning in order to obtain an optimal sequence of learning actions. We define position of particle as a sequence of learning actions with respect to Q-values and the velocity of particle as state transition probability values for choosing a particular action. Further, a Deep Q-Learning algorithm based on Particle Swarm Optimization (DQL-PSO) has been proposed for social bot detection by considering updation strategy of PSO and Q-value function. The experimental results illustrate the efficacy of the proposed algorithm by considering the real-world Twitter dataset.
Developing a Smart Home Solution Based on Personalized Intelligent Interior Units to Promote Activity and Customized Healthcare for Aging Society

Abstract The world’s population is aging at an unprecedented pace. Aging society is not only a severe crisis in the developed world, but also a rigorous challenge in emerging economies. Many age-related diseases are fostered by the lack of physical, cognitive, and social activities. Increasing the activity level has many benefits for the elderly and can improve their independence. Based on the analysis of the state-of-the-art and shortcomings of the current smart home solutions, the EU-funded research project REACH aims to develop a service system that will turn clinical and care environments into personalized modular sensing, prevention, and intervention systems that encourage the elderly to become healthy through various activities. To achieve that goal, the research team developed a series of smart furniture (PI 2 Us) that materialize the research concepts and functionality seamlessly into the various environments. Specifically, the development process and the functionality testing results of the PI 2 Us are reported in detail. In addition, a simulated smart home solution concept (TRACK) integrating key assistive technologies developed is proposed to create a comprehensive interior living and care environment for elderly users. Due to its modularity and flexibility, the partial or entire solution can be easily adapted and rapidly deployed in different use case settings in Europe, which allows the project consortium to execute different testing activities. In the next phase, the research team will focus on enhancing the usability and performance of the prototypes in order to eventually push them to the market. In conclusion, this research provides an innovative smart home solution for the industry to mitigate the impact of aging society.
Application of Music Artificial Intelligence in Preschool Music Education

Abstract AI teaching is an important part of Internet + education and intelligent education, and also an inevitable trend of the times. Through the automatic language and language sense recognition processing technology, the robot can sense the life scene and voice emotion of infants, and automatically recognize and play functional music. Artificial intelligence develops rapidly and is widely used in various fields. Music robot with certain neural network can understand music, analyze music and create music. In the field of professional music education, all kinds of new interactive teaching music intelligent system based on music artificial intelligence technology will be a new mode of perceiving music, cognitive music, creating music and music education.
Optimal Feature Selection for Big Data Classification: Firefly with Lion-Assisted Model

In this article, the proposed method develops a big data classification model with the aid of intelligent techniques. Here, the Parallel Pool Map reduce Framework is used for handling big data. The model involves three main phases, namely (1) feature extraction, (2) optimal feature selection, and (3) classification. For feature extraction, the well-known feature extraction techniques such as principle component analysis, linear discriminate analysis, and linear square regression are used. Since the length of feature vector tends to be high, the choice of the optimal features is complex task. Hence, the proposed model utilizes the optimal feature selection technology referred as Lion-based Firefly (L-FF) algorithm to select the optimal features. The main objective of this article is projected on minimizing the correlation between the selected features. It results in providing diverse information regarding the different classes of data. Once, the optimal features are selected, the classification algorithm called neural network (NN) is adopted, which effectively classify the data in an effective manner with the selected features. Furthermore, the proposed L-FF+NN model is compared with the traditional methods and proves the effectiveness over other methods. Experimental analysis shows that the proposed L-FF+NN model is 92%, 28%, 87%, 82%, and 78% superior to the state-of-art models such as GA+NN, FF+NN, PSO+NN, ABC+NN, and LA+NN, respectively.
Three-Dimensional Shape Detection for Non Uniform Reflective Objects: Combination of Color Light Projection and Camera’s Exposure Adjustment

For non uniform reflective objects, a new three-dimensional (3-D) shape detection method that combines color light projection with camera exposure adjustment is proposed. First, the intensity of the projected fringes is set to maximum. Second, we combine red, green and blue light projection with the camera exposure adjustment to obtain fringe sequences with different intensity and contrast. Finally, the optimal set of fringes is extracted from raw fringes, and used for 3-D shape reconstruction. Experimental results show that the method can successfully reconstruct the 3-D shape of the specular and colored objects with minimum of MSE (mean square error) 0.125 mm.
COVIDSafe, Australia’s Digital Contact Tracing App: The Legal Issues

The Australian government has developed a digital contact tracing app, COVIDsafe, accompanied by a temporary legal framework that is designed to support its dep
Multi-view clustering on data with partial instances and clusters

Most multi-view clustering algorithms apply to data with complete instances and clusters in the views. Recently, multi-view clustering on data with partial instances has been studied. In this paper, we study the more general version of the problem, i.e., multi-view clustering on data with partial instances and clusters in the views. We propose a non-negative matrix factorization (NMF) based algorithm. For the special case with partial instances, it introduces an instance-view-indicator matrix to indicate whether an instance exists in a view. Then, it maps the instances representing the same object to the same vector, and maps the instances representing different objects to different vectors. For the general case with partial instances and clusters, it further introduces a cluster-view-indicator matrix to indicate whether a cluster exists in a view. In each view, it also maps the instances representing the same object to the same vector, but it further makes the elements of the vector 0 if the elements correspond to missing clusters. Then it minimizes the disagreements between the approximated indicator vectors of instances representing the same object. Experimental results show that the proposed algorithm performs well on data with partial instances and clusters, and outperforms existing algorithms on data with partial instances.
Approaches to Information Security in Educational Processes in the Context of Digitalization

The article identifies the goals and objectives of digitalization in education, and presents the forms of implementation of educational processes. It investigates the methods of ensuring information security in educational institutions, and identifying 4 basic types of risk to build a threat model. Investigations into the invasion of our institution were carried out, vulnerabilities were identified for 4 types of risk, the results in the form of successful attacks were shown and recommendations for their elimination were developed. After the recommendations had been implemented, repeated testing was carried out. The results showed a significant decline in the number of successful attacks.
UAV Trajectory Estimation and Deviation Analysis for Contingency Management in Urban Environments

Contingency management of Unmanned Aircraft System (UAS) is an essential part of UAS Traffic Management (UTM). One of such contingencies is the loss of positional information due to weak or loss of Global Positioning System (GPS) signals, which will compromise the safety of the overall UTM system. This paper investigates the UAV trajectory estimation problem when GPS signal is weak or unavailable in urban environments. This research serves two purposes: Firstly, the study employed Extended Kalman Filter (EKF) to estimate the UAV true trajectory when GPS signal is intermittent or unavailable; Secondly, this work analyzed the deviations between pre-defined trajectory and estimated ones in different signal scenarios. The simulation results show that: (1) for uniform distribution of GPS signal, the estimated trajectory will deviate linearly with the decrease of signal density up until a density of 0.1hz and increase exponentially after that; and (2) for non-uniform distribution, the Gaussian distribution performed the worst, followed by random distribution and Sine distribution.
Robotic Chinese language tutor: personalising progress assessment and feedback or taking over your job?

Purpose The shortage of Chinese language teachers have been identified as a pressing issue globally. This paper aims to respond to the needs by investigating and designing the learning innovation with autonomous programmable robot, NAO. Design/methodology/approach By thoughtfully embedding NAO robot into teaching basic Chinese language, this research demonstrates an inquiry qualitative case study of artificial intelligence design principles and learning engagement with rule-based reasoning and progress test design. Findings This state-of-the arts robot use its emotion recognition and body language automated (LED eye with various colours) to demonstrate the Chinese words, to increase learners’ understanding and enhance their memory of the words learned. The responses conclude that the novel learning experience is more fun and interesting, thus the engagement from the axis of novelty, interactivity, motivation and interest is enhanced. Research limitations/implications It is recognised that the number of research participants was small, but the qualitative finding demonstrate key issues and recommendation that may inspire future empirical research. Practical implications Today, robotics is a rapidly growing field and has received significant attention in education. Humanoid robots are now increasingly used in fields such as education, hospitality, entertainment and health care. Educational robots are anticipated to serve as teaching assistants. Originality/value The learning engagement paradigm has shifted from manual engagement to personal response systems or mixed-reality on mobile platforms, and now with the humanoid robot, the recommendation of four principles and future work and for designing humanoid robot as a language tutor are discussed. The educational robot model can be changed to a newer robot such as CANBOT U05E.
Deep reinforcement learning for acceptance strategy in bilateral negotiations

This paper introduces an acceptance strategy based on reinforcement learning for automated bilateral negotiation, where negotiating agents bargain on multiple issues in a variety of negotiation scenarios. Several acceptance strategies based on predefined rules have been introduced in the automated negotiation literature. Those rules mostly rely on some heuristics, which take time and/or utility into account. For some negotiation settings, an acceptance strategy solely based on a negotiation deadline might perform well; however, it might fail in another setting. Instead of following predefined acceptance rules, this paper presents an acceptance strategy that aims to learn whether to accept its opponent's offer or make a counter offer by reinforcement signals received after performing an action. In an experimental setup, it is shown that the performance of the proposed approach improves over time.
Convolutional Neural Network Based Breast Cancer Histopathology Image Classification

Breast cancer is a global health concern, with approximately 30 million new cases projected to be reported by 2030. While efforts are being channeled into curative measures, preventive and diagnostic measures also need to be improved to curb the situation. Convolutional Neural Networks (CNNs) are a class of deep learning algorithms that have been widely adopted for the computerized classification of breast cancer histopathology images. In this work, we propose a set of training techniques to improve the performance of CNN-based classifiers for breast cancer identification. We combined transfer learning techniques with data augmentation and whole image training to improve the performance of the CNN classifier. Instead of conventional image patch extraction for training and testing, we employed a high-resolution whole-image training and testing on a modified network that was pre-trained on the Imagenet dataset. Despite the computational complexity, our proposed classifier achieved significant improvement over the previously reported studies on the open-source BreakHis dataset, with an average image level accuracy of about 91% and patient scores as high as 95%.Clinical Relevance- this work improves on the performance of CNN for breast cancer histopathology image classification. An improved Breast cancer image classification can be used for the preliminary examination of tissue slides in breast cancer diagnosis.