Reviewerly/exHarmony huggingface dataset:
Noisy Toric code and random-bond Ising model: The error threshold in a dual picture

It is known that noisy topological quantum codes are related to random bond Ising models where the order-disorder phase transition in the classical model is mapped to the error threshold of the corresponding topological code. On the other hand, there is a dual mapping between classical spin models and quantum Calderbank-Shor-Stean (CSS) states where the partition function of a classical model defined on a hypergraph $H$ is written as an inner product of a product state and a CSS state on dual hypergraph $\tilde{H}$. It is then interesting to see what is the interpretation of the classical phase transition in the random bond Ising model within the framework of the above duality and whether such an interpretation has any connection to the error threshold of the corresponding topological CSS code. In this paper, we consider the above duality relation specifically for a two-dimensional random bond Ising model. We show that the order parameter of this classical system is mapped to a coherence order parameter in a noisy Toric code model. In particular, a quantum phase transition from a coherent phase to a non-coherent phase occurs when the initial coherent state is affected by two sequences of bit-flip quantum channels where a quenched disorder is induced by measurement of the errors after the first channel. On the other hand, the above transition is directly related to error threshold of the Toric code model. Accordingly, and since the noisy process can be applied to other topological CSS states, we conclude that the dual correspondence can also provide a useful tool for the study of error thresholds in different topological CSS codes.
Augmented Reality Approach for Marker-based Posture Measurement on Smartphones

Marker tracking for postural and range of motion (ROM) measurements transcends multiple disciplines (e.g., healthcare, ergonomics, engineering). A viable real-time mobile application is currently lacking for measuring limb angles and body posture. To address this need, a novel Android smartphone augmented-reality-based application was developed using the AprilTag2 fiducial marker system. To evaluate the app, two markers were printed on paper and attached to a wall. A Samsung S6 mobile phone was fixed on a tripod, parallel to the wall. The smartphone app tracked and recorded marker orientation and 2D position data in the camera frame, from front and rear cameras, for different smartphone placements. The average error between mobile phone and measured angles was less than 1 degree for all test settings (back camera=0.29°, front camera=0.33°, yaw rotation=0.75°, tilt rotation=0.22°). The average error between mobile phone and measured distance was less than 4 mm for all test settings (back camera=1.8 mm, front camera=2.5 mm, yaw rotation=3 mm, tilt rotation=3.8 mm). Overall, the app obtained valid and reliable angle and distance measurements with smartphone positions and cameras that would be expected in practice. Thus, this app is viable for clinical ROM and posture assessments.
Champion Team Paper: Dynamic Passing-Shooting Algorithm of the RoboCup Soccer SSL 2019 Champion

ZJUNlict became the Small Size League Champion of RoboCup 2019 with 6 victories and 1 tie for their 7 games. The overwhelming ability of ball-handling and passing allows ZJUNlict to greatly threaten its opponent and almost kept its goal clear without being threatened. This paper presents the core technology of its ball-handling and robot movement which consist of hardware optimization, dynamic passing and shooting strategy, and multi-agent cooperation and formation. We first describe the mechanical optimization on the placement of the capacitors, the redesign of the damping system of the dribbler and the electrical optimization on the replacement of the core chip. We then describe our passing point algorithm. The passing and shooting strategy can be separated into two different parts, where we search the passing point on SBIP-DPPS and evaluate the point based on the ball model. The statements and the conclusion should be supported by the performances and log of games on Small Size League RoboCup 2019.
Microsegmentation and Zero Trust: Introduction

When you implement Zero Trust micro-segmentation, all ingress/egress traffic hitting your virtual NIC cards will be compared against a configured list of firewall policies. The packet will be dropped if there is no rule matching the specific traffic flow. A default deny rule at the end ensures that all unrecognized traffic is denied at the vNIC itself. From a security perspective this is called whitelisting or a positive security model, whereby only things that are specifically allowed are accepted—everything else is rejected.
Tell Your Robot What to Do: Evaluation of Natural Language Models for Robot Command Processing

The use of natural language to indicate robot tasks is a convenient way to command robots. As a result, several models and approaches capable of understanding robot commands have been developed, which, however, complicates the choice of a suitable model for a given scenario. In this work, we present a comparative analysis and benchmarking of four natural language understanding models - Mbot, Rasa, LU4R, and ECG. We particularly evaluate the performance of the models to understand domestic service robot commands by recognizing the actions and any complementary information in them in three use cases: the RoboCup@Home General Purpose Service Robot (GPSR) category 1 contest, GPSR category 2, and hospital logistics in the context of the ROPOD project.
A Multi-language Platform for Generating Algebraic Mathematical Word Problems

Existing approaches for automatically generating mathematical word problems are deprived of customizability and creativity due to the inherent nature of template-based mechanisms they employ. We present a solution to this problem with the use of deep neural language generation mechanisms. Our approach uses a Character Level Long Short Term Memory Network (LSTM) to generate word problems, and uses POS (Part of Speech) tags to resolve the constraints found in the generated problems. Our approach is capable of generating Mathematics Word Problems in both English and Sinhala languages with an accuracy over 90%.
Low-Dimensional Decomposition of Manifolds in Presence of Outliers

Existing approaches to robust subspace recovery and outlier identification decompose the data into low-rank plus sparse matrices corresponding to inliers and outliers, respectively. This paper considers a more generalized scheme, whereby the data is decomposed into two components, one corresponding to inliers belonging to low-dimensional manifolds and one to outliers not conforming to the inliers' structure. The inliers may not form a low-rank matrix, nor the outliers a sparse one. An informative encoding of the data is obtained through a kernel-based setting, which captures the underlying structure of the manifold effectively, and leads to a distinguishable representation of outliers from inliers. We introduce a novel measure, gauging each point's ability to represent the whole dataset, through which we certify the identity of a data point to be an inlier or an outlier. An efficient and parallelizable algorithm is developed to improve the scalability of the resulting convex program. Finally, the superiority of the proposed method is showcased with regard to resilience to considerable numbers of random outliers, and robustness to structured outliers and noise components for manifold data.
Residual CRNN and Its Application to Handwritten Digit String Recognition

Recently the Convolutional Recurrent Neural Network (CRNN) architecture has shown success in many string recognition tasks and residual connections are applied to most network architectures. In this paper, we embrace these observations and present a new string recognition model named Residual Convolutional Recurrent Neural Network (Residual CRNN, or Res-CRNN) based on CRNN and residual connections. We add residual connections to convolutional layers as well as recurrent layers in CRNN. With residual connections, the proposed method extracts more efficient image features and make better predictions than ordinary CRNN. We apply this new model to handwritten digit string recognition task (HDSR) and obtain significant improvements on HDSR benchmarks ORAND-CAR-A and ORAND-CAR-B.
Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening

Automated analysis of digital cervix images acquired during Visual Inspection with Acetic acid (VIA) is found to be of great help to aid the physicians to diagnose cervical cancer. Traditional classification methods require many features to distinguish between normal and abnormal cervix. Selection of distinct visual features which well represent the data and at the same time are capable of performing discriminative learning is complex. This problem can be overcome using deep learning approaches. Transfer learning is one of the deep learning approaches, which facilitates the use of a pre-trained network for a specific problem at hand. This paper presents a transfer learning using AlexNet, which is a pre-trained convolutional neural network, for classification of the cervix images into two classes namely negative and positive. This study used 2198 cervix images with 1090 belonging to negative class and 1108 to positive class. Our experiment using AlexNet for transfer learning achieved an accuracy of 0.934.
Depth estimation meets inverse rendering for single image novel view synthesis

In this paper we propose a method for estimating geometry, lighting and albedo from a single image of an uncontrolled outdoor scene. To do so, we combine state-of-the-art deep learning based methods for single image depth estimation and inverse rendering. The depth estimate provides coarse geometry that is refined using the inverse rendered surface normal estimates. Combined with the inverse rendered albedo map, this provides a model that can be used for novel view synthesis with both viewpoint and lighting changes. We show that, on uncontrolled outdoor images, our approach yields geometry that is qualitatively superior to that of the depth estimation network alone and that the resulting models can be re-illuminated without artefacts.
Improving long-distance distribution of entangled coherent state with the method of twin-field quantum key distribution

The twin-field quantum key distribution (TFQKD) protocol has garnered considerable attention in quantum communication because it overcomes the well-known fundamental limit of the secret key rate without quantum repeaters. In this study, we employ this scheme to demonstrate the long-distance distribution of entangled coherent state (ECS), which has not been addressed in the existing literature. We show a scheme for the distribution of ECS with a yield of η, where η is the total efficiency of the whole transmission link. Compared to the cat-state based scheme, the success probability for fidelity is enhanced by 1.86 to 11.54 times in our new scheme, where the ECS is |ψECS(α = 2.0)〉 and the fixed fidelity (F) ranges from 0.99 to 0.75. The performance of our scheme in the presence of realistic on-off photon detector has also been investigated. Our work provides the application of TFQKD method toward continuous variable entanglement distribution and we believe that its application to other quantum information processing protocols are worth investigation in the near future.
Enabling GPU-Enhanced Computer Vision and Machine Learning Research Using Containers

Video analytics frameworks often rely on Neural Networks to perform their tasks. For example, a “You Only Look Once” object detection algorithm applies a single neural network to each image, divides the image into regions, and predicts bounding boxes (weighted by the predicted probabilities) with probabilities for each region. Those algorithms often run more efficiently on hardware accelerators. Libraries which use CUDA enabled GPUs can achieve tremendous advances in speed for those functionalities. Frequently, video analytic researchers develop large solutions to allow them to solve problems with complex setup procedures for other researchers to be able to duplicate their efforts. Here we present a software solution that can be run on multiple computer environments without having to customize systems and software, and support the measurement of the performance of machine learning algorithms on disparate datasets. In this publication, we introduce a common base container that provides GPU-optimized access to common Computer Vision (CV) and Machine Learning (ML) libraries, and can be used as the building container (think Docker FROM) for complex analytics to be interactively designed and tested, and as the base for Docker container images that can be shared between analytics researchers.
A novel Kalman Filter based shilling attack detection algorithm

Collaborative filtering has been widely used in recommendation systems to recommend items that users might like. However, collaborative filtering based recommendation systems are vulnerable to shilling attacks. Malicious users tend to increase or decrease the recommended frequency of target items by injecting fake profiles. In this paper, we propose a Kalman filter-based attack detection model, which statistically analyzes the difference between the actual rating and the predicted rating calculated by this model to find the potential abnormal time period. The Kalman Filter filters out suspicious ratings based on the abnormal time period and identifies suspicious users based on the source of these ratings. The experimental results show that our method performs much better detection performance for the shilling attack than the traditional methods.
Conditional Generative Adversarial Networks for Data Augmentation in Breast Cancer Classification

Automatic breast cancer classification benefits pathologists in obtaining fast and precise diagnoses and improving early detection. However, the performance of deep learning models depends greatly on the quality and quantity of the datasets used. Due to the complexity and high costs of patient data collection, many medical datasets, particularly for pathological conditions, suffer from small sample sizes. Hence, developing a deep learning solution for breast cancer classification is still challenging. Data augmentation is one of the popular approaches to bridge this gap. In this work, we propose to use Conditional Generative Adversarial Networks (CGANs) for data augmentation. The aim of training CGANs is to generate a new set of realistic synthetic images and combine these together with real images to form a new augmented training set. The experiments show that most of the images produced by CGAN are reliable and classification performance with CGAN-based data augmentation can achieve good results. This method, unlike traditional data augmentation, can produce histopathological images that are completely different from the existing data. Therefore, this technique has the potential to address data scarcity and to directly benefit the training of deep learning models.
Social QA in non-CQA platforms

Community Question Answering (cQA) sites have emerged as platforms designed specifically for the exchange of questions and answers among communities of users. Although users tend to find good quality answers in cQA sites, there is evidence that they also engage in a significant volume of QA in other types of social sites, such as microblog platforms. Research indicates that users opt for these non-specific QA social networks because they contain up-to-date information on current events, also due to their rapid information propagation, and social trust. In this sense, we propose that microblog platforms can emerge as a novel, valuable source of information for QA information retrieval tasks. However, we have found that it is not straightforward to transfer existing approaches for automatically retrieving relevant answers in traditional cQA platforms for use in microblogs. This occurs because there are unique characteristics that differentiate microblog data from that of traditional cQA, such as noise and very short text length. In this work, we study (1) if microblog data can be used to automatically provide relevant answers for the QA task, and, in addition, (2) which features contribute the most for finding relevant answers for a particular query. In particular, we introduce a conversation (thread)-level document model, as well as a machine learning ranking framework for microblog QA. We validate our proposal by using factoid-QA as a proxy task, showing that Twitter conversations can indeed be used to automatically provide relevant results for QA. We are able to identify the importance of different features that contribute the most for QA ranking. In addition, we provide evidence that our method allows us to retrieve complex answers in the domain of non-factoid questions.
Play in the smart city context: exploring interactional, bodily, social and spatial aspects of situated media interfaces

Urban media interfaces can assume many forms, offering rich possibilities for interactions and social encounters. We focus on a particular type of urban interfaces, namely urban media installations, which are situated, fixed (i.e. non-mobile) and outdoor interactive interfaces enhanced by digital technologies. Drawing on research on play in the context of the smart city, we aim to clarify how the digital and the physical worlds of urban media installations can coalesce into enjoyable and socially thriving playscapes. Two case studies of urban media installations are presented: the Appearing Rooms, a seasonal art installation in London, and the Mirror Pool, a permanent, large-scale urban installation in Bradford, UK. Each of these interfaces incorporates a design paradigm that differs from the other in terms of context, duration of implementation, and scale. Following a longitudinal approach based on non-participant observations and time-lapse photography, we analyse emergent interactions and focus in particular on playful encounters at different levels and scales: from the micro scale of the bodily engagement to the macro scale of the spatial and social configurations. Our case studies highlight that the urban spatial layout is a key element in defining the emerging interactions and encounters around the urban situated interfaces.
The Analysis of Gomory Algorithm Convergence in Integer Linear Optimization Problem: Projects Management

The obtained result confirms that the process of convergence of the first algorithm of Gomorr may be slow in the process of calculating the command resources necessary for the project implementation with the help of models and methods of discrete optimization. Each iteration of algorithms can be interpreted as an intermediate approximated distribution result in project management tasks. This ensures controlled management of the process of forming a team to effectively achieve the objectives of the project.
Analysis of Key Randomness in Improved One-Time Pad Cryptography

In cryptography, one-time pad (OTP) is claimed to be the perfect secrecy algorithm in several works if all of its features are applied correctly. Its secrecy depends mostly on random keys, which must be truly random and unpredictable. Random number generators are used in key generation. In Psuedo Random Number Generator (PRNG), the possibility of producing numbers that are predictable and repeated exists. In this study, a proposed method using True Random Number Generator (TRNG) and Fisher-Yates shuffling algorithm are implemented to generate random keys for OTP. Frequency (monobit) test, frequency test within a block, and runs tests are performed and showed that the proposed method produces more random keys. Sufficient confusion and diffusion properties are obtained using Pearson correlation analysis.
Improved Ant Colony Optimization with Local Search for Traveling Salesman Problem

Improved Ant Colony Optimization (IACO) with a local search algorithm is proposed for better finding near optimal solution of Traveling Salesman Problem (TSP). In this work, IACO was combined with each of four local search operators in order to enhance IACO exploration capacity. The performances of IACO and the four local search were comparatively tested on a set of benchmark symmetric TSP instances. The experimental results show that IACO.
Apprenticeships as the answer to closing the cyber skills gap

It's become increasingly accepted that the cyber security landscape has become more and more hostile in recent years. Even with the most rose-tinted view of the situation, it's clear that both the volume and sophistication of cyberthreats has continued to climb. Companies have become increasingly aware of the damage that cyber attacks can inflict. But this doesn't always translate into the organisation taking the right action. The focus still tends to fall on the technical aspect of security, overlooking the issue of security skills. Apprenticeships can enable organisations to quickly acquire essential capabilities by upskilling their existing personnel and taking on new candidates to train up. With the cyber landscape getting more hostile, now is the time to explore new options for improving defences, says Steve Thompson of Raytheon Professional Services.
Universal Approximation with Certified Networks

Training neural networks to be certifiably robust is critical to ensure their safety against adversarial attacks. However, while promising, state-of-the-art results with certified training are far from satisfactory. Currently, it is very difficult to train a neural network that is both accurate and certifiably robust. In this work we take a step towards addressing this challenge. We prove that for every continuous function $f$, there exists a network $n$ such that: (i) $n$ approximates $f$ arbitrarily close, and (ii) simple interval bound propagation of a region $B$ through $n$ yields a result that is arbitrarily close to the optimal output of $f$ on $B$. Our result can be seen as a Universal Approximation Theorem for interval-certified ReLU networks. To the best of our knowledge, this is the first work to prove the existence of accurate, interval-certified networks.