Reviewerly/exHarmony huggingface dataset:
Deep Learning for Scene Recognition from Visual Data: A Survey

The use of deep learning techniques has exploded during the last few years, resulting in a direct contribution to the field of artificial intelligence. This work aims to be a review of the state-of-the-art in scene recognition with deep learning models from visual data. Scene recognition is still an emerging field in computer vision, which has been addressed from a single image and dynamic image perspective. We first give an overview of available datasets for image and video scene recognition. Later, we describe ensemble techniques introduced by research papers in the field. Finally, we give some remarks on our findings and discuss what we consider challenges in the field and future lines of research. This paper aims to be a future guide for model selection for the task of scene recognition.
Memory-assisted decoder for approximate Gottesman-Kitaev-Preskill codes

We propose a quantum error correction protocol for continuous-variable finite-energy, approximate Gottesman-Kitaev-Preskill (GKP) states undergoing small Gaussian random displacement errors, based on the scheme of Glancy and Knill [Phys. Rev. A {\bf 73}, 012325 (2006)]. We show that combining multiple rounds of error-syndrome extraction with Bayesian estimation offers enhanced protection of GKP-encoded qubits over comparible single-round approaches. Furthermore, we show that the expected total displacement error incurred in multiple rounds of error followed by syndrome extraction is bounded by $2\sqrt{\pi}$. By recompiling the syndrome-extraction circuits, we show that all squeezing operations can be subsumed into auxiliary state preparation, reducing them to beamsplitter transformations and quadrature measurements.
Ambient Assisted Living Technologies and Environments: Literature review and research agenda

Increasing number of older adults suffer from various physical and cognitive impairments. Therefore, they have difficulties with basic activities of daily living and are exposed to the risk of falls. In the last 20 years, emerging technologies such as wireless communication, internet of things (IoT), sensors, ambient intelligence and cloud computing, have opened up the possibilities to develop various technologies for supporting older adults to remain physically and mentally active and live autonomously, while being engaged in their communities. The aim of this paper is to review the development of Ambient Assistant Technologies and propose a future research agenda.
Tunable and weakly invasive probing of a superconducting resonator based on electromagnetically induced transparency

Superconducting cavities with high quality factors play an essential role in circuit quantum electrodynamics and quantum computing. In measurements of the the intrinsic loss rates of high frequency modes, it can be challenging to design an appropriate coupling to the measurement circuit in such a way that the resulting signal is sufficiently strong but also that this coupling does not lead to unwanted loading circuit, obscuring the intrinsic internal loss rates. Here, we propose and demonstrate a spectroscopic probe of high-Q resonators based on the phenomena of electromagnetically-induced transparency (EIT) between the resonator and qubit in the weak dispersive coupling regime. Applying a sideband drive signal to the qubit, we observe an interference dip originated from EIT in the qubit spectroscopy, originating from the quantum interference between the qubit probe signal and sideband transition. From the width and the depth of the dip, we are able to extract the single-photon linewidth of the resonator from an analytical model. Working in a previously unexplored regime in which the qubit has a larger linewidth than the resonator reduces the technical challenge of making a high-coherence qubit and is advantageous for remaining in the weakly-invasive limit of coupling to the resonator. Furthermore, the sideband and the dispersive coupling between the resonator and the qubit can be tuned $in~situ$ controlling the strength of the sideband drive power. This $in-situ$ tuneability allows the technique to be applied for efficient measurement of the resonator loss rate for any quality factor below a fixed upper bound, on the order of $10^8$ for our device, allowing a wide range of quality factors to probed using a single design.
Evaluating the Performance of Balance Physiotherapy Exercises Using a Sensory Platform: The Basis for a Persuasive Balance Rehabilitation Virtual Coaching System

Rehabilitation programs play an important role in improving the quality of life of patients with balance disorders. Such programs are usually executed in a home environment, due to lack of resources. This procedure usually results in poorly performed exercises or even complete drop outs from the programs, as the patients lack guidance and motivation. This paper introduces a novel system for managing balance disorders in a home environment using a virtual coach for guidance, instruction, and inducement. The proposed system comprises sensing devices, augmented reality technology, and intelligent inference agents, which capture, recognize, and evaluate a patient's performance during the execution of exercises. More specifically, this work presents a home-based motion capture and assessment module, which utilizes a sensory platform to recognize an exercise performed by a patient and assess it. The sensory platform comprises IMU sensors (Mbientlab MMR© 9axis), pressure insoles (Moticon©), and a depth RGB camera (Intel D415©). This module is designed to deliver messages both during the performance of the exercise, delivering personalized notifications and alerts to the patient, and after the end of the exercise, scoring the overall performance of the patient. A set of proof of concept validation studies has been deployed, aiming to assess the accuracy of the different components for the sub-modules of the motion capture and assessment module. More specifically, Euler angle calculation algorithm in 2D (R2 = 0.99) and in 3D (R2 = 0.82 in yaw plane and R2 = 0.91 for the pitch plane), as well as head turns speed (R2 = 0.96), showed good correlation between the calculated and ground truth values provided by experts' annotations. The posture assessment algorithm resulted to accuracy = 0.83, while the gait metrics were validated against two well-established gait analysis systems (R2 = 0.78 for double support, R2 = 0.71 for single support, R2 = 0.80 for step time, R2 = 0.75 for stride time (WinTrack©), R2 = 0.82 for cadence, and R2 = 0.79 for stride time (RehaGait©). Validation results provided evidence that the proposed system can accurately capture and assess a physiotherapy exercise within the balance disorders context, thus providing a robust basis for the virtual coaching ecosystem and thereby improve a patient's commitment to rehabilitation programs while enhancing the quality of the performed exercises. In summary, virtual coaching can improve the quality of the home-based rehabilitation programs as long as it is combined with accurate motion capture and assessment modules, which provides to the virtual coach the capacity to tailor the interaction with the patient and deliver personalized experience.
Representation Sharing for Fast Object Detector Search and Beyond

Region Proposal Network (RPN) provides strong support for handling the scale variation of objects in two-stage object detection. For one-stage detectors which do not have RPN, it is more demanding to have powerful sub-networks capable of directly capturing objects of unknown sizes. To enhance such capability, we propose an extremely efficient neural architecture search method, named Fast And Diverse (FAD), to better explore the optimal configuration of receptive fields and convolution types in the sub-networks for one-stage detectors. FAD consists of a designed search space and an efficient architecture search algorithm. The search space contains a rich set of diverse transformations designed specifically for object detection. To cope with the designed search space, a novel search algorithm termed Representation Sharing (RepShare) is proposed to effectively identify the best combinations of the defined transformations. In our experiments, FAD obtains prominent improvements on two types of one-stage detectors with various backbones. In particular, our FAD detector achieves 46.4 AP on MS-COCO (under single-scale testing), outperforming the state-of-the-art detectors, including the most recent NAS-based detectors, Auto-FPN [42] (searched for 16 GPU-days) and NAS-FCOS [39] (28 GPU-days), while significantly reduces the search cost to 0.6 GPU-days. Beyond object detection, we further demonstrate the generality of FAD on the more challenging instance segmentation, and expect it to benefit more tasks.
A deep learning based wearable system for food and drink intake recognition

Abstract Eating difficulties and the subsequent need for eating assistance are a prevalent issue within the elderly population. Besides, a poor diet is considered a confounding factor for developing chronic diseases and functional limitations. Driven by the above issues, this paper proposes a wrist-worn tri-axial accelerometer based food and drink intake recognition system. First, an adaptive segmentation technique is employed to identify potential eating and drinking gestures from the continuous accelerometer readings. A posteriori, a study upon the use of Convolutional Neural Networks for the recognition of eating and drinking gestures is carried out. This includes the employment of three time series to image encoding frameworks, namely the signal spectrogram, the Markov Transition Field and the Gramian Angular Field, as well as the development of various multi-input multi-domain networks. The recognition of the gestures is then tackled as a 3-class classification problem (‘Eat’, ‘Drink’ and ‘Null’), where the ‘Null’ class is composed of all the irrelevant gestures included in the post-segmentation gesture set. An average per-class classification accuracy of 97.10% was achieved by the proposed system. When compared to similar work, such accurate classification performance signifies a great contribution to the field of assisted living.
A framework for integration of artificial intelligence into digital marketing in Jordanian commercial banks.

This paper investigates the relationship between artificial intelligence (AI), and digital marketing in the Jordanian banking sector. It outlines the main implications of information gathering, data modelling, and processing &amp; delivery, as well, the importance of human communication and ethical implications. Banks need a coherent foundation when employing AI. This paper provides a theoretical background for AI developers, policymakers and marketers in the banking sector, and academics. Despite the extensive employment of artificial intelligence in numerous global and local businesses, few studies addressed the use of AI in the Jordanian banking sector. As well, AI has rapidly changed digital marketing practices, particularly in the light of the Coronavirus (COVID_19) pandemic. Banks in Jordan are oblivious to the challenges they face when integrating AI into their digital marketing services. This paper derives a general framework for integrating AI techniques into digital marketing practices in Jordanian banks. Recommendations designed to assist banks in targeting their clients more efficiently also presented in this paper.&#x0D;
Quantum Error Correction Thresholds for the Universal Fibonacci Turaev-Viro Code

We consider a two-dimensional quantum memory of qubits on a torus which encode the extended Fibonacci string-net code and devise strategies for error correction when those qubits are subjected to depolarizing noise. Building on the concept of tube algebras, we construct a set of measurements and of quantum gates which map arbitrary qubit errors to the string-net subspace and allow for the characterization of the resulting error syndrome in terms of doubled Fibonacci anyons. Tensor network techniques then allow us to quantitatively study the action of Pauli noise on the string-net subspace. We perform Monte Carlo simulations of error correction in this Fibonacci code and compare the performance of several decoders. For the case of a fixed-rate sampling depolarizing noise model, we find an error correction threshold of 4.7% using a clustering decoder.17 MoreReceived 27 April 2021Revised 8 December 2021Accepted 15 February 2022DOI:https://doi.org/10.1103/PhysRevX.12.021012Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum error correctionTopological quantum computingQuantum Information
RepShard: Reputation-based Sharding Scheme Achieves Linearly Scaling Efficiency and Security Simultaneously

Sharding technology is becoming a promising candidate to address the scalability issues in blockchain. The key concept behind sharding technology is to partition the network status into multiple distinct smaller committees, each of which handles a disjoint set of transactions to leverage its capability of parallel processing. However, when introducing sharding technology to blockchain, several key challenges need to be resolved, such as security and heterogeneity among the participating nodes. This paper introduces RepShard, a reputation-based blockchain sharding scheme that aims to achieve both linearly scaling efficiency and system security simultaneously. RepShard adopts a two-layer hierarchical chain structure, consisting of a reputation chain and independent transaction chains. Each transaction chain is maintained within its shard to record transactions, while the reputation chain is maintained by all shards to update the reputation score of each participating node. We leverage a novel reputation scheme to record each participating node's integrated and valid contribution to the system, in which we consider the heterogeneity of participating nodes (e.g., computational resources). The reputation score used in sharding and leader election processes maintains the balance and security of each shard. RepShard relies on verifiable relay transactions for cross-shard transactions to ensure consistency between distinct shards. By integrating reputation into the sharding protocol, our scheme can offer both scalability and security at the same time.
PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector

Positive-unlabeled learning (PU learning) is an important case of binary classification where the training data only contains positive and unlabeled samples. The current state-of-the-art approach for PU learning is the cost-sensitive approach, which casts PU learning as a cost-sensitive classification problem and relies on unbiased risk estimator for correcting the bias introduced by the unlabeled samples. However, this approach requires the knowledge of class prior and is subject to the potential label noise. In this paper, we propose a novel PU learning approach dubbed PULNS, equipped with an effective negative sample selector, which is optimized by reinforcement learning. Our PULNS approach employs an effective negative sample selector as the agent responsible for selecting negative samples from the unlabeled data. While the selected, likely negative samples can be used to improve the classifier, the performance of classifier is also used as the reward to improve the selector through the REINFORCE algorithm. By alternating the updates of the selector and the classifier, the performance of both is improved. Extensive experimental studies on 7 real-world application benchmarks demonstrate that PULNS consistently outperforms the current state-of-the-art methods in PU learning, and our experimental results also confirm the effectiveness of the negative sample selector underlying PULNS.
Texture features in the Shearlet domain for histopathological image classification

Abstract Background A various number of imaging modalities are available (e.g., magnetic resonance, x-ray, ultrasound, and biopsy) where each modality can reveal different structural aspects of tissues. However, the analysis of histological slide images that are captured using a biopsy is considered the gold standard to determine whether cancer exists. Furthermore, it can reveal the stage of cancer. Therefore, supervised machine learning can be used to classify histopathological tissues. Several computational techniques have been proposed to study histopathological images with varying levels of success. Often handcrafted techniques based on texture analysis are proposed to classify histopathological tissues which can be used with supervised machine learning. Methods In this paper, we construct a novel feature space to automate the classification of tissues in histology images. Our feature representation is to integrate various features sets into a new texture feature representation. All of our descriptors are computed in the complex Shearlet domain. With complex coefficients, we investigate not only the use of magnitude coefficients, but also study the effectiveness of incorporating the relative phase (RP) coefficients to create the input feature vector. In our study, four texture-based descriptors are extracted from the Shearlet coefficients: co-occurrence texture features, Local Binary Patterns, Local Oriented Statistic Information Booster, and segmentation-based Fractal Texture Analysis. Each set of these attributes captures significant local and global statistics. Therefore, we study them individually, but additionally integrate them to boost the accuracy of classifying the histopathology tissues while being fed to classical classifiers. To tackle the problem of high-dimensionality, our proposed feature space is reduced using principal component analysis. In our study, we use two classifiers to indicate the success of our proposed feature representation: Support Vector Machine (SVM) and Decision Tree Bagger (DTB). Results Our feature representation delivered high performance when used on four public datasets. As such, the best achieved accuracy: multi-class Kather (i.e., 92.56%), BreakHis (i.e., 91.73%), Epistroma (i.e., 98.04%), Warwick-QU (i.e., 96.29%). Conclusions Our proposed method in the Shearlet domain for the classification of histopathological images proved to be effective when it was investigated on four different datasets that exhibit different levels of complexity.
Deep web, dark web, dark net

Recently, online black markets and anonymous virtual currencies have become the subject of academic research, and we have gained a certain degree of knowledge about the dark web. However, as terms such as deep web and dark net, which have different meanings, have been used in similar contexts, discussions related to the dark web tend to be confusing. Therefore, in this paper, we discuss the differences between these concepts in an easy-to-understand manner, including their historical circumstances, and explain the technology known as onion routing used on the dark web.
Data-driven thresholding in denoising with Spectral Graph Wavelet Transform

This paper is devoted to adaptive signal denoising in the context of Graph Signal Processing (GSP) using Spectral Graph Wavelet Transform (SGWT). This issue is addressed via a data-driven thresholding process in the transformed domain by optimizing the parameters in the sense of the Mean Square Error (MSE) using the Stein’s Unbiased Risk Estimator (SURE). The SGWT considered is built upon a partition of unity making the transform semi-orthogonal so that the optimization can be performed in the transformed domain. However, since the SGWT is over-complete, the divergence term in the SURE needs to be computed in the context of correlated noise. Two thresholding strategies called coordinatewise and block thresholding process are investigated. For each of them, the SURE is derived for a whole family of elementary thresholding functions among which the soft threshold and the James–Stein threshold. This multi-scales analysis shows better performance than the most recent methods from the literature. That is illustrated numerically for a series of signals on different graphs.
Embedding Dynamic Attributed Networks by Modeling the Evolution Processes

Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors.While fairly successful, most existing works focus on the embedding techniques for static networks.But in practice, there are many networks that are evolving over time and hence are dynamic, e.g., the social networks.To address this issue, a high-order spatio-temporal embedding model is developed to track the evolutions of dynamic networks.Specifically, an activeness-aware neighborhood embedding method is first proposed to extract the high-order neighborhood information at each given timestamp.Then, an embedding prediction framework is further developed to capture the temporal correlations, in which the attention mechanism is employed instead of recurrent neural networks (RNNs) for its efficiency in computing and flexibility in modeling.Extensive experiments are conducted on four realworld datasets from three different areas.It is shown that the proposed method outperforms all the baselines by a substantial margin for the tasks of dynamic link prediction and node classification, which demonstrates the effectiveness of the proposed methods on tracking the evolutions of dynamic networks.
EMLight: Lighting Estimation via Spherical Distribution Approximation

Illumination estimation from a single image is critical in 3D rendering and it has been investigated extensively in the computer vision and computer graphic research community. On the other hand, existing works estimate illumination by either regressing light parameters or generating illumination maps that are often hard to optimize or tend to produce inaccurate predictions. We propose Earth Mover Light (EMLight), an illumination estimation framework that leverages a regression network and a neural projector for accurate illumination estimation. We decompose the illumination map into spherical light distribution, light intensity and the ambient term, and define the illumination estimation as a parameter regression task for the three illumination components. Motivated by the Earth Mover distance, we design a novel spherical mover's loss that guides to regress light distribution parameters accurately by taking advantage of the subtleties of spherical distribution. Under the guidance of the predicted spherical distribution, light intensity and ambient term, the neural projector synthesizes panoramic illumination maps with realistic light frequency. Extensive experiments show that EMLight achieves accurate illumination estimation and the generated relighting in 3D object embedding exhibits superior plausibility and fidelity as compared with state-of-the-art methods.