Reviewerly/exHarmony huggingface dataset:
Universal Classical Optical Computing Inspired by Quantum Information Process

Quantum computing has attracted much attention in recent decades, since it is believed to solve certain problems substantially faster than traditional computing methods. Theoretically, such an advance can be obtained by networks of the quantum operators in universal gate sets, one famous example of which is formed by CNOT gate and single qubit gates. However, realizing a device that performs practical quantum computing is tricky. This is because it requires a scalable qubit system with long coherence time and good controls, which is harsh for most current platforms. Here, we demonstrate that the information process based on a relatively stable system -- classical optical system, can be considered as an analogy of universal quantum computing. By encoding the information via the polarization state of classical beams, the optical computing elements that corresponds to the universal gate set are presented and their combination for a general information process are theoretically illustrated. Taking the analogy of two-qubit processor as an example, we experimentally verify that our proposal works well. Considering the potential of optical system for reliable and low-energy-consuming computation, our results open a new way towards advanced information processing with high quality and efficiency.
MICAR: multi-inhabitant context-aware activity recognition in home environments

Abstract The sensor-based recognition of Activities of Daily Living (ADLs) in smart-home environments enables several important applications, including the continuous monitoring of fragile subjects in their homes for healthcare systems. The majority of the approaches in the literature assume that only one resident is living in the home. Multi-inhabitant ADLs recognition is significantly more challenging, and only a limited effort has been devoted to address this setting by the research community. One of the major open problems is called data association , which is correctly associating each environmental sensor event (e.g., the opening of a fridge door) with the inhabitant that actually triggered it. Moreover, existing multi-inhabitant approaches rely on supervised learning, assuming a high availability of labeled data. However, collecting a comprehensive training set of ADLs (especially in multiple-residents settings) is prohibitive. In this work, we propose MICAR: a novel multi-inhabitant ADLs recognition approach that combines semi-supervised learning and knowledge-based reasoning. Data association is performed by semantic reasoning, combining high-level context information (e.g., residents’ postures and semantic locations) with triggered sensor events. The personalized stream of sensor events is processed by an incremental classifier, that is initialized with a limited amount of labeled ADLs. A novel cache-based active learning strategy is adopted to continuously improve the classifier. Our results on a dataset where up to 4 subjects perform ADLs at the same time show that MICAR reliably recognizes individual and joint activities while triggering a significantly low number of active learning queries.
Nonclassical correlated deterministic single-photon pairs for a trapped atom in bimodal cavities

Single photons and single-photon pairs, inherently nonclassical in their nature, are fundamental elements of quantum sciences and technologies. Here, we propose to realize the nonclassical correlated deterministic photon pairs at the single-photon level for a single atom trapped in bimodal cavities. It is shown that the photon emissions for bimodal cavities exhibit a high single-photon purity and relatively large intracavity photon numbers, which are ascribed to the combination of optical Stark-shift enhanced energy-spectrum anharmonicity and quantum interference suppressed two-photon excitation. Our scheme generates photon-photon pairs with strong photon blockade for cavity modes via constructing a highly tunable cavity-enhanced deterministic parametric down-conversion process. Furthermore, we show that the cross-correlation function between the two cavity modes vastly violates the Cauchy-Schwarz inequality of the classical boundary, which unambiguously demonstrates the nonclassicality of the single-photon pairs. Our result reveals a prominent strategy to generate the high-quality two-mode single-photon sources with strong nonclassical correlation, which could provide versatile applications in distribution of quantum networks, long-distance quantum communication, and fundamental tests of quantum physics.
A Data Flow Design Tool and Method for IoT System Using Program Net

The bad design of IoT system data flow causes errors under certain circumstances. In this paper, we propose a kind of web application named data flow design tool, which can simulate data flow to discover data pile-up and help user redesign the IoT system. Using an autonomous driving system as an example, we present the usage of our tool and illustrate how data pile-up may cause problem in the data flow design. Then, we show that a user can redesign the system to avoid data pile-up and verify the data flow new design using our design tool.
Cloud computing security challenges, analysis of security problems and cloud computing forensics issues

Cloud computing is a new computing model that allows businesses to adopt IT without making a large upfront commitment. Despite the potential benefits of the internet, model security remains a concern, which has a negative influence on cloud adoption. The security challenge gets more difficult under the data center, as additional dimensions such as model design, multitenancy, elasticity, and the layers dependency stack have been added to the problem scope. We present a thorough examination of the cloud security challenge in this article. We looked at the issue from the standpoints of network infrastructure, cloud-provided features, cloud consumers, and cloud service delivery methods. We develop a thorough characterization of the data protection challenge and essential aspects that any suggested security solution should address based on this research. Cloud computing is a fast-expanding field of research that depends on spreading computing power instead of using a dedicated server or smart devices to run programmers. The majority of the growth in this sector is attributable to the transition from a standard IT subscription model to a unique cloud model, as well as the widespread availability of electronic and digital gadgets. Cloud computing offered a significant danger and difficulty for information system projects, but it also provided them with several possibilities to improve their data processing. Furthermore, cloud users and consumers have yet to develop appropriate forensic abilities to aid in the detection of illegal activity in the cloud. Although the cloud offers some potential technological and economic benefits, consumers continue to be hesitant to adopt it, mostly owing to security concerns and the difficulty of conducting cloud proper investigation. Some study has been done in this area, and strategies for conducting forensic investigations have been proposed. In this research paper, we begin by analyzing the intrusion detection progress made by other academics, and then we analyze and evaluate our conclusions in order to assess the potential and difficulties that cloud forensics face based on these findings.
Heavy Rain Face Image Restoration: Integrating Physical Degradation Model and Facial Component-Guided Adversarial Learning

With the recent increase in intelligent CCTVs for visual surveillance, a new image degradation that integrates resolution conversion and synthetic rain models is required. For example, in heavy rain, face images captured by CCTV from a distance have significant deterioration in both visibility and resolution. Unlike traditional image degradation models (IDM), such as rain removal and super resolution, this study addresses a new IDM referred to as a scale-aware heavy rain model and proposes a method for restoring high-resolution face images (HR-FIs) from low-resolution heavy rain face images (LRHR-FI). To this end, a two-stage network is presented. The first stage generates low-resolution face images (LR-FIs), from which heavy rain has been removed from the LRHR-FIs to improve visibility. To realize this, an interpretable IDM-based network is constructed to predict physical parameters, such as rain streaks, transmission maps, and atmospheric light. In addition, the image reconstruction loss is evaluated to enhance the estimates of the physical parameters. For the second stage, which aims to reconstruct the HR-FIs from the LR-FIs outputted in the first stage, facial component-guided adversarial learning (FCGAL) is applied to boost facial structure expressions. To focus on informative facial features and reinforce the authenticity of facial components, such as the eyes and nose, a face parsing-guided generator and facial local discriminators are designed for FCGAL. The experimental results verify that the proposed approach based on a physical-based network design and FCGAL can remove heavy rain and increase the resolution and visibility simultaneously. Moreover, the proposed heavy rain face image restoration outperforms state-of-the-art models of heavy rain removal, image-to-image translation, and super resolution.
Self-attention-guided scale-refined detector for pedestrian detection

Abstract Pedestrian detection has been researched for decades. Recently, an anchor-free method CSP is proposed to generate the pedestrian bounding box directly. When the predicted center deviates from the ground truth in the testing phase, the CSP model generates deviated pedestrian bounding box, which leads to false detection in occlusion situations. To handle this problem, we refine the scale regression branch of the CSP model to generate a more accurate prediction. The new scale regression branch outputs the distances between the center and the four edges of the pedestrian bounding box. Even if the predicted center deviates from the ground truth, an accurate bounding box can still be obtained. Moreover, we integrate a self-attention module into our model to take full advantage of the features in different depth layers. Our proposed model achieves better performance than the state-of-the-art detectors in comparison experiments on the two datasets, i.e., Citypersons and Caltech.
More than feelings? How Facebook reaction icons affect online users' behavioral intentions toward online health rumor posts

Purpose When the spread of online health rumors on social media causes public concerns, the public is calling for action. However, little study has investigated how Facebook reaction icons (expressing feelings function) affect online users' behavioral intentions (intention to trust and share) toward online health rumor posts. The current study addresses this gap by focusing on the effect of Facebook reaction icons in two conditions: Facebook reaction icons' presence (versus absence), and Facebook reaction icons' emotional valence (positive versus negative versus neutral). Moreover, the authors also investigated the interaction between Facebook reaction icons' emotional valence and online health rumor posts' framing headlines (gain versus loss). Design/methodology/approach The authors used a 7 (Facebook reaction icons: Love, Like, Haha, Wow, Sad, Angry and no icon) × 2 (Facebook framing headlines: gain and loss) between-subjects design, analyzing 507 samples from online users with one-way ANOVA and MANOVA. Findings Results show that online health rumor posts without Facebook reaction icons are more likely to negatively change online users' behavioral intentions than the posts with Facebook reaction icons; negative reaction icons (Sad and Angry) lower online users' behavioral intentions than positive reaction icons (Love and Like). Further, the incongruency effect of interaction (i.e. positive reaction icons with a negative message) would have more negative effects on online users' behavioral intentions than the congruency effect (i.e. positive reaction icons with a positive message). Originality/value This study has rich contributions to theoretical and practical implications for the Facebook platform and Facebook users to apply Facebook reaction icons against online health rumor posts.
Persona-Guided Planning for Controlling the Protagonist’s Persona in Story Generation

Endowing the protagonist with a specific personality is essential for writing an engaging story.In this paper, we aim to control the protagonist's persona in story generation, i.e., generating a story from a leading context and a persona description, where the protagonist should exhibit the specified personality through a coherent event sequence.Considering that personas are usually embodied implicitly and sparsely in stories, we propose a planning-based generation model named CONPER to explicitly model the relationship between personas and events.CON-PER first plans events of the protagonist's behavior which are motivated by the specified persona through predicting one target sentence, then plans the plot as a sequence of keywords with the guidance of the predicted persona-related events and commonsense knowledge, and finally generates the whole story.Both automatic and manual evaluation results demonstrate that CONPER outperforms state-of-the-art baselines for generating more coherent and persona-controllable stories.
Computing the lowest eigenstate of tight-binding Hamiltonians using quantum walks

Finding or estimating the lowest eigenstate of quantum system Hamiltonians is an important problem for quantum computing, quantum physics, quantum chemistry, and material science. Several quantum computing approaches have been developed to address this problem. The most frequently used method is variational quantum eigensolver (VQE). Many quantum systems, and especially nanomaterials, are described using tight-binding Hamiltonians, but until now no quantum computation method has been developed to find the lowest eigenvalue of these specific, but very important, Hamiltonians. We address the problem of finding the lowest eigenstate of tight-binding Hamiltonians using quantum walks. Quantum walks is a universal model of quantum computation equivalent to the quantum gate model. Furthermore, quantum walks can be mapped to quantum circuits comprising qubits, quantum registers, and quantum gates and, consequently, executed on quantum computers. In our approach, probability distributions, derived from wave function probability amplitudes, enter our quantum algorithm as potential distributions in the space where the quantum walk evolves. Our results showed the quantum walker localization in the case of the lowest eigenvalue is distinctive and characteristic of this state. Our approach will be a valuable computation tool for studying quantum systems described by tight-binding Hamiltonians.
Fast and Effective: A Novel Sequential Single-Path Search for Mixed-Precision-Quantized Networks

Model quantization can reduce the model size and computational latency, it has been successfully applied for many applications of mobile phones, embedded devices, and smart chips. Mixed-precision quantization models can match different bit precision according to the sensitivity of different layers to achieve great performance. However, it is difficult to quickly determine the quantization bit precision of each layer in deep neural networks under some constraints (for example, hardware resources, energy consumption, model size, and computational latency). In this article, a novel sequential single-path search (SSPS) method for mixed-precision model quantization is proposed, in which some given constraints are introduced to guide the searching process. A single-path search cell is proposed to combine a fully differentiable supernet, which can be optimized by gradient-based algorithms. Moreover, we sequentially determine the candidate precisions according to the selection certainties to exponentially reduce the search space and speed up the convergence of the searching process. Experiments show that our method can efficiently search the mixed-precision models for different architectures (for example, ResNet-20, 18, 34, 50, and MobileNet-V2) and datasets (for example, CIFAR-10, ImageNet, and COCO) under given constraints, and our experimental results verify that SSPS significantly outperforms their uniform-precision counterparts.
Online Deep Learning from Doubly-Streaming Data

This paper investigates a new online learning problem with doubly-streaming data, where the data streams are described by feature spaces that constantly evolve, with new features emerging and old features fading away. A plausible idea to deal with such data streams is to establish a relationship between the old and new feature spaces, so that an online learner can leverage the knowledge learned from the old features to better the learning performance on the new features. Unfortunately, this idea does not scale up to high-dimensional multimedia data with complex feature interplay, which suffers a tradeoff between onlineness, which biases shallow learners, and expressiveness, which requires deep models. Motivated by this, we propose a novel OLD3S paradigm, where a shared latent subspace is discovered to summarize information from the old and new feature spaces, building an intermediate feature mapping relationship. A key trait of OLD3S is to treat the model capacity as a learnable semantics, aiming to yield optimal model depth and parameters jointly in accordance with the complexity and non-linearity of the input data streams in an online fashion. Both theoretical analysis and empirical studies substantiate the viability and effectiveness of our proposed approach. The code is available online at https://github.com/X1aoLian/OLD3S.
Dynamics of skew information-based quantum coherence under correlated noisy channels

In this paper, we study analytically the skew information-based coherence, and compare its dynamics with that of the l1 norm of coherence for two-qubit Bell-diagonal states under the influence of correlated Pauli channels, such as correlated dephase, depolarizing and classical noisy channels, respectively. The similar as well as different features between the skew information-based coherence and l1 norm of coherence are discussed. In addition, the physical mechanism of preserving quantum coherence based on memory coefficient of correlated channels and non-Markovian memory effect is also expounded.
The security economics of EdTech: vendors’ responsibility and the cybersecurity challenge in the education sector

Purpose The education sector is increasingly targeted by malicious cyber incidents, resulting in huge financial losses, cancelation of classes and exams and large-scale breaches of students’ and staff’s data. This paper aims to investigate education technology (EdTech) vendors’ responsibility for this cyber (in)security challenge, with a particular focus on EdTech in India as a case study. Design/methodology/approach Theoretically, building on the security economics literature, the paper establishes a link between the dynamics of the EdTech market and the education sector’s cyber insecurities and investigates the various economic barriers that stand in the way of improving EdTech vendors’ security practices. Empirically, the paper analyses publicly reported cyber incidents targeting the Indian education sector and EdTech companies in the past 10 years as published in newspapers, using the LexisNexis database. It also examines existing EdTech procurement challenges in India and elsewhere and develops a number of policy recommendations to address the misaligned incentives and information asymmetries between EdTech vendors and educational institutions. Findings Market forces alone cannot create sufficient incentives for EdTech vendors to prioritise security in product design. Considering the infant stage of the EdTech industry, the lack of evidence about the efficacy of EdTech tools, the fragmentation in the EdTech market and the peculiarities of educational institutions as end-users, a regulatorily and policy intervention is needed to secure education through procurement processes. Originality/value This paper introduces a novel exploration to the cybersecurity challenge in the education sector, an area of research and policy analysis that remains largely understudied. By adding a cybersecurity angle, the paper also contributes to the literature using a political economy approach in scrutinising EdTech.
Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features

The increase in the number of vehicles every year results in traffic jams. So it is necessary to identify the type of vehicle, so that the vehicle can be arranged according to the path. This study aims to develop a system that can identify the type of vehicle using the Learning Vector Quantization (LVQ) algorithm. In order for LVQ to work well in identifying, information in the form of characteristics of the object is needed. For this reason, the LVQ algorithm is combined with morphological feature extraction using the parameters of area, circumference, eccentricity, major axis length, and minor axis length to obtain shape features. Based on the test results using a confusion matrix by calculating precision, recall and accuracy, it is obtained that the precision value is 85%, recall is 82% and accuracy is 83%. This paper shows that for vehicle identification, the combination of morphological feature extraction and LVQ algorithm produces a model that can identify vehicles based on their shape and classify classes through competitive layers that are supervised by a single layer network architecture, this makes the computational process faster and does not burden the computational process.&#x0D;
<i>CLIN-X</i>: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain

The field of natural language processing (NLP) has recently seen a large change toward using pre-trained language models for solving almost any task. Despite showing great improvements in benchmark datasets for various tasks, these models often perform sub-optimal in non-standard domains like the clinical domain where a large gap between pre-training documents and target documents is observed. In this article, we aim at closing this gap with domain-specific training of the language model and we investigate its effect on a diverse set of downstream tasks and settings.We introduce the pre-trained CLIN-X (Clinical XLM-R) language models and show how CLIN-X outperforms other pre-trained transformer models by a large margin for 10 clinical concept extraction tasks from two languages. In addition, we demonstrate how the transformer model can be further improved with our proposed task- and language-agnostic model architecture based on ensembles over random splits and cross-sentence context. Our studies in low-resource and transfer settings reveal stable model performance despite a lack of annotated data with improvements of up to 47 F1 points when only 250 labeled sentences are available. Our results highlight the importance of specialized language models, such as CLIN-X, for concept extraction in non-standard domains, but also show that our task-agnostic model architecture is robust across the tested tasks and languages so that domain- or task-specific adaptations are not required.The CLIN-X language models and source code for fine-tuning and transferring the model are publicly available at https://github.com/boschresearch/clin_x/ and the huggingface model hub.