Reviewerly/exHarmony huggingface dataset:
Processing-Speed Enhancement in a Delay-Laser-Based Reservoir Computer by Optical Injection

A delay-laser-based reservoir computer (RC) usually has its processing speed limited by the transient response of laser dynamics. Here, we study a simple all-optical approach to enhancing the processing speed by introducing optical injection to the reservoir layer of conventional RC that consists of a semiconductor laser with a delay loop. Using optical injection, the laser’s transient response effectively accelerates due to the speeded carrier-photon resonance. In the chaotic time-series prediction task, the proposed RC achieves good performance in a flexible range of injection detuning frequency under sufficient injection rate. Using proper injection parameters, the prediction error is significantly reduced and stabilized when using high processing speed. For achieving a prediction error below 0.006, the optical injection enhances the processing speed by an order of magnitude of about 5 GSample/s. Moreover, the proposed RC extends the advantage to the handwritten digit recognition task by achieving better word error rate.
VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks

The rapid development of Convolutional Neural Networks (CNNs) in recent years has triggered significant breakthroughs in many machine learning (ML) applications. The ability to understand and compare various CNN models available is thus essential. The conventional approach with visualizing each model's quantitative features, such as classification accuracy and computational complexity, is not sufficient for a deeper understanding and comparison of the behaviors of different models. Moreover, most of the existing tools for assessing CNN behaviors only support comparison between two models and lack the flexibility of customizing the analysis tasks according to user needs. This paper presents a visual analytics system, VAC-CNN (Visual Analytics for Comparing CNNs), that supports the in-depth inspection of a single CNN model as well as comparative studies of two or more models. The ability to compare a larger number of (e.g., tens of) models especially distinguishes our system from previous ones. With a carefully designed model visualization and explaining support, VAC-CNN facilitates a highly interactive workflow that promptly presents both quantitative and qualitative information at each analysis stage. We demonstrate VAC-CNN's effectiveness for assisting novice ML practitioners in evaluating and comparing multiple CNN models through two use cases and one preliminary evaluation study using the image classification tasks on the ImageNet dataset.
KdTNet: Medical Image Report Generation via Knowledge-Driven Transformer

Writing medical image reports is an inefficient and time-consuming task for doctors. Automatically generating medical reports is an essential task of medical data mining, which can alleviate the workload of doctors and improve the standardization of reports. However, the existing methods mainly adopt the CNN-RNN structure to align image features with text features. This structure has difficulty dealing with the dependencies between distant text locations, leading to inconsistent context and semantics in the generated report. In this paper, we propose a knowledge-driven transformer (KdTNet) model for generating coherent medical reports. First, the visual grid and graph convolutional modules are devised to extract fine-grained visual features. Second, we adopt the transformer decoder to generate the hidden semantic states. Subsequently, a BERT-based auxiliary language module is employed to obtain the context language features of reports from the pre-defined medical term knowledge. We design a multimodal information fusion module to adaptively calculate the contribution of visual and linguistic features to the report for report generation. Extensive experiments on two real datasets explicate that our KdTNet model has achieved superior performance in captioning metrics and human evaluation compared with the state-of-the-art methods.
Do Reviewers’ Words and Behaviors Help Detect Fake Online Reviews and Spammers? Evidence From a Hierarchical Model

Although numerous studies have investigated spam detection and spammer detection on online platforms, they have ignored the fact that reviews written by the same reviewer may be correlated because each reviewer has their own distinct style.The traditional logistic regression model cannot handle this type of data because they violate the independence of residuals assumption.Furthermore, relatively few studies related to fake review detection have considered linguistic and behavioral aspects simultaneously.Thus, we propose a hierarchical logistic regression (HLR)-based model for detecting fake reviews that considers both linguistic and behavioral characteristics.With this outcome, our kernel also has multiple applications, including the detection of review spammers as a pre-module of quality in machine learning.The experimental results demonstrate that HLR can classify fake reviews and review spammers more accurately than the standard machine-learning algorithms.
Retransmission-Based TCP Fingerprints for Fine-Grain IoV Edge Device Identification

Due to the deployment of 5G technology, the number of IoV (Internet of Vehicle) devices connected to the Internet will explosively grow. However, as a kind of edge network device, IoV devices also face some problems including weak password authentication, lack of security protection, and lagged firmware updating, which largely threaten the security and legitimacy of these devices. IoV device identification is important in discovering, monitoring, and protecting these devices. Although existing proactive identification methods based on device fingerprints can be used to identify the large-scale Internet-connected IoV devices, they can not meet the fine-grained requirements for security risk assessment. Due to the increase in the types and brands of IoV devices, the fingerprint granularity will be insufficient. In this paper, we proposed a retransmission-based TCP fingerprints for large-scale fine-grained proactive device identification. Firstly, a probing scheme was designed to obtain TCP retransmission packet and increase the granularity space of traditional TCP fingerprint by selecting the multi-group features of TCP retransmission messages. Then, according to the bagging strategy of ensemble learning, a combined classifier with five predominant machine learning algorithms was generated. The experimental results showed that the identification accuracy and recall of IoV devices respectively reached 96.7% and 95.2%.
Multi-View Multi-Human Association With Deep Assignment Network

Identifying the same persons across different views plays an important role in many vision applications. In this paper, we study this important problem, denoted as Multi-view Multi-Human Association (MvMHA), on multi-view images that are taken by different cameras at the same time. Different from previous works on human association across two views, this paper is focused on more general and challenging scenarios of more than two views, and none of these views are fixed or priorly known. In addition, each involved person may be present in all the views or only a subset of views, which are also not priorly known. We develop a new end-to-end deep-network based framework to address this problem. First, we use an appearance-based deep network to extract the feature of each detected subject on each image. We then compute pairwise-similarity scores between all the detected subjects and construct a comprehensive affinity matrix. Finally, we propose a Deep Assignment Network (DAN) to transform the affinity matrix into an assignment matrix, which provides a binary assignment result for MvMHA. We build both a synthetic dataset and a real image dataset to verify the effectiveness of the proposed method. We also test the trained network on other three public datasets, resulting in very good cross-domain performance.
Image steganography performance analysis using discrete wavelet transform and alpha blending for secure communication

The opportunities to exchange information in the current web era have risen. The increased popularity of the media has posed real challenges to security-related issues. Steganography is a technology for the secure exchange of information. A video, audio, or image intended to raise no suspicion may be the carrier. After concealing the secret information, steganography techniques produce an identical cover image. This will prevent outside observers from noticing the existence of secret information. In the proposed work, alpha is a scaling parameter. Cover and payload images of different types and dimensions, live images from a webcam, and predefined images of other formats have been normalized and preprocessed. A Haar Discrete Wavelet Transformation (DWT) is applied to both the cover and payload images. To generate a stego image, the payload image is encrypted and fused with the cover image. The result parameters such as PSNR, MSE, and Entropy are measured.
An optimized approach for mapping solar irradiance in a mid-low latitude region based on a site-adaptation technique using Himawari-8 satellite imageries

Solar technologies play an important role in the renewable electric energy budget, so accurate solar maps are a crucial point for finding a suitable place for solar panel installation. This study proposes a method for solar irradiance mapping in mid-low latitude regions, and the method's site-adaptation process is performed by optimizing the Heliosat method through the REST2 clear-sky model, cloud albedo selection, new clear-sky index, and linear subtraction for bias removal. A local station with two pyranometers provided ground measurements. Site-adapted model results were used to create a calibrated solar map by linear regression adaptation. This study also provides the evaluation and site-adaption of another irradiance dataset in the Asian region from the Japan Aerospace Exploration Agency (JAXA). Heliosat model results with optimal cloud albedo showed high accuracy of 4.78 W/m2 MBE and 63.11 W/m2 RMSE, which can be improved using the site-adaptation process to 0.71 W/m2 MBE and 57.42 W/m2 RMSE. The selection of an optimal cloud albedo improved the model by approximately 20%. The JAXA dataset obtained a large overestimation of 56.72 W/m2 MBE, thereby highlighting the importance of site adaptation. This research's findings pave a new way for the creation of accurate site-adapted solar maps and databases.
Chinese Short Text Classification by ERNIE Based on LTC_Block

Short text classification, an important direction of the basic research of natural language processing, has extensive applications. Its effect depends on feature extraction methods and feature representation methods. This paper proposed an LTC_Block-based short text classification model named ERNIE to classify Chinese short texts and extract semantics in the corpus to address the polysemy problem in the text. In this model, LTC_Block, a double-channel structural unit composed of BiLSTM and TextCNN, was used to extract the contextual sequences and overall features of semantics, and residual connection was used to integrate features and further classify short texts. Experiments on two different datasets showed that ERNIE achieved a better classification effect than mainstream models, proving its feasibility and effectiveness.
Kernel-based ensemble gaussian mixture filtering for orbit determination with sparse data

In this paper, a modified kernel-based ensemble Gaussian mixture filtering (EnGMF) is introduced to produce fast and consistent orbit determination capabilities in a sparse measurement environment. The EnGMF is based on kernel density estimation (KDE) to combine particle filters and Gaussian sum filters. This work proposes using Silverman’s rule of thumb to reduce the computational burden of KDE. Equinoctial orbital elements are used to improve the accuracy of the KDE bandwidth parameter in the modified EnGMF. A bi-fidelity approach to propagation and an adaptation algorithm for selecting the appropriate number of particles are also applied to the EnGMF to reduce the computational burden with an acceptable loss in accuracy for long time propagation. Through numerical simulation, the proposed implementation is compared to state-of-the-art approaches in terms of accuracy, consistency, and computational speed.
The 5th International Workshop on Narrative Extraction from Texts: Text2Story 2022

Narrative extraction, understanding, verification, and visualization are currently popular topics for users interested in achieving a deeper understanding of text, researchers who want to develop accurate methods for text mining, and commercial companies that strive to provide efficient tools for that. Information Retrieval (IR), Natural Language Processing (NLP), Machine Learning (ML) and Computational Linguistics (CL) already offer many instruments that aid the exploration of narrative elements in text and within unstructured data. Despite evident advances in the last couple of years, the problem of automatically representing narratives in a structured form and interpreting them, beyond the conventional identification of common events, entities and their relationships, is yet to be solved. This workshop held virtually on April 10th, 2022 in conjunction with the 44th European Conference on Information Retrieval (ECIR'22) aims at presenting and discussing current and future directions for IR, NLP, ML and other computational linguistics-related fields capable of improving the automatic understanding of narratives. It includes sessions devoted to research, demo, position papers, work-in-progress, project description, nectar, and negative results papers, keynote talks and space for an informal discussion of the methods, of the challenges and of the future of this research area.
Chinese Text Classification Based on ERNIE-RNN

Text classification is a popular task of natural language processing. At present, text classification has been applied to multiple language like English, Chinese, Arabic et.al. However, Chinese text classification has many challenges especially in feature extraction and feature selection. This paper proposes the structure of ERNIE-RNN model. We use ERNIE to learn the complete semantic representation of a semantic unit instead of the traditional model to generate the word vector. RNN model is attached to the ERNIE model which can better process sequence information especially when preceding input is related to the following input for some certain task like text classification. In this paper, we use the data from our project which is tax consulting questions correspond to tax application scenarios. Our experimental results showed solid performance of all models on our data with a top performance of 94.63%, achieved by our model ERNIE-RNN.
Privacy-Enhanced Federated Generative Adversarial Networks for Internet of Things

Abstract Federated generative adversarial networks are designed to collaborate across the communication and privacy-constrained edge servers participating in training. However, in the Internet of Things scenario, local updates uploaded by edge servers can lead to the risk of privacy breaches. Gradient-sanitized-based approaches can transmit sanitized sensitive data with strict privacy guarantees, but gradient clipping and perturbation severely degrade convergence performance. In this paper, our proposed algorithm enhances the privacy of terminated raw data through differential privacy before it is transmitted to the edge server. The edge server trains the local generator and discriminator using the perturbed data, which provides privacy guarantees for the gradient attack on the FedGAN without compromising the gradient accuracy. The results of the experimental evaluation show that the algorithm generates images with slightly better quality than that generated by the gradient-sanitized-based approaches while maintaining privacy.
Digital contact tracing in the pandemic cities: Problematizing the regime of traceability in South Korea

Since 2020, many countries worldwide have deployed digital contact tracing programs that rely on a range of digital sensors in the city to locate and map the routes of viral spread. Many critical commentaries have raised concerns about the privacy risks and trustworthiness of these programs. Extending these analyses, this paper opens up a different line of questioning that goes beyond privacy-centered single-axis critique of surveillance by considering digital contact tracing symptomatic of the broader changes in modes of urban governance that renders our cities traceable, knowable, and governable through data. Based on archival and real-time analysis of South Korean national and local COVID-19 dashboards, online forums, and interviews with South Korean public health practitioners, this paper offers a sociotechnical analysis of digital contact tracing that looks at the various intersections of state-political, bio-political, and techno-political power dynamics. In contrast to popular narratives that attributed the success of the Korean approach to digital contact tracing to its collectivist culture and smart city infrastructures, this paper suggests that the case can be better understood by looking at both the macro-level shift in the forms of governance that takes on a spatialized and networked character and the micro-level formation of moral responsibility that shape one's conduct as a health and safety-conscious citizens. As the latest realization of the expanding regime of traceability in digital/urban governance, the development of digital contract tracing is seen to parallel with concurrent changes occurring in multiple domains of life including knowledge production, cultural memory, and individual subjectivity.
LANNS

Nearest neighbor search (NNS) has a wide range of applications in information retrieval, computer vision, machine learning, databases, and other areas. Existing state-of-the-art algorithm for nearest neighbor search, Hierarchical Navigable Small World Networks (HNSW), is unable to scale to large datasets of 100M records in high dimensions. In this paper, we propose LANNS, an end-to-end platform for Approximate Nearest Neighbor Search, which scales for web-scale datasets. Library for Large Scale Approximate Nearest Neighbor Search (LANNS) is deployed in multiple production systems for identifying top-K (100 ≤ k ≤ 200) approximate nearest neighbors with a latency of a few milliseconds per query, high throughput of ~2.5k Queries Per Second (QPS) on a single node, on large (e.g., ~ 180M data points) high dimensional (50-2048 dimensional) datasets.
Omnidirectional Image Super-Resolution via Latitude Adaptive Network

Omnidirectional images (ODI), also known as 360 images, have recently attracted extensive attention from both academia and industry. However, due to storage and transmission limitations, ODIs are usually at extremely low resolution. Thus, it is necessary to restore a high-resolution ODI from a low-resolution ODI, i.e., omnidirectional image super-resolution (ODI-SR). Different from traditional two-dimensional (2D) image SR, the challenge of ODI-SR is the nonuniformly distributed pixel density and geometric distortion across latitudes, which makes traditional SR methods difficult to be applied in ODI-SR. Towards ODI-SR, we propose in this paper a novel latitude-aware upscaling network, namely LAU-Net+, which fully considers the above characteristics of ODIs. In our network, different latitude bands can learn to adopt distinct upscaling factors, which significantly saves the computational resources and improves the SR efficiency. Specifically, a Laplacian multilevel pyramid network is introduced in which the upscaling factor is gradually increased with the number of levels. Each level is composed of a feature enhancement module (FEM), a drop-band decision module (DDM) and a high-latitude enhancement module (HEM). The FEM module serves to enhance the high-level features extracted from the input ODI, while the role of DDM is to dynamically drop the unnecessary high latitude bands and send the remained bands to the next level. The HEM is adopted to further enhance high-level features of dropped latitude bands with a lightweight architecture. In DDM, we develop a reinforcement learning scheme with a latitude adaptive reward to determine which band should be dropped. To the best of our knowledge, our method is the first work which considers the latitude characteristics for ODI-SR task. Extensive experimental results demonstrate that our LAU-Net+ achieves state-of-the-art results on ODI-SR both quantitatively and qualitatively on various ODI datasets.
Incorporating the effects of objects in an approximate model of light transport in scattering media

A computationally efficient radiative transport model is presented that predicts a camera measurement and accounts for the light reflected and blocked by an object in a scattering medium. The model is in good agreement with experimental data acquired at the Sandia National Laboratory Fog Chamber Facility (SNLFC). The model is applicable in computational imaging to detect, localize, and image objects hidden in scattering media. Here, a statistical approach was implemented to study object detection limits in fog.
Quantum search degeneration under amplitude noise in queries to the oracle

We examine how amplitude noise in queries to the oracle degrades a performance of quantum search algorithm. The Grover search and similar techniques are widely used in various quantum algorithms, including cases where rival parties are fighting over confidential data. Hence, the oracle-box wires become the subject of competing activity with an alteration of their normal functioning. Of course, many kinds of errors could arise in this way. Possible influence of dephasing on quantum search was already addressed in the literature. Amplitude damping is another type of errors that should be analyzed first. To study the problem, we introduce a simple model of collective distortions with the use of amplitude damping channel. All the quantities of interest should be considered as functions of the number of Grover’s iterations. In particular, we investigate the success probability with respect to the parameter that characterizes the level of amplitude errors. The success probability degrades significantly even if the error amount is not essential. Namely, this probability soon enough reduces to a value close to one half. We also study trade-off relations between quantum coherence and the success probability in the presence of amplitude noise.