Reviewerly/exHarmony huggingface dataset:
Non-Interactive Anonymous Router

Anonymous routing is one of the most fundamental online privacy problems and has been studied extensively for decades. Almost all known approaches for anonymous routing (e.g., mix-nets, DC-nets, and others) rely on multiple servers or routers to engage in some interactive protocol; and anonymity is guaranteed in the threshold model, i.e., if one or more of the servers/routers behave honestly. Departing from all prior approaches, we propose a novel non-interactive abstraction called a Non-Interactive Anonymous Router (NIAR), which works even with a single untrusted router. In a NIAR scheme, suppose that n senders each want to talk to a distinct receiver. A one-time trusted setup is performed such that each sender obtains a sending key, each receiver obtains a receiving key, and the router receives a token that “encrypts” the permutation mapping the senders to receivers. In every time step, each sender can encrypt its message using its sender key, and the router can use its token to convert the n ciphertexts received from the senders to n transformed ciphertexts. Each transformed ciphertext is delivered to the corresponding receiver, and the receiver can decrypt the message using its receiver key. Imprecisely speaking, security requires that the untrusted router, even when colluding with a subset of corrupt senders and/or receivers, should not be able to compromise the privacy of honest parties, including who is talking to who, and the message contents. We show how to construct a communication-efficient NIAR scheme with provable security guarantees based on the standard Decision Linear assumption in suitable bilinear groups. We show that a compelling application of NIAR is to realize a Non-Interactive Anonymous Shuffler (NIAS), where an untrusted server or data analyst can only decrypt a permuted version of the messages coming from n senders where the permutation is hidden. NIAS can be adopted to construct privacy-preserving surveys, differentially private protocols in the shuffle model, and pseudonymous bulletin boards. Besides this main result, we also describe a variant that achieves fault tolerance when a subset of the senders may crash. Finally, we further explore a paranoid notion of security called full insider protection, and show that if we additionally assume sub-exponentially secure Indistinguishability Obfuscation and as sub-exponentially secure one-way functions, one can construct a NIAR scheme with paranoid security.
Bridging the Socio-Technical Gaps in Body-worn Interpersonal Live-Streaming Telepresence through a Critical Review of the Literature

It is important to learn from the past as we endeavor into this uncharted territory of mobile, human-to-human, one-to-one telepresence for interpersonal use. With the ever-increasing access to live-streaming cameras, we are now at the cusp of being able to create novel, immersive, and interpersonal telepresence activities that have the potential to change how humans interact with one another on a daily basis. Due to its novelty, there are likely socio-technical gaps between the needs of users and the technical specifications of the prototypes that are currently being designed to support the complex social interactions of human-to-human telepresence. Therefore, in this paper, we use a socio-technical lens to conduct a systematic literature review of 52 peer-reviewed articles of early work in this space. Overall, we found that while progress has been made to address the social needs of those involved in one-to-one telepresence scenarios, there are discontinuities within the existing literature that need to be addressed, particularly with the way we attempt to measure and quantify human-centered outcomes with unvalidated instruments. We also found that the social needs of on-site users have been neglected, as in many articles the user was merely treated as a surrogate, or reported feeling socially awkward or unsafe, due to the conspicuous nature of the body-worn technology in public environments. These findings are prevalent, even as researchers consider adding to this body-worn burden in an attempt to improve the receiving users' sense of immersion and presence. To preserve the beneficial nature of telepresence interaction while ensuring that all users' needs are met, researchers should endeavor to further understand the dynamics of the relationship between all parties in the remote environment. Our paper creates a future research agenda that emphasizes the importance of ensuring that all parties involved feel comfortable in their role during interpersonal telepresence interactions.
A unified model for egocentric video summarization: an instance-based approach

Video summarization generates compact representations of videos in the form of summaries. The proposed framework is a unified model for instance-driven egocentric video summarization addressing generic and query-based summarization along with multi-video summarization. The model employs deep learning for object detection and semantic web technologies in the form of ontologies for query inferences. Combining user preferences in the form of object queries has aided in producing summaries that are subjective in nature. Quantitative evaluations performed on two novel datasets namely, ‘vehicle expo’ and ‘academic inspection’ prove that the proposed framework produces remarkable results with the employment of instance-driven modules for summarization. Additional experimental analysis for shot boundary detection have been conducted based on proposed method and conventional methods establishing the significance of the instance-based model. Moreover, qualitative evaluations further ensure that the summaries are concise, representative, diverse and semantically relevant further substantiating the need for instance-driven models in video summarization.
Human Activity Recognition based on smart home environment and their applications, challenges

Progress in technology aspect, Internet of Things (IoT) takes an obvious quantity in the development of smart applications in numerous area such as smart farming, smart grid, smart cities, smart industry, smart hospital, and so on. IoT is the method of emerging IoT applications and connected products that can be monitored, measured remotely in a given environment via external data sources and sensors, other related IoT enabled devices. Detection of human activity is a set of techniques that can be used in wide range of applications, including medical health care and smart home. With an advance development and commercialization of IoT enabled devices and crucial demands, human activity measuring the efficient states or health of individual in smart home-based environment has been a highly dynamic and important topic in recent years since its related to human life. Meanwhile, the intrinsic of human activities characteristic is considered by a high degree of ambiguity, and complexity, it's a gives a great challenge to design and develop a strong activity recognition model. The objectives of this article aim to exploit the applications of human activity recognition systems applications, challenges, and surveys their state of the art in smart home environment. We classify such smart home applications into vision based, sensor-based (wearable devices, object-tagged, dense sensing). Within these types, the applications are categorized according to the methodology, datasets, features, limitations used for identifying human behaviors. Finally, the article concludes with an evaluation of the existing approaches which, let to frame research problems and questions for future direction which, when applied to real-world scenarios.
Stealthy Backdoors as Compression Artifacts

Model compression is a widely-used approach for reducing the size of deep learning models without much accuracy loss, enabling resource-hungry models to be compressed for use on resource-constrained devices. In this paper, we study the risk that model compression could provide an opportunity for adversaries to inject stealthy backdoors. In a backdoor attack on a machine learning model, an adversary produces a model that performs well on normal inputs but outputs targeted misclassifications on inputs containing a small trigger pattern. We design stealthy backdoor attacks such that the full-sized model released by adversaries appears to be free from backdoors (even when tested using state-of-the-art techniques), but when the model is compressed it exhibits a highly effective backdoor. We show this can be done for two common model compression techniques—model pruning and model quantization—even in settings where the adversary has limited knowledge of how the particular compression will be done. Our findings demonstrate the importance of performing security tests on the models that will actually be deployed not in their precompressed version. Our implementation is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yulongtzzz/Stealthy-Backdoors-as-Compression-Artifacts</uri> .
Approximate Inference and Learning of State Space Models With Laplace Noise

State space models have been extensively applied to model and control dynamical systems in disciplines including neuroscience, target tracking, and audio processing. A common modeling assumption is that both the state and data noise are Gaussian because it simplifies the estimation of the system's state and model parameters. However, in many real-world scenarios where the noise is heavy-tailed or includes outliers, this assumption does not hold, and the performance of the model degrades. In this paper, we present a new approximate inference algorithm for state space models with Laplace-distributed multivariate data that is robust to a wide range of non-Gaussian noise. Exact inference is combined with an expectation propagation algorithm, leading to filtering and smoothing that outperforms existing approximate inference methods for Laplace-distributed data, while retaining a fast speed similar to the Kalman filter. Further, we present a maximum posterior expectation-maximization (EM) algorithm that learns the parameters of the model in an unsupervised way, automatically avoids over-fitting the data, and provides better model estimation than existing methods for the Gaussian model. The quality of the inference and learning algorithms are exemplified through a diverse set of experiments and an application to non-linear tracking of audio frequency.
Deep Learning Sentiment Classification Based on Weak Tagging Information

The purpose of sentiment classification is to solve the problem of automatic judgment of text sentiment tendency. In the sentiment classification task of online reviews, traditional deep learning sentiment classification models focus on algorithm optimization to improve the classification performance of the model, but when the sample data for manually labeling sentiment tendencies is insufficient, the classification performance of the model will be poor. The deep learning sentiment classification model based on weak tagging information, on the one hand, introduces weak tagging information into the training process of the model to reduce the use of manually tagging data. On the other hand, weak tagging information can represent the sentiment tendency of reviews to a certain extent, but it also contains noise, the model reduces the negative impact of the noise in weak tagging information in order to improve the classification performance of the sentiment classification model. The experimental results show that in the sentiment classification task of hotel online reviews, the deep learning sentiment classification model based on weak tagging information has superior classification performance than the traditional deep model without increasing labor cost.
Multi-Patch and Feature Fusion Network for Single Image Dehazing

The dehazing method based on deep learning has made significant progress in the field of image dehazing, but most methods still have the problem of incomplete dehazing and color distortion. To solve this problem, an image dehazing network based on multi-patch and feature fusion is proposed. The network consists of preprocessing, feature extraction, feature fusion and post-processing modules. The preprocessing module can adaptively extract image feature information from the patch. The feature extraction module uses cascaded dense residual blocks to extract deep feature information. The feature fusion module performs channel weighting and pixel weighting on the feature map to achieve the fusion of main features. The post-processing module performs nonlinear mapping on the fused feature map to obtain a dehazing image. Experiments show that this network has achieved ideal dehazing effects on both synthetic and real-world images, and can avoid color distortion after dehazing.
Lightweight 8‐bit S‐box and combined S‐box/S‐box<sup>−1</sup> for cryptographic applications

Summary In this paper, a lightweight 8‐bit S‐box and combined S‐box/S‐box −1 with a security level equal to the AES S‐box is presented. From the viewpoint of hardware implementation, the S‐box has better hardware and timing complexities. The structure is based on an efficient field inversion and a low‐cost affine transformation. The field multiplications over in the inversion circuit are implemented by resource sharing to reduces logic gates. The original equations of the inversion over are optimally rewritten. In addition, a part of the S‐box called Part1 is optimized for reducing area and delay. This part consists of two addition operations, one multiplication, field squaring, and multiplication by constant λ , which all operations are over . The subblocks of Part1 are combined to generate a unified structure. Security analysis of the proposed S‐box showed that the structure has a security level equal to the Advanced Encryption Standard (AES) S‐box. The implementation results in 180‐ and 65‐nm Complementary metal oxide semiconductor (CMOS) technologies show the proposed S‐box and combined S‐box/S‐box −1 are comparable in terms of area, delay, and area × delay than most of the famous S‐boxes. The proposed S‐box is a high‐secure and area‐efficient S‐box as a good candidate for block ciphers.
Single Image Deraining via detail-guided Efficient Channel Attention Network

Single image deraining is an important problem in many computer vision tasks since rain streaks can severely hamper and degrade the visibility of images. Exisiting methods either focus on extracting rain streaks and ignore the background recovery, or the network structure is extremely complex and the number of parameters is quite large. Although some methods mention background restoration work, they generally ignore effective contextual information and result in unsatisfactory results. In this paper, we propose a novel network single image Deraining via detail-guided Efficient Channel Attention Network (DECAN) to remove rain streaks from rainy images. Specifically, we introduce two sub-networks with a comprehensive loss function that synergize to remove rain streaks and recover the background of the derained image. For completing rain streaks removal, we construct a rain streaks removal network with detail-guided efficient-channel-attention module to identify effective low-level features. For background recovery, we present a specialized background repair network consisting of well-designed blocks, named background details recovery network, to repair the background with effective contextual information for eliminating image degradations. Experiments on four synthetic datasets and some real-world rainy image sets show visual and numerical improvements of proposed method over the state-of-the-arts considerably.
The Plausibility Paradox for Resized Users in Virtual Environments

This paper identifies and confirms a perceptual phenomenon: when users interact with simulated objects in a virtual environment where the users' scale deviates greatly from normal, there is a mismatch between the object physics they consider realistic and the object physics that would be correct at that scale. We report the findings of two studies investigating the relationship between perceived realism and a physically accurate approximation of reality in a virtual reality experience in which the user has been scaled by a factor of ten. Study 1 investigated perception of physics when scaled-down by a factor of ten, whereas Study 2 focused on enlargement by a similar amount. Studies were carried out as within-subjects experiments in which a total of 84 subjects performed simple interaction tasks with objects under two different physics simulation conditions. In the true physics condition, the objects, when dropped and thrown, behaved accurately according to the physics that would be correct at that either reduced or enlarged scale in the real world. In the movie physics condition, the objects behaved in a similar manner as they would if no scaling of the user had occurred. We found that a significant majority of the users considered the movie physics condition to be the more realistic one. However, at enlarged scale, many users considered true physics to match their expectations even if they ultimately believed movie physics to be the realistic condition. We argue that our findings have implications for many virtual reality and telepresence applications involving operation with simulated or physical objects in abnormal and especially small scales.
Underwater image super-resolution using multi-stage information distillation networks

Recently, single image super-resolution (SISR) has been widely applied in the fields of underwater robot vision and obtained remarkable performance. However, most current methods generally suffered from the problem of a heavy burden on computational resources with large model sizes, which limited their real-world underwater robotic applications. In this paper, we introduce and tackle the super resolution (SR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present a novel lightweight multi-stage information distillation network, named MSIDN, for better balancing performance against applicability, which aggregates the local distilled features from different stages for more powerful feature representation. Moreover, a novel recursive residual feature distillation (RRFD) module is constructed to progressively extract useful features with a modest number of parameters in each stage. We also propose a channel interaction & distillation (CI&D) module that employs channel split operation on the preceding features to produce two-part features and utilizes the inter channel-wise interaction information between them to generate the distilled features, which can effectively extract the useful information of current stage without extra parameters. Besides, we present USR-2K dataset, a collection of over 1.6K samples for large-scale underwater image SR training, and a testset with an additional 400 samples for benchmark evaluation. Extensive experiments on several standard benchmark datasets show that the proposed MSIDN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.
Non-Rigid Shape From Water

We introduce a novel 3D sensing method for recovering a consistent, dense 3D shape of a dynamic, non-rigid object in water. The method reconstructs a complete (or fuller) 3D surface of the target object in a canonical frame (e.g., rest shape) as it freely deforms and moves between frames by estimating underwater 3D scene flow and using it to integrate per-frame depth estimates recovered from two near-infrared observations. The reconstructed shape is refined in the course of this global non-rigid shape recovery by leveraging both geometric and radiometric constraints. We implement our method with a single camera and a light source without the orthographic assumption on either by deriving a practical calibration method that estimates the point source position with respect to the camera. Our reconstruction method also accounts for scattering by water. We prototype a video-rate imaging system and show 3D shape reconstruction results on a number of real-world static, deformable, and dynamic objects and creatures in real-world water. The results demonstrate the effectiveness of the method in recovering complete shapes of complex, non-rigid objects in water, which opens new avenues of application for underwater 3D sensing in the sub-meter range.
Blockchain-Based Multi-Role Healthcare Data Sharing System

Blockchain has unique advantages in data privacy protection and data integrity. We can solve many security problems, such as the single point of failure and data sharing in the current centralized system through blockchain approach. Existing studies have demonstrated that the application of blockchain in the medical system could improve the patient's medical experience. However, we found that the blockchain-based medical systems didn't consider the problems of long insurance claim cycle and complicated procedures. There are also very few healthcare systems that provide targeted sharing protocols for medical data and personal health data. In this paper, we propose a multi-role healthcare data sharing system framework based on blockchain. In this system, we reduce the storage cost through the collaborative storage of blockchain and IPFS. Then we design a smart contract on insurance to help patients achieve automatic insurance claim. In addition, we design two different sharing protocols to realize the fine management of personal data. The system analysis shows that our proposed blockchain-based multi-role healthcare data sharing system can effectively address the actual needs of users and has perfect performance in data storage, privacy protection, insurance claims and personal data management.
Development of a Web-Based System of Automatic Content Retrieval Database

In this work, the database was designed and implemented in accordance with the requirements of the relational model, which ensures the storage and collective access to the information of the auto-filling system and CMS WordPress data. Algorithms of system functioning were developed, the order of interaction of classes during program code execution was determined, as a result of which the application was implemented. Template Method architectural pattern was chosen to implement the web-based automatic content filling system. The following tools and technologies were selected to create the software package HTML markup language for HTML documents; programming language PHP; MySQL database management environment; Apache web server; the OpenServer package. The algorithms of the basic processes of content filling automation were considered and the interaction of the system classes during the processes of parsing, filtering and storing of information were analyzed. The developed system does not require specialized hardware, additional settings and deployment tools other than the standard ones for such plugins. This application is mostly for the site administrator and does not have user interface. That is why the features of the plugin automation system configuration interface; RSS feeds view and management interface, as well as the RSS feed configuration interface are described in detail. In the future, this system can be improved by introducing new functionality and improving the algorithm for reading data.