Reviewerly/exHarmony huggingface dataset:
Practical Identity Based Online/Off-Line Signcryption Scheme for Secure Communication in Internet of Things

Contemporary developments in providing proper security in the Internet of Things (IoT) have been made signcryption scheme highly suitable for various applications such as smart cities, smart healthcare and smart agriculture. However, security and privacy are the primary concerns in protecting data as it was highly sensitive. This paper is proposing a new Identity based online/off-line signcryption scheme suitable to provide secure message communication among IoT devices, gateway, and the server. This method is divided into the online and offline phases, where heavy mathematical computations are carried out in the offline phase and light computations in the online phase. This scheme provides a security solution for integrating Wireless Sensor Networks (WSNs) into the IoT. Experimentation was done and finally compared against existing techniques and proved that the proposed mechanism reduces the computation time in online by performing a greater number of operations in off-line signcryption phase. Further, it provides indistinguishability against adaptive chosen ciphertext attacks and existential unforgeability against adaptive chosen messages attacks.
Bound and subradiant multiatom excitations in an atomic array with nonreciprocal couplings

Collective decays of multiply excited atoms become subradiant and bound in space when they are strongly coupled to the guided modes in an atom-waveguide interface. In this interface, we analyze their average density-density and modified third-order correlations via Kubo cumulant expansions, which can arise, and finite correlations can be sustained for long time. The shape-preserving dimers and trimers of atomic excitations emerge in the most subradiant coupling regime of light-induced dipole-dipole interactions. This leads to a potential application of quantum information processing and quantum storage in the encoded nonreciprocal spin diffusion, where its diffusion speed depends on the initial coherence between the excited atoms and is robust to their relative phase fluctuations. The state-dependent photon routing can be viable as well in this interface.
Demystifying the Role of zk-SNARKs in Zcash

Zero-knowledge proofs have always provided a clear solution when it comes to conveying information from a prover to a verifier or vice versa without revealing essential information about the process. Advancements in zero-knowledge have helped develop proofs which are succinct and provide non-interactive arguments of knowledge along with maintaining the zero-knowledge criteria. zk-SNARKs (Zero knowledge Succinct Non-Interactive Argument of Knowledge) are one such method that outshines itself when it comes to advancement of zero-knowledge proofs. The underlying principle of the Zcash algorithm is such that it delivers a full-fledged ledger-based digital currency with strong privacy guarantees and the root of ensuring privacy lies fully on the construction of a proper zk-SNARK. In this paper we elaborate and construct a concrete zk-SNARK proof from scratch and explain its role in the Zcash algorithm.
Quantum capacity analysis of multi-level amplitude damping channels

Abstract Evaluating capacities of quantum channels is the first purpose of quantum Shannon theory, but in most cases the task proves to be very hard. Here, we introduce the set of Multi-level Amplitude Damping quantum channels as a generalization of the standard qubit Amplitude Damping Channel to quantum systems of finite dimension d . In the special case of d = 3, by exploiting degradability, data-processing inequalities, and channel isomorphism, we compute the associated quantum and private classical capacities for a rather wide class of maps, extending the set of models whose capacity can be computed known so far. We proceed then to the evaluation of the entanglement assisted quantum and classical capacities.
Modeling Content and Context with Deep Relational Learning

Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the existing frameworks for combining neural and symbolic representations have been designed for classic relational learning tasks that work over a universe of symbolic entities and relations. In this paper, we present DRaiL, an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios. Our framework supports easy integration with expressive language encoders, and provides an interface to study the interactions between representation, inference and learning.
Unlocking history through automated virtual unfolding of sealed documents imaged by X-ray microtomography

Abstract Computational flattening algorithms have been successfully applied to X-ray microtomography scans of damaged historical documents, but have so far been limited to scrolls, books, and documents with one or two folds. The challenge tackled here is to reconstruct the intricate folds, tucks, and slits of unopened letters secured shut with “letterlocking,” a practice—systematized in this paper—which underpinned global communications security for centuries before modern envelopes. We present a fully automatic computational approach for reconstructing and virtually unfolding volumetric scans of a locked letter with complex internal folding, producing legible images of the letter’s contents and crease pattern while preserving letterlocking evidence. We demonstrate our method on four letterpackets from Renaissance Europe, reading the contents of one unopened letter for the first time. Using the results of virtual unfolding, we situate our findings within a novel letterlocking categorization chart based on our study of 250,000 historical letters.
Unpredictable Oscillations for Hopfield-Type Neural Networks with Delayed and Advanced Arguments

This is the first time that the method for the investigation of unpredictable solutions of differential equations has been extended to unpredictable oscillations of neural networks with a generalized piecewise constant argument, which is delayed and advanced. The existence and exponential stability of the unique unpredictable oscillation are proven. According to the theory, the presence of unpredictable oscillations is strong evidence for Poincaré chaos. Consequently, the paper is a contribution to chaos applications in neuroscience. The model is inspired by chaotic time-varying stimuli, which allow studying the distribution of chaotic signals in neural networks. Unpredictable inputs create an excitation wave of neurons that transmit chaotic signals. The technique of analysis includes the ideas used for differential equations with a piecewise constant argument. The results are illustrated by examples and simulations. They are carried out in MATLAB Simulink to demonstrate the simplicity of the diagrammatic approaches.
A Study of Face Obfuscation in ImageNet

Face obfuscation (blurring, mosaicing, etc.) has been shown to be effective for privacy protection; nevertheless, object recognition research typically assumes access to complete, unobfuscated images. In this paper, we explore the effects of face obfuscation on the popular ImageNet challenge visual recognition benchmark. Most categories in the ImageNet challenge are not people categories; however, many incidental people appear in the images, and their privacy is a concern. We first annotate faces in the dataset. Then we demonstrate that face obfuscation has minimal impact on the accuracy of recognition models. Concretely, we benchmark multiple deep neural networks on obfuscated images and observe that the overall recognition accuracy drops only slightly (<= 1.0%). Further, we experiment with transfer learning to 4 downstream tasks (object recognition, scene recognition, face attribute classification, and object detection) and show that features learned on obfuscated images are equally transferable. Our work demonstrates the feasibility of privacy-aware visual recognition, improves the highly-used ImageNet challenge benchmark, and suggests an important path for future visual datasets. Data and code are available at https://github.com/princetonvisualai/imagenet-face-obfuscation.
Optic Flow-Based Reactive Collision Prevention for MAVs Using the Fictitious Obstacle Hypothesis

Optical flow sensors and optical flow divergence (OFD) have offered partial solutions for obstacle avoidance, landing, and perching with micro aerial vehicles.Theoretically, OFD can indicate the risk of collision, providing that the sensors' field of view is bounded within a single flat surface on the obstacle.However, in the real world, directly measuring the risk of collision with OFD generates false alarms due to rapidly changing speeds and irregular surroundings.In this letter, we present a new obstacle detection strategy based on an extended Kalman filter (EKF) combining the OFD with inertial sensing.The introduction of a fictitious obstacle hypothesis and the use of the EKF estimates enable us to differentiate the surrounding-generated OFD from the OFD caused by the actual obstacle.An embedded constant zero-OFD controller is then used for post-detection emergency deceleration.The ultra-light OFD estimation and control system, with a mass of 20 g, estimates OFD at 160 Hz.The system was validated on a 158 g mini quadrotor in both laboratory and field tests.Experimental results illustrate that the presented system can achieve accurate obstacle detection, near-obstacle distance estimation, and controlled deceleration to prevent collisions [Video attachment: https://youtu.be/yIyYHYN0jOw].
Adaptive online incremental learning for evolving data streams

Recent years have witnessed growing interests in online incremental learning. However, there are three major challenges in this area. The first major difficulty is concept drift, that is, the probability distribution in the streaming data would change as the data arrives. The second major difficulty is catastrophic forgetting, that is, forgetting what we have learned before when learning new knowledge. The last one we often ignore is the learning of the latent representation. Only good latent representation can improve the prediction accuracy of the model. Our research builds on this observation and attempts to overcome these difficulties. To this end, we propose an Adaptive Online Incremental Learning for evolving data streams (AOIL). We use auto-encoder with the memory module, on the one hand, we obtained the latent features of the input, on the other hand, according to the reconstruction loss of the auto-encoder with memory module, we could successfully detect the existence of concept drift and trigger the update mechanism, adjust the model parameters in time. In addition, we divide features, which are derived from the activation of the hidden layers, into two parts, which are used to extract the common and private features respectively. By means of this approach, the model could learn the private features of the new coming instances, but do not forget what we have learned in the past (shared features), which reduces the occurrence of catastrophic forgetting. At the same time, to get the fusion feature vector we use the self-attention mechanism to effectively fuse the extracted features, which further improved the latent representation learning.
Adversarial Incomplete Multiview Subspace Clustering Networks

Multiview clustering aims to leverage information from multiple views to improve the clustering performance. Most previous works assumed that each view has complete data. However, in real-world datasets, it is often the case that a view may contain some missing data, resulting in the problem of incomplete multiview clustering (IMC). Previous approaches to this problem have at least one of the following drawbacks: 1) employing shallow models, which cannot well handle the dependence and discrepancy among different views; 2) ignoring the hidden information of the missing data; and 3) being dedicated to the two-view case. To eliminate all these drawbacks, in this work, we present the adversarial IMC (AIMC) framework. In particular, AIMC seeks the common latent representation of multiview data for reconstructing raw data and inferring missing data. The elementwise reconstruction and the generative adversarial network are integrated to evaluate the reconstruction. They aim to capture the overall structure and get a deeper semantic understanding, respectively. Moreover, the clustering loss is designed to obtain a better clustering structure. We explore two variants of AIMC, namely: 1) autoencoder-based AIMC (AAIMC) and 2) generalized AIMC (GAIMC), with different strategies to obtain the multiview common representation. Experiments conducted on six real-world datasets show that AAIMC and GAIMC perform well and outperform the baseline methods.
Early Breast Cancer Detection Utilizing Artificial Neural Network

Breast cancer is one of the most dangerous cancer diseases for women in worldwide. A Computeraided diagnosis system is very helpful for radiologist for diagnosing micro calcification patterns earlier and faster than typical screening techniques. Maximum breast cancer cells are eventually form a lump or mass called a tumor. Moreover, some tumors are cancerous and some are not cancerous. The cancerous tumors are called malignant and non-cancerous tumors are called benign. The benign tumors are not dangerous to health. But the unchecked malignant tumors have the ability to spread in other organs of the body. For that early detection of benign and malignant tumor is important for confining the death of breast cancer. In these research study different neural networks such as, Multilayer Perceptron (MLP) Neural Network, Jordan/Elman Neural Network, Modular Neural Network (MNN), Generalized Feed-Forward Neural Network (GFFNN), Self-Organizing Feature Map (SOFM) Neural Network, Support Vector Machine (SVM) Neural Network, Probabilistic Neural Network (PNN) and Recurrent Neural Network (RNN) are used for classifying breast cancer tumor. And compare the results of these networks to find the best neural network for detecting breast cancer. The networks are tested on Wisconsin breast cancer (WBC) database. Finally, the comparing result showed that Probabilistic Neural Network shows the best detection result than other networks.
Bounded Model Checking for Hyperproperties

Abstract This paper introduces a bounded model checking (BMC) algorithm for hyperproperties expressed in HyperLTL, which — to the best of our knowledge — is the first such algorithm. Just as the classic BMC technique for LTL primarily aims at finding bugs, our approach also targets identifying counterexamples. BMC for LTL is reduced to SAT solving, because LTL describes a property via inspecting individual traces. Our BMC approach naturally reduces to QBF solving, as HyperLTL allows explicit and simultaneous quantification over multiple traces. We report on successful and efficient model checking, implemented in our tool called , of a rich set of experiments on a variety of case studies, including security, concurrent data structures, path planning for robots, and mutation testing.
Transient Voltage Control of Sending-End Wind Farm Using a Synchronous Condenser Under Commutation Failure of HVDC Transmission System

When the large-scale wind power is sent out through the high voltage direct current (HVDC) transmission system and a DC commutation failure occurs, the voltage of AC bus at the sending end decreases first and then increases. Suppose the reactive power supported in the low voltage ride-through process by various reactive resources is not timely returned. In that case, it may aggravate the voltage rise caused by the commutation failure, and the off-grid risk of wind turbine under high-voltage will be aggravated. In order to reduce the off-grid risk of wind turbines caused by the DC commutation failure, a transient voltage control strategy of DC sending-end regulator based on the online sequential extreme learning machine (OS-ELM) voltage prediction model is proposed. Firstly, the influence factors of commutation failures are analyzed. Aiming at the key factors, the real-time voltage comprehensive prediction model based on OS-ELM is used to predict the voltage increase during the commutation failure process and uses the voltage prediction results to optimize the transient response of the synchronous condenser. A large-scale wind farm together with the HVDC system is established in PSCAD to verify the effectiveness of the proposed scheme. Simulation results show that the proposed scheme can reduce the risk of wind power off-grid risk under DC commutation failures and increase the speed of voltage recovery at the point of common coupling.
Intelligent Monitoring System of Residential Environment Based on Cloud Computing and Internet of Things

With the development of society and the improvement of living standards, people pay more and more attention to their own living environment and life safety, and more people are gradually paying attention to the impact of the quality of the living environment on their health and work efficiency, and to meet this demand of people, it is necessary to effectively monitor and control the living environment. Based on this, this article applies cloud computing and Internet of things technologies that have developed rapidly in recent years, and proposes to design a residential environment intelligent monitoring system based on cloud computing and Internet of things, and use Internet of things and sensor technologies to achieve target connection and communication. It then uses distributed computing, a cloud computing technology, to implement integrated, standardized management of this system for truly intelligent monitoring. In this article, we first gathered a large amount of information through literature search methods, systematically introduced cloud computing and Internet of Things technologies, and introduced two applications in environmental intelligent monitoring. Next, we propose a design experiment of a residential environment intelligent monitoring system based on cloud computing and the Internet of Things, and propose an overall concept of system design, system hardware and software design requirements, and device selection and comfort evaluation. And living environment data integration is introduced in detail. We then tested the performance of the system in a specific application of the system in a real residential environment, using data parameters collected at different points in time as experimental data. Finally, it is concluded that the fuzzy close fusion algorithm used in the experiment can obtain data parameter values that are in good agreement with the real values, and the error range is controlled within 0–0.01; the indoor environment comfort is judged by 1 as the standard. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PMV&gt;1$ </tex-math></inline-formula> indicates that the comfort level is excellent, and in the test experiment, each parameter value at 5 time points is all > 1, indicating that the indoor environment comfort level detected by the system is excellent.
An Overview of Human Activity Recognition Using Wearable Sensors: Healthcare and Artificial Intelligence

With the rapid development of the internet of things (IoT) and artificial intelligence (AI) technologies, human activity recognition (HAR) has been applied in a variety of domains such as security and surveillance, human-robot interaction, and entertainment. Even though a number of surveys and review papers have been published, there is a lack of HAR overview papers focusing on healthcare applications that use wearable sensors. Therefore, we fill in the gap by presenting this overview paper. In particular, we present our projects to illustrate the system design of HAR applications for healthcare. Our projects include early mobility identification of human activities for intensive care unit (ICU) patients and gait analysis of Duchenne muscular dystrophy (DMD) patients. We cover essential components of designing HAR systems including sensor factors (e.g., type, number, and placement location), AI model selection (e.g., classical machine learning models versus deep learning models), and feature engineering. In addition, we highlight the challenges of such healthcare-oriented HAR systems and propose several research opportunities for both the medical and the computer science community.
Initiative-Aware Self-Supervised Learning for Knowledge-Grounded Conversations

In the knowledge-grounded conversation (KGC) task systems aim to produce more informative responses by leveraging external knowledge. KGC includes a vital part, knowledge selection, where conversational agents select the appropriate knowledge to be incorporated in the next response. Mixed initiative is an intrinsic feature of conversations where the user and the system can both take the initiative in suggesting new conversational directions. Knowledge selection can be driven by the user's initiative or by the system's initiative. For the former, the system usually selects knowledge according to the current user utterance that contains new topics or questions posed by the user; for the latter, the system usually selects knowledge according to the previously selected knowledge. No previous study has considered the mixed-initiative characteristics of knowledge selection to improve its performance.
R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition

Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.
Deepfake Videos in the Wild: Analysis and Detection

AI-manipulated videos, commonly known as deepfakes, are an emerging problem. Recently, researchers in academia and industry have contributed several (self-created) benchmark deepfake datasets, and deepfake detection algorithms. However, little effort has gone towards understanding deepfake videos in the wild, leading to a limited understanding of the real-world applicability of research contributions in this space. Even if detection schemes are shown to perform well on existing datasets, it is unclear how well the methods generalize to real-world deepfakes. To bridge this gap in knowledge, we make the following contributions: First, we collect and present the largest dataset of deepfake videos in the wild, containing 1,869 videos from YouTube and Bilibili, and extract over 4.8M frames of content. Second, we present a comprehensive analysis of the growth patterns, popularity, creators, manipulation strategies, and production methods of deepfake content in the real-world. Third, we systematically evaluate existing defenses using our new dataset, and observe that they are not ready for deployment in the real-world. Fourth, we explore the potential for transfer learning schemes and competition-winning techniques to improve defenses.