Reviewerly/exHarmony huggingface dataset:
Identity-Based Integrity Verification (IBIV) Protocol for Cloud Data Storage

With meteoric advancement in quantum computing, the traditional data integrity verifying schemes are no longer safe for cloud data storage. A large number of the current techniques are dependent on expensive Public Key Infrastructure (PKI). They cost computationally and communicationally heavy for verification which do not stand with the advantages when quantum computing techniques are applied. Hence, a quantum safe and efficient integrity verification protocol is a research hotspot. Lattice-based signature constructions involve matrix-matrix or matrix vector multiplications making computation competent, simple and resistant to quantum computer attacks. Study in this paper uses Bloom Filter which offers high efficiency in query and search operations. Further, we propose an Identity-Based Integrity Verification (IBIV) protocol for cloud storage from Lattice and Bloom filter. We focus on security against attacks from Cloud Service Provider (CSP), data privacy attacks against Third Party Auditor (TPA) and improvement in efficiency.
Bayesian Causal Inference in Probit Graphical Models

We consider a binary response which is potentially affected by a set of continuous variables. Of special interest is the causal effect on the response due to an intervention on a specific variable. The latter can be meaningfully determined on the basis of observational data through suitable assumptions on the data generating mechanism. In particular we assume that the joint distribution obeys the conditional independencies (Markov properties) inherent in a Directed Acyclic Graph (DAG), and the DAG is given a causal interpretation through the notion of interventional distribution. We propose a DAG-probit model where the response is generated by discretization through a random threshold of a continuous latent variable and the latter, jointly with the remaining continuous variables, has a distribution belonging to a zero-mean Gaussian model whose covariance matrix is constrained to satisfy the Markov properties of the DAG; the latter is assigned a DAG-Wishart prior through the corresponding Cholesky parameters. Our model leads to a natural definition of causal effect conditionally on a given DAG. Since the DAG which generates the observations is unknown, we present an efficient MCMC algorithm whose target is the posterior distribution on the space of DAGs, the Cholesky parameters of the concentration matrix, and the threshold linking the response to the latent. Our end result is a Bayesian Model Averaging estimate of the causal effect which incorporates parameter, as well as model, uncertainty. The methodology is assessed using simulation experiments and applied to a gene expression data set originating from breast cancer stem cells.
The impact of haze on photovoltaic systems: a case study

Urban haze has a multiple hazard in human living environment. It is not only harmful to human health but also affects the light passing through the atmosphere. This paper presents a study explaining the impact of haze on the performance of photovoltaic (PV) panels in the same humidity season. Using continuous and high-resolution on-site photovoltaic data from Shanghai, the paper calculates the attenuation of irradiance and the shortening of power generation hours due to changing concentrations of fine particulate matter. For Shanghai, we find that the relative loss of power generation was 14.09 MJ with loss ratio of 8.1% in total and the effective power generation hours were only 29.66 hours in the most serious air pollution month between December 2017 and February 2018. Economic damage from air pollution should not be ignored. Three economic models under the condition of haze are presented. The winter yield of the optimal economic model is 249.43 USD. Comparing the months with severe haze pollution and slight haze pollution, the revenue loss is estimated to be 59.17 USD. Meanwhile, the annual revenue without air pollution was estimated and the payback period was extended by 0.52 years due to the haze. This study has certain implications for PV installers and investors.
Robust Real-time Obstacle Avoidance of Wheeled Mobile Robot based on Multi-Sensor Data Fusion

Wheeled mobile robot has the advantages of strong execution ability, stable operation and fast response speed. It is widely utilized in smart autonomous robots, automated operation platforms, urban rail transit and some other related fields. Currently, the most of researches on wheeled mobile robot autonomous motion are based on single-sensor data to detect surrounding objects, which are susceptible to the environmental interference. This paper adopts the centralized multi-sensor fusion algorithm to achieve obstacle avoidance based on Kalman filter. Through multi-objective optimization, all the sub-behaviors among movement process are weighted, which improves the real-time and robustness of the system. Finally, the effectiveness of the algorithm is verified through prototype testing.
Simultaneous Multi-face Zoom Tracking for 3-D People-Flow Analysis with Face Identification

We developed a dual-camera-based multi-face zoom-tracking system that captures zoomed targets simultaneously by combining a mirror-drive pan-tilt camera with ultrafast gaze control for zoomed views and an RGB-D camera for 3D wide view. In our system, the RGB-D camera detects multiple persons in the wide view and outputs the 3-D positions of their keypoints with CNN-based pose estimation. The mirror-drive pan-tilt camera simultaneously switches its viewpoints to the directions of the nose keypoints to zoom on their face images to perform person identification. We demonstrate our system performance using experimental results of a 3-D people-flow analysis with person identification, where the mirror-drive pantilt camera with 120-viewpoints/s-switching functions effectively as five virtual 24-fps pan-tilt cameras to track and zoom on the faces of five walking persons in an indoor scene.
An Effective Facial Expression Recognition System

This paper propounds an automated Facial Expression Recognition (FER) system based on Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). The proposed method recognizes facial expressions for static images and real-time applications. Firstly, face and facial parts are detected from the input image. Preprocessing is performed over the detected parts using histogram equalization and image sharpening technique to reduce the illumination effect. Then HOG is used to extract the distinctive image features from facial regions and concatenated into a single feature vector. Finally, the SVM is used for expression classification with a polynomial kernel function. The proposed system is evaluated on renowned JAFFE and Cohn-Kanade facial expression database for seven basic facial expressions. It reveals that the proposed FER system yields an accuracy up to 97.62% and 98.61% for JAFFE and Cohn-Kanade databases, respectively.
A Meta-theory for Big-step Semantics

It is well known that big-step semantics is not able to distinguish stuck and non-terminating computations. This is a strong limitation as it makes it very difficult to reason about properties involving infinite computations, such as type soundness, which cannot even be expressed. We show that this issue is only apparent: the distinction between stuck and diverging computations is implicit in any big-step semantics and it just needs to be uncovered. To achieve this goal, we develop a systematic study of big-step semantics: we introduce an abstract definition of what a big-step semantics is, we define a notion of computation by formalizing the evaluation algorithm implicitly associated with any big-step semantics, and we show how to canonically extend a big-step semantics to characterize stuck and diverging computations. Building on these notions, we describe a general proof technique to show that a predicate is sound, that is, it prevents stuck computation, with respect to a big-step semantics. One needs to check three properties relating the predicate and the semantics, and if they hold, the predicate is sound. The extended semantics is essential to establish this meta-logical result but is of no concerns to the user, who only needs to prove the three properties of the initial big-step semantics. Finally, we illustrate the technique by several examples, showing that it is applicable also in cases where subject reduction does not hold, and hence the standard technique for small-step semantics cannot be used.
Some Algorithms of Differential-Ranging Acoustic Positioning System Intended for AUV Group Navigation

The paper considers the task of AUV group navigation using a long-base line differential-ranging acoustic positioning system. For solving the difference-ranging (DR) problem two navigation algorithms are developed and presented. The first of them implements enumeration search and the second allows to find the solution by analytical way. The simulation results of developed algorithms work are presented.
First-Order Efficient General-Purpose Clean-Label Data Poisoning

As one of the recently emerged threats to Deep Learning (DL) models, clean-label data poisoning can teach DL models to make wrong predictions on specific target data, such as images or network traffic packets, by injecting a small set of poisoning data with clean labels into the training datasets. Although several clean-label poisoning methods have been developed before, they have two main limitations. First, the methods developed with bi-level optimization or influence functions usually require second-order information, leading to substantial computational overhead. Second, the methods based on feature collision are not very transferable to unseen feature spaces or generalizable to various scenarios. To address these limitations, we propose a first-order efficient general-purpose clean-label poisoning attack in this paper. In our attack, we first identify the first-order model update that can push the model towards predicting the target data as the attack targeted label. We then formulate a necessary condition based on the model update and other first-order information to optimize the poisoning data. Theoretically, we prove that our first-order poisoning method is an approximation of a second-order approach with theoretically-guaranteed performance. Empirically, extensive evaluations on image classification and network traffic classification demonstrate the outstanding efficiency, transferability, and generalizability of our poisoning method.
3D Medical Image Registration Based on Spatial Attention

Learning-based image registration methods have confirmed the ability of neural network and achieved impressive performance. Recently, the convolutional neural network model based on encoding and decoding structure has shown great advantages in the task of medical image registration. In this work, an unsupervised end-to-end registration method based on spatial attention is proposed for deformable 3D medical images. Specifically, we use multi-scale feature fusion to reduce the difference between low-level features and high-level features. And spatial attention is introduced to enhance the salient areas of feature map and the interpretability of convolutional neural network. At the same time, we use dilated convolution to solve the problem of information loss in the process of increasing the receptive field, which facilitates the accuracy improvement. We evaluated the proposed method on four datasets OASIS, ADNI, ABIDE and ADHD200. The experimental results show that our method achieves promising results compared with the popular method SyN.
An Indirect Adaptive Control Approach to Image Based Visual Servoing for Translational Trajectory Tracking

In this work, it is considered an image based visual servoing control problem, for uncertain robot manipulators. Visual feedback is provided by a fixed monocular camera with uncertain parameters, for the purpose of tracking translational trajectories of a spherical target, both the trajectory on image plane and depth. Based on a cascade structure, the proposed adaptive visual servoing is combined with an adaptive motion control strategy. Stability and passivity properties are analyzed with the Lyapunov method. Simulation results illustrate and highlight performance of the proposed controller.
Utilizing Morphological Features for Part-of-Speech Tagging of Bahasa Indonesia in Bidirectional LSTM

Research in the area of Part of Speech (PoS) Tagging has been widely explored especially for high resource language, such as English. However, there are only a small number of studies that have been conducted for Bahasa Indonesia. In this study, we present our experiment on utilizing morphological features for PoS tagging of Bahasa Indonesia in Bidirectional Long Short Term Memory architecture. Three different features including prefix, suffix, and capitalization have been examined. The results of our study show that combining morphological features with word embedding is effective for improving the tagger performance. Our study also provides more detailed explanation on which morphological features are useful for the PoS tagging task.
Real Time Emotion Detection Using Deep Learning

Facial expressions depict emotions and produce information on the personalities and thoughts of people. The machine performs different tasks constantly in order to increase its use in public. Machines that are able to understand emotions can be used to execute a wide range of tasks. Machine perception demands machines to grasp information about their environment. The understanding of human facial expressions is a key component to understand emotions and also to find broad applications in the field of human-computer interaction. Tensorflow framework, Keras library and the Xception Architecture of CNN are used to train the model on the Fer2013 dataset.
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter IT

End-to-end Automatic Speech Recognition (ASR) models are commonly trained over spoken utterances using optimization methods like Stochastic Gradient Descent (SGD). In distributed settings like Federated Learning, model training requires transmission of gradients over a network. In this work, we design the first method for revealing the identity of the speaker of a training utterance with access only to a gradient. We propose Hessian-Free Gradients Matching, an input reconstruction technique that operates without second derivatives of the loss function (required in prior works), which can be expensive to compute. We show the effectiveness of our method using the DeepSpeech model architecture, demonstrating that it is possible to reveal the speaker’s identity with 34% top-1 accuracy (51% top-5 accuracy) on the LibriSpeech dataset. Further, we study the effect of Dropout on the success of our method. We show that a dropout rate of 0.2 can reduce the speaker identity accuracy to 0% top-1 (0.5% top-5).
Robust 360° Video Streaming via Non-Linear Sampling

We propose CoRE, a 360° video streaming approach that reduces bandwidth requirements compared to transferring the entire 360° video. CoRE uses non-linear sampling in both the spatial and temporal domains to achieve robustness to view direction prediction error and to transient wireless network bandwidth fluctuation. Each CoRE frame samples the environment in all directions, with full resolution over the predicted field of view and gradually decreasing resolution at the periphery, so that missing pixels are avoided, irrespective of the view prediction error magnitude. A CoRE video chunk has a main part at full frame rate, and an extension part at a gradually decreasing frame rate, which avoids stalls while waiting for a delayed transfer. We evaluate a prototype implementation of CoRE through trace-based experiments and a user study, and find that, compared to tiling with low-resolution padding, CoRE reduces data transfer amounts, stalls, and H.264 decoding overhead, increases frame rates, and eliminates missing pixels.
Unlimited Resolution Image Generation with R2D2-GANs

In this paper we present a novel simulation technique for generating high quality images of any predefined resolution. This method can be used to synthesize sonar scans of size equivalent to those collected during a full-length mission, with across track resolutions of any chosen magnitude. In essence, our model extends Generative Adversarial Networks (GANs) based architecture into a conditional recursive setting, that facilitates the continuity of the generated images. The data produced is continuous, realistically -looking, and can also be generated at least two times faster than the real speed of acquisition for the sonars with higher resolutions, such as EdgeTech. The seabed topography can be fully controlled by the user. The visual assessment tests demonstrate that humans cannot distinguish the simulated images from real. Moreover, experimental results suggest that in the absence of real data the autonomous recognition systems can benefit greatly from training with the synthetic data, produced by the R2D2-GANs.
ProbWalk: A random walk approach in weighted graph embedding

There are many weighted graphs in the real-world networks, such as social networks, communication networks, citation networks, etc. Along with successful application of deep learning in graph embedding, we study how to embed weight graph, because weights on the edges also play an important role in the graph. We propose a novel algorithm called ProbWalk, which take advantage of edge weights and convert the weights into transition probabilities. Our proposed method specifies the strategy of sampling the surrounding vertices by weights and generate the random walk for graph embedding according to transition probability. We evaluate our methods on tasks including multi-label classification and link prediction. Experimental results show that our method performs better than competed method on several weighted graph datasets.
Blockchain aided Vehicle Certification (BVC): A Secured E-Governance Framework for Transport Stakeholders

The exponential progression of digitalization created extensive prospects of e-governance, especially for transport stakeholders. Therefore, involving the transport participants remotely with governing activity digitally in the ease with the certification procedure in a paperless approach is exceedingly achievable. However, the major challenge for such real-time large-scale data management in a secure, scalable, and confidential manner increases the research opportunity. Consequently, we have proposed a Blockchain-enabled Vehicle Certification (BVC) framework where the government body, car owner, driver, and other vendors are connected through a secured channel. Here, we have adopted the Blockchain in the core business layer of the architecture through which the stakeholders can communicate with each other and store data in a confidential and secure access approach. Similarly, for interoperability and peer-to-peer communication, we have designed block binding and digital ring signature techniques. Nevertheless, we have developed a simple prototype of this BVC framework with a pilot database to test the stakeholders' interaction and functionality securely. Also, rigorous load testing, response time monitoring, and other performance requirements have been tested to evaluate the framework's robustness, scalability, and adaptability.
Product Innovation: A Multimodal Interaction Design Method based on HCI and TRIZ

Abstract The border between tangible and digital products is getting more diffuse, which has caused the focus of product innovation to migrate from technology to user experience and Human-Computer Interaction (HCI). In HCI, the comprehensive interactions between users and products are collectively referred to as multimodal interaction, and multimodal interaction defines the user experience of the product. However, there is currently a lack of design methods for multimodal interaction. This paper presents a multimodal interaction design method by exploiting the synergy of HCI and TRIZ. This method covers the whole design process, from the analysis of the user needs to the generation of the design solutions and it contains tools as flexible as active, a more valid view on user experience is provided during all design stages. A case experiment validated the effectiveness of the method. The implications of this study will help designers to innovate products with multimodal interaction.
Fool Me Once: Robust Selective Segmentation via Out-of-Distribution Detection with Contrastive Learning

In this work, a neural network is trained to simultaneously perform segmentation and pixel-wise Out-of-Distribution (OoD) detection, such that the segmentation of unknown regions of scenes can be rejected. This is made possible by leveraging an OoD dataset with a novel contrastive objective and data augmentation scheme. By including unknown classes in the training data, a more robust feature representation is learned with known classes represented distinctly from those unknown. In comparison, when presented with unknown classes or conditions, many current approaches for segmentation frequently exhibit high confidence in their inaccurate segmentations and cannot be trusted in many operational environments. We validate our system on a real-world dataset of unusual driving scenes, and show that by selectively segmenting scenes based on what is predicted as OoD, we can increase the segmentation accuracy by an IoU of 0.2 with respect to alternative techniques.
Recognition of Suspicious Human Activities using KLT and Kalman Filter for ATM Surveillance System

One of the active research areas in the field of Computer Vision in today's era is recognizing human activity under video surveillance. To resolve suspicious activity, sensitive and public places such as school, college, jewellery store, railway stations, a temple, bank, etc. can be monitored using video surveillance. It is mind-numbing and time-consuming to track such public areas for a long time. One such area is the Automated Teller Machine (ATM), monitored by a surveillance system. An intelligent monitoring system is proposed to classify real-time based human behaviour and categories them into regular and unusual activities to ensure the safety aspect of ATM and can cause different levels of alarm. This paper proposes a real-time system using the Kalman Filter and the Kanade-Lucas-Tomasi (KLT) Tracking Algorithm to detect and monitor suspicious or non-suspicious human behaviour for ATM video surveillance. On a real-time ATM Surveillance database, experimental results are carried out.
Shedding light on 19th century spectra by analyzing Lippmann photography

Significance Gabriel Lippmann won the 1908 Nobel Prize in Physics for his method of reproducing colors in photography. Despite the significance of this result, there are still misconceptions regarding the approach. We provide a complete end-to-end analysis of the process and show, both theoretically and experimentally, how the spectrum reflected from a Lippmann plate is not the same as the exposing spectrum. In addition, we demonstrate that, given the spectrum reflected from a Lippmann plate, together with the plate’s color absorption properties, the original exposing spectrum can be algorithmically recovered.