Reviewerly/exHarmony huggingface dataset:
Self-Supervised Domain Mismatch Estimation for Autonomous Perception

Autonomous driving requires self awareness of its perception functions. Technically spoken, this can be realized by observers, which monitor the performance indicators of various perception modules. In this work we choose, exemplarily, a semantic segmentation to be monitored, and propose an autoencoder, trained in a self-supervised fashion on the very same training data as the semantic segmentation to be monitored. While the autoencoder's image reconstruction performance (PSNR) during online inference shows already a good predictive power w.r.t. semantic segmentation performance, we propose a novel domain mismatch metric DM as the earth mover's distance between a pre-stored PSNR distribution on training (source) data, and an online-acquired PSNR distribution on any inference (target) data. We are able to show by experiments that the DM metric has a strong rank order correlation with the semantic segmentation within its functional scope. We also propose a training domain-dependent threshold for the DM metric to define this functional scope.
A Parallel Filtering-Communication-Based Cardinality Consensus Approach for Real-Time Distributed PHD Filtering

This paper proposes a new cardinality consensus (CC) approach called “prior-CC” to distributed probability hypothesis density (PHD) filtering based on a decentralized sensor network. In our approach, network-wide average consensus is sought with respect to the prior cardinality estimate. Unlike existing serial filtering-communication approaches, the prior-CC scheme allows the internode communication to be performed in parallel with the local filter calculation and requires only a small amount of interfacing fusion calculation and communication. This enables real time filtering that minimizes data delay and is of great significance in realistic tracking systems. We provide details of the Gaussian mixture implementation of the proposed prior-CC-PHD filter based on a diffuse target birth model and analyze the filtering-communication parallelization. In addition, we evaluate the gain of the prior-CC scheme with respect to the filtering accuracy in comparison with the standard CC scheme via simulations using a stationary linear sensor network and a dynamic nonlinear sensor network, respectively.
BYNQNet: Bayesian Neural Network with Quadratic Activations for Sampling-Free Uncertainty Estimation on FPGA

An efficient inference algorithm for Bayesian neural network (BNN) named BYNQNet, Bayesian neural network with quadratic activations, and its FPGA implementation are proposed. As neural networks find applications in mission critical systems, uncertainty estimations in network inference become increasingly important. BNN is a theoretically grounded solution to deal with uncertainty in neural network by treating network parameters as random variables. However, an inference in BNN involves Monte Carlo (MC) sampling, i.e., a stochastic forwarding is repeated N times with randomly sampled network parameters, which results in N times slower inference compared to non-Bayesian approach. Although recent papers proposed sampling-free algorithms for BNN inference, they still require evaluation of complex functions such as a cumulative distribution function (CDF) of Gaussian distribution for propagating uncertainties through nonlinear activation functions such as ReLU and Heaviside, which requires considerable amount of resources for hardware implementation. Contrary to conventional BNN, BYNQNet employs quadratic nonlinear activation functions and hence the uncertainty propagation can be achieved using only polynomial operations. Our numerical experiment reveals that BYNQNet has comparative accuracy with MC-based BNN which requires N=10 forwardings. We also demonstrate that BYNQNet implemented on Xilinx PYNQ-Z1 FPGA board achieves the throughput of 131×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> images per second and the energy efficiency of 44.7×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> images per joule, which corresponds to 4.07× and 8.99× improvements from the state-of-the-art MC-based BNN accelerator.
Ballot-Marking Devices Cannot Ensure the Will of the Voters

The complexity of U.S. elections usually requires computers to count ballots—but computers can be hacked, so election integrity requires a voting system in which paper ballots can be recounted by hand. However, paper ballots provide no assurance unless they accurately record the votes as expressed by the voters. Voters can express their intent by indelibly hand-marking ballots or using computers called ballot-marking devices (BMDs). Voters can make mistakes in expressing their intent in either technology, but only BMDs are also subject to hacking, bugs, and misconfiguration of the software that prints the marked ballots. Most voters do not review BMD-printed ballots, and those who do often fail to notice when the printed vote is not what they expressed on the touchscreen. Furthermore, there is no action a voter can take to demonstrate to election officials that a BMD altered their expressed votes, nor is there a corrective action that election officials can take if notified by voters—there is no way to deter, contain, or correct computer hacking in BMDs. These are the essential security flaws of BMDs. Risk-limiting audits can ensure that the votes recorded on paper ballots are tabulated correctly, but no audit can ensure that the votes on paper are the ones expressed by the voter on a touchscreen: Elections conducted on current BMDs cannot be confirmed by audits. We identify two properties of voting systems, contestability and defensibility, necessary for audits to confirm election outcomes. No available BMD certified by the Election Assistance Commission is contestable or defensible.
Interactive Transport Enquiry with AI Chatbot

Public transportation is used efficiently by millions of people all over the world. People tend to travel to different places and at certain times they may feel completely lost in a new place. Our chatbot comes to rescue at this time. A Chatbot is often described as one of the most promising tools for communication between humans and machines using artificial intelligence. It is a software application that is used to conduct an online chat conversation via text by using natural language processing (NLP) and deep learning techniques. It provides direct contact with a live human agent in the form of GUI. This AI chatbot confirms the current location and the final destination of the user by asking a few questions. It examines the user's query and extracts the appropriate entries from the database. The deep learning techniques that are used in this chatbot are responsible for understanding the user intents accurately to avoid any misconceptions. Once the user's intention has been recognized, the chatbot provides the most relevant response for the user's query request. Then the user gets all the information about the bus names along with their numbers so that the person can travel safely to the desired location. Our chatbot is implemented in python's Keras library and used Tkinter for GUI.
Noise is Inside Me! Generating Adversarial Perturbations with Noise Derived from Natural Filters

Deep learning solutions are vulnerable to adversarial perturbations and can lead a "frog" image to be misclassified as a "deer" or random pattern into "guitar". Adversarial attack generation algorithms generally utilize the knowledge of database and CNN model to craft the noise. In this research, we present a novel scheme termed as Camera Inspired Perturbations to generate adversarial noise. The proposed approach relies on the noise embedded in the image due to environmental factors or camera noise incorporated. We extract these noise patterns using image filtering algorithms and incorporate them into images to generate adversarial images. Unlike most of the existing algorithms that require learning of noise, the proposed adversarial noise can be applied in real-time. It is model-agnostic and can be utilized to fool multiple deep learning classifiers on various databases. The effectiveness of the proposed approach is evaluated on five different databases with five different convolutional neural networks such as ResNet-50, VGG-16, and VGG-Face. The proposed attack reduces the classification accuracy of every network, for instance, the performance of VGG-16 on the Tiny ImageNet database is reduced by more than 33%. The robustness of the proposed adversarial noise is also evaluated against different adversarial defense algorithms.
The effects of globalisation techniques on feature selection for text classification

Text classification (TC) is very important and critical task in the 21th century as there exist high volume of electronic data on the Internet. In TC, textual data are characterised by a huge number of highly sparse features/terms. A typical TC consists of many steps and one of the most important steps is undoubtedly feature selection (FS). In this study, we have comprehensively investigated the effects of various globalisation techniques on local feature selection (LFS) methods using datasets with different characteristics such as multi-class unbalanced (MCU), multi-class balanced (MCB), binary-class unbalanced (BCU) and binary-class balanced (BCB). The globalisation techniques used in this study are summation (SUM), weighted-sum (AVG), and maximum (MAX). To investigate the effect of globalisation techniques, we used three LFS methods named as Discriminative Feature Selection (DFSS), odds ratio (OR) and chi-square (CHI2). In the experiments, we have utilised four different benchmark datasets named as Reuters-21578, 20Newsgroup., Enron1, and Polarity in addition to Support Vector Machines (SVM) and Decision Tree (DT) classifiers. According to the experimental results, the most successful globalisation technique is AVG while all situations are taken into account. The experimental results indicate that DFSS method is more successful than OR and CHI2 methods on datasets with MCU and MCB characteristics. However, CHI2 method seems more accurate than OR and DFSS methods on datasets with BCU and BCB characteristics. Also, SVM classifier performed better than DT classifier in most cases.
Dive Into Deep Learning Based Automatic Modulation Classification: A Disentangled Approach

Recently, deep learning (DL) based automatic modulation classification (AMC) has received much attention. Various network structures with higher complexity are utilized to boost the performance of classification model. We divide the issue of AMC into two objectives and propose a disentangled approach with a signal processing module. Unlike popular end-to-end training strategy, we first consider a simple model with much fewer trainable parameters to learn accurate modulation features for classification. Then a U-net based signal processing module using a specially designed function is introduced to transfer the knowledge stored in classification module. We compare the performance of the proposed method with several baseline models on two well known datasets. Experimental results demonstrate that the proposed method gives superior performance with lower computational complexity compared with other methods. Furthermore, we also verify the feasibility and huge potential of the knowledge transferring in the field of wireless communications.
Learning Bayesian Networks with the Saiyan Algorithm

Some structure learning algorithms have proven to be effective in reconstructing hypothetical Bayesian Network graphs from synthetic data. However, in their mission to maximise a scoring function, many become conservative and minimise edges discovered. While simplicity is desired, the output is often a graph that consists of multiple independent subgraphs that do not enable full propagation of evidence. While this is not a problem in theory, it can be a problem in practice. This article examines a novel unconventional associational heuristic called Saiyan, which returns a directed acyclic graph that enables full propagation of evidence. Associational heuristics are not expected to perform well relative to sophisticated constraint-based and score-based learning approaches. Moreover, forcing the algorithm to connect all data variables implies that the forced edges will not be correct at the rate of those identified unrestrictedly. Still, synthetic and real-world experiments suggest that such a heuristic can be competitive relative to some of the well-established constraint-based, score-based and hybrid learning algorithms.
A novel deep neural network for noise removal from underwater image

Underwater image processing technologies have always been challenging tasks due to the complex underwater environment. Images captured under water are not only affected by the water itself, but also by the diverse suspended particles that increase the effect of absorption and scattering. Moreover, these particles themselves are usually imaged on the picture, causing the spot noise signal to interfere with the target objects. To address this issue, we propose a novel deep neural network for removing the spot noise from underwater images. Its main idea is to train a generative adversarial network (GAN) to transform the noisy image to clean image. Based on the deep encoder and decoder framework, the skip connections are introduced to combine the features of low-level and high-level to help recover the original image. Meanwhile, the self-attention mechanism is employed to the generative network to capture global dependencies in the feature maps, which can generate the image with fine details at every location. Furthermore, we apply the spectral normalization to both the generative and discriminative networks to stabilize the training process. Experiments evaluated on synthetic and real-world images show that the proposed method outperforms many recent state-of-the-art methods in terms of quantitative and visual quality. Besides, the results also demonstrate that the proposed method has the good ability to remove the spot noise from underwater images while preserving sharp edge and fine details.
Crypto-Currencies and ICOs: Are They Scams? An Empirical Study

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Vulnerability of Person Re-Identification Models to Metric Adversarial Attacks

Person re-identification (re-ID) is a key problem in smart supervision of camera networks.Over the past years, models using deep learning have become state of the art.However, it has been shown that deep neural networks are flawed with adversarial examples, i.e. human-imperceptible perturbations.Extensively studied for the task of image closedset classification, this problem can also appear in the case of open-set retrieval tasks.Indeed, recent work has shown that we can also generate adversarial examples for metric learning systems such as re-ID ones.These models remain vulnerable: when faced with adversarial examples, they fail to correctly recognize a person, which represents a security breach.These attacks are all the more dangerous as they are impossible to detect for a human operator.Attacking a metric consists in altering the distances between the feature of an attacked image and those of reference images, i.e. guides.In this article, we investigate different possible attacks depending on the number and type of guides available.From this metric attack family, two particularly effective attacks stand out.The first one, called Self Metric Attack, is a strong attack that does not need any image apart from the attacked image.The second one, called Furthest-Negative Attack, makes full use of a set of images.Attacks are evaluated on commonly used datasets: Market1501 and DukeMTMC.Finally, we propose an efficient extension of adversarial training protocol adapted to metric learning as a defense that increases the robustness of re-ID models. 1
On quantum ensembles of quantum classifiers

Quantum machine learning seeks to exploit the underlying nature of a quantum computer to enhance machine learning techniques. A particular framework uses the quantum property of superposition to store sets of parameters, thereby creating an ensemble of quantum classifiers that may be computed in parallel. The idea stems from classical ensemble methods where one attempts to build a stronger model by averaging the results from many different models. In this work, we demonstrate that a specific implementation of the quantum ensemble of quantum classifiers, called the accuracy-weighted quantum ensemble, can be fully dequantised. On the other hand, the general quantum ensemble framework is shown to contain the well-known Deutsch-Jozsa algorithm that notably provides a quantum speedup and creates the potential for a useful quantum ensemble to harness this computational advantage.
Distributed V2V Computation Offloading Based on Dynamic Pricing Using Deep Reinforcement Learning

Vehicular computation offloading is a promising paradigm that improves the computing capability of vehicles to support autonomous driving and various on-board infotainment services. Comparing with accessing the remote cloud, distributed vehicle-to-vehicle (V2V) computation offloading is more efficient and suitable for delay-sensitive tasks by taking advantage of vehicular idle computing resources. Due to the high dynamic vehicular environment and the variation of available vehicular computing resources, it is a great challenge to design an effective task offloading mechanism to efficiently utilize vehicular computing resources. In this paper, we investigate the computation task allocation among vehicles, and propose a distributed V2V computation offloading framework, in which wireless channel states and variation of idle computing resources are both considered. Specially, we formulate the task allocation problem as a sequential decision making problem, which can be solved by using deep reinforcement learning. Considering that vehicles with idle computing resources may not share their computing resources voluntarily, we thus propose a dynamic pricing scheme that motivates vehicles to contribute their computing resources according to the price they receive. The performance of designed task allocation mechanism is validated by simulation results which reveal the effectiveness of our mechanism compared to the other algorithms.
Classification of functional and non-functional requirement in software requirement using Word2vec and fast Text

Abstract One of the needs in adopting a crowdsourcing approach in software requirement system (SRS) is to be able to perform text analytics to gain insight or knowledge from the crowd’s feedback. One of the expected text analytic tasks is to be able of analyzing the feedback automatically; such as, whether the feedback concerns about the functional requirement (FR) and non-functional requirements (NFR). To automatically do the FR and NFR identification, one can treat the problem as a text classification task. Performing automatic identification requires features (word representation) that contain sufficient information which can be used to do the identification. Thus, the types of features can be considered as an important step in performing automatic identification of FR and NFR. In this study, the RE’17 dataset challenge is used as the dataset. Using the dataset, we will like to find out the effect of word embedding against traditional features (such as bag-of-words) in NFR and FR classification. In addition, we also want to find out whether is necessary to use a complex neural classifier to obtain the best performance of NFR and FR classification. Based on the obtained results, using fastText seems to be the promising classification model since the model obtained the highest Fl-score of 92.8%.
Semantic approximation for reducing code bloat in Genetic Programming

Code bloat is a phenomenon in Genetic Programming (GP) characterized by the increase in individual size during the evolutionary process without a corresponding improvement in fitness. Bloat negatively affects GP performance, since large individuals are more time consuming to evaluate and harder to interpret. In this paper, we propose two approaches for reducing GP code bloat based on a semantic approximation technique. The first approach replaces a random subtree in an individual by a smaller tree of approximate semantics. The second approach replaces a random subtree by a smaller tree that is semantically approximate to the desired semantics. We evaluated the proposed methods on a large number of regression problems. The experimental results showed that our methods help to significantly reduce code bloat and improve the performance of GP compared to standard GP and some recent bloat control methods in GP. Furthermore, the performance of the proposed approaches is competitive with the best machine learning technique among the four tested machine learning algorithms.
Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent Networks

High-resolution satellite imagery is critical for various earth observation applications related to environment monitoring, geoscience, forecasting, and land use analysis. However, the acquisition cost of such high-quality imagery due to the scarcity of providers and needs for high-frequency revisits restricts its accessibility in many fields. In this work, we present a data-driven, multi-image super resolution approach to alleviate these problems. Our approach is based on an end-to-end deep neural network that consists of an encoder, a fusion module, and a decoder. The encoder extracts co-registered highly efficient feature representations from low-resolution images of a scene. A Gated Re-current Unit (GRU)-based module acts as the fusion module, aggregating features into a combined representation. Finally, a decoder reconstructs the super-resolved image. The proposed model is evaluated on the PROBA-V dataset released in a recent competition held by the European Space Agency. Our results show that it performs among the top contenders and offers a new practical solution for real-world applications.
Live Trojan Attacks on Deep Neural Networks

Like all software systems, the execution of deep learning models is dictated in part by logic represented as data in memory. For decades, attackers have exploited traditional software programs by manipulating this data. We propose a live attack on deep learning systems that patches model parameters in memory to achieve predefined malicious behavior on a certain set of inputs. By minimizing the size and number of these patches, the attacker can reduce the amount of network communication and memory overwrites, with minimal risk of system malfunctions or other detectable side effects. We demonstrate the feasibility of this attack by computing efficient patches on multiple deep learning models. We show that the desired trojan behavior can be induced with a few small patches and with limited access to training data. We describe the details of how this attack is carried out on real systems and provide sample code for patching TensorFlow model parameters in Windows and in Linux. Lastly, we present a technique for effectively manipulating entropy on perturbed inputs to bypass STRIP, a state-of-the-art run-time trojan detection technique.