Reviewerly/exHarmony huggingface dataset:
BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements

Deep neural networks (DNNs) have progressed rapidly during the past decade and have been deployed in various real-world applications. Meanwhile, DNN models have been shown to be vulnerable to security and privacy attacks. One such attack that has attracted a great deal of attention recently is the backdoor attack. Specifically, the adversary poisons the target model's training set to mislead any input with an added secret trigger to a target class. Previous backdoor attacks predominantly focus on computer vision (CV) applications, such as image classification. In this paper, we perform a systematic investigation of backdoor attack on NLP models, and propose BadNL, a general NLP backdoor attack framework including novel attack methods. Specifically, we propose three methods to construct triggers, namely BadChar, BadWord, and BadSentence, including basic and semantic-preserving variants. Our attacks achieve an almost perfect attack success rate with a negligible effect on the original model's utility. For instance, using the BadChar, our backdoor attack achieves a 98.9% attack success rate with yielding a utility improvement of 1.5% on the SST-5 dataset when only poisoning 3% of the original set. Moreover, we conduct a user study to prove that our triggers can well preserve the semantics from humans perspective.
Blockchain Enabled Transparent and Anti-Counterfeiting Supply of COVID-19 Vaccine Vials

The COVID-19 pandemic has profoundly affected almost all facets of peoples' lives, various economic areas and regions of the world. In such a situation implementation of a vaccination can be viewed as essential but its success will be dependent on availability and transparency in the distribution process that will be shared among the stakeholders. Various distributed ledgers (DLTs) such as blockchain provide an open, public, immutable system that has numerous applications due the mentioned abilities. In this paper the authors have proposed a solution based on blockchain to increase the security and transparency in the tracing of COVID-19 vaccination vials. Smart contracts have been developed to monitor the supply, distribution of vaccination vials. The proposed solution will help to generate a tamper-proof and secure environment for the distribution of COVID-19 vaccination vials. Proof of delivery is used as a consensus mechanism for the proposed solution. A feedback feature is also implemented in order to track the vials lot in case of any side effect cause to the patient. The authors have implemented and tested the proposed solution using Ethereum test network, RinkeyBy, MetaMask, one clicks DApp. The proposed solution shows promising results in terms of throughput and scalability.
Graph Convolutional Adversarial Networks for Spatiotemporal Anomaly Detection

Traffic anomalies, such as traffic accidents and unexpected crowd gathering, may endanger public safety if not handled timely. Detecting traffic anomalies in their early stage can benefit citizens' quality of life and city planning. However, traffic anomaly detection faces two main challenges. First, it is challenging to model traffic dynamics due to the complex spatiotemporal characteristics of traffic data. Second, the criteria of traffic anomalies may vary with locations and times. In this article, we propose a spatiotemporal graph convolutional adversarial network (STGAN) to address these above challenges. More specifically, we devise a spatiotemporal generator to predict the normal traffic dynamics and a spatiotemporal discriminator to determine whether an input sequence is real or not. There are high correlations between neighboring data points in the spatial and temporal dimensions. Therefore, we propose a recent module and leverage graph convolutional gated recurrent unit (GCGRU) to help the generator and discriminator learn the spatiotemporal features of traffic dynamics and traffic anomalies, respectively. After adversarial training, the generator and discriminator can be used as detectors independently, where the generator models the normal traffic dynamics patterns and the discriminator provides detection criteria varying with spatiotemporal features. We then design a novel anomaly score combining the abilities of two detectors, which considers the misleading of unpredictable traffic dynamics to the discriminator. We evaluate our method on two real-world datasets from New York City and California. The experimental results show that the proposed method detects various traffic anomalies effectively and outperforms the state-of-the-art methods. Furthermore, the devised anomaly score achieves more robust detection performances than the general score.
Multiphase Overtaking Maneuver Planning for Autonomous Ground Vehicles Via a Desensitized Trajectory Optimization Approach

This article studies the problem of trajectory optimization for autonomous ground vehicles with the consideration of irregularly placed on-road obstacles and multiple maneuver phases. By introducing a series of event sequences, a new multiphase constrained optimal control formulation is constructed to describe the automatic overtaking process. Although existing trajectory optimization techniques can be applied to address the constructed problem, they may suffer from poor or premature convergence issues due to the complexity of the mission formulation. Thus, to offer an effective alternative, a novel desensitized trajectory optimization method is designed and implemented to explore the optimal overtaking maneuver for the AGVs. The proposed method applies a double layer structure, where an enhanced intelligent optimization method is used in the outer layer such that the main inner optimization routine can be boosted by starting at a better reference solution. The algorithm convergence as well as the solution optimality conditions are theoretically analyzed. Numerical results are provided to illustrate the validity of the established formulation. Comparative case studies were executed to demonstrate the quality of the obtained solution and the enhanced performance of the proposed trajectory optimization method.
A modified bat algorithm with torus walk for solving global optimisation problems

Bat algorithm (BA) has been widely used to solve the diverse kinds of optimisation problems. In accordance with the optimisation problems, balance between the two major components: exploitation and exploration, plays a significant role in meta-heuristic algorithms. Several researchers have worked on the performance for the improvement of these algorithms. BA faces one of the major issues in high dimensions. In our work, we proposed a new variant of BA by introducing the torus walk (TW-BA) to solve this issue. To improve the local search capability instead of using the standard uniform walk, torus walk is incorporated in this paper. The simulation results performed on 19 standard benchmark functions depicts the efficiency and effectiveness of TW-BA compared with the traditional BA, directional bat algorithm, particle swarm optimisation, cuckoo search, harmony search algorithm, differential evolution and genetic algorithm. The promising experimental result suggests the superiority of proposed technique.
Indistinguishability Obfuscation from LPN over $$\mathbb {F}_p$$, DLIN, and PRGs in NC$$^0$$

In this work, we study what minimal sets of assumptions suffice for constructing indistinguishability obfuscation ( $$i\mathcal {O}$$ ). We prove: Theorem(Informal): Assume sub-exponential security of the following assumptions: – the Learning Parity with Noise ( $$\mathsf {LPN}$$ ) assumption over general prime fields $$\mathbb {F}_p$$ with polynomially many $$\mathsf {LPN}$$ samples and error rate $$1/k^\delta $$ , where k is the dimension of the $$\mathsf {LPN}$$ secret, and $$\delta >0$$ is any constant; – the existence of a Boolean Pseudo-Random Generator ( $$\mathsf {PRG}$$ ) in $$\mathsf {NC}^0$$ with stretch $$n^{1+\tau }$$ , where n is the length of the $$\mathsf {PRG}$$ seed, and $$\tau >0$$ is any constant; – the Decision Linear ( $$\mathsf {DLIN}$$ ) assumption on symmetric bilinear groups of prime order. Then, (subexponentially secure) indistinguishability obfuscation for all polynomial-size circuits exists. Further, assuming only polynomial security of the aforementioned assumptions, there exists collusion resistant public-key functional encryption for all polynomial-size circuits. This removes the reliance on the Learning With Errors (LWE) assumption from the recent work of [Jain, Lin, Sahai STOC’21]. As a consequence, we obtain the first fully homomorphic encryption scheme that does not rely on any lattice-based hardness assumption. Our techniques feature a new notion of randomized encoding called Preprocessing Randomized Encoding (PRE), that essentially can be computed in the exponent of pairing groups. When combined with other new techniques, PRE gives a much more streamlined construction of $$i\mathcal {O}$$ while still maintaining reliance only on well-studied assumptions.
Leveraging electronic health records for data science: common pitfalls and how to avoid them

Analysis of electronic health records (EHRs) is an increasingly common approach for studying real-world patient data. Use of routinely collected data offers several advantages compared with other study designs, including reduced administrative costs, the ability to update analysis as practice patterns evolve, and larger sample sizes. Methodologically, EHR analysis is subject to distinct challenges because data are not collected for research purposes. In this Viewpoint, we elaborate on the importance of in-depth knowledge of clinical workflows and describe six potential pitfalls to be avoided when working with EHR data, drawing on examples from the literature and our experience. We propose solutions for prevention or mitigation of factors associated with each of these six pitfalls-sample selection bias, imprecise variable definitions, limitations to deployment, variable measurement frequency, subjective treatment allocation, and model overfitting. Ultimately, we hope that this Viewpoint will guide researchers to further improve the methodological robustness of EHR analysis.
Power normalized cepstral robust features of deep neural networks in a cloud computing data privacy protection scheme

Deep Neural Networks (DNNs) have developed rapidly in data privacy protection applications such as medical treatment and finance. However, DNNs require high-speed and high-memory computers in terms of computation, otherwise training can be very lengthy. Furthermore, DNNs are often not available in resource-constrained mobile devices. Therefore, training and executing DNNs are increasingly using cloud computing. In the paper, the Power Normalized Cepstrum-based Robust Feature Detector (PNC-RFD), with deep learning in the cloud computing, is proposed for data privacy protection. The proposed PNC-RFD extracts a specified number of signal segments of high robustness used to embed and extract various data. For the sake of embedding and extracting the data, a method of information hiding employing Dual-Tree Complex Wavelet Packet Transform (DT CWPT) is therefore presented. The presented scheme simultaneously embeds multiple data into coefficients of the DT CWPT of signal segments. By embedding the data into the orthogonal spaces, the proposed method ensures the independent extraction of the multiple data. In line with the performance analysis, the superiority of the presented scheme is elaborated through making the comparison with the current state-of-the-art methods.
Exact quantum dynamics of XXZ central spin problems

We obtain analytically close forms of benchmark quantum dynamics of the collapse and revival (CR), the reduced density matrix, Von Neumann entropy, and fidelity for the XXZ central spin problem. These quantities characterize the quantum decoherence and entanglement of the system with any number of bath spins, and for a short to infinitely long time evolution. For the homogeneous central spin problem, the effective magnetic field $B$, coupling constant $A$, and longitudinal interaction $\mathrm{\ensuremath{\Delta}}$ significantly influence the time scales of the quantum dynamics of the central spin and the bath, providing a tunable resource for quantum metrology. Under the resonance condition $B=\mathrm{\ensuremath{\Delta}}=A$, the location of the $m\mathrm{th}$ revival peak in time reaches a simple relation ${t}_{r}\ensuremath{\simeq}\frac{\ensuremath{\pi}N}{A}m$ for a large $N$. For $\mathrm{\ensuremath{\Delta}}=0$, $N\ensuremath{\rightarrow}\ensuremath{\infty}$, and a small polarization in the initial spin coherent state, our analytical result for the CR recovers the known expression found in the Jaynes-Cummings model, thus building up an exact dynamical connection between the central spin problems and the light-matter interacting systems in quantum nonlinear optics. In addition, the CR dynamics is robust to a moderate inhomogeneity of the coupling amplitudes while disappearing at strong inhomogeneity.
Two Streams Multiple-Model Object Tracker for Thermal Infrared Video

Thermal infrared (TIR) visual object tracking has been applied in various applications, such as pedestrian detection, wildlife observation, surveillance systems, and so on. The tracker function is to track a particular object of interest and generate its trajectory which will be integrated into a decision making process. Many trackers that are based on fully convolutional neural networks (CNNs) have good performance for RGB input, but it is not the case for the TIR input. Its lack of texture information and the fact that it produces similar heat maps between two nearby objects make the tracking task very challenging. By relying on a fully CNN network alone, a tracker can learn the appearance model but it will not work well if the object heat map looks too similar to the background. Hence, a Siamese CNN network can be implemented to complement the fully CNN, as it allows a set of recent object templates to be used for matching purposes. Yet, the Siamese network alone is not accurate especially in the case of occlusions, as the stored templates rarely produce robust matching. Thus, we propose a two-stream CNN tracker that combines the fully CNN and the Siamese CNN such that each network keeps a set of matching models to cater to diverse appearance changes. Furthermore, the CNN layers are shared between both CNN streams to reduce computational burden. A single dense score map is produced by overlaying the normalized scores of the two streams. The experiments on VOT-TIR 2016 database show that our tracker works well for the datasets with high motion blur, occlusion, and appearance deformation. Besides, the Siamese CNN response map can also be used as an indicator to decide the size of the search region.
IoT Forensics: A State-of-the-Art Review, Challenges and Future Directions

The IoT is capable of communicating and connecting billions of things at the same time. The concept offers numerous benefits for consumers that alters how users interact with the technology. With this said, however, such monumental growth within IoT development also gives rise to a number of legal and technical challenges in the field of IoT forensics. Indeed, there exist many issues that must be overcome if effective IoT investigations are to be carried out. This paper presents a review of the IoT concept, digital forensics and the state-of-the-art on IoT forensics. Furthermore, an exploration of the possible solutions proposed in recent research and IoT forensics challenges that are identified in the current research literature are examined. Picks apart the challenges facing IoT forensics which have been established in recent literature. Overall, this paper draws attention to the obvious problems – open problems which require further efforts to be addressed properly.
Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations

We develop the mathematical foundations of the stochastic modified equations (SME) framework for analyzing the dynamics of stochastic gradient algorithms, where the latter is approximated by a class of stochastic differential equations with small noise parameters. We prove that this approximation can be understood mathematically as an weak approximation, which leads to a number of precise and useful results on the approximations of stochastic gradient descent (SGD), momentum SGD and stochastic Nesterov's accelerated gradient method in the general setting of stochastic objectives. We also demonstrate through explicit calculations that this continuous-time approach can uncover important analytical insights into the stochastic gradient algorithms under consideration that may not be easy to obtain in a purely discrete-time setting.
CompILE: Compositional Imitation Learning and Execution

We introduce Compositional Imitation Learning and Execution (CompILE): a framework for learning reusable, variable-length segments of hierarchically-structured behavior from demonstration data. CompILE uses a novel unsupervised, fully-differentiable sequence segmentation module to learn latent encodings of sequential data that can be re-composed and executed to perform new tasks. Once trained, our model generalizes to sequences of longer length and from environment instances not seen during training. We evaluate CompILE in a challenging 2D multi-task environment and a continuous control task, and show that it can find correct task boundaries and event encodings in an unsupervised manner. Latent codes and associated behavior policies discovered by CompILE can be used by a hierarchical agent, where the high-level policy selects actions in the latent code space, and the low-level, task-specific policies are simply the learned decoders. We found that our CompILE-based agent could learn given only sparse rewards, where agents without task-specific policies struggle.
A Novel Distribution-Embedded Neural Network for Sensor-Based Activity Recognition

Feature-engineering-based machine learning models and deep learning models have been explored for wearable-sensor-based human activity recognition. For both types of methods, one crucial research issue is how to extract proper features from the partitioned segments of multivariate sensor readings. Existing methods have different drawbacks: 1) feature-engineering-based methods are able to extract meaningful features, such as statistical or structural information underlying the segments, but usually require manual designs of features for different applications, which is time consuming, and 2) deep learning models are able to learn temporal and/or spatial features from the sensor data automatically, but fail to capture statistical information. In this paper, we propose a novel deep learning model to automatically learn meaningful features including statistical features, temporal features and spatial correlation features for activity recognition in a unified framework. Extensive experiments are conducted on four datasets to demonstrate the effectiveness of our proposed method compared with state-of-the-art baselines.
Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication

In this paper, a light-weight radio frequency fingerprinting identification (RFFID) scheme that combines with a two-layer model is proposed to realize authentications for a large number of resource-constrained terminals under the mobile edge computing (MEC) scenario without relying on encryption-based methods. In the first layer, signal collection, extraction of RF fingerprint features, dynamic feature database storage, and access authentication decision are carried out by the MEC devices. In the second layer, learning features, generating decision models, and implementing machine learning algorithms for recognition are performed by the remote cloud. By this means, the authentication rate can be improved by taking advantage of the machine-learning training methods and computing resource support of the cloud. Extensive simulations are performed under the IoT application scenario. The results show that the novel method can achieve higher recognition rate than that of traditional RFFID method by using wavelet feature effectively, which demonstrates the efficiency of our proposed method.
Unconstrained Monotonic Neural Networks

Monotonic neural networks have recently been proposed as a way to define invertible transformations. These transformations can be combined into powerful autoregressive flows that have been shown to be universal approximators of continuous probability distributions. Architectures that ensure monotonicity typically enforce constraints on weights and activation functions, which enables invertibility but leads to a cap on the expressiveness of the resulting transformations. In this work, we propose the Unconstrained Monotonic Neural Network (UMNN) architecture based on the insight that a function is monotonic as long as its derivative is strictly positive. In particular, this latter condition can be enforced with a free-form neural network whose only constraint is the positiveness of its output. We evaluate our new invertible building block within a new autoregressive flow (UMNN-MAF) and demonstrate its effectiveness on density estimation experiments. We also illustrate the ability of UMNNs to improve variational inference.
Gun and Knife Detection Based on Faster R-CNN for Video Surveillance

Public safety in public areas is nowadays one of the main concerns for governments and companies around the world. Video surveillance systems can take advantage from the emerging techniques of deep learning to improve their performance and accuracy detecting possible threats. This paper presents a system for gun and knife detection based on the Faster R-CNN methodology. Two approaches have been compared taking as CNN base a GoogleNet and a SqueezeNet architecture respectively. The best result for gun detection was obtained using a SqueezeNet architecture achieving a 85.44% $$AP_{50}$$ . For knife detection, the GoogleNet approach achieved a 46.68% $$AP_{50}$$ . Both results improve upon previous literature results evidencing the effectiveness of our detectors.
Arabic Offensive Language Classification on Twitter

Social media users often employ offensive language in their communication. Detecting offensive language on Twitter has many applications ranging from detecting/predicting conflict to measuring polarization. In this paper, we focus on building effective offensive tweet detection. We show that we can rapidly build a training set using a seed list of offensive words. Given the automatically created dataset, we trained a character n-gram based deep learning classifier that can effectively classify tweets with F1 score of 90%. We also show that we can expand our offensive word list by contrasting offensive and non-offensive tweets.
DELAY DIMINISHED EFFICIENT TASK SCHEDULING AND ALLOCATION FOR HETEROGENEOUS CLOUD ENVIRONMENT

Cloud computing being a promising paradigm has become very prominent among a wide range of applications due to their timely service rendering capability. Attracted to the type of servicing and the way of servicing lots and lots of users, adapt to the cloud computing. This makes the time servicing of the cloud computing a tedious job. So in order to effectively handle the tasks the scheduling approach is entailed in the cloud computing. The paper proposes an efficient task scheduling for the heterogeneous cloud to render service at a minimized delay utilizing the genetic algorithm. The proposed method is validated through the, cloud simulator to understand the efficiency of the same in terms of delay and the quality of service.