Reviewerly/exHarmony huggingface dataset:
Rethinking search

When experiencing an information need, users want to engage with a domain expert, but often turn to an information retrieval system, such as a search engine, instead. Classical information retrieval systems do not answer information needs directly, but instead provide references to (hopefully authoritative) answers. Successful question answering systems offer a limited corpus created on-demand by human experts, which is neither timely nor scalable. Pre-trained language models, by contrast, are capable of directly generating prose that may be responsive to an information need, but at present they are dilettantes rather than domain experts -- they do not have a true understanding of the world, they are prone to hallucinating, and crucially they are incapable of justifying their utterances by referring to supporting documents in the corpus they were trained over. This paper examines how ideas from classical information retrieval and pre-trained language models can be synthesized and evolved into systems that truly deliver on the promise of domain expert advice.
Short-term photovoltaic power forecasting method based on irradiance correction and error forecasting

Accurate photovoltaic (PV) power forecasting is of great significance for safe and stable operation for PV power plant and reasonable dispatching of power grids, and it is also a fundamental technology to ensure the high ratio of PV power generation access to the power grid, but its forecasting error cannot meet the actual requirement. Based on data analysis from a 10 kW distributed PV power plant and a 60 MW large-scale power plant, this study shows the PV power short-term forecasting error mainly comes from the numerical weather prediction (NWP) and forecasting process. To improve the forecasting performance, a short-term PV power forecasting method based on irradiance correction and error forecasting is proposed. First, the measured irradiance is used to correct the irradiance in the NWP. Then, the particle swarm optimization (PSO) algorithm is used to optimize the forecasting model. Finally, an error forecasting model is introduced to correct the forecasting result further. It is proved that the proposed method can effectively improve the short-term forecasting accuracy through verification with an actual power plant. Taking the 6 steps correction as an example, the NWP correction can reduce RMSE(1) by 3.0%, the model optimization can reduce RMSE(2) by 0.4%, and the error correction can reduce RMSE by 0.4%.
Europol: Internet Organised Crime Threat Assessment (IOCTA) 2019

Much of the debate around cybercrime focuses on novelty. Whether it's criminals deploying artificial intelligence (AI) to create cunning new exploits or the latest zero-day vulnerabilities, the security industry likes to emphasise what's new about the threats that face us.
Neural network decoder for topological color codes with circuit level noise

A quantum computer needs the assistance of a classical algorithm to detect and identify errors that affect encoded quantum information. At this interface of classical and quantum computing the technique of machine learning has appeared as a way to tailor such an algorithm to the specific error processes of an experiment --- without the need for a priori knowledge of the error model. Here, we apply this technique to topological color codes. We demonstrate that a recurrent neural network with long short-term memory cells can be trained to reduce the error rate $\epsilon_{\rm L}$ of the encoded logical qubit to values much below the error rate $\epsilon_{\rm phys}$ of the physical qubits --- fitting the expected power law scaling $\epsilon_{\rm L} \propto \epsilon_{\rm phys}^{(d+1)/2}$, with $d$ the code distance. The neural network incorporates the information from "flag qubits" to avoid reduction in the effective code distance caused by the circuit. As a test, we apply the neural network decoder to a density-matrix based simulation of a superconducting quantum computer, demonstrating that the logical qubit has a longer life-time than the constituting physical qubits with near-term experimental parameters.
Mixed grey wolf optimizer for the joint denoising and unmixing of multispectral images

Grey wolf optimizer (GWO) is a bio-inspired iterative optimization algorithm which simulates the hunting behaviour of a pack of wolves. Their position is updated across iterations in a search space under the leadership of the α, β, and δ wolves. In this work, a novel mixed GWO is proposed, which, for the first time, searches for parameter values in both continuous and discrete spaces. In the proposed approach, the update rules are as follows: the leaders guide the hunt, assisted by two random wolves during the first half of the iterations; for the continuous parameters, a weighted combination of the leaders’ contribution is calculated; for the discrete parameters, a random selection is performed instead, and the probability for the α to be selected increases at the detriment of the other leaders across iterations. The exploration and exploitation phases are distinguished for continuous and discrete parameters. The proposed mixed GWO is compared against other bio-inspired optimization methods, using several test problems which are either continuous, discrete or mixed: the proposed algorithm can significantly improve the performance metrics. Moreover, our method is adapted to simultaneously denoise and unmix real-world multispectral images.
Diagrammatic algebra: from linear to concurrent systems

We introduce the resource calculus, a string diagrammatic language for concurrent systems. Significantly, it uses the same syntax and operational semantics as the signal flow calculus --- an algebraic formalism for signal flow graphs, which is a combinatorial model of computation of interest in control theory. Indeed, our approach stems from the simple but fruitful observation that, by replacing real numbers (modelling signals) with natural numbers (modelling resources) in the operational semantics, concurrent behaviour patterns emerge. The resource calculus is canonical: we equip it and its stateful extension with equational theories that characterise the underlying space of definable behaviours---a convex algebraic universe of additive relations---via isomorphisms of categories. Finally, we demonstrate that our calculus is sufficiently expressive to capture behaviour definable by classical Petri nets.
Diagnosing Bottlenecks in Deep Q-learning Algorithms

Q-learning methods represent a commonly used class of algorithms in reinforcement learning: they are generally efficient and simple, and can be combined readily with function approximators for deep reinforcement learning (RL). However, the behavior of Q-learning methods with function approximation is poorly understood, both theoretically and empirically. In this work, we aim to experimentally investigate potential issues in Q-learning, by means of a unit testing framework where we can utilize oracles to disentangle sources of error. Specifically, we investigate questions related to function approximation, sampling error and nonstationarity, and where available, verify if trends found in oracle settings hold true with modern deep RL methods. We find that large neural network architectures have many benefits with regards to learning stability; offer several practical compensations for overfitting; and develop a novel sampling method based on explicitly compensating for function approximation error that yields fair improvement on high-dimensional continuous control domains.
A Baseline for 3D Multi-Object Tracking

3D multi-object tracking (MOT) is an essential component technology for many real-time applications such as autonomous driving or assistive robotics. However, recent works for 3D MOT tend to focus more on developing accurate systems giving less regard to computational cost and system complexity. In contrast, this work proposes a simple yet accurate real-time baseline 3D MOT system. We use an off-the-shelf 3D object detector to obtain oriented 3D bounding boxes from the LiDAR point cloud. Then, a combination of 3D Kalman filter and Hungarian algorithm is used for state estimation and data association. Although our baseline system is a straightforward combination of standard methods, we obtain the state-of-the-art results. To evaluate our baseline system, we propose a new 3D MOT extension to the official KITTI 2D MOT evaluation along with two new metrics. Our proposed baseline method for 3D MOT establishes new state-of-the-art performance on 3D MOT for KITTI, improving the 3D MOTA from 72.23 of prior art to 76.47. Surprisingly, by projecting our 3D tracking results to the 2D image plane and compare against published 2D MOT methods, our system places 2nd on the official KITTI leaderboard. Also, our proposed 3D MOT method runs at a rate of 214.7 FPS, 65 times faster than the state-of-the-art 2D MOT system. Our code is publicly available at this https URL
Practical lossless compression with latent variables using bits back coding

Deep latent variable models have seen recent success in many data domains. Lossless compression is an application of these models which, despite having the potential to be highly useful, has yet to be implemented in a practical manner. We present `Bits Back with ANS' (BB-ANS), a scheme to perform lossless compression with latent variable models at a near optimal rate. We demonstrate this scheme by using it to compress the MNIST dataset with a variational auto-encoder model (VAE), achieving compression rates superior to standard methods with only a simple VAE. Given that the scheme is highly amenable to parallelization, we conclude that with a sufficiently high quality generative model this scheme could be used to achieve substantial improvements in compression rate with acceptable running time. We make our implementation available open source at this https URL .
Discrete Flows: Invertible Generative Models of Discrete Data

While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown. In this paper, we show that flows can in fact be extended to discrete events---and under a simple change-of-variables formula not requiring log-determinant-Jacobian computations. Discrete flows have numerous applications. We consider two flow architectures: discrete autoregressive flows that enable bidirectionality, allowing, for example, tokens in text to depend on both left-to-right and right-to-left contexts in an exact language model; and discrete bipartite flows that enable efficient non-autoregressive generation as in RealNVP. Empirically, we find that discrete autoregressive flows outperform autoregressive baselines on synthetic discrete distributions, an addition task, and Potts models; and bipartite flows can obtain competitive performance with autoregressive baselines on character-level language modeling for Penn Tree Bank and text8.
Efficient and secure attribute-based heterogeneous online/offline signcryption for body sensor networks based on blockchain

In body sensor networks, both wearable and implantable biosensors are deployed in a patient body to monitor and collect patient health record information. The health record information is then transmitted toward the medical server via a base station for analysis, diagnosis, and treatment by medical experts. Advancement in wireless technology although improves the patient health–monitoring mechanism, but still there are some limitations regarding security, privacy, and efficiency due to open wireless channel and limited resources of body sensor networks. To overcome these limitations, we have proposed an efficient and secure heterogeneous scheme for body sensor networks, in which biosensor nodes use a certificate-less cryptography environment to resolve the key escrow and certificate-management problems, while MS uses a public key infrastructure environment to enhance the scalability of the networks. Furthermore, we design an online/offline signcryption method to overcome the burden on biosensor nodes. We split the signcryption process into two phases: offline phase and online phase. In the offline phase, the major operations are computed without prior knowledge of patient data. While in online phase, the minor operations are computed when patient data are known. Besides, we have used a new hybrid blockchain technology approach for the secure transmission of patient information along with attributes stored in the medical server toward the cloud that provides ease of patient data access remotely from anywhere by the authorized users and data backup in case of medical server failure. Moreover, hybrid blockchain provides advantages of interoperability, transparency traceability, and universal access. The formal security analysis of the proposed scheme is proved in the standard model, and informal security assures that our scheme provides resistance against possible attacks. As compared to other existing schemes, our proposed scheme consumes fewer resources and efficient in terms of processing cost, transmission overhead, and energy consumption.
Evaluation of Hand-Tracking Systems in Teleoperation and Virtual Dexterous Manipulation

The quality of robotic dexterous manipulation, in real or in virtual environments, relies on a fine control of the fingertips to perform stable grasps and inside-hand manipulation. In practice, teleoperating a robotic hand requires to capture the human hand configuration.If the user manipulates objects with fingertips, the acquisition of their motion must be accurate enough to produce realistic manipulation at the robot hand or its virtual avatar. In this context, one challenge is to accurately capture the motion of the human hand. The performances of three different hand-tracking devices are evaluated in this paper: two data gloves, the VRFree and the Manus VR, and a vision-based system, the Leap Motion Controller. To this end, the positions of the human hand joints and fingertips are captured while performing several tasks, with a high-precision motion capture system as reference, and with the tested devices. The accuracy of the measured joint angles and fingertips positions is compared for the different systems. Specific measurement configurations are considered by varying the hand orientation and the distances to the sensors. The strengths and weaknesses of these different systems are deduced from the experiments. This system review gives insights into the relevance of hand-tracking devices for remote robotic or virtual manipulation.
Facial Emotion Recognition Using Hybrid Features

Facial emotion recognition is a crucial task for human-computer interaction, autonomous vehicles, and a multitude of multimedia applications. In this paper, we propose a modular framework for human facial emotions’ recognition. The framework consists of two machine learning algorithms (for detection and classification) that could be trained offline for real-time applications. Initially, we detect faces in the images by exploring the AdaBoost cascade classifiers. We then extract neighborhood difference features (NDF), which represent the features of a face based on localized appearance information. The NDF models different patterns based on the relationships between neighboring regions themselves instead of considering only intensity information. The study is focused on the seven most important facial expressions that are extensively used in day-to-day life. However, due to the modular design of the framework, it can be extended to classify N number of facial expressions. For facial expression classification, we train a random forest classifier with a latent emotional state that takes care of the mis-/false detection. Additionally, the proposed method is independent of gender and facial skin color for emotion recognition. Moreover, due to the intrinsic design of NDF, the proposed method is illumination and orientation invariant. We evaluate our method on different benchmark datasets and compare it with five reference methods. In terms of accuracy, the proposed method gives 13% and 24% better results than the reference methods on the static facial expressions in the wild (SFEW) and real-world affective faces (RAF) datasets, respectively.
Fault Template Attacks on Block Ciphers Exploiting Fault Propagation

Fault attacks (FA) are one of the potent practical threats to modern cryptographic implementations. Over the years the FA techniques have evolved, gradually moving towards the exploitation of device-centric properties of the faults. In this paper, we exploit the fact that activation and propagation of a fault through a given combinational circuit (i.e., observability of a fault) is data-dependent. Next, we show that this property of combinational circuits leads to powerful Fault Template Attacks (FTA), even for implementations having dedicated protections against both power and fault-based vulnerabilities. The attacks found in this work are applicable even if the fault injection is made at the middle rounds of a block cipher, which are out of reach for most of the other existing fault analysis strategies. Quite evidently, they also work for a known-plaintext scenario. Moreover, the middle round attacks are entirely blind in the sense that no access to the ciphertexts (correct/faulty) or plaintexts are required. The adversary is only assumed to have the power of repeating an unknown plaintext several times. Practical validation over a hardware implementation of SCA-FA protected PRESENT, and simulated evaluation on a public software implementation of protected AES prove the efficacy of the proposed attacks.
Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs

Multimodal brain tumor segmentation challenge (BraTS) brings together researchers to improve automated methods for 3D MRI brain tumor segmentation. Tumor segmentation is one of the fundamental vision tasks necessary for diagnosis and treatment planning of the disease. Previous years winning methods were all deep-learning based, thanks to the advent of modern GPUs, which allow fast optimization of deep convolutional neural network architectures. In this work, we explore best practices of 3D semantic segmentation, including conventional encoder-decoder architecture, as well combined loss functions, in attempt to further improve the segmentation accuracy. We evaluate the method on BraTS 2019 challenge.
Small and Slim Deep Convolutional Neural Network for Mobile Device

Recent development of deep convolutional neural networks (DCNN) devoted in creating a slim model for devices with lower specification such as embedded, mobile hardware, or microcomputer. Slim model can be achieved by minimizing computational complexity which theoretically will make processing time faster. Therefore, our focus is to build an architecture with minimum floating-point operation per second (FLOPs). In this work, we propose a small and slim architecture which later will be compared to state-of-the-art models. This architecture will be implemented into two models which are CustomNet and CustomNet2. Each of these models implements 3 convolutional blocks which reduce the computational complexity while maintains its accuracy and able to compete with state-of-the-art DCNN models. These models will be trained using ImageNet, CIFAR 10, CIFAR 100 and other datasets. The result will be compared based on accuracy, complexity, size, processing time, and trainable parameter. From the result, we found that one of our models which is CustomNet2, is better than MobileNet, MobileNet-v2, DenseNet, NASNetMobile in accuracy, trainable parameter, and complexity. For future implementation, this architecture can be adapted using region based DCNN for multiple object detection.
Path following algorithm application to automatic berthing control

Abstract This paper aims to verify a new automatic berthing system using a path following algorithm. Berthing operation is one of the most burdensome tasks for crews among several ship operations. The maneuverability of a ship at low speed during berthing operation deteriorates and becomes more vulnerable to disturbances such as wind. Therefore, it is necessary to support and automate operations that require advanced skills such as berthing operation. Previous studies on automatic berthing have investigated various methods to handle the nonlinearity of ship maneuvering motion and determine the optimal control variable. There is a trade-off between accuracy and real-time performance of berthing control from these studies. The algorithms must have sufficiently real-time performance while maintaining the accuracy of control. For these purposes, we propose the automatic berthing system applied a path following algorithm for a ship with one propeller and one rudder in this paper. We show the mathematical model for numerical simulation of berthing control and carried out system identification of the subject ship. In full-scale experiments, the proposed system performed automatic berthing control in both calm wind conditions around 2 m/s and strong wind conditions around 6 m/s.
GAWA–A Feature Selection Method for Hybrid Sentiment Classification

Sentiment analysis or opinion mining is the key to natural language processing for the extraction of useful information from the text documents of numerous sources.Several different techniques, i.e., simple rule-based to lexicon-based and more sophisticated machine learning algorithms, have been widely used with different classifiers to get the factual analysis of sentiment.However, lexicon-based sentiment classification is still suffering from low accuracies, mainly due to the deficiency of domain-oriented competitive dictionaries.Similarly, machine learning-based sentiment is also tackling the accuracy constraints because of feature ambiguity from social data.One of the best ways to deal with the accuracy issue is to select the best feature-set and reduce the volume of the feature.This paper proposes a method (namely, GAWA) for feature selection by utilizing the Wrapper Approaches (WA) to select the premier features and the Genetic Algorithm (GA) to reduce the size of the premier features.The novelty of this work is the modified fitness function of heuristic GA to compute the optimal features by reducing the redundancy for better accuracy.This work aims to present a comprehensive model of hybrid sentiment by using the proposed method, GAWA.It will be valued in developing a new approach for the selection of feature-set with a better accuracy level.The experiments revealed that these techniques could reduce the feature-set up-to 61.95% without negotiating the accuracy level.The new optimal feature sets enhanced the efficiency of the Naïve Bayes algorithm up to 92%.This work is compared with the conventional method of feature selection and concluded the 11% better accuracy than PCA and 8% better than PSO.Furthermore, the results are compared with the literature work and found that the proposed method outperformed the previous research.