Reviewerly/exHarmony huggingface dataset:
“Unexpected Item in the Bagging Area”: Anomaly Detection in X-Ray Security Images

The role of anomaly detection in X-ray security imaging, as a supplement to targeted threat detection, is described, and a taxonomy of anomaly types in this domain is presented. Algorithms are described for detecting appearance anomalies of shape, texture, and density, and semantic anomalies of object category presence. The anomalies are detected on the basis of representations extracted from a convolutional neural network pre-trained to identify object categories in photographs, from the final pooling layer for appearance anomalies, and from the logit layer for semantic anomalies. The distribution of representations in normal data is modeled using high-dimensional, full-covariance, Gaussians, and anomalies are scored according to their likelihood relative to those models. The algorithms are tested on X-ray parcel images using stream-of-commerce data as the normal class, and parcels with firearms present the examples of anomalies to be detected. Despite the representations being learned for photographic images and the varied contents of stream-of-commerce parcels, the system, trained on stream-of-commerce images only, is able to detect 90% of firearms as anomalies, while raising false alarms on 18% of stream-of-commerce.
Towards a realization of device-independent quantum key distribution

In the implementation of device-independent quantum key distribution we are interested in maximizing the key rate, i.e. the number of key bits that can be obtained per signal, for a fixed security parameter. In the finite size regime, we furthermore also care about the minimum number of signals required before key can be obtained at all. Here, we perform a fully finite size analysis of device independent protocols using the CHSH inequality both for collective and coherent attacks. For coherent attacks, we sharpen the results recently derived in Arnon-Friedman et al., Nat. Commun. 9, 459 (2018), to reduce the minimum number of signals before key can be obtained. In the regime of collective attacks, where the devices are restricted to have no memory, we employ two different techniques that exploit this restriction to further reduce the number of signals. We then discuss experimental platforms in which DIQKD may be implemented. We analyse Bell violations and expected QBER achieved in previous Bell tests with distant setups and situate these parameters in the security analysis. Moreover, focusing on one of the experimental platforms, namely nitrogen-vacancy based systems, we describe experimental improvements that can lead to a device-independent quantum key distribution implementation in the near future.
Central pixel selection strategy based on local gray-value distribution by using gradient information to enhance LBP for texture classification

Local binary pattern (LBP) has been successfully used in computer vision and pattern recognition applications, such as biomedical image analysis, remote sensing and image retrieval. However, the current LBP-based features, which assign a fixed sampling radius for all pixels in a single scale, completely ignore the fact that different central pixels actually have different local gray-value distributions and the proper sampling radius should be different for pixels. In this paper, we propose a novel and effective central pixel selection (CPS) strategy by using gradient information to classify central pixels of a texture image into different classes based on their local gray-value distributions. Then, we introduce this CPS strategy into the LBP framework and assign an adaptive sampling radius for each central pixel according to the class it belongs to. As a preprocessing step of LBP framework, this CPS strategy can also be integrated into any other LBP variants so as to extract more effective local texture features. Extensive experiments on five representative texture databases of Outex, UIUC, CUReT, UMD and ALOT validate the efficiency of the proposed central pixel selection (CPS) strategy, which can achieve almost 16% improvement over the original LBP and 1%–10% improvement compared with the best classification accuracy among other benchmarked state-of-the-art LBP variants.
Griffin–Lim Like Phase Recovery via Alternating Direction Method of Multipliers

Recovering a signal from its amplitude spectrogram, or phase recovery, exhibits many applications in acoustic signal processing.When only an amplitude spectrogram is available and no explicit information is given for the phases, the Griffin-Lim algorithm (GLA) is one of the most utilized methods for phase recovery.However, GLA often requires many iterations and results in low perceptual quality in some cases.In this letter, we propose two novel algorithms based on GLA and the alternating direction method of multipliers (ADMM) for better recovery with fewer iteration.Some interpretation of the existing methods and their relation to the proposed method are also provided.Evaluations are performed with both objective measure and subjective test.
Linearized sigmoidal activation: A novel activation function with tractable non-linear characteristics to boost representation capability

Activation functions play a crucial role in discriminative capabilities of the deep neural networks. They are also one of the main reasons for revival of the neural networks. Although, recent activation functions provide solution to vanishing and exploding gradient problems, but they do not have sufficient capacity to model non-linear data. This paper proposes a novel activation function, which imparts neural networks with capability to model non-linear dependencies in data. Behavior of proposed activation function remains non-saturating even with non-linear structure. The activation function provides distinct activation behaviors to different data range segments. The paper presents two different variants of proposed function. The first, “linear sigmoidal activation” function is a fixed structure activation function with function coefficients defined at the start of model design. Whereas second, “adaptive linear sigmoidal activation” function is a trainable function which can adapt itself according to the complexity of the given data. Both of the proposed models are tested against the state of the art activation functions on benchmark datasets (CIFAR-10, MNIST, SVHN, FER-2013). The proposed activation function is able to outperforms every known activation functions in all the tests.
Multiview Classification With Cohesion and Diversity

Different views of multiview data share certain common information (consensus) and also contain some complementary information (complementarity). Both consensus and complementarity are of significant importance to the success of multiview learning. In this paper, we explicitly formulate both of them for multiview classification. On the one hand, a cohesion-increasing loss term with a learnable label-adjusting matrix is designed to facilitate consensus among views in the training stage. With this kind of loss, the learned classifiers of all views are more likely to obtain the correct classification, thereby maximizing the agreement among views. On the other hand, an independence measurement is adopted as the diversity-promoting regularization to encourage classifiers to be diverse such that more complementary information can be captured by these “diversified” classifiers. Overall, the resultant model is capable of achieving more comprehensive and accurate classification by exploring and exploiting the common and complementary information across multiple views more thoroughly. An iterative optimization algorithm with proved convergence is proposed for training the model. Extensive experimental results on various datasets have demonstrated the efficacy of the proposed method.
Tracking People in a Mobile Robot From 2D LIDAR Scans Using Full Convolutional Neural Networks for Security in Cluttered Environments

Tracking people has many applications, such as security or safe use of robots. Many onboard systems are based on Laser Imaging Detection and Ranging (LIDAR) sensors. Tracking peoples' legs using only information from a 2D LIDAR scanner in a mobile robot is a challenging problem because many legs can be present in an indoor environment, there are frequent occlusions and self-occlusions, many items in the environment such as table legs or columns could resemble legs as a result of the limited information provided by two-dimensional LIDAR usually mounted at knee height in mobile robots, etc. On the other hand, LIDAR sensors are affordable in terms of the acquisition price and processing requirements. In this article, we describe a tool named PeTra based on an off-line trained full Convolutional Neural Network capable of tracking pairs of legs in a cluttered environment. We describe the characteristics of the system proposed and evaluate its accuracy using a dataset from a public repository. Results show that PeTra provides better accuracy than Leg Detector (LD), the standard solution for Robot Operating System (ROS)-based robots.
Structure-Texture Image Decomposition Using Deep Variational Priors

Most variational formulations for structure-texture image decomposition force the structure images to have small norm in some functional spaces and to share a common notion of edges, i.e., large-gradients or large-intensity differences. However, such a definition makes it difficult to distinguish structure edges from oscillations that have fine spatial scale but high contrast. In this paper, we introduce a new model by learning deep variational priors for structure images without explicit training data. An alternating direction method of a multiplier algorithm and its modular structure are adopted to plug deep variational priors into an iterative smoothing process. The central observations are that convolution neural networks (CNNs) can replace the total variation prior, and are indeed powerful to capture the natures of structure and texture. We show that our learned priors using CNNs successfully differentiate high-amplitude details from structure edges, and avoid halo artifacts. Different from previous data-driven smoothing schemes, our formulation provides another degree of freedom to produce continuous smoothing effects. Experimental results demonstrate the effectiveness of our approach on various computational photography and image processing applications, including texture removal, detail manipulation, HDR tone-mapping, and non-photorealistic abstraction.
CMIP-5 models project photovoltaics are a no-regrets investment in Europe irrespective of climate change

Using projections of surface solar radiation and temperature from 23 CMIP5 global climate models for two climate change scenarios (RCP4.5 & 8.5) we quantify the average change in PV electricity production expected in the years 2060–2080 compared to the present (2007–2027). We upsample daily radiation data to hourly resolution with a sinusoidal diurnal cycle model and split it into direct and diffuse radiation with the semi-empirical BRL model as input to a PV electricity generation model. Locally, changes in PV potential from −6% to +3% in annual and −25% to +10% in monthly means are shown. These projections are combined with a PV deployment scenario and show countries benefitting from increased PV yields include Spain, France, Italy and Germany. We also calculate uncertainties when calculating PV yield with input data at daily or lower resolution, demonstrating that our method to derive synthetic hourly profiles should be of use for other researchers using input data with low temporal resolution. We conclude that PV is an attractive and no-regrets investment in Europe irrespective of future climate change, and can continue to play a key role in energy system decarbonisation.
Speckle noise removal in SAR images using Multi-Objective PSO (MOPSO) algorithm

SAR images are inherently affected by speckle noise, and although attempts made earlier to remove speckle succeeded, there is still the challenge of preserving the edges of images. This is due to the smoothing effect of most of the earlier algorithms that work on thresholding coefficients in the transform domain. There exists a trade-off between denoising and the ability to preserve edges in selecting a suitable threshold. Estimation of an optimal threshold is a major concern and is compounded by the requirement for concurrent smoothing of noise and preservation of structural/edge information in an image. Considering the search for an optimal threshold as exhaustive and the requirements as contradictory, we model this as a Multi-Objective Particle Swarm Optimization (MOPSO) task and propose a MOPSO framework for despeckling an SAR image using a Dual-Tree Complex Wavelet Transform (DTCWT) in the frequency domain. Two counteractive reference metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Mean Structural Similarity Index Metric (MSSIM), and non-reference metrics such as the alpha-beta (αβ) and Despeckling Evaluation Index (DEI) have been used as the objective functions of MOPSO. An optimal threshold derived from this multi-objective optimization is chosen for despeckling the SAR images. The proposed solution has been found to outperform state-of-the-art filters such as Lee, Kaun, Frost and SAR-BM3D filters. Also, the proposed MOPSO framework superior than the competing optimization technique Multi-Objective Evolutionary Algorithm (MOEA) based on Differential Evolution (DE) framework for despeckling.
On-Road Vehicle Tracking Using Part-Based Particle Filter

In this paper, we propose a part-based particle filter for on-road vehicle tracking. The proposed model combines a part-based strategy with a particle filter. By introducing a hidden state representing the center position of the vehicle, particles corresponding to vehicle parts sharing the same motion can be collectively updated in an efficient manner. By using a pre-trained appearance and geometric model, the tracker can distinguish parts with rich information from invalid parts to make more precise predictions. Meanwhile, some prior knowledge about the motion patterns of vehicles in a well-structured on-road environment is learned and can be used to infer measurement and motion models to improve tracking performance and efficiency. Experiments were conducted using the real data collected in Beijing to examine the performance of the method in different situations in terms of both its advantages and challenges. The collected Beijing highway dataset for on-road vehicle tracking will be made publicly available. We compare our method with the state-of-the-art approaches. The results demonstrate that the proposed algorithm is able to handle occlusion and the aspect ratio changes in the on-road vehicle tracking problem.
Route-Based Dynamics Modeling and Tracking With Application to Air Traffic Surveillance

In transportation networks, the majority of moving vehicles are route-based or trajectory-scheduled. Taking advantage of such predictive information generally produces more accurate dynamic models and better surveillance performance. This paper is concerned with the route-based dynamic modeling along with the route-aided tracking. First, the evolution of the positions across the route is formulated as a stationary Markov process from the characteristics of the route-based dynamics, which follows that the second- and third-order models of the straight-line route-based motions are constructed. This novel modeling strategy is in reverse to the conventional ones starting from the acceleration and its resultant dynamic models are easy to implement due to the linearity with respect to the system states. Second, an optimal initialization technique for route-aided tracking is proposed by utilizing the stationary process information sufficiently. Furthermore, an extension to the circular route-based dynamic modeling and a combinational modeling structure are also presented. Finally, in the context of aerial surveillance, numerical simulations are provided to show the effectiveness of the proposed dynamic modeling and to verify the theoretical results given in the paper.
A Tangible Solution for Hand Motion Tracking in Clinical Applications

Objective real-time assessment of hand motion is crucial in many clinical applications including technically-assisted physical rehabilitation of the upper extremity. We propose an inertial-sensor-based hand motion tracking system and a set of dual-quaternion-based methods for estimation of finger segment orientations and fingertip positions. The proposed system addresses the specific requirements of clinical applications in two ways: (1) In contrast to glove-based approaches, the proposed solution maintains the sense of touch. (2) In contrast to previous work, the proposed methods avoid the use of complex calibration procedures, which means that they are suitable for patients with severe motor impairment of the hand. To overcome the limited significance of validation in lab environments with homogeneous magnetic fields, we validate the proposed system using functional hand motions in the presence of severe magnetic disturbances as they appear in realistic clinical settings. We show that standard sensor fusion methods that rely on magnetometer readings may perform well in perfect laboratory environments but can lead to more than 15 cm root-mean-square error for the fingertip distances in realistic environments, while our advanced method yields root-mean-square errors below 2 cm for all performed motions.
Cold Start Recommendation Based on Attribute-Fused Singular Value Decomposition

Collaborative filtering plays an important role in promoting the service recommendation ecosystem, and the matrix decomposition technology has been proven to be one of the most effective recommendation methods. However, the traditional collaborative filtering algorithm has great shortcomings in the recommendation of cold start items, especially the emergence of new items will be largely ignored. This not only has a very bad impact on the development of the item, but also greatly reduces the diversity of the recommendation system. The rise of mobile devices has also brought a large number of mobile applications, and these emerging applications need to be promoted in order to maintain the robustness of the application system. In order to solve this problem, we propose a method of combining the attribute information of the item with the historical rating matrix to predict the potential preferences of the user. It combines the attribute and time information into a matrix decomposition model. By testing our method on the movielens and the climbed JD dataset, the experimental results show that, compared with the baseline method, the proposed method achieves a significant improvement in recommendation accuracy. Therefore, this method is an effective way to solve the cold start problem of new items.
Development and Experimental Validation of Aerial Vehicle With Passive Rotating Shell on Each Rotor

Aerial robotics is a fast-growing field of robotics and has been successfully used in various applications. Still, it faces many challenges, such as dealing with unavoidable obstacles in a cluttered environment. Recently, a flying robot with a protective shell that can rotate passively was introduced. The passive rotating mechanism is intended to reduce the impact force on the attitude of the UAV. However, such a system also has some limitations. Because the shell rotates passively, the ability to physically interact outside the shell is limited, and the onboard camera and other remote sensors are constantly obstructed. In this letter, a new idea is introduced in response to the limitations of the previous system while retaining the protective shell and maintaining some degrees of passive rotation of the shell. It is proposed to position two passive rotating hemispherical shells in each rotor to directly protect the propeller. This letter presents the concept, discusses the design and proof of concept, and validates the concept through experiments. Various experiments are conducted to demonstrate the capabilities of the proposed flying robot, resolve the problem of physical interaction and camera obstruction, and introduce new advantages.
Formalising investigative decision making in digital forensics: Proposing the Digital Evidence Reporting and Decision Support (DERDS) framework

In the field of digital forensics it is crucial for any practitioner to possess the ability to make reliable investigative decisions which result in the reporting of credible evidence. This competency should be considered a core attribute of a practitioner’s skill set and it is often taken for granted that all practitioners possess this ability; in reality this is not the case. A lack of dedicated research and formalisation of investigative decision making models to support digital forensics practitioner’s is an issue given the complexity of many digital investigations. Often, the ability to make forensically sound decisions regarding the reliability of any findings is arguably an assumed trait of the practitioner, rather than a formally taught competency. As a result, the digital forensic discipline is facing increasing recent scrutiny with regards to the quality and validity of evidence it’s practitioners are producing. This work offers the Digital Evidence Reporting and Decision Support (DERDS) framework, designed to help the practitioner assess the reliability of their ‘inferences, assumptions of conclusions’ in relation to any potentially evidential findings. The structure and application of the DERDS framework is discussed, demonstrating the stages of decision making a practitioner must undergo when evaluating the accuracy of their findings, whilst also recognising when content may be deemed unsafe to report.
Learning Composite Latent Structures for 3D Human Action Representation and Recognition

3D human action representation and recognition are important issues in many multimedia applications. While latent state approaches have been widely used for action modeling, previous works assume the latent states of actions are single attribute. This assumption is inaccurate for representing structures of complex actions. In this paper, we propose that latent states have composite attributes and introduce a novel composite latent structure (CLS) model to represent and recognize 3D human actions with skeleton sequences. A human action is modeled with a hierarchical graph, which represents the action sequence as sequential atomic actions. An atomic action is represented as a composite latent state, which is composed of a latent semantic attribute and a latent geometric attribute. A discriminative EM-like algorithm is proposed to learn the model parameters and the composite latent structures of human actions. Given a 3D skeleton sequence, a composite attribute iterative programming algorithm is proposed to recognize the action and infer the action's latent temporal structure. We evaluate the proposed method on three challenging 3D action datasets-MSR 3D Action Dataset, Multiview 3D Event Dataset, and UTKinect-Action 3D Dataset. Extensive experimental results on these datasets demonstrate the effectiveness and advantage of the proposed method.