Reviewerly/exHarmony huggingface dataset:
Co-Training for On-Board Deep Object Detection

Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models.This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature.Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e.objects) within the training images.Thus, object detection is one of such tasks for which human labeling is a major bottleneck.In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors.Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled.Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles.Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation.
Learnability and robustness of shallow neural networks learned by a performance-driven BP and a variant of PSO for edge decision-making

It may not be easy to implement complex AI models in edge devices without strong computing capacity (e.g. GPU). The Universal Approximation Theorem states that a shallow neural network (SNN) can represent any nonlinear function. In this paper, we focus on the learnability and robustness of SNNs, obtained by a greedy tight force heuristic algorithm [a Performance Driven Back-Propagation (PDBP)] and a loose force meta-heuristic algorithm [a variant of particle swarm optimization (VPSO)]. From the engineering prospective, all sensors are well justified for a specific task. Hence, all sensor readings should be strongly correlated to the target, and the structure of an SNN should depend on the dimensions of a problem space. The key findings of the research are summarized as follows: (1) The number of hidden neurons of an SNN depends on the nonlinearity of the training data, and the number of hidden neurons up to the dimension number of a problem space could be enough; (2) The learnability of SNNs, produced by error-driven PDBP, is always better than that of SNNs, optimized by error-driven VPSOs; (3) The performances of SNNs, obtained by PDBPs and VPSOs, do not change much for different training rates; and (4) Comparing with other classic machine learning algorithms, such as C4.5, NB and NN in literature, the SNNs, obtained by accuracy-driven PDBPs, win for all tested data sets, and the improvement percentage is up to 32.86%. Hence, the research could provide valued guidance for the implementation of edge intelligence.
Disaster Severity Analysis from Micro-Blog Texts Using Deep-NN

Wadawadagi, Ramesh Pagi, VeerappaThe current decade has witnessed a significant amount of research in the field of sentiment analysis (SA). Several applications have emerged to evidence the necessity of research in this area. On the contrary, the size of micro-blogs content is overgrowing and likely to increase even faster shortly. Social media applications have become part and parcel of our daily lives, as they urge the public to express their opinions and share information around the world. Especially during disasters, people are likely to utilize social media to communicate their hindrances. In this article, we investigate the severity of disaster events from micro-blog messages posted by people during natural calamities and emergencies using deep learning techniques. In particular, the work employs a joint model that combines the features of convolutional neural networks (CNN) with recurrent neural networks (RNN), taking account of the coarse-grained local features generated via CNN and long-range dependencies learned through RNN for analysis of small text messages. Furthermore, the proposed model is evaluated for both binary and fine-grained analyses tested over two different datasets. The accuracy of 87% is observed for binary classification and up to 65% for a three-class problem. The intended work finds usefulness in many instants of disaster relief and crisis management.
Unambiguous discrimination of fermionic states through local operations and classical communication

The paper studies unambiguous discrimination of Fermionic states through local operations and classical communication (LOCC). In the task of unambiguous discrimination, no error is tolerated but an inconclusive result is allowed. We show that contrary to the quantum case, it is not always possible to distinguish two Fermionic states through LOCC unambiguously with the same success probability as if global measurements were allowed. Furthermore, we prove that we can overcome such a limit through an ancillary system made of two Fermionic modes, independently of the dimension of the system, prepared in a maximally entangled state: in this case, LOCC protocols achieve the optimal success probability.
A Deep Model for Super-resolution Enhancement from a Single Image

This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks simultaneously. We use the residual layers in our model to make repetitive layers, increase the depth of the model, and make an end-to-end model. Furthermore, we employed a deep network in up-sampling step instead of bicubic interpolation method used in most of the previous works. Since the image resolution plays an important role to obtain rich information from the medical images and helps for accurate and faster diagnosis of the ailment, we use the medical images for resolution enhancement. Our model is capable of reconstructing a high-resolution image from low-resolution one in both medical and general images. Evaluation results on TSA and TZDE datasets, including MRI images, and Set5, Set14, B100, and Urban100 datasets, including general images, demonstrate that our model outperforms state-of-the-art alternatives in both areas of medical and general super-resolution enhancement from a single input image.
MCoMat

Existing performance metrics assess classifiers on single granularity layer. Having multi-layer labels is also possible such as activity recognition datasets. Semantic annotations could be given with multiple granularity layers in these datasets e.g., activity and the current step within that activity like: cooking and taking ingredients from fridge. Recognizing both layers is important i.e., remote monitoring of patients with dementia. To evaluate a classifier for both layers concurrently, a new performance metric is required. However, it is not easy to design as there are many underlying issues: the relation between the layers and the impact of class imbalance. This work proposes a new metric for evaluating multi-layer labeled dataset considering the mentioned factors and is applied on two datasets. It is found that it can assess the performance of a model classifying activities at two different granularity layers and give more insightful results i.e. reflecting performance for each layer.
Improving nonstoquastic quantum annealing with spin-reversal transformations

Nonstoquastic Hamiltonians are hard to simulate due to the sign problem in quantum Monte Carlo simulation. It is however unclear whether nonstoquasticity can lead to advantage in quantum annealing. Here we show that YY-interaction between the qubits makes the adiabatic path during quantum annealing, and therefore the performance, dependent on spin-reversal transformations. With the right choice of spin-reversal transformation, a nonstoquastic Hamiltonian with YY-interaction can outperform stoquastic Hamiltonians with similar parameters. We introduce an optimization protocol to determine the optimal transformation and discuss the effect of suboptimality.
Deep Semantic Parsing of Freehand Sketches With Homogeneous Transformation, Soft-Weighted Loss, and Staged Learning

In this paper, we propose a novel deep framework for part-level semantic parsing of freehand sketches, which makes three main contributions that are experimentally shown to have substantial practical merit. First, we propose a homogeneous transformation method to address the problem of domain adaptation. For the task of sketch parsing, there is no available data of labeled freehand sketches that can be directly used for model training. An alternative solution is to learn from datasets of real image parsing, while the domain adaptation is an inevitable problem. Unlike existing methods that utilize the edge maps of real images to approximate freehand sketches, the proposed homogeneous transformation method transforms the data from domains of real images and freehand sketches into a homogeneous space to minimize the semantic gap. Second, we design a soft-weighted loss function as guidance for the training process, which gives attention to both the ambiguous label boundary and class imbalance. Third, we present a staged learning strategy to improve the parsing performance of the trained model, which takes advantage of the shared information and specific characteristic from different sketch categories. Extensive experimental results demonstrate the effectiveness of the above three methods. Specifically, to evaluate the generalization ability of our homogeneous transformation method, additional experiments for the task of sketch-based image retrieval are conducted on the QMUL FG-SBIR dataset. Finally, by integrating the proposed three methods into a unified framework of deep semantic sketch parsing (DeepSSP), we achieve the state-of-the-art on the public SketchParse dataset.
When it is (also) Algorithms and AI that decide on Criminal Matters: In Search of an Effective Remedy

Abstract The paper presents the main areas of application of predictive systems based on algorithmic and AI technology, and analyses their impact on fundamental rights and fair trial principles. It focuses in particular on the definition of the right to an effective remedy against decisions taken (also) with the support of algorithmic and AI systems, and proposes some innovative solutions on how to ensure compliance with this right in technologically advanced criminal proceedings.
RE @ runtime : the challenge of change RE’20 Conference Keynote

Providing rigorous techniques and tools to support RE so that it could potentially be performed online, at runtime, is certainly challenging. However the rewards could be great. Performing RE at runtime has the potential not only to enable software self-adaptation, but also to provide support for change in general by suggesting possible remedies to engineers. This talk will present our motivation and vision, and suggest possible approaches using techniques such as model checking, learning and synthesis to try to support RE change and adaptation.
Cascade of phase transitions for multiscale clustering

We present a novel framework exploiting the cascade of phase transitions occurring during a simulated annealing of the Expectation-Maximisation algorithm to cluster datasets with multi-scale structures. Using the weighted local covariance, we can extract, a posteriori and without any prior knowledge, information on the number of clusters at different scales together with their size. We also study the linear stability of the iterative scheme to derive the threshold at which the first transition occurs and show how to approximate the next ones. Finally, we combine simulated annealing together with recent developments of regularised Gaussian mixture models to learn a principal graph from spatially structured datasets that can also exhibit many scales.
ZWT: A new cross-platform graphical interface framework for Java applications

The Java Programming Language revolutionized the world of software development in the last decades. Thanks to its portability, Java makes it possible to develop software that can run everywhere, in a truly cross-platform computing environment. Although running the same Java code anywhere works smoothly on major desktop and server platforms, this becomes much more complicated when different devices and platforms, such as smartphones or embedded systems, are taken into account. Furthermore, even if we consider devices that natively support the Java programming language, the same application may not run without re-writing part or the entire source code. This is mainly due to the existence of platform specific libraries for accessing input/output peripherals or system-specific features. In particular, the main limitation is usually associated to the different APIs that must be used for programming the Graphical User Interface (GUIs). In this paper, we present a novel framework that can be used by developers to write Java applications with portable GUIs that are truly platform-independent and thus can run on different systems such as PCs, Workstations, Android devices or mobile phones and embedded systems with Java MicroEdition (Java ME).
Bipartite quantum measurements with optimal single-sided distinguishability

We analyse orthogonal bases in a composite $N\times N$ Hilbert space describing a bipartite quantum system and look for a basis with optimal single-sided mutual state distinguishability. This condition implies that in each subsystem the $N^2$ reduced states form a regular simplex of a maximal edge length, defined with respect to the trace distance. In the case $N=2$ of a two-qubit system our solution coincides with the elegant joint measurement introduced by Gisin. We derive explicit expressions of an analogous constellation for $N=3$ and provide a general construction of $N^2$ states forming such an optimal basis in ${\cal H}_N \otimes {\cal H}_N$. Our construction is valid for all dimensions for which a symmetric informationally complete (SIC) generalized measurement is known. Furthermore, we show that the one-party measurement that distinguishes the states of an optimal basis of the composite system leads to a local quantum state tomography with a linear reconstruction formula. Finally, we test the introduced tomographical scheme on a complete set of three mutually unbiased bases for a single qubit using two different IBM machines.
Common Ontology of Sustainable Development

Sustainable development in its various aspects is a multifaceted and contradictory concept, interpreted by multiple sources. This study exposes the need to explore and develop a productive coevolutionary approach to build modular ontological metamodel of sustainable development including a set of interconnected ontologies and instance databases. Based on a review and on an analysis of existing ontologies in this area, a methodology for building common ontology of sustainable development is proposed as a top level of the metamodel. Using terminology from well-established and authoritative sources in the area, as well as the methods, the resources, and the standards of Semantic Web, the methodology contains stages: exploring the SD concepts; controlled vocabulary and thesaurus creating; ontology formalization, verification, querying, and publication. Relationships with the respective domain and other related to sustainable development ontologies are established. In conclusion, the results and the future development of the ontological metamodel are discussed.
On the Effectiveness of Digital Technologies in the Educational Environment of the University

The article discusses the main approaches to the organization of online education and assesses the prospects for their development in the context of digitalization of education in universities.
Bat-G2 Net: Bat-Inspired Graphical Visualization Network Guided by Radiated Ultrasonic Call

In this paper, a noise-immune Bat-inspired Graphical visualization network Guided by the radiated ultrasonic call (Bat-G2 net) that can reconstruct 3D shapes of a target from ultrasonic echoes is presented. The Bat-G2 net achieves noise-resiliency by emulating bat's auditory system that processes echoes along with the highly correlated radiated ultrasonic call (RUC). In order to extract the information contained in the echoes robustly and effectively, two implementation ideas have been applied to the Bat-G2 net: (1) RUC-guided attention, and (2) non-local attention. The Bat-G2 net is trained with ECHO-4CH dataset acquired by a custom-made Bat-I sensor. Noise-resistant property of the Bat-G2 net is demonstrated by comparing the reconstructed images with those from current state-of-the-art ultrasonic image reconstruction network under low SNR conditions. This study clearly demonstrates the implementation feasibility of the new modality of 'seeing by hearing' in practical environments.
GKSL generators and digraphs: computing invariant states

In recent years, digraph induced generators of quantum dynamical semigroups have been introduced and studied, particularly in the context of unique relaxation and invariance. In this article we define the class of pair block diagonal generators, which allows for additional interaction coefficients but preserves the main structural properties. Namely, when the basis of the underlying Hilbert space is given by the eigenbasis of the Hamiltonian (for example the generic semigroups), then the action of the semigroup leaves invariant the diagonal and off-diagonal matrix spaces. In this case, we explicitly compute all invariant states of the semigroup.
Condition for zero and nonzero discord in graph Laplacian quantum states

This work is at the interface of graph theory and quantum mechanics. Quantum correlations epitomize the usefulness of quantum mechanics. Quantum discord is an interesting facet of bipartite quantum correlations. Earlier, it was shown that every combinatorial graph corresponds to quantum states whose characteristics are reflected in the structure of the underlined graph. A number of combinatorial relations between quantum discord and simple graphs were studied. To extend the scope of these studies, we need to generalize the earlier concepts applicable to simple graphs to weighted graphs, corresponding to a diverse class of quantum states. To this effect, we determine the class of quantum states whose density matrix representation can be derived from graph Laplacian matrices associated with a weighted directed graph and call them graph Laplacian quantum states. We find the graph-theoretic conditions for zero and non-zero quantum discord for these states. We apply these results on some important pure two qubit states, as well as a number of mixed quantum states, such as the Werner, Isotropic, and $X$-states. We also consider graph Laplacian states corresponding to simple graphs as a special case.
Interpretability With Accurate Small Models

Models often need to be constrained to a certain size for them to be considered interpretable. For example, a decision tree of depth 5 is much easier to understand than one of depth 50. Limiting model size, however, often reduces accuracy. We suggest a practical technique that minimizes this trade-off between interpretability and classification accuracy. This enables an arbitrary learning algorithm to produce highly accurate small-sized models. Our technique identifies the training data distribution to learn from that leads to the highest accuracy for a model of a given size. We represent the training distribution as a combination of sampling schemes. Each scheme is defined by a parameterized probability mass function applied to the segmentation produced by a decision tree. An Infinite Mixture Model with Beta components is used to represent a combination of such schemes. The mixture model parameters are learned using Bayesian Optimization. Under simplistic assumptions, we would need to optimize for O(d) variables for a distribution over a d-dimensional input space, which is cumbersome for most real-world data. However, we show that our technique significantly reduces this number to a fixed set of eight variables at the cost of relatively cheap preprocessing. The proposed technique is flexible: it is model-agnostic, i.e., it may be applied to the learning algorithm for any model family, and it admits a general notion of model size. We demonstrate its effectiveness using multiple real-world datasets to construct decision trees, linear probability models and gradient boosted models with different sizes. We observe significant improvements in the F1-score in most instances, exceeding an improvement of 100% in some cases.
Photonic realization of erasure-based nonlocal measurements

Abstract Relativity theory severely restricts the ability to perform nonlocal measurements in quantum mechanics. Studying such nonlocal schemes may thus reveal insights regarding the relations between these two fundamental theories. Therefore, for the last several decades, nonlocal measurements have stimulated considerable interest. However, the experimental implementation of nonlocal measurements imposes profound restrictions because the interaction Hamiltonian cannot contain, in general, nonlocal observables such as the product of local observables belonging to different particles at spacelike-separated regions. In this work, we experimentally realize a scheme for nonlocal measurements with the aid of probabilistic quantum erasure. We apply this scheme to the tasks of performing high-accuracy nonlocal measurements of the parity, as well as measurements in the Bell basis, which do not necessitate classical communication between the parties. Unlike other techniques, the nonlocal measurement outcomes are available locally (upon successful postselection). The state reconstructed via performing quantum tomography on the system after the nonlocal measurement indicates the success of the scheme in retrieving nonlocal information while erasing any local data previously acquired by the parties. This measurement scheme allows to realize any controlled-controlled-gate with any coupling strength. Hence, our results are expected to have conceptual and practical applications to quantum communication and quantum computation.
Specifying the Unitary Evolution of a Qudit for a General Nonstationary Hamiltonian via the Generalized Gell-Mann Representation

Optimal realizations of quantum technology tasks lead to the necessity of a detailed analytical study of the behavior of a d-level quantum system (qudit) under a time-dependent Hamiltonian. In the present article, we introduce a new general formalism describing the unitary evolution of a qudit ( d ≥ 2 ) in terms of the Bloch-like vector space and specify how, in a general case, this formalism is related to finding time-dependent parameters in the exponential representation of the evolution operator under an arbitrary time-dependent Hamiltonian. Applying this new general formalism to a qubit case ( d = 2 ) , we specify the unitary evolution of a qubit via the evolution of a unit vector in R 4 , and this allows us to derive the precise analytical expression of the qubit unitary evolution operator for a wide class of nonstationary Hamiltonians. This new analytical expression includes the qubit solutions known in the literature only as particular cases.