Reviewerly/exHarmony huggingface dataset:
Multiple Learners Combination: Cascading

Machine learning techniques are valuable allies in bioinformatics and have been applied to a wide spectrum of applications, let’s think for example to prediction and classification problems. With ensemble learning methods we refer to the process of building a model by combining more base learners, in order to reach better performances than each single learner of the “committee”. In particular, this article presents Cascading, a general multistage method for combining multiple learners in a sequential fashion. In Cascading, in fact, base learners are ranked by their complexity, in terms of space and time, and the j–th learners is used if all the previous j−1 learners are not confident enough. This approach is different from other ensemble approaches, for example Bagging and Boosting, known to be multiexpert methods.
Dictionary-learning-based image deblurring for improving image performance in x-ray nondestructive testing

This study investigated a dictionary-learning (DL)-based image deblurring method for improving image performance in x-ray nondestructive testing. DL is a representation learning theory that aims to find a sparse representation of the input signal in the form of a linear combination of basic elements as well as those basic elements themselves. In this study, a DL-based algorithm was implemented, and a computational simulation and experiment were then performed to evaluate the algorithm’s effectiveness for image deblurring. The hardware system used in the experiment consisted of an x-ray tube with a focal spot size of 0.6 mm and a flat-panel detector with a pixel size of 100 μm2. X-ray images of several electronic components were acquired at x-ray tube conditions of 80 kVp and 1.25 mAs. The image characteristics of the deblurred images generated by the DL-based algorithm were quantitatively evaluated in terms of intensity profile, universal-quality index, and noise power spectrum. Our results indicate that our DL-based image deblurring method effectively improves image performance in x-ray nondestructive testing.
Do Deep Reinforcement Learning Agents Model Intentions?

Inferring other agents’ mental states, such as their knowledge, beliefs and intentions, is thought to be essential for effective interactions with other agents. Recently, multi-agent systems trained via deep reinforcement learning have been shown to succeed in solving various tasks. Still, how each agent models or represents other agents in their environment remains unclear. In this work, we test whether deep reinforcement learning agents trained with the multi-agent deep deterministic policy gradient (MADDPG) algorithm explicitly represent other agents’ intentions (their specific aims or plans) during a task in which the agents have to coordinate the covering of different spots in a 2D environment. In particular, we tracked over time the performance of a linear decoder trained to predict the final targets of all agents from the hidden-layer activations of each agent’s neural network controller. We observed that the hidden layers of agents represented explicit information about other agents’ intentions, i.e., the target landmark the other agent ended up covering. We also performed a series of experiments in which some agents were replaced by others with fixed targets to test the levels of generalization of the trained agents. We noticed that during the training phase, the agents developed a preference for each landmark, which hindered generalization. To alleviate the above problem, we evaluated simple changes to the MADDPG training algorithm which lead to better generalization against unseen agents. Our method for confirming intention modeling in deep learning agents is simple to implement and can be used to improve the generalization of multi-agent systems in fields such as robotics, autonomous vehicles and smart cities.
Linear Artificial Forces for Human Dynamics in Complex Contexts

In complex contexts, peopleCoscia, Pasquale need to adapt their behavior to interactBallan, Lamberto with the surrounding environmentPalmieri, Francesco A. N. to reach the intended destination or avertAlahi, Alexandre collisions. Motion dynamics should therefore include both social and kinematic rules. The proposed analysis aimsSavarese, Silvio at defining a linear dynamic model to predict future positions of different types of agents, namely pedestrians and cyclists, observing a limited number of frames. The dynamics are defined in terms of artificial potentials fields (APFs) obtained by static (e.g., walls, doors or benches) and dynamic (e.g, other agents) elements to produce attractive and repulsive forces that influence the motion. A linear combination of such forces affects the resulting behavior. We exploit the context using a semantic scene segmentation to derive static forces while the interactions between agents are defined in terms of their reciprocal physical distances. We conduct experiments both on synthetic and on subsets of publicly available datasets to corroborate the proposed model.
Semantically Selective Augmentation for Deep Compact Person Re-Identification

We present a deep person re-identification approach that combines semantically selective, deep data augmentation with clustering-based network compression to generate high performance, light and fast inference networks. In particular, we propose to augment limited training data via sampling from a deep convolutional generative adversarial network (DCGAN), whose discriminator is constrained by a semantic classifier to explicitly control the domain specificity of the generation process. Thereby, we encode information in the classifier network which can be utilized to steer adversarial synthesis, and which fuels our CondenseNet ID-network training. We provide a quantitative and qualitative analysis of the approach and its variants on a number of datasets, obtaining results that outperform the state-of-the-art on the LIMA dataset for long-term monitoring in indoor living spaces.
An IoT based modified graph cut segmentation with optimized adaptive connectivity and shape priors

Internet of Things (IoT) is the network of physical objects embedded with software, electronics, sensors and network connectivity which enables these objects to collect and exchange data. IoT based image processing is reaching up to a different level which is becoming a major part of everyone life. IoT based image segmentation is used to solve the real world problems like animal classification in wildlife monitoring and vehicle classification etc. Image segmentation is one of the processes of image processing technique which involves partitioning an image into multiple segments so that any region of interest can be obtained easily. The modified graph cut approach is one of the image segmentation techniques that avoid the boundary and region balance problem of graph cut technique by removing the regional term and strengthening the boundary term with the prior information. By combining shape priors in an adaptive way, a robust way to harness the shape prior was introduced in graphcut segmentation that reduced the memory size required for modified graph cut implementation. However, the structural regularities were not handled by the modified graph cut with adaptive shape prior method. Hence, in this proposed work the structural irregularities in image shapes are considered for image segmentation where the connectivity constraints are formulated such that corresponding optimization problems are all NP-hard which is solved by optimized DijkstraGC and problem decomposition approach. The segmentation with satisfied constraint results in an efficient image segmentation which reduces amount of time required for segmentation process. Furthermore, parameter of adaptive shape prior and connectivity constraints are optimized using BAT algorithm to improve the performance of image segmentation. Results clearly shows that proposed approach gives better results in terms of Root Mean Square, Energy Consumption and correlation coefficient.
Arbobanko - A Treebank for Esperanto

In this paper we describe and evaluate Arbobanko, a syntactic treebank for the artificial language Esperanto, as well as methods and tools used to produce the treebank. For an under-resourced language, the quality of automatic syntactic pre-annotation is of obvious importance, and by evaluating the parser associated with the treebank, we try to answer the question whether the language's extremely regular morphology and low lexical ambiguity carry over into a more regular syntax and higher parsing accuracy. On the linguistic side, the treebank allows us to address and quantify the typological issue of (free) word order in Esperanto.
Heating a Torus with Number Theory

SummaryWe discuss an amusing application of number theory: suppose you find yourself on the two-dimensional torus T2 equipped with N candles and want to position the candles in such a way that they heat up the room as efficiently as possible. To achieve this goal, we give a construction that uses number theory as the main ingredient and explain related results.
q-Neurons: Neuron Activations Based on Stochastic Jackson’s Derivative Operators

We propose a new generic type of artificial neurons called q-neurons. A q-neuron is a stochastic neuron with its activation function relying on Jackson's discrete q-derivative for a stochastic parameter q. We show how to generalize neural network architectures with q-neurons and demonstrate the scalability and ease of implementation of q-neurons into legacy deep learning frameworks. We report experimental results that consistently improve performance over state-of-the-art standard activation functions, both on training and test loss functions.
Blogging birds

The system transforms raw telemetric data into engaging and informative blog texts readily understood by all.
Solving Schrödinger equation for bipartite hard-core potential by virtue of the entangled state representation

We study the one-dimensional Schrödinger equation for the Hamiltonian involving the bipartite hard-core potential, [Formula: see text]. We select the suitable unitary transformation and employ the entangled state representation |η⟩, η = η 1 + iη 2 , to find the quantization condition for energy E, [Formula: see text] which seems new.
Fast In-Memory Key–Value Cache System with RDMA

The quick advances of Cloud and the advent of Fog computing impose more and more critical demand for computing and data transfer of low latency onto the underlying distributed computing infrastructure. Remote direct memory access (RDMA) technology has been widely applied for its low latency of remote data access. However, RDMA gives rise to a host of challenges in accelerating in-memory key–value stores, such as direct remote memory writes, making the remote system more vulnerable. This study presents an in-memory key–value system based on RDMA, named Craftscached, which enables: (1) buffering remote memory writes into a communication cache memory to eliminate direct remote memory writes to the data memory area; (2) dividing the communication cache memory into RDMA-writable and RDMA-readable memory zones to reduce the possibility of data corruption due to stray memory writes and caching data into an RDMA-readable memory zone to improve the remote memory read performance; and (3) adopting remote out-of-place direct memory write to achieve high performance of remote read and write. Experimental results in comparison with Memcached indicate that Craftscached provides a far better performance: (1) in the case of read-intensive workloads, the data access of Craftscached is about 7–43[Formula: see text] and 18–72.4% better than those of TCP/IP-based and RDMA-based Memcached, respectively; (2) the memory utilization of small objects is more efficient with only about 3.8% memory compaction overhead.
An Output-Sensitive Algorithm for the Minimization of 2-Dimensional String Covers

String covers are a powerful tool for analyzing the quasi-periodicity of 1-dimensional data and find applications in automata theory, computational biology, coding and the analysis of transactional data. A \emph{cover} of a string $T$ is a string $C$ for which every letter of $T$ lies within some occurrence of $C$. String covers have been generalized in many ways, leading to \emph{k-covers}, \emph{$\lambda$-covers}, \emph{approximate covers} and were studied in different contexts such as \emph{indeterminate strings}. In this paper we generalize string covers to the context of 2-dimensional data, such as images. We show how they can be used for the extraction of textures from images and identification of primitive cells in lattice data. This has interesting applications in image compression, procedural terrain generation and crystallography.
Augmented Reality and Experiences

Today technology evolves in two different directions. The first one is to create a new technology for our requirement and solve the problem, and the second one is to do it with the existing technology. This chapter will discuss in detail augmented reality and its use in the real world and also its application domains like medicine, education, health, gaming, tourism, film and entertainment, architecture, and development. Many think that AR is only for smartphones, but there are different ways to enhance the insight of the world. Augmented realities can be presented on an extensive range of displays, monitors, screens, handheld devices, or glasses. This chapter will provide the information about the key components of AR devices. This chapter gives a view on different types of AR and also projects how the technology can be adapted for multiple purposes based on the required type of view.
A Innovative Topic Based Customer Complaints Sentiment classification System

Text mining helps to convert big text data into a small amount of most relevant data for a particular problem, and also helps providing knowledge provenance and interpreting patterns. Text mining has to do with further analysing the relevant data to discover the actionable knowledge that can be directly useful for decision making or many other tasks. In the previous work, authors have used conditional entropy with maxima/minima boundaries in identifying aspects from given text in identifying aspects. In the present work, we concentrated on mining word associations using mutual information in measuring co-occurrence of words. The objective of our research is to propose a novel probabilistic latent aspect rating regression (LARR) model. In which, natural language processing is used to represent the text, discovering and analysing topics using expectation maximisation (EM) algorithm. In addition to that, we compared the performance of the proposed model with multiple ordinal logistic regression classifier (MLRC) and fuzzy the rating obtained from the model. The finding in our research is the use of mutual information to mine synatagmatic relations helps in identifying the aspect at document level at greater extend, intern helps to multi level sentiment polarity prediction.
Speeding Up Budgeted Stochastic Gradient Descent SVM Training with Precomputed Golden Section Search

Limiting the model size of a kernel support vector machine to a pre-defined budget is a well-established technique that allows to scale SVM learning and prediction to large-scale data. Its core addition to simple stochastic gradient training is budget maintenance through merging of support vectors. This requires solving an inner optimization problem with an iterative method many times per gradient step. In this paper we replace the iterative procedure with a fast lookup. We manage to reduce the merging time by up to $$65\%$$ and the total training time by $$44\%$$ without any loss of accuracy.
The Trade-off between Quantity and Quality. Comparing a Large Crawled Corpus and a Small Focused Corpus for Medical Terminology Extraction

Abstract We investigate the cost-effectiveness of special-purpose crawled corpora versus more focused corpora for automatic terminology extraction (ATE). Our focus is on medical terminology on heart failure for two languages, viz. English for which we have more web and specialized resources at our disposal and the less resourced Dutch. We show that, although term density in the dedicated corpora is larger for both languages, the potential for term extraction is higher in the crawled corpora than in the dedicated corpora. Furthermore, in a set of experiments in which we evaluate both types of corpora, while keeping size constant, we observe that more Gold Standard (GS) terms are covered by the “noisy” crawled corpus than with a dedicated corpus of the same size.
Recovering Affine Features from Orientation- and Scale-Invariant Ones

An approach is proposed for recovering affine correspondences (ACs) from orientation- and scale-invariant, e.g. SIFT, features. The method calculates the affine parameters consistent with a pre-estimated epipolar geometry from the point coordinates and the scales and rotations which the feature detector obtains. The closed-form solution is given as the roots of a quadratic polynomial equation, thus having two possible real candidates and fast procedure, i.e. <1 ms. It is shown, as a possible application, that using the proposed algorithm allows us to estimate a homography for every single correspondence independently. It is validated both in our synthetic environment and on publicly available real world datasets, that the proposed technique leads to accurate ACs. Also, the estimated homographies have similar accuracy to what the state-of-the-art methods obtain, but due to requiring only a single correspondence, the robust estimation, e.g. by Graph-Cut RANSAC, is an order of magnitude faster.
Robust Set Reconciliation via Locality Sensitive Hashing

We consider variations of set reconciliation problems where two parties, Alice and Bob, each hold a set of points in a metric space, and the goal is for Bob to conclude with a set of points that is close to Alice's set of points in a well-defined way. This setting has been referred to as robust set reconciliation. In one variation, the goal is for Bob to end with a set of points that is close to Alice's in earth mover's distance, and in another the goal is for Bob to have a point that is close to each of Alice's. The first problem has been studied before; while previous results achieved an $O(d)$ approximation, where d is the dimension of the space, we achieve an $O(łog n)$ approximation, where n is the number of points. The second problem appears new, and here we find schemes that, under certain conditions, use sublinear communication. Our primary novelty is utilizing Invertible Bloom Lookup Tables in combination with locality sensitive hashing. This combination allows us to cope with the geometric setting in a communication-efficient manner.
Collapse and revival oscillation in Double Jaynes–Cummings model

We develop a systematic method of solving two non-interacting Jaynes-Cummings models by using the dressed state formalism in Hilbert space $\mathcal{H}_{AB}^{(2\otimes2)}$. It is shown that such model, called Double Jaynes-Cummings model (D-JCM), can be exactly solved if we take the initial bare state as the linear superposition of two Bell states. The collapse and revival oscillation, which is the standard trait of typical Jaynes-Cummings model, can be recovered if we make measurement at each local sites. Some consequence of the entanglement-induced dressing is discussed.
Simulation and analysis of campus network based on OPNET

Due to the low efficiency of traditional network design, and long cycle of network construction, this paper simulated the campus network model based on the OPNET simulation platform. From the aspects of network nodes, link media, number of servers and settings, etc., the network performance of the campus network solution was improved, and the corresponding solution was verified through the OPNET simulation platform. Simulation experiments has verified the feasibility of network simulation in network planning and design.
Conditional Masking to Numerical Data

Protecting the privacy of datasets has become hugely important these days. Many real-life datasets like income data and medical data need to be secured before making it public. However, security comes at the cost of losing some useful statistical information about the dataset. Data obfuscation deals with this problem of masking a dataset in such a way that the utility of the data is maximized while minimizing the risk of the disclosure of sensitive information. Two popular approaches to data obfuscation for numerical data involve (i) data swapping and (ii) adding noise to data. While the former masks well sacrificing the whole of correlation information, the latter gives estimates for most of the popular statistics like mean, variance, quantiles and correlation but fails to give an unbiased estimate of the distribution curve of the original data. In this paper, we propose a mixed method of obfuscation combining the above two approaches and discuss how the proposed method succeeds in giving an unbiased estimation of the distribution curve while giving reliable estimates of the other well-known statistics like moments and correlation.
Iteration of polynomials AX + C over finite fields

For a polynomial f(X)=AXd+C∈Fp[X] with A≠0 and d≥2, we prove that if d|p−1 and f∘i(0)≠f∘j(0) for 0≤i<j≤N, then #f∘N(Fp)∼2p(d−1)N, where f∘N is the N-th iteration of f.
Orthogonalization of fermion<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>-body operators and representability

The reduced $k$-particle density matrix of a density matrix on finite-dimensional, fermion Fock space can be defined as the image under the orthogonal projection in the Hilbert-Schmidt geometry onto the space of $k$-body observables. A proper understanding of this projection is therefore intimately related to the representability problem, a long-standing open problem in computational quantum chemistry. Given an orthonormal basis in the finite-dimensional one-particle Hilbert space, we explicitly construct an orthonormal basis of the space of Fock space operators which restricts to an orthonormal basis of the space of $k$-body operators for all $k$.
User Loss A Forced-Choice-Inspired Approach to Train Neural Networks Directly by User Interaction

In this paper, we investigate whether is it possible to train a neural network directly from user inputs. We consider this approach to be highly relevant for applications in which the point of optimality is not well-defined and user-dependent. Our application is medical image denoising which is essential in fluoroscopy imaging. In this field every user, i.e. physician, has a different flavor and image quality needs to be tailored towards each individual. To address this important problem, we propose to construct a loss function derived from a forced-choice experiment. In order to make the learning problem feasible, we operate in the domain of precision learning, i.e., we inspire the network architecture by traditional signal processing methods in order to reduce the number of trainable parameters. The algorithm that was used for this is a Laplacian pyramid with only six trainable parameters. In the experimental results, we demonstrate that two image experts who prefer different filter characteristics between sharpness and de-noising can be created using our approach. Also models trained for a specific user perform best on this users test data. This approach opens the way towards implementation of direct user feedback in deep learning and is applicable for a wide range of application.