Reviewerly/exHarmony huggingface dataset:
Natural Language Search and Associative-Ontology Matching Algorithms Based on Graph Representation of Texts

The ability to freely publish any information content is causing rapid growth of unstructured, duplicated and unreliable information volumes with irregular dynamics. This significantly complicates timely access to actual reliable information especially in the tasks of the specific scientific topics monitoring or when it is necessary to get quick insight of adjacent scientific fields of interest. The paper contains the description of the technology of text representation as a semantic graph. The algorithmic implementation of proposed technology in the tasks of fuzzy and exploratory information search is developed. The problems of current search technologies are considered. The proposed ontology-associative graph matching approach to post-full-text search system development is capable of solving the problem of document search under conditions of insufficient initial data for correct query formation. The proposed graph representation of texts allows restricting usable ontology, which in turn gives the benefit of thematic localization of the search region in the field of knowledge.
Consciousness as a logically consistent and prognostic model of reality

The work demonstrates that brain might reflect the external world causal relationships in the form of a logically consistent and prognostic model of reality, which shows up as consciousness. The paper analyses and solves the problem of statistical ambiguity and provides a formal model of causal relationships as probabilistic maximally specific rules. We suppose that brain makes all possible inferences from causal relationships. We prove that the suggested formal model has a property of an unambiguous inference: from consistent premises we infer a consistent conclusion. It enables a set of all inferences to form a consistent model of the perceived world. Causal relationships may create fixed points of cyclic inter-predictable properties. We consider the “natural” classification introduced by John St. Mill and demonstrate that a variety of fixed points of the objects’ attributes forms a “natural” classification of the external world. Then we consider notions of “natural” categories and causal models of categories, introduced by Eleanor Rosch and Bob Rehder and demonstrate that fixed points of causal relationships between objects attributes, which we perceive, formalize these notions. If the “natural” classification describes the objects of the external world, and “natural” concepts the perception of these objects, then the theory of integrated information, introduced by G. Tononi, describes the information processes of the brain for “natural” concepts formation that reflects the “natural” classification. We argue that integrated information provides high accuracy of the objects identification. A computer-based experiment is provided that illustrates fixed points formation for coded digits.
OmniPD: One-Step Person Detection in Top-View Omnidirectional Indoor Scenes

We propose a one-step person detector for topview omnidirectional indoor scenes based on convolutional neural networks (CNNs). While state of the art person detectors reach competitive results on perspective images, missing CNN architectures as well as training data that follows the distortion of omnidirectional images makes current approaches not applicable to our data. The method predicts bounding boxes of multiple persons directly in omnidirectional images without perspective transformation, which reduces overhead of pre- and post-processing and enables real-time performance. The basic idea is to utilize transfer learning to fine-tune CNNs trained on perspective images with data augmentation techniques for detection in omnidirectional images. We fine-tune two variants of Single Shot MultiBox detectors (SSDs). The first one uses Mobilenet v1 FPN as feature extractor (moSSD). The second one uses ResNet50 v1 FPN (resSSD). Both models are pre-trained on Microsoft Common Objects in Context (COCO) dataset. We fine-tune both models on PASCAL VOC07 and VOC12 datasets, specifically on class person. Random 90-degree rotation and random vertical flipping are used for data augmentation in addition to the methods proposed by original SSD. We reach an average precision (AP) of 67.3 % with moSSD and 74.9 % with resSSD onthe evaluation dataset. To enhance the fine-tuning process, we add a subset of HDA Person dataset and a subset of PIROPOdatabase and reduce the number of perspective images to PASCAL VOC07. The AP rises to 83.2 % for moSSD and 86.3 % for resSSD, respectively. The average inference speed is 28 ms per image for moSSD and 38 ms per image for resSSD using Nvidia Quadro P6000. Our method is applicable to other CNN-based object detectors and can potentially generalize for detecting other objects in omnidirectional images.
Columnar data processing for HEP analysis

In the last stages of data analysis, physicists are often forced to choose between simplicity and execution speed. In High Energy Physics (HEP), high-level languages like Python are known for ease of use but also very slow execution. However, Python is used in speed-critical data analysis in other fields of science and industry. In those fields, most operations are performed on Numpy arrays in an array programming style; this style can be adopted for HEP by introducing variable-sized, nested data structures. We describe how array programming may be extended for HEP use-cases and an implementation known as awkward-array. We also present integration with ROOT, Apache Arrow, and Parquet, as well as preliminary performance results.
Sets of Pictures Avoiding Overlaps

A string set avoids overlaps if it has the property that the prefix of each string in the set does not coincide with the suffix of any string in the set. The problem of avoiding overlaps in strings is extensively investigated since it plays an important role in the context of string matching and coding. The notion of overlap can be extended naturally to two dimensions; two pictures [Formula: see text] and [Formula: see text] have an overlap if one can put one corner of [Formula: see text] on some position in [Formula: see text] in such a way that all symbols in the common positions coincide. A picture with no self-overlaps is called unbordered and it is a generalization in two dimensions of an unbordered (or bifix-free) string. We first investigate the problem of generating all unbordered pictures of fixed size. Then, we study particular sets of unbordered pictures that are non-overlapping each others. We show that the frame of the pictures has a special role in defining these sets. In particular, we prove that there exist kinds of synchronization frames that can be put around the pictures of any set to make it non-overlapping. Finally, we present a construction of non-expandable non-overlapping sets of pictures which starts from some property of frame-completeness.
Coherence of Superposition States*

Abstract We find tight upper bound on the coherence of a superposition of two states in terms of the coherence of the two states constituting the superposition with l 1 -norm of coherence. Our upper bound is tighter than the one presented by Liu, et al. [Quantum Inf. Process. 15 (2016) 4209.] We also generalize the results to the case that the superposition is constituted with more than two states in high dimension, and we give the corresponding upper bounds.
The Empirical Analysis of Bitcoin Market in the General Equilibrium Framework

This paper firstly pursues the fundamental price of Bitcoin in the general equilibrium framework and its empirical characteristics. Our theoretical model predicts that (i) the Bitcoin price and the total hash rate are determined simultaneously in the long-run and (ii) the hash rate of Bitcoin Granger-causes Bitcoin prices in the short-run. Using a cointegration framework, our empirical analysis provides consistent results with these theoretical predictions. Our empirical results suggest that the Bitcoin market has been under a fundamental value instead of speculative bubbles.
Bi-homomorphic Lattice-Based PRFs and Unidirectional Updatable Encryption

We define a pseudorandom function (PRF) $$F: \mathcal {K} \times \mathcal {X} \rightarrow \mathcal {Y}$$ to be bi-homomorphic when it is fully Key homomorphic and partially Input Homomorphic (KIH), i.e., given $$F(k_1, x_1)$$ and $$F(k_2, x_2)$$ , there is an efficient algorithm to compute $$F(k_1 \oplus k_2, x_1 \ominus x_2)$$ , where $$\oplus $$ and $$\ominus $$ are (binary) group operations. The homomorphism on the input is restricted to a fixed subset of the input bits, i.e., $$\ominus $$ operates on some pre-decided m-out-of-n bits, where $$|x_1| = |x_2| = n, m < n$$ , and the remaining $$n-m$$ bits are identical in both inputs. In addition, the output length, $$\ell $$ , of the operator $$\ominus $$ is not fixed and is defined as $$n \le \ell \le 2n$$ , hence leading to Homomorphically induced Variable input Length (HVL) as $$n \le |x_1 \ominus x_2| \le 2n$$ . We present a learning with errors (LWE) based construction for a HVL-KIH-PRF family. Our construction is inspired by the key homomorphic PRF construction due to Banerjee and Peikert (Crypto 2014). We use our novel PRF family to construct an updatable encryption scheme, named QPC-UE-UU, which is quantum-safe, post-compromise secure and supports unidirectional ciphertext updates, i.e., the tokens can be used to perform ciphertext updates, but they cannot be used to undo completed updates. Our PRF family also leads to the first left/right key homomorphic constrained-PRF family with HVL.
The Zeitgeist Juncture of “Big Data” and its Future Trends

With the prime objective of understanding the direction in which the field of big data is heading, this paper emphasizes on the recent and upcoming trends in big data. The human brain handles big data in all its forms very efficiently and almost instantaneously, no matter what is the granularity of the data. The idea behind the field of processing big data is to mimic the manner in which the natural brain works and handles big data. All the fields and subfields of machine intelligence are based on identifying and mimicking the natural methods which are used by mother nature to handle big data. The current trends are mainly moving towards processing big data using the cloud infrastructure and machine learning techniques. By and large the intention of artificial intelligence lies in developing a humanoid robot which can work, talk and think like a human being. The future trends of big data are being applied to such techniques as microprocessors processing big data in a robot’s-brain to plan its actions, express emotions and ultimately to think like a human being.
An $\ell_0$ -Norm-Based Centers Selection for Failure Tolerant RBF Networks

There are two important issues in the construction of a radial basis function (RBF) neural network. The first one is to select suitable RBF centers. The second one is that the resultant RBF network should be with good fault tolerance. This paper proposes an algorithm that is able to select RBF centers and to train fault tolerant RBF networks simultaneously. The proposed algorithm borrows the concept from sparse approximation. In our formulation, we first define a fault tolerant objective function based on all input vectors from the training samples. We then introduce the minimax concave penalty (MCP) function, which is an approximation of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -norm, into the objective function. The MCP term is able to force some unimportant RBF weights to zero. Hence the RBF node selection process can be achieved during training. As the MCP function is nondifferentiable and nonconvex, traditional gradient descent based algorithms are still unable to minimize the modified objective function. Based on the alternating direction method of multipliers (ADMM) framework, we develop an algorithm, called ADMM-MCP, to minimize the modified objective function. The convergent proof of the proposed ADMM-MCP algorithm is also presented. Simulation results show that the proposed ADMM-MCP algorithm is superior to many existing center selection algorithms under the concurrent fault situation.
Parallax Tolerant Light Field Stitching for Hand-Held Plenoptic Cameras

Light field (LF) stitching is a potential solution to improve the field of view (FOV) for hand-held plenoptic cameras. Existing LF stitching methods cannot provide accurate registration for scenes with large depth variation. In this paper, a novel LF stitching method is proposed to handle parallax in the LFs more flexibly and accurately. First, a depth layer map (DLM) is proposed to guarantee adequate feature points on each depth layer. For the regions of nondeterministic depth, superpixel layer map (SLM) is proposed based on LF spatial correlation analysis to refine the depth layer assignments. Then, DLM-SLM-based LF registration is proposed to derive the location dependent homography transforms accurately and to warp LFs to its corresponding position without parallax interference. 4D graph-cut is further applied to fuse the registration results for higher LF spatial continuity and angular continuity. Horizontal, vertical and multi-LF stitching are tested for different scenes, which demonstrates the superior performance provided by the proposed method in terms of subjective quality of the stitched LFs, epipolar plane image consistency in the stitched LF, and perspective-averaged correlation between the stitched LF and the input LFs.
A fully pipelined FPGA accelerator for scale invariant feature transform keypoint descriptor matching

The scale invariant feature transform (SIFT) algorithm is considered a classical feature extraction algorithm within the field of computer vision. SIFT keypoint descriptor matching is a computationally intensive process due to the amount of data consumed. In this work, we designed a novel fully pipelined hardware accelerator architecture for SIFT keypoint descriptor matching. The accelerator core was implemented and tested on a field programmable gate array (FPGA). The proposed hardware architecture is able to properly handle the memory bandwidth necessary for a fully-pipelined implementation and hits the roofline performance model, achieving the potential maximum throughput. The fully pipelined matching architecture was designed based on the consine angle distance method. Our architecture was optimized for 16-bit fixed-point operations and implemented on hardware using a Xilinx Zynq-based FPGA development board. Our proposed architecture shows a noticeable reduction of area resources compared with its counterparts in literature, while maintaining high throughput by alleviating memory bandwidth restrictions. The results show a reduction in consumed device resources of up to 91% in LUTs and 79% of BRAMs. Our hardware implementation is 15.7 × faster than the comparable software approach.
Convexity Preserving Contraction of Digital Sets

Convexity is one of the useful geometric properties of digital sets in digital image processing. There are various applications which require deforming digital convex sets while preserving their convexity. In this article, we consider the contraction of such digital sets by removing digital points one by one. For this aim, we use some tools of combinatorics on words to detect a set of removable points and to define such convexity-preserving contraction of a digital set as an operation of re-writing its boundary word. In order to chose one of removable points for each contraction step, we present three geometrical strategies, which are related to vertex angle and area changes. We also show experimental results of applying the methods to repair some non-convex digital sets, which are obtained by rotations of convex digital sets.
A Searching and Automatic Video Tagging Tool for Events of Interest during Volleyball Training Sessions

Quick and easy access to performance data during matches and training sessions is important for both players and coaches. While there are many video tagging systems available, these systems require manual effort. This paper proposes a system architecture that automatically supplements video recording by detecting events of interests in volleyball matches and training sessions to provide tailored and interactive multi-modal feedback.
Collaborative methodology and ICTs for Math Learning in undergraduate students

Since its creation in 2002, the students of Micaela Bastidas National University of Apurímac-UNAMBA, have registered discouraging results in mathematics subjects. This paper describes the use of active procedures (collaborative method) in mathematics learning and teaching processes. This proposal was evaluated by two teachers and 54 undergraduate students of Mathematics at the Professional School of Administration-UNAMBA. The results show that using the collaborative methodology and ICT tools, the students have greatly improved the quality of learning. Qualitative results show that in the interviews with students they think that the collaborative approach helps them to improve their knowledge, learn in a funny way with their classmates, and teacher uses the ICT resource to create and post the videos on YouTube. The quantitative results show that the students improve their score average from 7.28 to 9.92. Teachers agree with the methodology, but they think that it is required more time to teach in the classroom.
Visual Person Understanding Through Multi-task and Multi-dataset Learning

We address the problem of learning a single model for person re-identification, attribute classification, body part segmentation, and pose estimation. With predictions for these tasks we gain a more holistic understanding of persons, which is valuable for many applications. This is a classical multi-task learning problem. However, no dataset exists that these tasks could be jointly learned from. Hence several datasets need to be combined during training, which in other contexts has often led to reduced performance in the past. We extensively evaluate how the different task and datasets influence each other and how different degrees of parameter sharing between the tasks affect performance. Our final model matches or outperforms its single-task counterparts without creating significant computational overhead, rendering it highly interesting for resource-constrained scenarios such as mobile robotics.
Novel model for boosting security strength and energy efficiency in internet-of-things using multi-staged game

Security as well as energy efficiency is one of the most inevitable and challenging problems when it comes it large scale network deployment like INternet-of-Things (IoT). After reviewing existing research work on IoT, it was found that there are discrete set of solution for security as well as for energy. However, there is little research work that has jointly investigated both the problems with respect to IoT. Apart from this, there are also various form of attacks that cost energy of sensors that constitutes core physical devices in IoT. Therefore, these manuscripts present a novel idea for identifying and resisting the security breach within an IoT system ensuring energy efficiency too. Harnessing the modelling capability of game-theory, the proposed system offers a joint solution towards these problems. The simulated outcome of the study is found to offer balance performance for better energy efficiency and robust threat mitigation capability when compared with existing approaches.
Legal Challenges of Cryptocurrencies: Isn’t It Time to Regulate the Intermediaries?

In only one decade, cryptocurrencies have witnessed significant growth, with Bitcoin being the most dominant one. They are not efficient payment methods, although they bring some benefits linked to the underlying technology they use (Blockchain). In reality, they function more as investment assets than as payment instruments and pose various risks, which are very similar to those encountered on capital markets (price volatility, fraud, market manipulation). In order to deal with these risks, regulation should apply to intermediaries who provide services in relation to cryptocurrencies, such as crypto wallets, operation of crypto exchanges and brokerage. To this end, the European legislator should consider two options. It could, on the one hand, bring cryptocurrency service providers within the scope of the existing financial services regime and thus modify MIFID II in order to include cryptocurrencies in the list of financial instruments. The European legislator could also, on the other hand, aim at creating a new appropriate and proportionate regime that draws on the existing one. Insofar as the existing regulatory framework was not designed with cryptocurrencies in mind and as some of the current rules may not be tailored to the specificities of these assets, the second option is preferable.
Uncertainty-Aware Resource Provisioning for Workflow Scheduling in Edge Computing Environment

The workflow, in edge computing, is capable of orderly managing a set of computing tasks, via setting up a structured calculation process and flow. However in running time, the resources at edge nodes are often released by other workflows, which results in additional uncertainties on top of those caused by performance degradation and service failure. In addressing these new uncertainties, we propose in this paper an uncertainty-aware resource provisioning (UARP) method for scheduling workflow in the software-defined network-based(SDN-based) edge computing environment. The proposed UARP ensures optimal in that the non-dominated sorting genetic algorithm-III (NSGA-III) is employed to elaborate the workflow scheduling strategy. Extensive experiments are carried out for method evaluations and the results show that the UARP method is effective in reducing uncertainty, shortening processing time and bringing down energy consumption.
Fully Quantizing a Simplified Transformer for End-to-end Speech Recognition

While significant improvements have been made in recent years in terms of end-to-end automatic speech recognition (ASR) performance, such improvements were obtained through the use of very large neural networks, unfit for embedded use on edge devices. That being said, in this paper, we work on simplifying and compressing Transformer-based encoder-decoder architectures for the end-to-end ASR task. We empirically introduce a more compact Speech-Transformer by investigating the impact of discarding particular modules on the performance of the model. Moreover, we evaluate reducing the numerical precision of our network's weights and activations while maintaining the performance of the full-precision model. Our experiments show that we can reduce the number of parameters of the full-precision model and then further compress the model 4x by fully quantizing to 8-bit fixed point precision.
Detection of Subsurface Defects in Composite Panels Using Dynamic Speckle Patterns

A new method for subsurface defects detection in multilayer composites is proposed. It is based on estimation of dynamic speckle motion in different surface areas of a studied composite panel using dynamic speckle patterns. The system based on this method can be used both in laboratory and field conditions.