Reviewerly/exHarmony huggingface dataset:
Optimizing backbone filtering

In propositional formulas, literals are assigned to True or False in each assignment. Backbone literals are always assigned to True in every satisfiable assignment (model) of the given formula. Backbone literals are keys to improve the performance of SAT testing and SAT-based applications, such as model checking and program analysis. In this paper, we propose an optimized approach that combines COV, WHIT and 2LEN strategies to improve backbone computing. We have implemented it in EDUCIBone. COV and 2LEN are used for finding backbone literals, WHIT is used for finding non-backbone literals. COV uses the fact that for a backbone literal l, there must exist at least one clause ϕ in every model v of the given formula Φ such that for every other literal l′ in ϕ, the assignment of l′ is False in v. WHIT is based on the fact that for a literal l, and a model v of the given formula Φ, if for every clause ϕ that has l, there always exists another l′ also in ϕ such that the assignment of l′ is True, then l is a non-backbone literal. For a literal l, the naive way to decide if l is a backbone literal is to check the satisfiability of l∧Φ and ¬l∧Φ. 2LEN finds a set of unique free literals Fl for l, such that the satisfiability of ¬l∧Φ and ⋀l′∈Fll′∧Φ are the same. Experiments show that checking the satisfiability of ⋀l′∈Fll′∧Φ is faster than checking the satisfiability of ¬l∧Φ when the backbone computing time of the given formula is less than 3000 seconds. We deactivate 2LEN strategy when the formula has been computed for more than 3000 seconds. We evaluate the performance of EDUCIBone on formulas from industrial tracks in SAT Competitions from 2011 to 2017. Performance comparisons are conducted among EDUCIBone, DUCIBone and minibones-cb100. Results show that EDUCIBone solves the most formulas. For the formulas that are commonly solved by the three tools, EDUCIBone is the fastest in computing backbone literals from the formulas. We also analyze the efficiency of COV, WHIT and 2LEN strategies separately with detailed experiments. Results show that every strategy contributes to the performance of EDUCIBone, and they are all applied to various kinds of formulas.
Flexible calibration method of an FPP system based on a geometrical model and NLSM with fewer parameters: publisher’s note

This publisher's note amends the Funding section of Appl. Opt.58, A7 (2019)APOPAI0003-693510.1364/AO.58.0000A7.
My-AHA: Middleware Platform to Sustain Active and Healthy Ageing

Ageing population is growing every day, and there is an urge to provide the means for improved quality of life of the elderly. Different healthy and active ageing solutions use biosignals, movement, social activity and other parameters of senior people, aiming at understanding how their daily routine is affected by advanced age. Such solutions provide a vast range of information (e.g., physical activities, nutrition, social contacts, to name a few), focusing on identifying early signs of decline and providing queues, to caretakers and the seniors themselves, on how to ameliorate their current condition. Upon that, we propose My-AHA, a multi-platform middleware that seamlessly integrates different health and active ageing solutions, targeting senior well-being. To illustrate its potential, we present the application of My-AHA considering a frailty use case, and some insights on its current deployment and future work are provided.
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery

Human players in professional team sports achieve high level coordination by dynamically choosing complementary skills and executing primitive actions to perform these skills. As a step toward creating intelligent agents with this capability for fully cooperative multi-agent settings, we propose a two-level hierarchical multi-agent reinforcement learning (MARL) algorithm with unsupervised skill discovery. Agents learn useful and distinct skills at the low level via independent Q-learning, while they learn to select complementary latent skill variables at the high level via centralized multi-agent training with an extrinsic team reward. The set of low-level skills emerges from an intrinsic reward that solely promotes the decodability of latent skill variables from the trajectory of a low-level skill, without the need for hand-crafted rewards for each skill. For scalable decentralized execution, each agent independently chooses latent skill variables and primitive actions based on local observations. Our overall method enables the use of general cooperative MARL algorithms for training high level policies and single-agent RL for training low level skills. Experiments on a stochastic high dimensional team game show the emergence of useful skills and cooperative team play. The interpretability of the learned skills show the promise of the proposed method for achieving human-AI cooperation in team sports games.
On Development of Machine Learning Models with Aim of Medical Differential Diagnostics of the Comorbid States

The purpose of the work is to develop mathematical and software background for the development of machine learning (ML) models in differential diagnostics of comorbid states. Flowchart includes basic steps of ML model development, including the statement of task, the choice of method (learner), setting its parameters and model assessment. The problems dealing with dimension reduction which arise often in differential diagnostics of comorbid states are highlighted and solved with help of modified PCA method. As an example we consider the problem of development of classifier for chronic pancreatitis combined with ascaridosis where we solve all tasks of ML model development. With help of benchmark of learners in the package mlr we compare different methods of ML when applying them in differential diagnostics of comorbid states.
METHODS AND TECHNOLOGY FOR ASSESSMENT OF HUMAN CAPITAL OF A UNIVERSITY GRADUATE

Purpose: The article presents the author's approach to assessing the human capital of a graduate of a regional university. Emphasis is placed on the fact that the problem of assessing of human capital is especially acute for pedagogy, since the contribution of education to the formation of human capital is one of the determining factors, and the contribution of pedagogy to the study of the pedagogical aspects of this phenomenon is extremely small.&#x0D; Methodology: The authors propose a methodology and technology for assessing human capital. In particular, it was proposed to use an integrated approach to the assessment of human capital of a graduate of a regional university, the essence of which is to add the absolute values of indicators of individual components of capital, and then based on the data calculate the relative indicator of the quality of human capital.&#x0D; Result: The result of training and the main goal of improving the quality of training of specialists at a regional university will be to ensure the minimum deviation of the level of the human capital of the graduate achieved during the period of study at the university, from the level of human capital established by regulatory requirements.
Video Segmentation Learning Using Cascade Residual Convolutional Neural Network

Video segmentation consists of a frame-by-frame selection process of meaningful areas related to foreground moving objects. Some applications include traffic monitoring, human tracking, action recognition, efficient video surveillance, and anomaly detection. In these applications, it is not rare to face challenges such as abrupt changes in weather conditions, illumination issues, shadows, subtle dynamic background motions, and also camouflage effects. In this work, we address such shortcomings by proposing a novel deep learning video segmentation approach that incorporates residual information into the foreground detection learning process. The main goal is to provide a method capable of generating an accurate foreground detection given a grayscale video. Experiments conducted on the Change Detection 2014 and on the private dataset PetrobrasROUTES from Petrobras support the effectiveness of the proposed approach concerning some state-of-the-art video segmentation techniques, with overall F-measures of 0.9535 and 0.9636 in the Change Detection 2014 and PetrobrasROUTES datasets, respectively. Such a result places the proposed technique amongst the top 3 state-of-the-art video segmentation methods, besides comprising approximately seven times less parameters than its top one counterpart.
Sampling Shape Contours Using Optimization over a Geometric Graph

Consider selecting points on a contour in the x-y plane. In shape analysis, this is frequently referred to as contour sampling. It is important to select the points such that they effectively represent the shape of the contour. Generally, the stroke order and number of strokes are informative for that purpose. Several effective methods exist for sampling contours drawn with a certain stroke order and number of strokes, such as the English alphabet or Arabic figures. However, many contours entail an uncertain stroke order and number of strokes, such as pictures of symbols, and little research has focused on methods for sampling such contours. This is because selecting the points in this case typically requires a large computational cost to check all the possible choices. In this paper, we present a sampling method that is useful regardless of whether the contours are drawn with a certain stroke order and number of strokes or not. Our sampling method thereby expands the application possibilities of contour processing. We formulate contour sampling as a discrete optimization problem that can be solved using a type of direct search. Based on a geometric graph whose vertices are the points and whose edges form rectangles, we construct an effective objective function for the problem. Using different shape datasets, we demonstrate that our sampling method is effective with respect to shape representation and retrieval.
The study of bubbles in bitcoin behavior

Bitcoin is an online communication system that facilitates the use of virtual currency, including electronic payments. This paper aims at analyzing the behavior of Bitcoin returns as a proposal for future currencies while making a comparison between Bitcoin and other conventional currencies. This paper uses quantitative approach to analyze the time series of Bitcoin and that of other conventional currencies during the period 2010–2018. It uses 1) a descriptive statistics for the weekly returns for Bitcoin which includes the mean, standard deviation, maximum value, minimum value, skewness, kurtosis, and Jarque-Bera normal distribution test statistics, and 2) duration dependence test on Bitcoin weekly returns by extracting the weekly returns for the Bitcoin that behave in irregular way of the general Bitcoin return level through autocorrelation regression, and taking the residuals for this regression as a time series for irregular returns.This paper has confirmed no empirical evidence for the existence of a speculative bubble in the Bitcoin values and returns. In addressing the question of whether Bitcoin can act as a reliable substitute for conventional currencies, the returns based analysis shows a huge difference between the behavior of Bitcoin returns from conventional currency returns when comparing both aspects of level and stability. The paper concluded that bitcoin is more an investment than a currency. This paper represents a significant contribution in the path of financial economics and financial risk management, and represents a contribution to the stability of the financial system around the world and mitigating financial crises.
Significance of Semantic Web – Pitfalls and Benefits of Use in the Common Judiciary

Abstract Semantic Webs increasingly allow the processing of information over the Internet according to their content and purpose. The widespread use of metadata standards has, however, caused interoperability problems between different systems. Enabling users to search for different languages requires translation resources to cross the language barrier can be a serious challenge. The presented tendency to use semantic webs is an opportunity for legislation, facilitating the process of developing and providing legal regulations and managing the progress of legislative work and implementation of procedures. The aim of this article is to review semantic web theory in relation to the pitfalls of defining and applying ontology to the machine-based understanding of content stored in the network. The scope of this article is a preliminary analysis of aspects of interdisciplinary cooperation in the metadata creation phase – indexed according to the conceptual analysis (ontology) of a given field. The area of research interest was also the benefits and pitfalls of using open source, XML and the Legal Semantic Web in the common judiciary.
Improved memory in recurrent neural networks with sequential non-normal dynamics

Training recurrent neural networks (RNNs) is a hard problem due to degeneracies in the optimization landscape, a problem also known as the vanishing/exploding gradients problem. Short of designing new RNN architectures, various methods that have been proposed for dealing with this problem usually boil down to orthogonalization of the recurrent dynamics, either at initialization or during the entire training period. The basic motivation behind these methods is that orthogonal transformations are isometries of the Euclidean space, hence they preserve (Euclidean) norms and effectively deal with the vanishing/exploding gradients problem. However, this idea ignores the crucial effects of non-linearity and noise. In the presence of a non-linearity, orthogonal transformations no longer preserve norms, suggesting that alternative transformations might be better suited to non-linear networks. Moreover, in the presence of noise, norm preservation itself ceases to be the ideal objective. A more sensible objective is maximizing the signal-to-noise ratio (SNR) of the propagated signal instead. Previous work has shown that in the linear case, recurrent networks that maximize the SNR display strongly non-normal, sequential dynamics and orthogonal networks are highly suboptimal by this measure. Motivated by this finding, we investigate the potential of non-normal RNNs, i.e. RNNs with a non-normal recurrent connectivity matrix, in sequential processing tasks. Our experimental results show that non-normal RNNs outperform their orthogonal counterparts in a diverse range of benchmarks. We also find evidence for increased non-normality and hidden chain-like feedforward structures in trained RNNs initialized with orthogonal recurrent connectivity matrices.
Constructing Digitized Chaotic Time Series with a Guaranteed Enhanced Period

When chaotic systems are realized in digital circuits, their chaotic behavior will degenerate into short periodic behavior. Short periodic behavior brings hidden dangers to the application of digitized chaotic systems. In this paper, an approach based on the introduction of additional parameters to counteract the short periodic behavior of digitized chaotic time series is discussed. We analyze the ways that perturbation sources are introduced in parameters and variables and prove that the period of digitized chaotic time series generated by a digitized logistic map is improved efficiently. Furthermore, experimental implementation shows that the digitized chaotic time series has great complexity, approximate entropy, and randomness, and the perturbed digitized logistic map can be used as a secure pseudorandom sequence generator for information encryption.
Digital Watermarking Image Compression Method Based on Symmetric Encryption Algorithms

A digital watermarking image compression method based on symmetrical encryption algorithm is proposed in this study. First, the original image and scrambled watermarking image are processed by wavelet transform, and then the watermarking image processed by the Arnold replacement method is transformed into a meaningless image in the time domain to achieve the effect of encryption. Watermarking is generated by embedding the watermarking image into the important coefficients of the wavelet transform. As an inverse process of watermarking embedding, watermarking extraction needs to be reconstructed by the wavelet transform. Finally, the watermarking is extracted from the inverse scrambled watermarking image, and a new symmetrically encrypted digital watermarking image is obtained. The compression method compresses the embedded digital watermarking image, so that the volume of the compressed watermarking image is greatly reduced when the visual difference is very small. The experimental results show that the watermarking image encrypted by this method not only has good transparency, but also has strong anti-brightness/contrast attack, anti-shearing, and anti-noise performance. When the volume of the compressed image is greatly reduced, the root mean square error and visual difference measurement of the watermarking image are very small.
A Comparative Study of Three Blockchain Emerging Technologies: Bitcoin, Ethereum and Hyperledger

A blockchain is a decentralized peer to peer data sharing mechanism that enables fast and trusted transactions between users on Internet by using cryptographic techniques. This new technology can be used for data control and doing transactions, secure payment and smart contracts, and much more. The aim of this paper is to explain the main concepts of blockchains. Also we give a comparative study of three well-known emerging technologies which are: Bitcoin, Ethereum and Hyperledger.
Weakly Supervised Clustering by Exploiting Unique Class Count

A weakly supervised learning based clustering framework is proposed in this paper. As the core of this framework, we introduce a novel multiple instance learning task based on a bag level label called unique class count ($ucc$), which is the number of unique classes among all instances inside the bag. In this task, no annotations on individual instances inside the bag are needed during training of the models. We mathematically prove that with a perfect $ucc$ classifier, perfect clustering of individual instances inside the bags is possible even when no annotations on individual instances are given during training. We have constructed a neural network based $ucc$ classifier and experimentally shown that the clustering performance of our framework with our weakly supervised $ucc$ classifier is comparable to that of fully supervised learning models where labels for all instances are known. Furthermore, we have tested the applicability of our framework to a real world task of semantic segmentation of breast cancer metastases in histological lymph node sections and shown that the performance of our weakly supervised framework is comparable to the performance of a fully supervised Unet model.
Strategies for an Autonomous Agent Playing the “Werewolf game” as a Stealth Werewolf

The "Werewolf game" is a popular multiplayer game wherein "villagers" try to figure out who is a "werewolf" through conversations.Werewolves usually pretend to be villagers.In this paper, we studied conversations in game logs in order to investigate how werewolves' cooperation contributed to increasing the winning percentage of the werewolves' team.As the number of "whispers" that are utterances via werewolves' private chat may be regarded as a measure of the werewolves' cooperation, we investigated the relation between the number of whispers and the winning percentage.As the result, we observed that the winning percentage of werewolves' team increased by 63 points at most when the number of whispers of at least two werewolves was more than 106.
Constructive Modeling of Lightning Activity in Thunderstorm Front

Using the tools of constructive-synthesizing modeling, constructors of fractal time series which determine the location, magnitude and rate of damping of lighting discharges are developed. Model video images of lightning in the thunderstorm front are formed according to constructors' implementation. The adequacy of the model is verified by comparison of the model video image with the same produced by NASA satellite.
Addressing the Implementation Challenges of Institutional Obligations and Reporting Requirements under the Nigerian Freedom of Information Act 2011

Access to information is critical to the realisation of an accountable government.The enactment of Nigeria's Freedom of Information Act in 2011 ushered in a fresh hope of imminent victory over the culture of secrecy that had always defined the Nigerian public service.The Act provides for public access to public record and information; obligates public institutions to maintain records and allow public access to information, among other things.It establishes procedures for the achievement of these purposes.This paper examines the institutional and reporting requirements provisions, which constitute part of the goal of the Act to enhance the availability and accessibility of public records and information.The paper finds that, lofty as these provisions are, there are gaps in the law.These gaps include ambiguity in some of the provisions and failure to provide requisite sanctions for noncompliance with certain provisions of the law.There are also the challenges of the pervasive culture of not keeping record by public institutions and lack of sufficient will on the part of the Attorney-General of the Federation and Minister of Justice, who is the chief implementing officer of the Act, to ensure the success of the goal of the Act.In order to address these challenges, the paper recommends, among others, that the identified gaps be filled, adequate sanctions be provided for noncompliance with relevant provisions of the law while the few sanctions already provided for in the Act should be effectively enforced.