Reviewerly/exHarmony huggingface dataset:
Experiments regarding implementation of a virtual training environment for automotive industry

Training operators for activities ran on automotive manufacturing lines is a long, thorough and laborious process that aims to generate a correct collection of information and actions as well as adequate staff training. Schooling operators has always been costly; therefore, developing an application will facilitate learning the necessary skills. The time until an operator will become productive will be significantly shorter and the exposure to certain risks will also be removed. Learning process can be monitorized for each operator, keeping track of his progress. This allows to estimate completion time of the training and to express an overview on future work activities. At the same time, the operator will acquire skills faster, which normally require long practice and a high level of dexterity. Thus, the authors' proposal is to develop an application for the operators training, within the manufacturing lines, aiming to experiment, in a virtual environment the simulation and the orderly assembly of objects as a stack form. Also, it will be necessary to develop a glove prototype that will have the ability to manipulate and control objects in a virtual environment. The end of this paper will reveal reasons for investing and implementing a new way of teaching, making use of such an application.
Classification of the relative positions between a small ellipsoid and an elliptic paraboloid

We classify all the relative positions between an ellipsoid and an elliptic paraboloid when the ellipsoid is small in comparison with the paraboloid (small meaning that the two surfaces cannot be tangent at two points simultaneously when one is moved with respect to the other). This provides an easy way to detect contact between the two surfaces by a direct analysis of the coefficients of a fourth degree polynomial.
Improving Prediction Efficacy Through Abnormality Detection and Data Preprocessing

Abnormal testing data can severely reduce model performance if not processed properly.In this paper, we propose a preprocessing system to handle different types of commonly seen abnormal testing data.The system consists of an aberrant data detector and an aberrant data corrector.The aberrant data detector is responsible for classifying the type of incoming data.Based on the data type, the aberrant data corrector will take different actions to amend testing data.Users can then apply their preferred prediction methods on the corrected testing data.Specifically, corrupted and adversarial images are used as examples of abnormal data.We show that corrupted data can be reconstructed through a Gaussian locally linear mappings method, and the prediction performance of adversarial samples can be improved by using the nearest neighbors as a surrogate.We compare the proposed aberrant data detector and corrector with existing and well-recognized alternatives.These approaches are published individually and do not put two components together as a pre-processing system.The numerical outcomes show that our proposed components, standing alone, are competitive.The proposed system is a generic method that can be applied to different downstream predictive models.We use three existing prediction methods to illustrate the general usage of the proposed system and its capability of improving prediction efficacy.
ETA-RULES IN MARTIN-LÖF TYPE THEORY

Abstract The eta rule for a set A says that an arbitrary element of A is judgementally identical to an element of constructor form. Eta rules are not part of what may be called canonical Martin-Löf type theory. They are, however, justified by the meaning explanations, and a higher order eta rule is part of that type theory. The main aim of this article is to clarify this somewhat puzzling situation. It will be argued that lower order eta rules do not, whereas the higher order eta rule does, accord with the understanding of judgemental identity as definitional identity. A subsidiary aim is to clarify precisely what an eta rule is. This will involve showing how such rules relate to various other notions of type theory, proof theory, and category theory.
Analysis of Hannan consistent selection for Monte Carlo tree search in simultaneous move games

Hannan consistency, or no external regret, is a key concept for learning in games. An action selection algorithm is Hannan consistent (HC) if its performance is eventually as good as selecting the best fixed action in hindsight. If both players in a zero-sum normal form game use a Hannan consistent algorithm, their average behavior converges to a Nash equilibrium of the game. A similar result is known about extensive form games, but the played strategies need to be Hannan consistent with respect to the counterfactual values, which are often difficult to obtain. We study zero-sum extensive form games with simultaneous moves, but otherwise perfect information. These games generalize normal form games and they are a special case of extensive form games. We study whether applying HC algorithms in each decision point of these games directly to the observed payoffs leads to convergence to a Nash equilibrium. This learning process corresponds to a class of Monte Carlo Tree Search algorithms, which are popular for playing simultaneous-move games but do not have any known performance guarantees. We show that using HC algorithms directly on the observed payoffs is not sufficient to guarantee the convergence. With an additional averaging over joint actions, the convergence is guaranteed, but empirically slower. We further define an additional property of HC algorithms, which is sufficient to guarantee the convergence without the averaging and we empirically show that commonly used HC algorithms have this property.
Blockchain-based risk mitigation for invoice financing

The market for invoice financing has been steadily growing in the last few years and has been the third financing market in size in 2016. Most solutions in this field are based on private platforms and even the new proposals based on blockchain are mostly adopting a private, permissioned blockchain. In this paper, we propose an idea based on a public blockchain that allows both fully open and group-restricted auctioning of invoices. Furthermore, our proposal introduces a reputation system that is based on the past behavior of entities, as it is photographed by the public blockchain, to allow insurance companies modulate the cost of the insurance contracts they offer. This combination guarantees the complete transparency and tamperproof-ness of a public blockchain, while it allows reducing insurance costs and fraud possibilities.
Enhancing Engagement in Token-Curated Registries via an Inflationary Mechanism

Token Curated Registries (TCR) are decentralized recommendation systems that can be implemented using Blockchain smart contracts. They allow participants to vote for or against adding items to a list through a process that involves staking tokens intrinsic to the registry, with winners receiving the staked tokens for each vote. A TCR aims to provide incentives to create a well-curated list. In this work, we consider a challenge for these systems - incentivizing token-holders to actually engage and participate in the voting process. We propose a novel token-inflation mechanism for enhancing engagement, whereby only voting participants see their token supply increased by a predefined multiple after each round of voting. To evaluate this proposal, we propose a simple 4-class model of voters that captures all possible combinations of two key dimenions: whether they are engaged (likely to vote at all for a given item) or disengaged, and whether they are informed (likely to vote in a way that increases the quality of the list) or uninformed, and a simple metric to evaluate the quality of the list as a function of the vote outcomes. We conduct simulations using this model of voters and show that implementing token-inflation results in greater wealth accumulation for engaged voters. In particular, when the number of informed voters is sufficiently high, our simulations show that voters that are both informed and engaged see the greatest benefits from participating in the registry when our proposed token-inflation mechanism is employed.
Performance optimization of decoy-state BB84- and MDI- QKD protocol and their key integrating application strategy for power dispatching

Quantum cryptography is an effective means to improve the information security level of power dispatch data networks. From the perspective of quantum key supply and demand balance, this paper adopts the local search algorithm to implement full parameter optimization (including both the pulse intensities and all the probability parameters) for both decoy-state biased-basis based BB84 and measurement-device-independent (MDI) quantum key distribution (QKD) protocols, and compares their key application performance. Then, the BB84 and MDI quantum key integrated application strategy is designed for multi-service power dispatching systems, and the tests of jointly dynamic adjustment to the multi-node key-update frequency and application mode are carried out. The test results show that the proposed integrated application strategy not only fully considers the differences of information security requirements of various services, but also maximizes the actual security of power dispatch data transmission.
Geochronological and geochemical studies of adakites from Tethyan Belt, Western Pakistan: A clue to geodynamics and Cu-Au mineralization

Large porphyry Cu-Au deposits are associated with Early Miocene intrusive rocks in Tethyan belt, discovered along Chagai magmatic arc in Western Pakistan, adjacent to Southeast Iran. Two types of rocks were discriminated as granodiorite and monzodiorite from Saindak area. The granodiorites are associated with regional large Cu-Au mineralization, while the monzodiorites are mostly ore-barren. LA-ICP-MS U-Pb dating yielded weighted mean 206Pb/238U ages of 22.16–24.2 Ma for granodiorites and ca. 22.6 Ma for a monzodiorite. These intrusive rocks in the Saindak area are all calc-alkaline rocks, with enriched LILEs and depleted HFSEs, and without Eu negative anomalies. The felsic granodiorites are characterized by high Sr contents and Sr/Y ratios, with intermediate to high (La/Yb)N ratios that identify them as typical high-silica adakites. In contrast, the basaltic-andesitic monzodiorites are just normal arc-related rocks, showing less fractionated REE patterns than the granodiorites, with systematically lower LREE and higher MREE and HREE. Low K2O/Na2O ratios and decoupled Sr/Y-(La/Yb)N ratios indicate the characteristics of slab-derived adakites for the granodiorites, most likely originated through partial melting of the subducted Neotethys oceanic crust beneath Eurasian continent followed by subsequent mantle interaction. The almost simultaneously melting of subcontinental lithospheric mantle induced by dehydration of Neotethys plate gave rise to the formation of the barren monzodiorites. These intrusive rocks in the Saindak area are characterized by similar Sr-Nd-Pb isotopes with high 87Sr/86Sri ratios, slightly negative εNd(t) values and radiogenic Pb isotopes, plotting in the field between the MORB and EM-II mantle endmembers or the Average Cadomian Lower Crust, suggesting subducting sediments or old continental crustal materials have contributed into the compositions of these rocks by source enrichment or crustal contamination. The slab-melting derived adakite is favourable for regional massive Cu-Au mineralization in the Saindak area.
Applying Encryption Algorithm on Text Steganography Based on Number System

Nowadays as there is huge transfer of information throughout the world so there are adequate chances of leakage of information. Standard algorithms fail to protect data/information. This is the primary purpose for developing a number system-based technique for data/information hiding. This is a combination of art and science where the original data is hidden, which appears as nonexistent, so keeping it out of sight. All the characters, numbers can be hidden by using a novel method, and so it is applicable to any language. Any message can be hidden by transmitting it in the encoded form over any electronic media. This work is not a comparison or improvement of the previous works but it is a different approach to text Steganography to hide the information. A superb blend of number, word to hide information. For developing the application, this paper could be very useful for any defence, force to transmit highly secret data securely.
Computational Hermeneutics: An Integrated Approach for the Logical Analysis of Natural-Language Arguments

We utilize higher order automated deduction technologies for the logical analysis of natural-language arguments. Our approach, termed computational hermeneutics, is grounded on recent progress in the area of automated theorem proving for classical and nonclassical higher order logics, and it integrates techniques from argumentation theory. It has been inspired by ideas in the philosophy of language, especially semantic holism and Donald Davidson’s radical interpretation; a systematic approach to interpretation that does justice to the inherent circularity of understanding: the whole is understood compositionally on the basis of its parts, while each part is understood only in the context of the whole (hermeneutic circle). Computational hermeneutics is a holistic, iterative approach where we evaluate the adequacy of some candidate formalization of a sentence by computing the logical validity of (i) the whole argument it appears in and (ii) the dialectic role the argument plays in some piece of discourse.
Rough-Set Based Hotspot Detection in Spatial Data

A special type of cluster is called hotspots in the sense that objects in the hotspot are more active as compared to all others (appearance, density, etc.). The object in a general cluster has a similarity which is less than the object in the hotspot. In spatial data mining hotspots detection is a process of identifying the region where events are more likely to happen than the others. Hotspot analysis is mainly used in the analysis of health and crime data. In this paper, the health care data set is used to find the Hotspot of the health condition in India. The clustering algorithm is used to find the hotspot. Two clustering algorithm K-medoid and Rough K-medoid are implemented to find the cluster. K-medoid is used to find the spatial cluster, Rough K-medoid finds the cluster by removing boundary points and in this way find cluster which is denser. Granules are created on the clusters created using K-medoid and Rough K-medoid and point lying in each granule is counted. Granule containing points above a particular threshold is considered as a potential hotspot. To find the footprint of the hotspot convex hull is created on each detected hotspot. Also in this paper hotspot and footprint is defined mathematically.
A Model for Visual and Intuitive Crime Investigation Based on Associative Rule Mining Technique (VICIBARM): A Case Study of Kenya

Crime has been part of our society ever since the concept of laws was first approved. There is an increased concern at governance level due to escalating levels of crime both internationally as well as locally in Kenya. Large amounts of raw data are recorded in the occurrence books of many police stations. This data can be intelligently analyzed and visualized to assist law enforcement agencies in understanding these crimes and taking steps to ensure they remain ahead of the criminal elements. Data mining and visual analysis can be used for advanced crime analysis. In this article, the researcher suggests the use of Associative Rule Mining to come up with a model suitable for crime analysis and prevention using Apriori algorithm to represent mutual implications among criminal occurrences. This model is aimed at providing better understanding of crime patterns and helping law enforcement plan on preventive future crime policy.
Eigenvalues, absolute continuity and localizations for periodic unitary transition operators

The localization phenomenon for periodic unitary transition operators on a Hilbert space consisting of square summable functions on an integer lattice with values in a finite-dimensional Hilbert space, which is a generalization of the discrete-time quantum walks with constant coin matrices, is discussed. It is proved that a periodic unitary transition operator has an eigenvalue if and only if the corresponding unitary matrix-valued function on a torus has an eigenvalue which does not depend on the points on the torus. It is also proved that the continuous spectrum of a periodic unitary transition operator is absolutely continuous. As a result, it is shown that the localization happens if and only if there exists an eigenvalue, and when there exists only one eigenvalue, the long-time limit of transition probabilities coincides with the point-wise norm of the projection of the initial state to the eigenspace. The results can be applied to certain unitary operators on a Hilbert space on a covering graph, called a topological crystal, over a finite graph. An analytic perturbation theory for matrices in several complex variables is employed to show the result about absolute continuity for periodic unitary transition operators.
Towards easier and faster sequence labeling for natural language processing: A search-based probabilistic online learning framework (SAPO)

There are two major approaches for sequence labeling. One is the probabilistic gradient-based methods such as conditional random fields (CRF) and neural networks (e.g., RNN), which have high accuracy but drawbacks: slow training, and no support of search-based optimization (which is important in many cases). The other is the search-based learning methods such as structured perceptron and margin infused relaxed algorithm (MIRA), which have fast training but also drawbacks: low accuracy, no probabilistic information, and non-convergence in real-world tasks. We propose a novel and “easy” solution, a search-based probabilistic online learning method, to address most of those issues. The method is “easy”, because the optimization algorithm at the training stage is as simple as the decoding algorithm at the test stage. This method searches the output candidates, derives probabilities, and conducts efficient online learning. We show that this method with fast training and theoretical guarantee of convergence, which is easy to implement, can support search-based optimization and obtain top accuracy. Experiments on well-known tasks show that our method has better accuracy than CRF and BiLSTM.1
Parametric t-Distributed Stochastic Exemplar-Centered Embedding

Parametric embedding methods such as parametric t-distributed Stochastic Neighbor Embedding (pt-SNE) enables out-of-sample data visualization without further computationally expensive optimization or approximation. However, pt-SNE favors small mini-batches to train a deep neural network but large mini-batches to approximate its cost function involving all pairwise data point comparisons, and thus has difficulty in finding a balance. To resolve the conflicts, we present parametric t-distributed stochastic exemplar-centered embedding. Our strategy learns embedding parameters by comparing training data only with precomputed exemplars to indirectly preserve local neighborhoods, resulting in a cost function with significantly reduced computational and memory complexity. Moreover, we propose a shallow embedding network with high-order feature interactions for data visualization, which is much easier to tune but produces comparable performance in contrast to a deep feedforward neural network employed by pt-SNE. We empirically demonstrate, using several benchmark datasets, that our proposed method significantly outperforms pt-SNE in terms of robustness, visual effects, and quantitative evaluations.
GraphMP: I/O-Efficient Big Graph Analytics on a Single Commodity Machine

Recent studies showed that single-machine graph processing systems can be as highly competitive as cluster-based approaches on large-scale problems. While several out-of-core graph processing systems and computation models have been proposed, the high disk I/O overhead could significantly reduce performance in many practical cases. In this paper, we propose GraphMP to tackle big graph analytics on a single machine. GraphMP achieves low disk I/O overhead with three techniques. First, we design a vertex-centric sliding window (VSW) computation model to avoid reading and writing vertices on disk. Second, we propose a selective scheduling method to skip loading and processing unnecessary edge shards on disk. Third, we use a compressed edge cache mechanism to fully utilize the available memory of a machine to reduce the amount of disk accesses for edges. Extensive evaluations have shown that GraphMP could outperform existing single-machine out-of-core systems such as GraphChi, X-Stream and GridGraph by up to 30, and can be as highly competitive as distributed graph engines like Pregel+, PowerGraph and Chaos.
Proof Theory for Positive Logic with Weak Negation

Proof-theoretic methods are developed for subsystems of Johansson’s logic obtained by extending the positive fragment of intuitionistic logic with weak negations. These methods are exploited to establish properties of the logical systems. In particular, cut-free complete sequent calculi are introduced and used to provide a proof of the fact that the systems satisfy the Craig interpolation property. Alternative versions of the calculi are later obtained by means of an appropriate loop-checking history mechanism. Termination of the new calculi is proved, and used to conclude that the considered logical systems are PSPACE-complete.
Norms and Extended Argumentation Frameworks

This paper discusses reasoning about norms using Extended Argumentation Frameworks. We argue that norms emerge from a process of value based practical reasoning. We model practical reasoning using Action Based Alternating Transition Systems with Values, with the resulting arguments evaluated in an Extended Argumentation Framework, which permits arguments to attack attacks as well as arguments, and so provides a natural way of representing exceptions. Following our consideration of how norms are justified, we consider their application. Our representation of norms in EAFs enables us to offer an accessible account of permissions and exceptions. We illustrate our approach with an example relating to some US Supreme Court Fourth Amendment cases.
Adapting Image Super-Resolution State-Of-The-Arts and Learning Multi-Model Ensemble for Video Super-Resolution

Recently, image super-resolution has been widely studied and achieved significant progress by leveraging the power of deep convolutional neural networks. However, there has been limited advancement in video super-resolution (VSR) due to the complex temporal patterns in videos. In this paper, we investigate how to adapt state-of-the-art methods of image super-resolution for video super-resolution. The proposed adapting method is straightforward. The information among successive frames is well exploited, while the overhead on the original image super-resolution method is negligible. Furthermore, we propose an learning-based method to ensemble the outputs from multiple super-resolution models. Our methods show superior performance and rank second in the NTIRE2019 Video Super-Resolution Challenge Track 1.