Reviewerly/exHarmony huggingface dataset:
Refining Concepts by Machine Learning

In this paper we deal with machine learning methods and algorithms applied in learning simple concepts by their refining or explication.The method of refining a simple concept of an object O consists in discovering a molecular concept that defines the same or a very similar object to the object O. Typically, such a molecular concept is a professional definition of the object, for instance a biological definition according to taxonomy, or legal definition of roles, acts, etc.Our background theory is Transparent Intensional Logic (TIL).In TIL concepts are explicated as abstract procedures encoded by natural language terms.These procedures are defined as six kinds of TIL constructions.First, we briefly introduce the method of learning with a supervisor that is applied in our case.Then we describe the algorithm 'Framework' together with heuristic methods applied by it.The heuristics is based on a plausible supply of positive and negative (near-miss) examples by which learner's hypotheses are refined and adjusted.Given a positive example, the learner refines the hypothesis learnt so far, while a near-miss example triggers specialization.Our heuristic methods deal with the way refinement is applied, which includes also its special cases generalization and specialization.
Quantum entanglement in one-dimensional anyons

Anyons in one spatial dimension can be defined by correctly identifying the configuration space of indistinguishable particles and imposing Robin boundary conditions. This allows an interpolation between the bosonic and fermionic limits. In this paper, we study the quantum entanglement between two one-dimensional anyons on a real line as a function of their statistics.
Deep Stroke-Based Sketched Symbol Reconstruction and Segmentation

Hand-drawn objects usually consist of multiple semantically meaningful parts. In this article, we propose a neural network model that segments sketched symbols into stroke-level components. Our segmentation framework has two main elements: a fixed feature extractor and a multilayer perceptron (MLP) network that identifies a component based on the feature. As the feature extractor we utilize an encoder of a stroke-rnn, which is our newly proposed generative variational auto-encoder (VAE) model that reconstructs symbols on a stroke-by-stroke basis. Experiments show that a single encoder could be reused for segmenting multiple categories of sketched symbols with negligible effects on segmentation accuracies. Our segmentation scores surpass existing methodologies on an available small state-of-the-art dataset. Moreover, extensive evaluations on our newly annotated big dataset demonstrate that our framework obtains significantly better accuracies as compared to baseline models. We release the dataset to the community.
Deep unsupervised state representation learning with robotic priors: a robustness analysis

Our understanding of the world depends highly on our capacity to produce intuitive and simplified representations which can be easily used to solve problems. We reproduce this simplification process using a neural network to build a low dimensional state representation of the world from images acquired by a robot. As in Jonschkowski et al. 2015, we learn in an unsupervised way using prior knowledge about the world as loss functions called robotic priors and extend this approach to high dimension richer images to learn a 3D representation of the hand position of a robot from RGB images. We propose a quantitative evaluation metric of the learned representation that uses nearest neighbors in the state space and allows to assess its quality and show both the potential and limitations of robotic priors in realistic environments. We augment image size, add distractors and domain randomization, all crucial components to achieve transfer learning to real robots. Finally, we also contribute a new prior to improve the robustness of the representation. The applications of such low dimensional state representation range from easing reinforcement learning (RL) and knowledge transfer across tasks, to facilitating learning from raw data with more efficient and compact high level representations. The results show that the robotic prior approach is able to extract high level representation as the 3D position of an arm and organize it into a compact and coherent space of states in a challenging dataset.
Deep Independently Recurrent Neural Network (IndRNN)

Recurrent neural networks (RNNs) are known to be difficult to train due to the gradient vanishing and exploding problems and thus difficult to learn long-term patterns and construct deep networks. To address these problems, this paper proposes a new type of RNNs with the recurrent connection formulated as Hadamard product, referred to as independently recurrent neural network (IndRNN), where neurons in the same layer are independent of each other and connected across layers. Due to the better behaved gradient backpropagation, IndRNN with regulated recurrent weights effectively addresses the gradient vanishing and exploding problems and thus long-term dependencies can be learned. Moreover, an IndRNN can work with non-saturated activation functions such as ReLU (rectified linear unit) and be still trained robustly. Different deeper IndRNN architectures, including the basic stacked IndRNN, residual IndRNN and densely connected IndRNN, have been investigated, all of which can be much deeper than the existing RNNs. Furthermore, IndRNN reduces the computation at each time step and can be over 10 times faster than the commonly used Long short-term memory (LSTM). Experimental results have shown that the proposed IndRNN is able to process very long sequences and construct very deep networks. Better performance has been achieved on various tasks with IndRNNs compared with the traditional RNN, LSTM and the popular Transformer.
Genetic global optimization algorithms

Two new facts regarding genetic extremum search algorithms are presented. The first one, which is based on the use of a modification of the simulated annealing method, makes it possible to distinguish close extrema at the initial stage and indicates the strategy of behavior in the case of many equal extrema. The second fact (a lemma and its consequence) points out new methods for implementing the evolution of the covariance matrix. Numerical examples are given.
Success Factors for Cyber Security Operation Center (SOC) Establishment

The boundless in the digital world is one of the terms used to describe the present state where everything depends mostly on the use of technology.The increased dependency on these technology services has indirectly increased the risk of threats and cyber-attacks.One of the popular solutions to defend against these threats is by implementing the Cyber Security Operation Center (SOC) to monitor, track and handle the cyber incidents.However, there are a number of factors that affect the success of the SOC.Therefore, this paper aims to highlight the importance of the human, process and technology factors towards the establishment of SOC.A comparison of the previous establishment of SOC from the literature is made.The inputs from the literature come from the journal, proceeding, report starting from the year 2011 until 2018.From the result of the comparison, it presents the requirement of human, process, and technology to make sure the SOC work efficiently to defend against the cyber-attack.
Performance Improvement of Underwater Continuous-Variable Quantum Key Distribution via Photon Subtraction

Considering the ocean water’s optical attenuation is significantly larger than that of Fiber Channel, we propose an approach to enhance the security of underwater continuous-variable quantum key distribution (CVQKD). In particular, the photon subtraction operation is performed at the emitter to enhance quantum entanglement, thereby improving the underwater transmission performance of the CVQKD. Simulation results show that the photon subtraction operation can effectively improve the performance of CVQKD in terms of underwater transmission distance. We also compare the performance of the proposed protocol in different water qualities, which shows the advantage of our protocol against water deterioration. Therefore, we provide a suitable scheme for establishing secure communication between submarine and submarine vehicles.
Towards Designing Conversational Agent Systems

Conversation is interactive communication between two and more people which enhances knowledge among these people. It is key to exchange thoughts and ideas while listening to each other. Based on this idea the advances in artificial intelligence started to develop technologies in which computer can communicate with human in a more natural way. A computer program which acts as an automated conversation agent is also called as a Chatbot. Chatbots are useful in many different applications like health care, education, financial marketing, banking, agriculture, etc. This paper presents a survey on different issues in designing conversational agents. The paper discusses the types and applications of Chatbot; it lists research challenges while designing and implementing these systems. The paper presents a study and comparison of different techniques like NLP (Natural Language Processing), Deep Learning and Neural Networks used for designing these systems. The paper also presents various datasets being used by popular Chatbots in the industry. The paper ends by summarizing scope for future work in this domain.
Data Science: Technologies for Better Software

Data science is mandatory in today's business to capitalize on achievements and assets. This specifically holds for modern software development, where data science facilitates analyzing product, process, and usage and thus managing evolution and performance. With the convergence of embedded and IT domains, such as the Internet of Things (IoT) and automotive systems, software systems are becoming more complex. Complexity has two faces. On one hand it means more functionality and fluid delivery models, thus offering markets more value, such as the ability to deliver a single-customer focus. Complexity, however, also means the growth of technical debt, which slows productivity and lowers quality. As software engineering generates ever larger and more varied data sets, such as feature usage, code analysis, test coverage, error logs, and maintenance data, companies face the challenge of unlocking the value of that data.
A Role and Trust Access Control Model for Preserving Privacy and Image Anonymization in Social Networks

Over the last decade Online Social Networks (OSN) privacy has been thoroughly studied in many aspects. Some of these privacy related aspects are trust and credibility involving the OSN user-data conveyed by different relationships in the network. One of OSN major problems is that users expose their information in a manner thought to be relatively private, or even partially public, to unknown and possibly unwanted entities, such as adversaries, social bots, fake users, spammers or data-harvesters. Preventing this information leakage is the target of many OSN privacy models, such as Access Control, Relationship based models, Trust based models and many others. In this paper we suggest a new Role and Trust based Access Control model, denoted here as RTBAC, in which roles, that manifest different permissions, are assigned to the users connected to the Ego-node (the user sharing the information), and in addition, every user is evaluated trust wise by several criteria, such as total number of friends, age of user account, and friendship duration. An interesting extension of the model of image anonymization is also given, where a user that has a certain role with a proper permission can access a partial instance of the data, if a sufficient trust level is not achieved. These role and trust assessments provide more precise and viable information sharing decisions and enable better privacy control in the social network.
Transformed-Domain Robust Multiple-Exposure Blending With Huber Loss

Pixel-domain weighting methods for multiple-exposure blending can efficiently remove noise and under-/over-exposed pixels simultaneously in high dynamic range (HDR) image generation.Various types of noise such as non-Gaussian noise, e.g., Poisson, impulse noise, and pixel saturation, are often superimposed to multiple-exposure images taken with a high ISO setting in a low-light condition.Because almost all existing methods assume Gaussian noise, these methods cannot sufficiently reduce these types of noise.To achieve high-quality HDR image generation in such difficult conditions, we propose a novel multiple-exposure blending method in which image blending is performed in a wavelet domain so as to enhance the denoising performance.In addition, the Huber loss function is utilized as a fidelity measure in blending to make the method robust against outliers.We also introduce an efficient algorithm based on a primal-dual splitting method for solving our optimization problem.The experimental results demonstrate the advantages of the proposed method over several conventional methods.
Exploitation Techniques and Defenses for Data-Oriented Attacks

Data-oriented attacks manipulate non-control data to alter a program's benign behavior without violating its control-flow integrity. It has been shown that such attacks can cause significant damage even in the presence of control-flow defense mechanisms. However, these threats have not been adequately addressed. In this systematization of knowledge (SoK) paper, we first map data-oriented exploits, including Data-Oriented Programming (DOP) and Block-Oriented Programming attacks, to their assumptions/requirements and attack capabilities. We also compare known defenses against these attacks, in terms of approach, detection capabilities, overhead, and compatibility. Then we discuss the possible frequency anomalies of data-oriented attacks, especially the frequency anomalies of DOP attacks with experimental proofs. It is generally believed that control flows may not be useful for data-oriented security. How-ever, the frequency anomalies show that data-oriented attacks (especially DOP attacks) may generate side-effects on control-flow behavior in multiple dimensions. In the end, we discuss challenges for building deployable data-oriented defenses and open research questions.
Key Classes in Object-Oriented Systems: Detection and Assessment

Inadequate documentation of software design has been known to be a barrier for developers. Interestingly, several relevant object-oriented systems have their design documented using key classes, which are meant to represent key concepts of the systems. In order to fill the gap of under-documented design, we present Keecle, an approach for detecting a predefined number of key classes in a semi-automatic way. The main challenge is to reduce the space of potentially thousands of classes to just a few representatives of the main concepts of a system, while maintaining high precision. The approach is evaluated with 13 systems in order to assess its correctness. The ground-truth is obtained either from the original documentation, or from third-party, or from the respective developers. The results were analyzed in terms of precision and recall, and have shown to be superior compared to the state-of-the-art approach. In order to evaluate if key classes are more critical from the design point of view, we evaluated whether they are associated with cohesion and coupling metrics. We found that although key classes, in general, are critical from the point of view of design, there are other classes that are also critical, suggesting that being aware of key classes encompass information not available in structural metrics, and could be useful as a additional facet for design assessment.
Bagged ensemble of fuzzy classifiers and feature selection for handwritten signature verification

Handwritten signature verification is an important research area in the field of person authentication and biometric identification. There are two known methods for handwriting signature verification: if it is possible to digitize the speed of pen movement, then verification is said to be on-line or dynamic; otherwise, when only an image of handwriting is available, verification is said to be off-line or static. It is proved that when using dynamic verification, a greater accuracy is achieved than when using static verification. In the present work, the amplitudes, frequencies, and phases of the harmonics extracted from the signature signals of the X and Y coordinates of the pen movement using a discrete Fourier transform are used as characteristics of the signature. All signals are pre-processed in advance, including the elimination of gaps, the elimination of the angle of inclination, the normalization of position and scaling. A fuzzy classifier is proposed as a signature verification tool based on the features obtained. The work examines the effectiveness of this tool in the ensemble, as well as using a procedure for feature selection. To build an ensemble of classifiers, a well-known bagging method is used, and the feature selection is based on the determination of mutual information between a feature and a class of an object. Experiments on signature verification on the SVC2004 data set with the construction of a fuzzy classifier and ensembles of three, five, seven and nine fuzzy classifiers were conducted. Experiments were carried out both with the use of the feature selection procedure and without selection. The efficiency of the classifiers constructed is compared with each other and with known analogues: decision trees, support vector machines, discriminant analysis and k-nearest neighbors.
Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling

Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems. In particular, there has been a growing interest in adapting structure-aware neural methods to work with the underlying graph representations of logical expressions. While more effective than character and token-level approaches, graph-based methods have often made representational trade-offs that limited their ability to capture key structural properties of their inputs. In this work we propose a novel approach for embedding logical formulae that is designed to overcome the representational limitations of prior approaches. Our architecture works for logics of different expressivity; e.g., first-order and higher-order logic. We evaluate our approach on two standard datasets and show that the proposed architecture achieves state-of-the-art performance on both premise selection and proof step classification.
Extracting unambiguous information from a single qubit by sequential observers

In a recent paper [Phys. Rev. Lett. 111, 100501 (2013)], a scheme was proposed where subsequent observers can extract unambiguous information about the initial state of a qubit, with finite joint probability of success. Here, we generalize the problem for arbitrary preparation probabilities (arbitrary priors). We discuss two different schemes: one where only the joint probability of success is maximized and another where, in addition, the joint probability of failure is also minimized. We also derive the mutual information for these schemes and show that there are some parameter regions for the scheme without minimizing the joint failure probability where, even though the joint success probability is maximum, no information is actually transmitted by Alice.
On the Value of Structural Health Information

Structural Health Information (SHI) may be provided by various sources such as e.g. structural health monitoring, inspections, testing and digital technologies and networks as by the Industry Internet of Things (IIOT). SHI holds a high potential for industrial, life safety and sustainability value and facilitates an optimal and intelligent life cycle management of infrastructure and machinery systems. The paper documents the utility, decision and reliability theoretical basis for the quantification of the value of SHI â€“ as explicated by the scientific networking project COST Action TU1402 â€“ and the basics of detection and Bayes risk theory, which have shaped the field of structural health monitoring. Both approaches are illustrated with a generic analysis. The value assessment of different SHI types is described and the boundaries for achieving a high industrial and societal value of SHI are explicated.
Online Robustness Training for Deep Reinforcement Learning.

In deep reinforcement learning (RL), adversarial attacks can trick an agent into unwanted states and disrupt training. We propose a system called Robust Student-DQN (RS-DQN), which permits online robustness training alongside Q networks, while preserving competitive performance. We show that RS-DQN can be combined with (i) state-of-the-art adversarial training and (ii) provably robust training to obtain an agent that is resilient to strong attacks during training and evaluation.
Human face detection and recognition using contour generation and matching algorithm

Face detection, monitoring of human face and recognition is a high-quality venture in safety, surveillance and regulation enforcement systems with greater accuracy. There are many motives at the back of in this trouble. Which include function of the human face; pose version, negative environmental conditions, terrible lightning and picture tilt etc,. So we must locate a few feasible solutions to clear up those issues by means of imposing a brand new system. Our proposed venture might be improving the issues on Face detection, monitoring of human face and recognition issues.
ERASER: A Benchmark to Evaluate Rationalized NLP Models

State-of-the-art models in NLP are now predominantly based on deep neural networks that are opaque in terms of how they come to make predictions. This limitation has increased interest in designing more interpretable deep models for NLP that reveal the `reasoning' behind model outputs. But work in this direction has been conducted on different datasets and tasks with correspondingly unique aims and metrics; this makes it difficult to track progress. We propose the Evaluating Rationales And Simple English Reasoning (ERASER) benchmark to advance research on interpretable models in NLP. This benchmark comprises multiple datasets and tasks for which human annotations of (supporting evidence) have been collected. We propose several metrics that aim to capture how well the rationales provided by models align with human rationales, and also how faithful these rationales are (i.e., the degree to which provided rationales influenced the corresponding predictions). Our hope is that releasing this benchmark facilitates progress on designing more interpretable NLP systems. The benchmark, code, and documentation are available at this https URL