Reviewerly/exHarmony huggingface dataset:
Artificial General Intelligence and Contract

Abstract The aim of this article is to inquire whether contract law can operate in a state of affairs in which artificial general intelligence (AGI) exists and has the cognitive abilities to interact with humans to exchange promises or otherwise engage in the sorts of exchanges typically governed by contract law. AGI is a long way off, but its emergence may be sudden and come in the lifetimes of some people who are alive today. How might contract law adapt to a situation in which at least one of the contract parties could, from the standpoint of a capacity to engage in promising and exchange, be an AGI? This is not a situation in which artificial intelligence (AI) operates as an agent of a human or a firm, a frequent occurrence right now. Rather, the question is whether an AGI could constitute a principal—a contract party on its own. Contract law is a good place to start a discussion about adapting the law for an AGI future because it already incorporates a version of what is known as weak AI in its objective standard for contract formation and interpretation. Contract law, in some limited sense, takes on issues of relevance from the philosophy of the mind. AGI holds the potential to transform a solution to an epistemological problem of how to prove a contract exists into solution to an ontological problem about the capacity to contract. An objection might be that contract law presupposes the existence of a person who the law recognizes as possessing the capacity to contract. Contract law itself may not be able to answer the prior question of legally recognized personhood. The answer will be to focus on how AGI cognitive architecture could be designed for compatibility for human interaction. This article focuses on that question as well.
Reinforcement Learning When All Actions are Not Always Available

The Markov decision process (MDP) formulation used to model many real-world sequential decision making problems does not efficiently capture the setting where the set of available decisions (actions) at each time step is stochastic. Recently, the stochastic action set Markov decision process (SAS-MDP) formulation has been proposed, which better captures the concept of a stochastic action set. In this paper we argue that existing RL algorithms for SAS-MDPs can suffer from potential divergence issues, and present new policy gradient algorithms for SAS-MDPs that incorporate variance reduction techniques unique to this setting, and provide conditions for their convergence. We conclude with experiments that demonstrate the practicality of our approaches on tasks inspired by real-life use cases wherein the action set is stochastic.
Role of p53 and Ki-67 in prognostication of carcinoma breast

Role of p53 and Ki-67 in prognostication of carcinoma breast - IJPO- Print ISSN No: - 2394-6784 Online ISSN No:- 2394-6792 Article DOI No:- 10.18231/j.ijpo.2019.051, Indian Journal of Pathology and Oncology-Indian J Pathol Oncol
A computational Bayesian approach for estimating density functions based on noise-multiplied data

In this big data era, an enormous amount of personal and company information can be easily collected by third parties. Sharing the data with the public and allowing data users to access the data for data mining often bring many benefits to the public. However, sharing the microdata with the public usually causes the issue of data privacy. Protecting data privacy through noise-multiplied data is one of the approaches studied in the literature. This paper introduces the B-M L2014 Approach for estimating the density function of the original data based on noise-multiplied microdata. This paper shows applications of the B-M L2014 Approach and demonstrates that the statistical information of the original data can be retrieved from their noise-multiplied data reasonably while the disclosure risk is under control. The B-M L2014 Approach provides a new data mining technique for big data when data privacy is concerned.
Hybrid Post-Quantum Key Encapsulation Methods (PQ KEM) for Transport Layer Security 1.2 (TLS)

Hybrid key exchange refers to executing two independent key exchangesand feeding the two resulting shared secrets into a Pseudo RandomFunction (PRF), with the goal of deriving a secret which is as secureas the stronger of the two key exchanges. This document describes newhybrid key exchange schemes for the Transport Layer Security 1.2 (TLS)protocol. The key exchange schemes are based on combining EllipticCurve Diffie-Hellman (ECDH) with a post-quantum key encapsulationmethod (PQ KEM) using the existing TLS PRF. In particular, thisdocument specifies the use of the Bit Flipping Key Exchange (BIKE) andSupersingular Isogeny Key Exchange (SIKE) schemes in combination withECDHE as a hybrid key agreement in a TLS 1.2 handshake.
Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation

Generating long and semantic-coherent reports to describe medical images poses great challenges towards bridging visual and linguistic modalities, incorporating medical domain knowledge, and generating realistic and accurate descriptions. We propose a novel Knowledge-driven Encode, Retrieve, Paraphrase (KERP) approach which reconciles traditional knowledge- and retrieval-based methods with modern learning-based methods for accurate and robust medical report generation. Specifically, KERP decomposes medical report generation into explicit medical abnormality graph learning and subsequent natural language modeling. KERP first employs an Encode module that transforms visual features into a structured abnormality graph by incorporating prior medical knowledge; then a Retrieve module that retrieves text templates based on the detected abnormalities; and lastly, a Paraphrase module that rewrites the templates according to specific cases. The core of KERP is a proposed generic implementation unit---Graph Transformer (GTR) that dynamically transforms high-level semantics between graph-structured data of multiple domains such as knowledge graphs, images and sequences. Experiments show that the proposed approach generates structured and robust reports supported with accurate abnormality description and explainable attentive regions, achieving the state-of-the-art results on two medical report benchmarks, with the best medical abnormality and disease classification accuracy and improved human evaluation performance.
Fractional convolution and nonlinear operations applied to the image encryption

Abstract We use the fractional convolution, nonlinear operations and random phase masks (RPMs) in order to encrypt images. The definition used in this paper of the fractional convolution is based on the fractional Fourier transform (FrFT). The amplitude and phase truncation are nonlinear operations that allow to select a specific information of a complex-valued image. The encryption and decryption systems are based on the double random phase encoding (DRPE). At the beginning of the encryption process, the image to encrypt is encoded in phase. The rest of the encryption and decryption process use two RPMs, the fractional convolution and the amplitude and phase truncation operations in a sequential way in order to get the encrypted and decrypted image. The amplitude and phase truncation and the phase encoding are nonlinear operations that improving the security of the encrypted image, because the obtained key space of the proposed encryption process is very larger. The fractional order of the FrFT introduces a new key for the security system and the encrypted image of the proposed encryption system is a real-valued image. The encryption-decryption system has five security keys. All these five keys with their correct values are necessary in the decryption process with the purpose of obtaining the original image that was encrypted in the encryption process.
The Elephant in the Room

“Good morning,” said the little prince.“Good morning,” said the merchant.This was a merchant who sold pills that had been invented to quench thirst. You need only swallow one pill a week, and you w...
Gamut-Extension Methods Considering Color Information Restoration

Recently, ultra high definition television (UHDTV) services have become popular using satellites and the Internet. However, there are expansive volumes of high definition television (HDTV) and standard definition television (SDTV) contents held by broadcasting companies and in storage devices. Herein we propose two color space conversion (also known as gamut mapping) methods from BT.709 (used for current HDTV broadcast) to BT.2020 (used for UHDTV broadcast) that restore or estimate lost color information. One of our methods anisotropically diffuses the BT.709 chromaticities with regard to the direction of the original chromaticities in the BT.2020 color space, generating chromaticities out of BT.709 gamut. The other learns an end-to-end conversion method from a BT.709 image to a BT.2020 image and restores lost color information using convolutional neural network (CNN). Using these methods along with BT.709 images, we obtain BT.2020 images with chromaticities from the BT.709 color gamut.
Coppersmith's lattices and “focus groups”: An attack on small-exponent RSA

We present a principled technique for reducing the lattice and matrix size in some applications of Coppersmith's lattice method for finding roots of modular polynomial equations. It relies on extrapolating patterns from the actual behavior of Coppersmith's attack for smaller parameter sizes, which can be thought of as “focus group” testing. When applied to the small-exponent RSA problem, our technique reduces lattice dimensions and consequently running times, and hence can be applied to a wider range of exponents. Moreover, in many difficult examples our attack is not only faster but also more successful in recovering the RSA secret key. We include a discussion of subtleties concerning whether or not existing metrics (such as enabling condition bounds) are decisive in predicting the true efficacy of attacks based on Coppersmith's method. Finally, indications are given which suggest certain lattice basis reduction algorithms (such as Nguyen-Stehlé's L2) may be particularly well-suited for Coppersmith's method.
Motion Detection in Video Retrieval Using Content-Based Video Retrieval

The most important challenge in content-based video retrieval is detection of moving objects accurately. The videos are stored in the database and their features are extracted. The features of the input query clip were also extracted and compared with the database features. If the features are matched up to the threshold values then the similar videos are identified. This paper proposes a technique to detect the moving objects in the videos and in order to detect the objects an abstract difference technique is used. This technique is followed by noise filtering.
Development of an automated obstacle detector for blind people

Eyes play a vital role in our life. Usually, all of us have seen the visually impaired people and know the problems that they face in their daily life. In order to detect the obstacles, blind people use sticks when they are walking but this instrument just can help them find objects on the ground. Obstacle detection is a field of effort that has led to vast progress in primary safety systems and in the primary-secondary safety systems interaction. Obstacle detector for the blind is an automated device for blind people. The main objective of this device is to make easy the walking environment for blind people. In this paper, a device is made which help the blind people by assisting through an android application. First, the device is made and then its performance is tested in three demanding conditions, for instance, normal, windy, and rainy conditions. The device shows a satisfactory level of accuracy in the three conditions. It helps the user to hear the distance and the location in an automated human voice.
Introducing Contextual Reasoning to the Semantic Web with OWL $$^{C}$$

Representing the context of triples and reasoning on contextualized triples is an open problem in the semantic web. In this paper, we present $$OWL^{C}$$ : a contextual two-dimensional web ontology language. Using the first dimension, we can define contexts-dependent classes, properties, and axioms and using the second dimension, we can express knowledge about contexts which we consider formal objects, as proposed by McCarthy [17]. Moreover, we describe a contextual extension of the OWL entailment rules, and we present a new set of rules for reasoning on contexts. We demonstrate the modeling strength and reasoning capabilities of $$OWL^{C}$$ with a practical scenario from the digital humanity domain. We chose the FDS project in virtue of its inherent contextual nature, as well as its notable complexity which allow us to highlight many issues connected with contextual knowledge representation and reasoning.
Modular Descriptions of Regular Functions

We discuss various formalisms to describe string-to-string transformations. Many are based on automata and can be seen as operational descriptions, allowing direct implementations when the input scanner is deterministic. Alternatively, one may use more human friendly descriptions based on some simple basic transformations (e.g., copy, duplicate, erase, reverse) and various combinators such as function composition or extensions of regular operations.
Machine Learning Technique for Automatic Intruder Identification and Alerting

Security has become an important factor. Intruders have become prominent factors for all the data/property theft. The basic idea in this paper is to identify the intruder and alert owner/administrator in different possible ways. This paper discusses different ways such as “a message (SMS)”, “WhatsApp message”, “location of intruder”, “an immediate call”, and “intruder’s image to owner’s/administrator’s WhatsApp” to alert owner/administrator. For identifying the intruder, machine learning algorithm is used. A camera placed at the locality is trained such that it can identify the familiar people and it is “on” all the time. Whenever an unknown/unidentified person comes to the vicinity of the camera, all the above-said features get activated and the owner gets alerted. The idea can be applied in many real-life situations, like thief identification near the house.
The Space Between Worlds: Liminality, Multidimensional Virtual Reality and Deep Immersion

Normal, everyday consciousness is a mixture of mental states which change, dependent upon both internal and external factors, ordinarily without any particular focused direction. When deliberate change is desired, however, there are mechanisms of transition available. These gateways allow smooth induction between stable, sustained, brain states. Building upon the concept of Deep Immersion, multidimensional virtual reality is explored utilizing personal narrative, portals and symbolic anchoring links as mechanisms of transit. Together, these target specific states within the individual, using biofeedback and fluid, self-adapting environments. This work is informed by Buddhist, yogic and other spiritual traditions, employing the latest technologies in Virtual and Augmented Reality, combined with mobile sensors and EEG equipment.
Let AI Clothe You: Diversified Fashion Generation

In this paper, we demonstrate automation of fashion assortment generation that appeals widely to consumer tastes given context in terms of attributes. We show how we trained generative adversarial networks to automatically generate an assortment given a fashion category (such as dresses and tops etc.) and its context (neck type, shape, color etc.), and describe the practical challenges we faced in terms of increasing assortment diversity. We explore different GAN architectures in context based fashion generation. We show that by providing context better quality images can be generated. Examples of taxonomy of design given a fashion article and finally automate generation of new designs that span the created taxonomy is shown. We also show a designer-in-loop process of taking a generated image to production level design templates (tech-packs). Here the designers bring their own creativity by adding elements, suggestive from the generated image, to accentuate the overall aesthetics of the final design.
The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning

Recent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. However, given the lack of theoretical insight, it remains unclear what the employed neural networks are learning, or how we should enhance their representational power to address the problems on which they fail. In this work, we empirically investigate the representational power of various network architectures on a series of one-shot games. Despite their simplicity, these games capture many of the crucial problems that arise in the multi-agent setting, such as an exponential number of joint actions or the lack of an explicit coordination mechanism. Our results quantify how well various approaches can represent the requisite value functions, and help us identify issues that can impede good performance.
Ensemble-based Top-k Recommender System Considering Incomplete Data

Recommender systems have been widely used in e-commerce applications. They are a subclass of information filtering system, used to either predict whether a user will prefer an item (prediction problem) or identify a set of k items that will be user-interest (Top-k recommendation problem). Demanding sufficient ratings to make robust predictions and suggesting qualified recommendations are two significant challenges in recommender systems. However, the latter is far from satisfactory because human decisions affected by environmental conditions and they might change over time. In this paper, we introduce an innovative method to impute ratings to missed components of the rating matrix. We also design an ensemble-based method to obtain Top-k recommendations. To evaluate the performance of the proposed method, several experiments have been conducted based on 10-fold cross validation over real-world data sets. Experimental results show that the proposed method is superior to the state-of-the-art competing methods regarding applied evaluation metrics.
Language Project: the importance of educational evolution in the improvement of communicative competence

Over the last decade, it is being confirmed that language projects are improving the traditional poor results in communicative and linguistic competence as well as in the educative disjointed and unsuccessful measures in Spain. In the present times we can discern a new and more satisfactory era based on a set of actions that a language project completely adapted to the context is taking place in some Spanish schools. Language politics in some Spanish regions are improving communicative competence in students as an integral and innovative response to the detected weaknesses. This article comes to outline the last implemented measures in Spain forward in this respect.
Recreation of Containers for High Availability Architecture and Container-Based Applications

Over the past few years containers have become a very popular solution for virtualization purposes. They have all major advantages of virtual machines and, additionally, reduced hardware and time requirements and built-in support for detecting faults in containers and recreating the ones which are not working. Docker is an open platform which enables running applications in a loosely connected environment called a container. A Docker Swarm framework may be used on a Docker platform to cluster and schedule containers. The time needed for detecting and recreating failed containers was tested in this paper. The time needed to recreate a given container depends on the size of the container and the reason of its failure. Docker Swarm needs from 8 to 31 s to recreate a failed container, however, for some purposes, e.g. e-commerce systems, it is too long. In such systems the administrators attempt to minimize the time of website unavailability. To mitigate this problem, we propose the architecture of a lightweight system for fast monitoring of containers and scheduling their recreation. The prototype based on the proposed architecture is insensitive to a container image size, and recreation times depend only on the reason of a container failure. The recreation times range from 3 to 10 s. If we omit the time needed to create containers, which depends solely on internal Docker Engine implementation, our solution detects a container failure and schedules the recreation of a container instance almost instantaneously. This paper presents a part of the holistic solution of fast and lightweight container creation, monitoring and responding to failures.
Scientific Researches Analysis in Digital Multitasking Field of Educational Process in Modern Universities and Determination of New Conceptual Boundaries of It

This paper is a review of recent researches on digital multitasking. Claiming that digital technologies are changing the traditional roles of a teacher and a student, we must clearly understand a kind of effects arised at the moment digital technologies are used in the classroom. Moreover, the methods of applying digital technologies for the success of students’ academic progress are not always obvious to the teacher himself. The goal of the research analysis on digital multitasking in education problem is to identify shifts in settings and accents from the operationalism methodology to attempts of application of analytical philosophy of consciousness methodology and/or postmodern philosophy. This allows us to analyze the intentions, motives of the educational process participants and thereby define new conceptual boundaries of digital multitasking.
Towards Comprehension of the Multi-Programming Language Software Systems

A software system changes over time to meet the new needs of users as well as to adapt to the environment. Therefore, it deviates from its original and documented structure. So, after a while, its understanding becomes harder and new changes cannot be made easily. Modularization is used to extract the software systems structure for understanding it. Recently, there exists a meaningful increasing is written an application by a collaboration of multiple programming languages, for example, Mozilla Firefox 3.7 is written by more than twenty programming languages. Understanding such applications for maintaining and developing them is a challengeable problem. Existing modularization methods for understanding an application are only applicable to programs written by one programming language. This paper introduces the concept of multiprogramming language software systems modularization to support such software systems comprehension. This paper discusses the problems of existing modularization techniques in dealing with systems written with multiple programming languages, the reason for using multilingual software systems, and ultimately will provide a modularization process for such systems.
Serendipity-Based Recommendation Framework for SNS Users Using Tie Strength and Relation Clustering

As contents are overflowing in Social Network Services (SNSs), the Recommender System (RS) in SNS became increasingly important. Traditional RSs focus on the relevance of contents to the users and therefore recommend obvious contents over and over again. To solve this problem, many researches have sought to find serendipity, but they have the limitation of recommending obvious or absurd posts. In this paper, we propose a novel method to recommend serendipity using tie strength of the users’ social relationships. Through the implementation of this method, serendipity can be recommended without analyzing user preferences or contents. We developed an illustrative example to prove validity of our framework.