Reviewerly/exHarmony huggingface dataset:
Lightweight Delegatable Proofs of Storage to Avoid Data Spillage using AVL Tree

Cloud storage is associate progressively standard application of cloud computing, which might offer on-demand outsourcing data services for each organizations and people.Due to the information outsourcing, however, this new paradigm of knowledge hosting service jointly introduces new security challenges that desire associate freelance auditing service to check the information integrity inside the cloud.Some existing remote integrity checking ways that can exclusively serve for static archive knowledge and thus can't be applied to the auditing service since the information inside the cloud square measure typically dynamically updated.We consider the errand of allowing a Third Party Auditor (TPA), for the benefit of the cloud customer, to confirm the trustworthiness of the dynamic data hangs on inside the cloud. Proofs of storage (including Proofs of Irretrievability and demonstrable knowledge Possession) could be a cryptographic tool, that permits data owner or third party auditor to audit integrity of knowledge hold on remotely in Associate in Nursing extremely cloud storage server, while not keeping a region copy of data or downloading data back throughout auditing.
Constructing glossiness perception model of computer graphics with sounds

In this paper, we construct a model for cross-modal perception of glossiness by investigating the interaction between sounds and graphics. First, we conduct evaluation experiments on cross-modal glossiness perception using sounds and graphics stimuli. There are three types of stimuli in the experiments. The stimuli are visual stimuli (22 stimuli), audio stimuli (15 stimuli) and audiovisual stimuli (330 stimuli). Also, there are three sections in the experiments. The first one is a visual experiment, the second one is an audiovisual experiment, and the third one is an auditory experiment. For the evaluation of glossiness, the magnitude evaluation method is applied. Second, we analyze the influence of sounds on glossiness perception from the experimental results. The results suggest that the cross-modal perception of glossiness can be represented as a combination of visual-only perception and auditory-only perception. Then, based on the results, we construct a model by a linear sum of computer graphics and sound parameters. Finally, we confirm the feasibility of the cross-modal glossiness perception model through a validation experiment.
Inducing Hypernym Relationships Based On Order Theory.

This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints. We apply it to induce hypernymy relations by training with is-a pairs. We also present an augmented variant of SPON that can generalize type information learned for in-vocabulary terms to previously unseen ones. An extensive evaluation over eleven benchmarks across different tasks shows that SPON consistently either outperforms or attains the state of the art on all but one of these benchmarks.
Spread-gram: A spreading-activation schema of network structural learning

Network representation learning has exploded recently. However, existing studies usually reconstruct networks as sequences or matrices, which may cause information bias or sparsity problem during model training. Inspired by a cognitive model of human memory, we propose a network representation learning scheme. In this scheme, we learn node embeddings by adjusting the proximity of nodes traversing the spreading structure of the network. Our proposed method shows a significant improvement in multiple analysis tasks based on various real-world networks, ranging from semantic networks to protein interaction networks, international trade networks, human behavior networks, etc. In particular, our model can effectively discover the hierarchical structures in networks. The well-organized model training speeds up the convergence to only a small number of iterations, and the training time is linear with respect to the edge numbers.
Efficient Emergency Pharmaceutical Service Via Cloud Server

In the past few years it was clear that mobile cloud computing was established via integrating both mobile computing and cloud computing to be add in both storage space and processing speed. These advancements in mobile computing has a potential impact to improve health care delivery, reduce health care costs, make health care services more convenient to patients and increase the overall efficiency and effectiveness of health care providers. The system provides assistance to patients identifies and selects doctors based on the location and the specialties of the doctors. The system allows patients to make appointments with doctors and assigns reminders to take the prescribed medications and vaccinations. The results of testing the applications show a big saving of time and mobility of doctors and patients. The system allows patients to take the prescribed medications and vaccinations. Generally referred to as m-Health, mobile devices are used in conjunction with other information and communication technology facilities to deliver care. A promise of m-Health is its capacity to facilitate the consumption of Electronic Health Record (EHR) data using mobile devices, which is central to promoting remote healthcare delivery. Through mobile technology, physicians are facilitated to interact with patients in a more efficient manner. m-Health employs mobile technology, remote healthcare delivery can be facilitated through patient monitoring, patient data collection, out of health facility patient care, and cost management I.INTRODUCTION Efficient Emergency Pharmaceutical Service via Cloud Server that is used to focuses on supporting patients (persons with tender hemophilia) to self-regulate wounds in occasions of minor scenes.In this Application patients to self-manage injuries in cases of minor incidents.If patients have any query about the minor health problem , they will send the query and get the information about how to manage the injuries.This involves bi-directional exchanges of the Electronic Health Record (EHR) amongst patients and the care facility.In any case, mobile phones rely on wireless communication channels (e.g., 3.5/4G, et cetera.) to transmit data and these channels can experience sporadic disconnections due to bandwidth fluctuations and user mobility.This work took advantage of the ubiquitous nature of mobile cloud computing and proposes a middleware, which facilitates efficient process of medical data synchronization, and with minimal latency.The work details state-of-the-art architecture of the cloud-based middleware that is built and tested for real-world use following four methodologies namely: reflective, tuple space, contextawareness, and event-based.
Physical Activities and Fitness of College Students by Obesity

The purpose of this study is to define the relationship between obesity, physical activity, health-related variables, and figure out physical activity and health-related fitness of college students by obesity. To achieve this goal, 77 students (45 males and 32 females) at a University in Gyeongsang Province were selected as the study subject by convenience sampling method. As the variables, obesity (BMI), subjective physical activity, health-related physical fitness (muscle strength, muscle endurance, flexibility, cardiorespiratory endurance) were measured. As the analysis method, descriptive statistics and independent t-test were used to analyze the general characteristics of the gender of subjects. Descriptive statistics and 2-way ANOVA were used to analyze the characteristics of physical activity and health-related physical fitness by gender and obesity. Pearson
Segnali discorsivi in Giordano da Pisa e Bernardino da Siena

This paper analyses pragmatic and discourse markers in a corpus of mediaeval sermons, a genre that is strongly context dependent and characterised by spoken features. More specifically, it considers a number of sermons delivered by Giordano of Pisa (1260-1311) and Bernardino of Siena (1380-1444). The analysis throws up some remarkable findings. As regards the forms, the list of items that can have pragmatic meaning has been expanded; as for the functions, some markers display a wide range of pragmatic uses (multi-functionality).
Quantum Algorithm for Finding the Optimal Variable Ordering for Binary Decision Diagrams

An ordered binary decision diagram (OBDD) is a directed acyclic graph that represents a Boolean function. OBDDs are also known as special cases of oblivious read-once branching programs in the field of complexity theory. Since OBDDs have many nice properties as data structures, they have been extensively studied for decades in both theoretical and practical fields, such as VLSI design, formal verification, machine learning, and combinatorial problems. Arguably, the most crucial problem in using OBDDs is that they may vary exponentially in size depending on their variable ordering (i.e., the order in which the variable are to read) when they represent the same function. Indeed, it is NP hard to find an optimal variable ordering that minimizes an OBDD for a given function. Hence, numerous studies have sought heuristics to find an optimal variable ordering. From practical as well as theoretical points of view, it is also important to seek algorithms that output optimal solutions with lower (exponential) time complexity than trivial brute-force algorithms do. Friedman and Supowit provided a clever deterministic algorithm with time/space complexity $O^\ast(3^n)$, where $n$ is the number of variables of the function, which is much better than the trivial brute-force bound $O^\ast(n!2^n)$. This paper shows that a further speedup is possible with quantum computers by demonstrating the existence of a quantum algorithm that produces a minimum OBDD together with the corresponding variable ordering in $O^\ast(2.77286^n)$ time and space with an exponentially small error. Moreover, this algorithm can be adapted to constructing other minimum decision diagrams such as zero-suppressed BDDs, which provide compact representations of sparse sets and are often used in the field of discrete optimization and enumeration.
On some cryptographic properties of Boolean functions and their second-order derivatives

In this paper some cryptographic properties of Boolean functions, including weight, balancedness and nonlinearity, are studied, particularly focusing on splitting functions and cubic Boolean functions. Moreover, we present some quantities derived from the behaviour of second-order derivatives which allow us to determine whether a quadratic or cubic function is APN.
Organic Geochemistry Significance of the Chang 7 Member of the Triassic Yanchang Formation in the Mid-West of Ordos Basin, China

Preview this conference paper: Organic Geochemistry Significance of the Chang 7 Member of the Triassic Yanchang Formation in the Mid-West of Ordos Basin, China, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/2214-4609/2019/P293-1.gif
Active Goal Recognition

To coordinate with other systems, agents must be able to determine what the systems are currently doing and predict what they will be doing in the future---plan and goal recognition. There are many methods for plan and goal recognition, but they assume a passive observer that continually monitors the target system. Real-world domains, where information gathering has a cost (e.g., moving a camera or a robot, or time taken away from another task), will often require a more active observer. We propose to combine goal recognition with other observer tasks in order to obtain \emph{active goal recognition} (AGR). We discuss this problem and provide a model and preliminary experimental results for one form of this composite problem. As expected, the results show that optimal behavior in AGR problems balance information gathering with other actions (e.g., task completion) such as to achieve all tasks jointly and efficiently. We hope that our formulation opens the door for extensive further research on this interesting and realistic problem.
Research on "4A" Model of Museum Social Education Based on U-Learning Theory

Under the background of the current realization of the Chinese Dream of national rejuvenation, the U-Learning theory is applied to the museum social education to actively explore the "4A" model of museum social education to make museums carry out social education activities to anyone, at anytime and anywhere with any device to further develop their social education function and role.
Life Skills Of Youth In Chennai Slums

Slums are universal in character and no country is able to get rid of them. Large-scale migration to the cities due to employment opportunities and other community facilities, an income level which is inadequate to maintain decent standards of living are some of the major contributory factors responsible for the growth of slums especially in Chennai as per Tamilnadu slum clearance board. Life Skills have been defined by the World Health Organization (WHO) as psychosocial abilities for adaptive and positive behavior that enable individuals to deal effectively with the demands and challenges of everyday life. The youth from slums due to lack of effective utilization of their skills are still in a lower level jobs and have not climbed to great ladders. The youth pursuing their graduation or pursuing their post graduation belonging to the age group between eighteen to twenty four years of age from Zone 8 slum recognized by the slum clearance board alone are the respondents. Strength of thirty samples by using simple random sampling method was used for the research. A majority 73.3% of the respondents were in poor condition level in self awareness dimension, while 26.7% of them were in average level. It is inferred that the respondents various life skills from different dimensions namely interpersonal relationship, critical thinking, decision making, problem solving and coping with emotions do differ with regard to their gender. It is observed that there is strong and significant association between various dimensions of life skills namely Interpersonal relationship (X2-4.562,df-1), Effective communication (X2-1.425,df-1) and Critical thinking (X2-4.124,df-1). The research suggests the need for bettering the life skill education among the youth from slums at the time of their academics itself
Artificial Intelligence Assisted Learning (AIAL) Blueprint

Education is facing various challenges at the moment and needs to be reinvented. Some of the methods used have been inspired by the industrial revolution when an assembly line one-size-fits-all approach was setup in schools. Today, teachers are struggling to manage the number of students in a class thus making the quality of teaching inconsistent. Furthermore, they have to deal with students having different abilities in the same class which makes it impossible to give each and every student the individual attention they deserve. Through the artificial intelligence assisted learning (AIAL) system, the authors believe that they can personalise the learning and thus free a lot of time for the teacher which can be used to focus on those students that are really in need. This will be done on a case by case basis, thus creating a fairer educational system which is personalized for the needs of each and every student which guarantees equity.
Towards Multimodal Understanding of Passenger-Vehicle Interactions in Autonomous Vehicles: Intent/Slot Recognition Utilizing Audio-Visual Data

Understanding passenger intents from spoken interactions and car's vision (both inside and outside the vehicle) are important building blocks towards developing contextual dialog systems for natural interactions in autonomous vehicles (AV). In this study, we continued exploring AMIE (Automated-vehicle Multimodal In-cabin Experience), the in-cabin agent responsible for handling certain multimodal passenger-vehicle interactions. When the passengers give instructions to AMIE, the agent should parse such commands properly considering available three modalities (language/text, audio, video) and trigger the appropriate functionality of the AV system. We had collected a multimodal in-cabin dataset with multi-turn dialogues between the passengers and AMIE using a Wizard-of-Oz scheme via realistic scavenger hunt game. In our previous explorations, we experimented with various RNN-based models to detect utterance-level intents (set destination, change route, go faster, go slower, stop, park, pull over, drop off, open door, and others) along with intent keywords and relevant slots (location, position/direction, object, gesture/gaze, time-guidance, person) associated with the action to be performed in our AV scenarios. In this recent work, we propose to discuss the benefits of multimodal understanding of in-cabin utterances by incorporating verbal/language input (text and speech embeddings) together with the non-verbal/acoustic and visual input from inside and outside the vehicle (i.e., passenger gestures and gaze from in-cabin video stream, referred objects outside of the vehicle from the road view camera stream). Our experimental results outperformed text-only baselines and with multimodality, we achieved improved performances for utterance-level intent detection and slot filling.
Semi-supervised classification on graphs using explicit diffusion dynamics

Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples. Intuitively, the graph acts as a conduit to channel and bias the inference of class labels. Here, we study classification methods that consider the graph as the originator of an explicit graph diffusion. We show that appending graph diffusion to feature-based learning as an \textit{a posteriori} refinement achieves state-of-the-art classification accuracy. This method, which we call Graph Diffusion Reclassification (GDR), uses overshooting events of a diffusive graph dynamics to reclassify individual nodes. The method uses intrinsic measures of node influence, which are distinct for each node, and allows the evaluation of the relationship and importance of features and graph for classification. We also present diff-GCN, a simple extension of Graph Convolutional Neural Network (GCN) architectures that leverages explicit diffusion dynamics, and allows the natural use of directed graphs. To showcase our methods, we use benchmark datasets of documents with associated citation data.
Learning Category Correlations for Multi-label Image Recognition with Graph Networks

Multi-label image recognition is a task that predicts a set of object labels in an image. As the objects co-occur in the physical world, it is desirable to model label dependencies. Previous existing methods resort to either recurrent networks or pre-defined label correlation graphs for this purpose. In this paper, instead of using a pre-defined graph which is inflexible and may be sub-optimal for multi-label classification, we propose the A-GCN, which leverages the popular Graph Convolutional Networks with an Adaptive label correlation graph to model label dependencies. Specifically, we introduce a plug-and-play Label Graph (LG) module to learn label correlations with word embeddings, and then utilize traditional GCN to map this graph into label-dependent object classifiers which are further applied to image features. The basic LG module incorporates two 1x1 convolutional layers and uses the dot product to generate label graphs. In addition, we propose a sparse correlation constraint to enhance the LG module and also explore different LG architectures. We validate our method on two diverse multi-label datasets: MS-COCO and Fashion550K. Experimental results show that our A-GCN significantly improves baseline methods and achieves performance superior or comparable to the state of the art.
Ontology Based Information Integration: A Survey

An ontology makes a special vocabulary which describes the domain of interest and the meaning of the term on that vocabulary. Based on the precision of the specification, the concept of the ontology contains several data and conceptual models. The notion of ontology has emerged into wide ranges of applications including database integration, peer-to-peer systems, e-commerce, semantic web, etc. It can be considered as a practical tool for conceptualizing things which are expressed in computer format. This paper is devoted to ontology matching as a mean or information integration. Several matching solutions have been presented from various areas such as databases, information systems and artificial intelligence. All of them take advantages of different attributes of ontology like, structures, data instances, semantics and labels and its other valuable properties. The solutions have some common techniques and cope with similar problems, but use different methods for combining and exploiting their results. Information integration is among the first classes of applications at which matching was considered as a probable solution. Information integration contains many fields including, data integration, schema integration, catalogue integration and semantic integration. We cover these notions in term of ontology in our proposed paper.
Independent Generative Adversarial Self-Imitation Learning in Cooperative Multiagent Systems.

Many tasks in practice require the collaboration of multiple agents through reinforcement learning. In general, cooperative multiagent reinforcement learning algorithms can be classified into two paradigms: Joint Action Learners (JALs) and Independent Learners (ILs). In many practical applications, agents are unable to observe other agents' actions and rewards, making JALs inapplicable. In this work, we focus on independent learning paradigm in which each agent makes decisions based on its local observations only. However, learning is challenging in independent settings due to the local viewpoints of all agents, which perceive the world as a non-stationary environment due to the concurrently exploring teammates. In this paper, we propose a novel framework called Independent Generative Adversarial Self-Imitation Learning (IGASIL) to address the coordination problems in fully cooperative multiagent environments. To the best of our knowledge, we are the first to combine self-imitation learning with generative adversarial imitation learning (GAIL) and apply it to cooperative multiagent systems. Besides, we put forward a Sub-Curriculum Experience Replay mechanism to pick out the past beneficial experiences as much as possible and accelerate the self-imitation learning process. Evaluations conducted in the testbed of StarCraft unit micromanagement and a commonly adopted benchmark show that our IGASIL produces state-of-the-art results and even outperforms JALs in terms of both convergence speed and final performance.
Two-stage Image Classification Supervised by a Single Teacher Single Student Model

The two-stage strategy has been widely used in image classification. However, these methods barely take the classification criteria of the first stage into consideration in the second prediction stage. In this paper, we propose a novel two-stage representation method (TSR), and convert it to a Single-Teacher Single-Student (STSS) problem in our two-stage image classification framework. We seek the nearest neighbours of the test sample to choose candidate target classes. Meanwhile, the first stage classifier is formulated as the teacher, which holds the classification scores. The samples of the candidate classes are utilized to learn a student classifier based on L2-minimization in the second stage. The student will be supervised by the teacher classifier, which approves the student only if it obtains a higher score. In actuality, the proposed framework generates a stronger classifier by staging two weaker classifiers in a novel way. The experiments conducted on several face and object databases show that our proposed framework is effective and outperforms multiple popular classification methods.