Reviewerly/exHarmony huggingface dataset:
Disruptive Innovations: Blockchain and Spinoffs

Blockchain is creating many opportunities for business as it is transforming the way sustaining technologies operate in organizations. In blockchain, transactions are recorded as digital blocks and contain immutable properties to preserve data validation, encryption, and algorithms. The goal is to provide uniqueness and resistant to fraud. For example, digital currencies use a shared network to permanently record transactions but operate in decentralized mode to secure independence among participants. The potential applications of blockchain are unlimited and proliferating although several initiatives are still in development. Many industries are already capitalizing on experimental blockchain projects. Leaders in those industries are partnering with R&D and start-up companies to determine opportunities. Also, research universities have dedicated labs to focus on new theoretical concepts or improvements that can be leveraged in healthcare, global rights management, and decentralized publishing to name a few. Blockchain is called as “trustless system” because it can disrupt entire industries. This chapter explores the blockchain origins and its underlying technologies to understand concepts and become familiar with the latest development. A crypto-digital currency like Bitcoin will be introduced in some detail to bring awareness to the benefits, risks, and ethical concerns. A discussion on regulations will be included to investigate how government policy affects cryptocurrencies and related security. In the interest of blockchain inspired projects, the chapter will also introduce a broader discussion on new ventures adopting blockchain attributes and the trends of gradual technology implementation among early adapters.
Document Expansion Method for Digital Resource Objects

Digital Resource Objects (DROs) suffer from shortage of its contents, and this leads to reduction in the effectiveness in its retrieval results. In order to increase the retrieval effectiveness of DRO, adding extra information to its contents is required. However, the extra information must be related to the structure of DRO. Each document contains metadata units with multiple topics. Document expansion (DE) methods utilize the unstructured documents to increase the document contents. In the same way, in this paper, an Enhanced Document Expansion (EDE) method is proposed by utilizing structured documents. DE method is a way of feeding and providing documents with new information to increase the effectiveness of the documents. Usually, traditional DE methods add terms to the original documents. In the proposed EDE method, a new procedure to increase the information content according to specific steps is added and aimed at adding new information which is more relevant and closer to each metadata unit in each document. The proposed EDE method calculates the nearest sentences to the content of the metadata unit by improving the probability estimation equation. The experiments which are conducted on cultural heritage CHiC2013 collections show a statistically significant improvement over the traditional document expansion methods.
Generalizable Adversarial Attacks Using Generative Models.

Adversarial attacks on deep neural networks traditionally rely on a constrained optimization paradigm, where an optimization procedure is used to obtain a single adversarial perturbation for a given input example. In this work we frame the problem as learning a distribution of adversarial perturbations, enabling us to generate diverse adversarial distributions given an unperturbed input. We show that this framework is domain-agnostic in that the same framework can be employed to attack different input domains with minimal modification. Across three diverse domains---images, text, and graphs---our approach generates whitebox attacks with success rates that are competitive with or superior to existing approaches, with a new state-of-the-art achieved in the graph domain. Finally, we demonstrate that our framework can efficiently generate a diverse set of attacks for a single given input, and is even capable of attacking \textit{unseen} test instances in a zero-shot manner, exhibiting attack generalization.
Trustworthy AI for 5G: Telco Experience and Impact in the 5G ESSENCE

This paper discusses how it is possible to implement a general model to cope with the network and service management when the AI (Artificial Intelligence) capabilities are integrated in a trustworthy AI for 5G solutions framework. We discuss the general regulatory considerations related to the AI introduction in the 5G network management as this has been examined as part of the H2020 Project 5GESSENCE.
Object Exchangability in Reinforcement Learning

Although deep reinforcement learning has advanced significantly over the past several years, sample efficiency remains a major challenge. Careful choice of input representations can help improve efficiency depending on the structure present in the problem. In this work, we present an attention-based method to project inputs into an efficient representation space that is invariant under changes to input ordering. We show that our proposed representation results in a search space that is a factor of m! smaller for inputs of m objects. Our experiments demonstrate improvements in sample efficiency for policy gradient methods on a variety of tasks. We show that our representation allows us to solve problems that are otherwise intractable when using naive approaches.
Aspects of Transparency in Machine Learning

The fact that machine learning is growing more and more entrenched in almost every aspect of society, combined with the opacity of various of its algorithms has induced the relatively young research area of transparent machine learning. The aim of this domain is to provide explanations for automated decisions to increase public trust. In my thesis, I am going to consider certain problems that arise from this research agenda. Particularly, I so far considered, the dilemma of conflicting explanations and the issue of privacy concerns arising from transparency.
Cooperative Multi-Agent Deep Reinforcement Learning in Soccer Domains

In multi-robot reinforcement learning the goal is to enable a group of robots to learn coordinated behaviors from direct interaction with the environment. Here, we provide a comparison of two main approaches designed for tackling this challenge; namely, independent learners (IL) and joint-action learners (JAL). We evaluate these methods in a multi-robot cooperative and adversarial soccer scenario, called 2 versus 2 free-kick task, with simulated NAO humanoid robots as players. Our findings show that both approaches can achieve satisfying solutions, with JAL outperforming IL.
Performance and Cost Analysis between Elasticity Strategies over Pipeline-structured Applications

With the advances in eScience-related areas and the growing complexity of scientific analysis, more and more scientists are interested in workflow systems. There is a class of scientific workflows that has become a standard to stream processing, called pipeline-structured application. Due to the amount of data these applications need to process nowadays, Cloud Computing has been explored in order to accelerate such processing. However, applying elasticity over stage-dependent applications is not a trivial task since there are some issues that must be taken into consideration, such as the workload proportionality among stages and ensuring the processing flow. There are studies which explore elasticity on pipeline-structured applications but none of them compare or adopt different strategies. In this paper, we present a comparison between two elasticity approaches which consider not only CPU load but also workload processing time information to reorganize resources. We have conducted a number of experiments in order to evaluate the performance gain and cost reduction when applied our strategies. As results, we have reached an average of 72% in performance gain and 73% in cost reduction when comparing non-elastic and elastic executions.
Moment-Based Variational Inference for Markov Jump Processes

We propose moment-based variational inference as a flexible framework for approximate smoothing of latent Markov jump processes. The main ingredient of our approach is to partition the set of all transitions of the latent process into classes. This allows to express the Kullback-Leibler divergence between the approximate and the exact posterior process in terms of a set of moment functions that arise naturally from the chosen partition. To illustrate possible choices of the partition, we consider special classes of jump processes that frequently occur in applications. We then extend the results to parameter inference and demonstrate the method on several examples.
Metric Hybrid Factored Planning in Nonlinear Domains with Constraint Generation

We introduce a novel planner SCIPPlan for metric hybrid factored planning in nonlinear domains with general metric objectives, transcendental functions such as exponentials, and instantaneous continuous actions. Our key contribution is to leverage the spatial branch-and-bound solver of SCIP inside a nonlinear constraint generation framework where we iteratively check relaxed plans for temporal feasibility using a domain simulator, and repair the source of the infeasibility through a novel nonlinear constraint generation methodology. We experimentally evaluate SCIPPlan on a variety of domains, showing it is competitive with, or outperforms, ENHSP in terms of run time and makespan and handles general metric objectives. SCIPPlan is also competitive with a general metric-optimizing unconstrained Tensorflow-based planner (TF-Plan) in nonlinear domains with exponential transition functions and metric objectives. Overall, this work demonstrates the potential of combining nonlinear optimizers with constraint generation for planning in expressive metric nonlinear hybrid domains.
Opinion mining: a hybrid framework based on lexicon and machine learning approaches

Sentiment analysis is a practical technique that allows businesses, researchers, governments, politicians, and organizations to know about people's sentiments, which play an important role in decision-making processes. Sentiment classification techniques are mainly divided into lexicon-based methods, machine learning methods, and hybrid methods. There are limitations in each approach; Traditional machine learning approaches are based on complex features extraction process, and lexicon-based approaches suffer from scalability and are limited by unreliable sentiment lexicons that are commonly created manually by experts. In this paper, we seek to improve the performance of machine learning techniques by integrating it with our enhanced lexicon based technique Sum-of-Votes Model. Sum-of-Votes model is a generic extendable lexicon based model that beats traditional lexicon based models in accuracy and provides good solutions to previous challenges and drawbacks such as scalability, domain dependency, and unreliability, but its accuracy was 81.62%. So, in this paper we proposed a novel framework based upon both Sum-of-Votes and Bag-of-Words models; we applied them, then their outputs were fed as features to Machine Learning Classifiers. We got higher accuracy than all the individual lexicons and the entire old framework.
A method based on Multi-Convolution layers Joint and Generative Adversarial Networks for Vehicle Detection

In order to achieve rapid and accurate detection of vehicle objects in complex traffic conditions, we propose a novel vehicle detection method.Firstly, more contextual and small-object vehicle information can be obtained by our Joint Feature Network (JFN).Secondly, our Evolved Region Proposal Network (EPRN) generates initial anchor boxes by adding an improved version of the region proposal network in this network, and at the same time filters out a large number of false vehicle boxes by soft-Non Maximum Suppression (NMS).Then, our Mask Network (MaskN) generates an example that includes the vehicle occlusion, the generator and discriminator can learn from each other in order to further improve the vehicle object detection capability.Finally, these candidate vehicle detection boxes are optimized to obtain the final vehicle detection boxes by the Fine-Tuning Network(FTN).Through the evaluation experiment on the DETRAC benchmark dataset, we find that in terms of mAP, our method exceeds Faster-RCNN by 11.15%, YOLO by 11.88%, and EB by 1.64%.Besides, our algorithm also has achieved top2 comaring with MS-CNN, RefineNet, RetinaNet, of vehicle category in KITTI dataset.
An Anti-occlusion and Scale Adaptive Kernel Correlation Filter for Visual Object Tracking

Focusing on the issue that the conventional Kernel Correlation Filter (KCF) algorithm has poor performance in handling scale change and obscured objects, this paper proposes an anti-occlusion and scale adaptive tracking algorithm in the basis of KCF.The average Peak-to Correlation Energy and the peak value of correlation filtering response are used as the confidence indexes to determine whether the target is obscured.In the case of non-occlusion, we modify the searching scheme of the KCF.Instead of searching for a target with a fixed sample size, we search for the target area with multiple scales and then resize it into the sample size to compare with the learnt model.The scale factor with the maximum filter response is the best target scaling and is updated as the optimal scale for the following tracking.Once occlusion is detected, the model updating and scale updating are stopped.Experiments have been conducted on the OTB benchmark video sequences for compassion with other state-of-the-art tracking methods.The results demonstrate the proposed method can effectively improve the tracking success rate and the accuracy in the cases of scale change and occlusion, and meanwhile ensure a real-time performance.
Two Alternatives for Disjunction: an Inquisitive Reconciliation

There are two prominent treatments of disjunction in formal semantics. Traditionally, disjunction is taken to express an operator that applies to any two elements A and B of a Boolean algebra and yields their join. In particular, if A and B are propositions, then disjunction delivers their union, . Another, more recent proposal is to treat disjunction as expressing an operator that can apply to any two objects of the same semantic type, and yields the set consisting of these two objects. In particular, if disjunction applies to two propositions A and B, it delivers a set of propositional alternatives, . Each of the two approaches has certain merits that the other one lacks. Thus, it would be desirable to reconcile the two, combining their respective strengths. This paper shows that this is indeed possible, if we adopt a notion of meaning that does not just take truth-conditional, informative content into consideration, but also inquisitive content.
Increasing the Chance of Interest Learning in the User-Aware Information Distribution System Using a Smart Watch

In order to solve information overload in news information on the Internet, smartphone applications that provide news suitable for personal interests have been developed in previous research. However, if the user does not use the application, even if interesting news is distributed, since the news is not browsed, a loss of opportunity to learn the user's interest in the application has occurred. In this research, in order to prevent the loss of learning opportunities, we proposed an information distribution application that facilitates startup and browsing using smart watch. As a result of evaluation, no increase in the number of times of learnings was seen by using smart watch with this application. The cause is that the information displayed on the smart watch with a small screen size does not satisfy the amount of information desired by the user. Devices to provide users with saved information are required.
Learning to learn by Self-Critique

In few-shot learning, a machine learning system learns from a small set of labelled examples relating to a specific task, such that it can generalize to new examples of the same task. Given the limited availability of labelled examples in such tasks, we wish to make use of all the information we can. Usually a model learns task-specific information from a small training-set (support-set) to predict on an unlabelled validation set (target-set). The target-set contains additional task-specific information which is not utilized by existing few-shot learning methods. Making use of the target-set examples via transductive learning requires approaches beyond the current methods; at inference time, the target-set contains only unlabelled input data-points, and so discriminative learning cannot be used. In this paper, we propose a framework called Self-Critique and Adapt or SCA, which learns to learn a label-free loss function, parameterized as a neural network. A base-model learns on a support-set using existing methods (e.g. stochastic gradient descent combined with the cross-entropy loss), and then is updated for the incoming target-task using the learnt loss function. This label-free loss function is itself optimized such that the learnt model achieves higher generalization performance. Experiments demonstrate that SCA offers substantially reduced error-rates compared to baselines which only adapt on the support-set, and results in state of the art benchmark performance on Mini-ImageNet and Caltech-UCSD Birds 200.
A Functional Model of Information System for IT Education Company

Object Oriented Analysis and Design uses diagrams for three types of modeling: structural, dynamic and functional. For external point of view, functional modelling is used to define business requirements through users’ requirements, to refine system functionality and to define processes that have to be enabled with the objects, containing attributes and methods. The functional model specifies the meanings of operations over the objects and actions of the dynamic modelling. The acquired functional models are visualized by UML use case diagrams and use case scenarios according to the software engineering principles. As a result of using use case diagrams, activity diagrams have to be done as a part of the functional model. These components of the model define the basic building blocks, roles and activities as well as the rules. They are used in the next software development phases in order the developers to have a clear understanding of the whole project. The paper presents the practical application of the functional modelling by using use case diagrams through scenario methods and highlighted processes with the use case and activity diagrams for information system for IT education company management
Specifying Industrial System Requirements using Specification Patterns: A Case Study of Evaluation with Practitioners

With the ever-increasing size and complexity of the industrial software systems there is an imperative need for an automated, systematic and exhaustive verification of various software artifacts, s ...
A Static Hand Gesture Recognition for Peruvian Sign Language Using Digital Image Processing and Deep Learning

The work consists in recognizing the gestures of the alphabet in Peruvian sign language using techniques of digital image processing and a model of Deep Learning (CNN). Image processing techniques are used for segmentation and tracking of the hand of the person making the gestures. Once the image of the segmented hand is used, a CNN classification model is used to be able to recognize the gesture. The image processing and CNN algorithms were implemented in the Python programming language. The database used was 23,000 images divided into 70% for training, 15% for testing and 15% for validation. Likewise, said data corresponds to 1000 images for each non-mobile gesture of the alphabet. The results obtained for the precision of the classifier were 99.89, 99.88 and 99.85% for the data of training, test and validation respectively. In the case of the Log Loss parameter, 0.0132, 0.0036, and 0.0107 were obtained for the training, testing and validation data, respectively.
Estimation and testing of nonparametric hidden Markov model with application in stock market

In this paper, a fully nonparametric Hidden Markov Model is proposed, where both the state dependent emission model and transition matrix are all nonparametric and depend on a covariate. By reducing modeling bias, the new model extends the applicability of both the semiparametric HMM with nonparametric emission model and nonhomogeneous HMM. A modified EM algorithm, which combines the kernel regression technique, is proposed for estimation. Model selection procedures are provided and a conditional bootstrap method is employed to assess the standard errors of the estimates. A generalized likelihood ratio test procedure is further proposed and the Wilk’s type of phenomenon is verified to hold for the proposed model. Simulation studies are conducted to demonstrate proposed procedure, and the proposed model is applied in analyzing the impact of economic on stock market.
ANALYTICAL STUDY OF COOLING AND HEATING LOADS DUE TO RECENT NATIONWIDE WEATHER DATA

According to the Meteorological Agency 'Climate Change Monitoring Report 2016', the average annual temperature in Japan has risen.
OFFLINE STUDY FOR IMPLEMENTING HUMAN COMPUTER INTERFACE FOR ELDERLY PARALYZED PATIENTS USING ELECTROOCULOGRAPHY AND NEURAL NETWORKS

Earlier day's people with disability face lot of difficulty in communication due to neuromuscular attack. They are unable to share ideas and thoughts with others so they need some assist to overcome this condition. To overcome the condition, in this paper, we discussed the capabilities of designing electrooculogram (EOG)-based human computer interface (HCI) by ten subjects using power spectral density techniques and neural network. In this study, we compare the right hander performance with left hander performance. Outcomes of the study concluded that lefthander performance was marginally appreciated compared to right hander performance in terms of classification accuracy with an average accuracy of 93.38% for all left hand subjects and 91.38% for all the right subjects using probabilistic neural network (PNN) and also we analysed that during the training left handers were interestingly participated and also they can able to perform the following eleven tasks easily compared with right handers.
Embedding Imputation with Grounded Language Information

Due to the ubiquitous use of embeddings as input representations for a wide range of natural language tasks, imputation of embeddings for rare and unseen words is a critical problem in language processing. Embedding imputation involves learning representations for rare or unseen words during the training of an embedding model, often in a post-hoc manner. In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph. This is in contrast to existing approaches which typically make use of vector space properties or subword information. We propose an online method to construct a graph from grounded information and design an algorithm to map from the resulting graphical structure to the space of the pre-trained embeddings. Finally, we evaluate our approach on a range of rare and unseen word tasks across various domains and show that our model can learn better representations. For example, on the Card-660 task our method improves Pearson's and Spearman's correlation coefficients upon the state-of-the-art by 11% and 17.8% respectively using GloVe embeddings.