Reviewerly/exHarmony huggingface dataset:
An overview of a novel analysis approach for enhancing context awareness in smart environments

This work is part of context aware applications design and development, and smart environments in which context changes frequently. The objective of the work is to facilitate the design and the development of context aware applications able to detect context changes and to predict context. In the paper, two analysis tasks are proposed. An analysis task for detection aiming at supporting application designers to conceive easily context aware applications able to detect context changes and an analysis task for prediction aiming at helping the application designers to conceive context aware applications able to predict context. The paper details also an analysis module that implements the functionalities of the analysis tasks. The analysis module helps the application developers to develop context aware applications. Finally, the paper introduces a case study related to smart buildings in order to show the usefulness of the analysis tasks. The paper shows an application scenario related to smart buildings and particularly water consumption prediction. Also the paper presents experiments related to memory consumption introduced by the use of our analysis module. The application scenario illustrates the usefulness of the analysis approach. The overhead introduced by the analysis module is negligeable.
Linear classifier design in the weight space

In this paper, we propose a linear classifier design method in the weight space. A linear classifier is completely determined by a weight vector. To design a linear classifier is equivalent to finding a weight vector. When there are a number of training samples, each training sample represents a plane in the weight space. On one side of the plane, the training sample is correctly classified while it is incorrectly classified on the other side. Thus, finding the optimal linear classifier can be formulated as finding a subspace that provides the best classification accuracy. In this paper, we have developed an algorithm to find such optimal subspaces in the weight space. Although we could not find the optimal linear classifier due to prohibitive computational complexity, the proposed design method produced noticeable improvements in some cases. Experimental results show that the proposed linear classifier performed better than or equivalent to existing linear classifiers.
Multi modal registration of structural features and mutual information of medical image

Based on automatic matching of medical images synthesized by complementary characteristic, this paper makes full use of SURF algorithm’s good scale rotation invariance and HOG algorithm to describe local shape information better, showing good robustness when affected by geometric and optical deformation of graphics. Each performance of algorithm is comprehensively evaluated through the experiment, with accuracy of reaching 98%, meeting the actual application requirements. The algorithm shows good performance and application potential in the field of medical image matching. At the same time, since medical images are affected by lighting, equipment, scene and other factors in the acquisition process, there are large differences, which affect the robustness of the algorithm to a certain extent. Experiments show that this method has high accuracy in digital medical image matching and can be used to construct automatic matching system of medical images.
Faster manipulation of large quantum circuits using wire label reference diagrams

Large scale quantum computing is highly anticipated, and quantum circuit design automation needs to keep up with the transition from small scale to large scale problems. Methods to support fast quantum circuit manipulations (e.g. gate replacement, wire reordering, etc.) or specific circuit analysis operations have not been considered important and have been often implemented in a naive manner thus far. For example, quantum circuits are usually represented in term of one-dimensional gate lists or as directed acyclic graphs. Although implementations for quantum circuit manipulations are often only of polynomial complexity, the sheer number of possibilities to consider with increasing scales of quantum computations make these representations highly inefficient – constituting a serious bottleneck. At the same time, quantum circuits have structural characteristics, which allow for more specific and faster approaches. This work utilises these characteristics by introducing a dedicated representation for large quantum circuits, namely wire label reference diagrams. We apply the representation to a set of very common circuit transformations, and develop corresponding solutions which achieve orders of magnitude performance improvements for circuits which include up to 80 000 qubits and 200 000 gates. The implementation of the proposed method is available online.
Towards Robust Neural Networks with Lipschitz Continuity

Deep neural networks have shown remarkable performance across a wide range of vision-based tasks, particularly due to the availability of large-scale datasets for training and better architectures. However, data seen in the real world are often affected by distortions that not accounted for by the training datasets. In this paper, we address the challenge of robustness and stability of neural networks and propose a general training method that can be used to make the existing neural network architectures more robust and stable to input visual perturbations while using only available datasets for training. Proposed training method is convenient to use as it does not require data augmentation or changes in the network architecture. We provide theoretical proof as well as empirical evidence for the efficiency of the proposed training method by performing experiments with existing neural network architectures and demonstrate that same architecture when trained with the proposed training method perform better than when trained with conventional training approach in the presence of noisy datasets.
Re-KISSME: A robust resampling scheme for distance metric learning in the presence of label noise

Distance metric learning aims to learn a metric with the similarity of samples. However, the increasing scalability and complexity of dataset or complex application brings about inevitable label noise, which frustrates the distance metric learning. In this paper, we propose a resampling scheme robust to label noise, Re-KISSME, based on Keep It Simple and Straightforward Metric (KISSME) learning method. Specifically, we consider the data structure and the priors of labels as two resampling factors to correct the observed distribution. By introducing the true similarity as latent variable, these two factors are integrated into a maximum likelihood estimation model. As a result, Re-KISSME can reason the underlying similarity of each pair and reduce the influence of label noise to estimate the metric matrix. Our model is solved by iterative algorithm with low computational cost. With synthetic label noise, the experiments on UCI datasets and two application datasets of person re-identification confirm the effectiveness of our proposal.
U-LITE, a Private Cloud Approach for Particle Physics Computing

The computing infrastructure of Laboratori Nazionali del Gran Sasso (LNGS) is the primary platform for data storage, analysis, computing and simulation of the LNGS-based experiments, which are part of the research activities of the Istituto Nazionale di Fisica Nucleare (INFN). Scientific collaborations running such experiments have diverse needs and adopt different approaches in developing the computing frameworks that support their activities. The emergence of resource virtualization and the cloud paradigm has allowed a complete change to the way of operating and managing the LNGS computing infrastructure, and realize U-LITE, a private versatile environment apt at hosting such varied ecosystem and providing LNGS scientific users a familiar computing interface which hides all the complexities of a modern data center management. Since 2011, U-LITE has proven to be a valuable tool for the LNGS experiments, and provides an example of effective use of a private Cloud computing approach in a real scientific context.
Service matchmaking for Internet of Things based on probabilistic topic model

The Internet of Things (IoT) is one of the most rapidly growing technologies which enables things to interact with each other through a global network of machines and devices. In order to facilitate the user to query, search and discover appropriate services, service matchmaking is a critical and challenging task in the IoT. Currently, semantic modeling methods are mainly used to solve service matchmaking problems. However, as the text for service description is often short, these methods face many challenges such as sparse and high-dimensional features. To solve these issues, we propose a service matchmaking method based on Weighted-Word Latent Dirichlet Allocation (WW-LDA). By distinguishing and incorporating the importance of different words, WW-LDA is proposed as a probabilistic topic model to extract latent semantic factors. Based on the result of WW-LDA, we further design a new service matchmaking algorithm to discover IoT services. Experimental results show that our proposed method performs much better than other existing methods in terms of the precision rate and recall rate on real-world datasets.
Toward Data-Driven Safety: An Ontology-Based Information System

In the age of data, enterprise-related data can be efficiently used for supporting safety management decisions. In this paper, the state of the art in the domain of aviation safety is reviewed and problems that current aviation safety information systems suffer from are identified. Based on the outcomes, ontologies are defended as a technology for safety data management and the design and implementation of an ontology-based information system for aviation safety data integration is presented. The system was developed in cooperation with the Civil Aviation Authority of the Czech Republic, and it is described in terms of design, core technologies, and achieved functionalities. Subsequently, it is compared to other available solutions in the domain. The results indicate that, by linking the features to ontologies, many desired characteristics of the safety information system are achieved due to proper utilization of top-level ontologies and that the solution offers a systematic and sustainable way for improving aviation safety.
Computability evaluation of RESTful API using Primitive Recursive Function

Web services are moving toward a new emerging technology lead to the migration of SOAP to RESTful API, which is an Architectural Style that holds Lightweight, Stateless, Uniform Interface, etc., as its constraints. Various sources of clusters of resources, entity, database relations are access throughout the distributed environment across the internet. Generative Power of the RESTful API witnesses the emergence of many companies whose whole business process is based upon the building applications. Since the Syntactic essentials of RESTful Web Services are mainly concerned with the RESTful API, there is a need for evaluation of those essentials whether they are computable or not. The proposed work is carried out on taking resources as simple and effective using Primitive Recursive Function (PRF). Primitive Recursive Function (PRF) uses Turing Machine for REST API capability evaluation and the Service Invocation along with the Application Logic and AppState Logic in order to handle manageability of the RESTful resources via computability evaluation with or without security. To demonstrate the effectiveness of our evaluation process, we conduct a case study on the available REST web services using Primitive Recursive Resources (PRR). The results of our case study show that our evaluation process achieves greater portability, reliability, scalability, etc., which in turn results in high performance.
Data Science and Data Visualization

This chapter focuses on a select number of effective multidimensional techniques and their application in the transportation domain. Generating an effective multivariate visualization is more challenging and usually involves more compromises than visualizing two or three variables. This section focuses on interactive visualization techniques that can be used to explore the data and uncover clusters and relationships. Parallel coordinates, t-distributed stochastic neighbor embedding (t-SNE), and multidimensional scaling (MDS) are applied on a number of publicly available transportation data sets. It is shown that MDS and t-SNE provide an effective way to visualize dozens or hundreds of variables through dimensionality reduction.
Predicting insertion positions in word-level machine translation quality estimation

Word-level machine translation (MT) quality estimation (QE) is usually formulated as the task of automatically identifying which words need to be edited (either deleted or replaced) in a translation T produced by an MT system. The advantage of estimating MT quality at the word level is that this information can be used to guide post-editors since it enables the identification of the specific words in T that need to be edited in order to ease their work. However, word-level MT QE, as defined in the current literature, has an obvious limitation: it does not identify the positions in T in which missing words need to be inserted. To deal with this limitation, we propose a method which identifies both word deletions and insertion positions in T. This is, to the best of our knowledge, the first approach allowing the identification of insertion positions in word-level MT QE. The method proposed can use any source of bilingual information – such as MT, dictionaries, or phrase-level translation memories – to extract features that are then used by a neural network to produce a prediction for both words and insertion positions (gaps between words) in the translation T. In this paper, several feature sets and neural network architectures are explored and evaluated on publicly-available datasets used in previous evaluation campaigns for word-level MT QE. The results confirm the feasibility of the proposed approach, as well as the usefulness of sharing information between the two prediction tasks in order to obtain more reliable quality estimations.
Robust content fingerprinting algorithm based on invariant and hierarchical generative model

Content fingerprinting has been widely used for protecting the copyright of on-line digital media. By aggregating the perceptual attributes of digital media into an invariant digest, content fingerprinting enables user-generated-contents (UGC) networks to identify the unauthorized distribution of copyrighted contents. In this paper, we propose an image fingerprinting algorithm based on invariant generative model. The proposed work formulates fingerprinting algorithm as a hierarchy of parametric models. For better generalization performance, we first train the models to learn generic visual patterns from local image structures, which is accomplished by fitting the statistical distribution of local patches. The learned models are then fine-tuned to address the robustness and discriminability requirements of content fingerprinting. Moreover, our training scheme also regularizes the norm of gradients to force the models to learn visual features that are insensitive to distortion. The learned models are cascaded with a pooling operation to form the building block of fingerprinting algorithm. Considering the security requirement of copyright protection, we also develop a key-dependent scheme to randomize fingerprint computation. Experimental results validate that the proposed work can withstand a wide variety of distortions and achieve a higher content identification accuracy than competing algorithms.
Modeling adaptive access control policies using answer set programming

Many of the existing management platforms such as pervasive computing systems implement policies that depend on dynamic operational environment changes. Existing formal approaches for automatically enforcing access control policies are primarily expressed in conventional logic programming, also known as monotonic logics, e.g., First Order Logic (FOL). The major issue with monotonic logics is that they are not devised to invalidate initial believes in the light of further observations. This limitation makes these traditional logical approaches less suitable for modeling and analyzing context-aware access control policies, where exceptional policies are introduced incrementally and adaptively during runtime. The inability to invalidate initial policies when an exception needs to be enforced might result in inconsistencies and violations that need to be resolved manually by human entities. To address the problems with conventional logical approaches and more importantly prevent such inconsistencies, this paper presents a non-monotonic logic-based reasoning scheme for modeling and analyzing adaptive access control policies. In the proposed formalism, unavailable context data and incomplete access control policies can be explicitly expressed. To do so, the paper distinguishes three kinds of policies: default, context-dependent and exception policies. The proposed formalism is based on Answer Set Programming (ASP), a non-monotonic logic programming language that allows elegant representation of unavailability of context data in adaptive systems. We devise non-monotonic policy inference rules such that, when exception policies are defined, they take precedence over default and context-dependent policies automatically. The results of two case studies are reported to demonstrate the feasibility of the proposed policy representation scheme compared to the Organizational-Based Access Control (OrBAC) model.
Multimodal medical image registration via common representations learning and differentiable geometric constraints

Multimodal medical image registration remains a challenging problem when strong appearance variations and imprecise alignment exist in images. Previous deep network approaches cannot handle such a high degree of variability and prohibit the use of strong geometric constraints. The authors introduce a novel deep architecture that not only produces image representations that are well-suited for this challenging task, but also leverages knowledge of the geometry constraints for robust registration. By enforcing the representations of different modalities living in a common semantic space, they obtain convolutional features tending to respond to object parts consistently across modality. This yields a unique description for all object fragments and allows the end user to know the model's decision process, whereas most existing models remain unclear and difficult to explain. By using the differentiable spatial transformer to compensate transform, they integrate geometric consensus into the cost function to enable end-to-end model optimisation which has not yet been exploited before. The authors evaluate their method on a very challenging medical image dataset. Experiments demonstrate that the proposed method provides a plausible representation and outperforms state-of-the-art approaches by a significant margin.
Concept detection using text exemplars aligned with a specialized ontology

Knowledge extraction from text documents requires identifying and classifying semantic content. Utilizing an appropriate domain ontology can facilitate this process if words and phrases can be linked to the classes and relationships within the ontology. This paper presents an exemplar-based algorithm to link text to semantically similar classes within an ontology constructed for the chronic pain medicine domain. Human annotators linked classes to text segments within a random document set for construction of an exemplar dictionary, which we examined for completeness using Zipf plot analysis. An algorithm was created to use this dictionary on previously unseen text to form a map between sentence text and probable class assignments. We performed a 5×5 cross-validation between human and algorithm annotations and examined both ROC and precision versus recall curves to show that the algorithm can identify the many medical and biopsychosocial components from the texts. We briefly describe a use case for detecting pain relief from various interventions utilizing the word-by-class maps. We conclude that an exemplar-based method can be a valuable tool in knowledge extraction from texts that share similar construction, such as medical progress notes.
A systematic method for building Internet of Agents applications based on the Linked Open Data approach

The Internet of Agents (IoA) is an emerging paradigm whose objective is to mitigate the deficiencies of devices of Internet of Things (IoT) in terms of reasoning and social capabilities, in order to improve proactivity, intelligence and interoperability. This paper presents the guidelines to develop IoA applications based on described semantic agents following the Linked Open Data (LOD) approach and the specifications of the IoA-OWL ontology—a specialized full ontology that formally defines the main aspects related to a novel approach such as IoA. These guidelines have been drawn up via a systematic method created from the best practices of Agile Software Development Methodologies. This method creates smart, autonomous, collaborative IoA applications based on novel Linked Open Agents (LOAs) that are driven by Linked-Agent Contracts (LACs) and Workflows for Agent Control (WACs). From a practical perspective, our method separates the modeling of components using two levels, microscopic (at agent level) and macroscopic (at agent society level) facilitated the planning, configuring and implementation of each agent in the IoA ecosystem. Moreover, the method facilitates the agent creation automation process, reducing the time required for its development and simplifying the design complexity. These achievements were demonstrated through the modeling of an Ambient Intelligence (AmI) scenario on an office composed by a set of collaborative agents in order to provide smart comfort.
Modeling positive and negative feedback for improving document retrieval

Pseudo-relevance feedback (PRF) has evident potential for enriching the representation of short queries. Traditional PRF methods treat top-ranked documents as feedback, since they are assumed to be relevant to the query. However, some of these feedback documents may actually distract from the query topic for a range of reasons and accordingly downgrade PRF system performance. Such documents constitute negative examples (negative feedback) but could also be valuable in retrieval. In this paper, a novel framework of query language model construction is proposed in order to improve retrieval performance by integrating both positive and negative feedback. First, an improvement-based method is proposed to automatically identify the types of feedback documents (i.e. positive or negative) according to whether the document enhances the retrieval’s effectiveness. Subsequently, based on the learned positive and negative examples, the positive feedback models and the negative feedback models are estimated using an Expectation-Maximization algorithm with the assumptions: the positive term distribution is affected by the context term distribution and the negative term distribution is affected by both the positive term distribution and the context term distribution (such that the positive feedback model upgrades the rankings of relevant documents and the negative feedback model prunes the irrelevant documents from a query). Finally, a content-based representativeness criterion is proposed in order to obtain the representative negative feedback documents. Experiments conducted on the TREC collections demonstrate that our proposed approach results in better retrieval accuracy and robustness than baseline methods.