Reviewerly/exHarmony huggingface dataset:
Ensemble Validation: Selectivity has a Price, but Variety is Free

For an ensemble classifier that is composed of classifiers selected from a hypothesis set of classifiers, and that selects one of its constituent classifiers at random to use for each classification, we present ensemble error bounds consisting of the average of error bounds for the individual classifiers in the ensemble, a term that depends on the fraction of hypothesis classifiers selected for the ensemble, and a small constant term and multiplier. There is no penalty for using a richer hypothesis set, if the same fraction of the hypothesis classifiers are selected for the ensemble.
Joint Graph Based Embedding and Feature Weighting for Image Classification

The graph-based embedding is an effective and useful method in reducing the dimension and extracting relevant data. This paper introduces a framework for classifying high dimensional data via a joint graph-based embedding and weighting method which could be used in semi-supervised or supervised learning. We design on effective optimization algorithm to solve the objective function. Experiments on image classification show that our proposed method can have a performance that is better than that of many state-of-the-art methods including linear and nonlinear methods.
An Educational Strategy Based on Virtual Reality and QFD to Develop Soft Skills in Engineering Students

This work exposes an educational strategy based on gamification for the development of soft skills in engineering students, through the simulation of the process of product development based on QFD (Quality Function Deployment) in a virtual reality environment using "Second Life". The steps carried out in this work are explained starting from the proposal design process, followed by the development of the proposal and the evaluation. Finally, the findings showed that the proposal achieved their goals; thanks to that, the virtual environment furnished conditions very close to the QFD implementation in a company, which helped to promote positive interactions among students who overcame the apathy and fear among them and finished with a positive perception of the process.
A Metrics Framework for Product Development in Software Startups

Business cases and customer problem spaces are evolving quicker than ever before and more startups are moving to adopt the lean startup methodology to match this speed of changing customer needs. This phenomenon, however, comes with its own set of opportunities and challenges for startups to build great products, while catering to customer pain points. To this end, there is a need for a metrics framework which can help startups succeed in creating good software solutions and building successful business models around these solutions. Metrics can help measure the effectiveness of the product in relation to the customer problem and help drive key decisions in both the product and business aspects of the startup. This paper reviews current frameworks on metrics for software products, studies the appropriateness in the context of software startups and proposes a metrics framework to help provide good software experiences, while subsequently building good business models around these experiences. The framework is designed to cover aspects of both the product and business space, ranging from considerations of the problem space identification to the evolution of the solution. The proposed framework is validated using a case study approach of a successful startup. The framework aims to help startups in their journey to success by providing an end to end, structured approach to metric identification.
Special issue: Different perspectives on proper noun modifiers

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From the Myth of Babel to Google Translate: Confronting Malicious Use of Artificial Intelligence – Copyright and Algorithmic Biases in Online Translation Systems

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Keyed learning: An adversarial learning framework—formalization, challenges, and anomaly detection applications

We propose a general framework for keyed learning, where a secret key is used as an additional input of an adversarial learning system. We also define models and formal challenges for an adversary who knows the learning algorithm and its input data but has no access to the key value. This adversarial learning framework is subsequently applied to a more specific context of anomaly detection, where the secret key finds additional practical uses and guides the entire learning and alarm-generating procedure.
Conforming test of Blockchain for 5G Enabled IoT

While the question of blockchain strengthening the IoT is raised, the conformance test of 5G for blockchain applications becomes inevitable, since 5G is an enabler of IoT. Blockchain demands point-to-point communication to have transaction and 5G is best suited for such LOS communication. The convergence of technological, economic and environmental forces, are driving the 5G and blockchain simultaneously, then each drives the other forward. Be it the 5G driving blockchain or vice-versa, the continued expansion of each is good for the other. The complete suitability of 5G for blockchain applications is yet to be conducted. In this paper, we bring out the individual attributes and expectations of 5G and blockchain and carry out a study on how both can complement each other. The study encompasses how well communication constraints in blockchain is resolved by 5G communication and how the security and privacy issues in 5G are resolved by blockchain. The research we did in the blockchain and 5G, as well as mmWave communication and MIMO technologies, evoke visions of a vast network of connected devices balancing and securing in a deeply connected world. The proliferation of blockchain enabled 5G (or 5G enabled blockchain) will open up new frontiers of services and applications for next-generation communication systems.
The age distribution of facial metrics in two large Korean populations

Abstract Growth and alterations in craniofacial morphology have attracted interest in many fields of science, especially physical anthropology, genetics and forensic sciences. We performed an analysis of craniofacial morphology alterations by gender and ageing stage in Korean populations. We studied 15 facial metrics using two large Korean populations (1,926 samples from the Korea Medicine Data Center cohort and 5,643 samples from the Ansan-Ansung cohort). Among the 15 metrics, 12 showed gender differences and tended to change with age. In both of the independent populations, brow ridge height, upper lip height, nasal tip height, and profile nasal length tended to increase with age, whereas outer canthal width, right palpebral fissure height, left palpebral fissure height, right upper lip thickness, left upper lip thickness, nasal tip protrusion, facial base width, and lower facial width tended to decrease. In conclusion, our findings suggest that ageing (past 40 years of age) might affect eye size, nose length, upper lip thickness, and facial width, possibly due to loss of elasticity in the face. Therefore, these facial metric changes could be applied to individual age prediction and aesthetic facial care.
A Subjective Logic Based Approach to Handling Inconsistencies in Ontology Merging

Ontologies reflect their creators’ view of the domain at hand and are thus subjective. For specific applications it may be necessary to combine several of these ontologies into a more comprehensive domain model by merging them. However, due to the subjective nature of the source ontologies, this can result in inconsistencies. Handling these inconsistencies is a challenging task even for modestly sized ontologies. Therefore, in this paper, we propose a Subjective Logic based approach to cope with inconsistencies originating in the ontology merging process. We formulate subjective opinions about the inconsistency causing axioms based on several pieces of evidence such as provenance information and structural relevance by utilizing consensus and conditional deduction operators. This allows creating an environment that supports handling of these inconsistencies. It provides the necessary mechanisms to capture the subjective opinion of different communities represented by the input ontologies on the trustworthiness of each axiom in the merged ontology and identifies the least trustworthy axioms. It suggests remedies of the inconsistencies, e.g. deleting or rewriting axioms, to the user. Our experimental results show that with this approach it is possible to overcome the inconsistency problem in ontology merging and that the approach is feasible and effective.
Brain-vascular segmentation for SEEG planning via a 3D fully-convolutional neural network

Three dimensional visualization of vascular structures can assist clinicians in preoperative planning, intra-operative guidance, and post-operative decision-making. The goal of this work is to provide an automatic, accurate and fast method for brain vessels segmentation in Contrast Enhanced Cone Beam Computed Tomography (CE-CBCT) dataset based on a residual Fully Convolutional Neural Network (FCNN). The proposed NN embeds in an encoder-decoder architecture residual elements which decreases the vanishing effect due to deep architecture while accelerating the convergence. Moreover, a two-stage training has been proposed as a countermeasure for the unbalanced nature of the dataset. The FCNN training was performed on 20 CE-CBCT volumes exploiting mini-batch gradient descent and the Adam optimizer. Binary cross-entropy was used as loss function. Performance evaluation was conducted considering 5 datasets. A median value of Dice, Precision and Recall of 0.79, 0.8 and 0.69 were obtained with respect to manual annotations.
An Efficient Object Detection Model Using Convolution Neural Networks

Image processing and computer vision have gained an enormous advance in the field of machine learning techniques. Some of the major research areas within machine learning are Object detection and Scene Recognition. Though there are numerous existing works related to the specified fields object detection still encounters numerous challenges when it comes to implementing in the real-time scenario. The problem occurs in the detection due to various objects present in the background. Object detection mechanism detects a specified object when a particular scene is given. Classifiers like SVM and Neural Networks are used to train the classifier in such a way they are able to detect an object when a new image is given. In this paper, we have proposed a model which detects texts from an image. Bounding boxes are used to detect the texts and localize it. The neural network is used to train the model where numerous images having texts are given as the training set. The performance evaluation is done on the model and it is observed that it detects the texts when a new image is given. Object detection is a fundamental problem in computer vision, which aims to detect general objects in images.
Enhancing the Proficiency of Artificial Neural Network on Prediction with GPU

Prior efforts in research exhibit that Artificial Neural Network (ANN) proves out to give better and satisfactory results for prediction. This would be helpful for accomplishing decision procedure for future. Apart from this, Graphical Processing Units (GPUs) has a parallel computing environment, which expands the computation ability in a meaningful manner. But there is no specified model to conduct computation for this environment. The application of ANN includes excessive number of matrix operation, for these types of operations; GPUs perform best as they provide parallel computation. In this paper an ANN model with GPU has been proposed that may provide improved results of prediction with GPU over CPU.
Modeling Conversational Agents for Service Systems

Service providers are increasingly exploring the use of conversational agents (CA) or dialogue based systems to support end customers, as a CA promises natural method for users to interact and a convenient channel for customer service. Commercial CAs, excel in addressing specific tasks or functions such as searching for restaurants, providing location directions, or scheduling meetings, with small variations in the user request. Designing a CA for a more complex service system, requires sufficient knowledge of its services such as the service capabilities, their constraints, and effects, in addition to understanding user utterances. The design of a CA is typically an independent activity and its linkages to the service system it supports are left to the designers. In this paper, we study existing work with respect to text-based CAs and identify the conceptual elements of a CA. Further, a linkage between the model elements of a CA and service model of the service system it supports is established and presented. We show that interesting insights can be derived from the linkages, that can be useful to CA designers.
Key time steps selection for CFD data based on deep metric learning

As one of the main technologies of flow visualization, key time steps selection plays a key role in solving storage limit and has been intensively studied. In this paper, we introduce Deep Metric Learning (DML) into key time steps selection for Computational Fluid Dynamics (CFD) data and propose a local selection method based on DML. In specific, the proposed method samples small patches from CFD data, trains a Siamese deep neural network which has a symmetry structure with two Convolutional Neural Networks (CNN), and then selects the key time steps according to the similarities between consecutive time steps which are assessed by the networks. Compared with one of the existing local selection methods, the Myers’s method, our method has advantages in accuracy, precision and recall, and the selection results are better. Experimental results also demonstrate the good generalization of the proposed method on CFD datasets.
Unsupervised Lesion Detection with Locally Gaussian Approximation

Generative models have recently been applied to unsupervised lesion detection, where a distribution of normal data, i.e. the normative distribution, is learned and lesions are detected as out-of-distribu-tion regions. However, directly calculating the probability for the lesion region using the normative distribution is intractable. In this work, we address this issue by approximating the normative distribution with local Gaussian approximation and evaluating the probability of test samples in an iterative manner. We show that the local Gaussian approximator can be applied to several auto-encoding models to perform image restoration and unsupervised lesion detection. The proposed method is evaluated on the BraTS Challenge dataset, where the proposed method shows improved detection and achieves state-of-the-art results.
Towards a Logic for Conditional Local Strategic Reasoning

We consider systems of rational agents who act in pursuit of their individual and collective objectives and we study the reasoning of an agent or an external observer about the consequences from the expected choices of action of the other agents based on their objectives, in order to assess the reasoner’s ability to achieve his own objective. To formalize such reasoning we introduce new modal operators of conditional strategic reasoning and use them to extend Coalition Logic in order to capture variations of conditional strategic reasoning. We provide formal semantics for the new conditional strategic operators, introduce the matching notion of bisimulation for each of them and discuss and compare briefly their expressiveness.
An Open-source Based Automatic Car Detection System using IoT

Cars have been employed as a tool to commit a crime such as for escaping, carrying drugs, or bombs to attack especially in the three southern borders of Thailand. To detect a suspicious car from a blacklist, government officers normally check this manually which may have some errors. This blacklist can be updated time by time. In addition, to avoid being detected, the criminal may change the car license plate. Therefore, it is better to have an automatic system that can detect suspicious cars. Though there are some open-source programs that can detect car such as by its license plate, they have many limitations; for instance, cannot recognize Thai license plate, cannot detect car logo; especially there is no system that can perform these two functions together. This project thus proposes an automatic car detection system that can recognize a Thai license plate and car logo in one system. We aim to build this system to be portable using low-cost IoT devices and to develop it with open-source programs. In addition, we will publish the Thai license plate recognition and logo detection models for the benefit of Thai society.
Product Line Verification via Modal Meta Model Checking

Modal Meta Model Checking (M3C) is a method and tool supporting meta-level product lining and evolution that comprises both context-free system structure and modal refinement. The underlying Context-Free Modal Transition Systems (CFMTSs) can be regarded as loose specifications of meta models, and modal refinement as a way to increase the specificity of allowed domain specific languages (DSLs) by constraining the range of allowed syntax specifications. Model checking with M3C allows one to verify properties specified in a branching-time logic for all DSLs of a given level of specificity in one go. The paper illustrates the impact of M3C in an industrial setting where well-formed documents serve as contracts between a provider and its customers in two steps: it establishes CFMTS as a formalism to specify product lines of document description types (DTDs – or related formalisms like JSON schema), and it shows how M3C-based product line verification can be used to guarantee that violations of essential well-formedness constraints of a corresponding user document are detected by standard DTD-checkers. The resulting hierarchical product line verification allows Creios GmbH, a service provider for E-commerce systems to provide a wide range of tailored shop applications whose essential business rules are checked by a standard DTD-checker.
Brain Segmentation from k-Space with End-to-End Recurrent Attention Network

The task of medical image segmentation commonly involves an image reconstruction step to convert acquired raw data to images before any analysis. However, noises, artifacts and loss of information due to the reconstruction process are almost inevitable, which compromises the final performance of segmentation. We present a novel learning framework that performs magnetic resonance brain image segmentation directly from k-space data. The end-to-end framework consists of a unique task-driven attention module that recurrently utilizes intermediate segmentation estimation to facilitate image-domain feature extraction from the raw data, thus closely bridging the reconstruction and the segmentation tasks. In addition, to address the challenge of manual labeling, we introduce a novel workflow to generate labeled training data for segmentation by exploiting imaging modality simulators and digital phantoms. Extensive experimental results show that the proposed method outperforms several state-of-the-art methods.
Relevance Feedback

Document retrieval methods that utilize relevance feedback often induce a single query model from the set of feedback documents, specifically, the relevant documents. We empirically show that for a few state-of-the-art query-model induction methods, retrieval performance can be significantly improved by constructing the query model from a subset of the relevant documents rather than from all of them. Motivated by this finding, we propose a new approach for relevance-feedback-based retrieval. The approach, derived from the risk minimization framework, is based on utilizing multiple query models induced from all subsets of the given relevant documents. Empirical evaluation shows that the approach posts performance that is statistically significantly better than that of applying the standard practice of utilizing a single query model induced from the relevant documents. While the average relative improvements are small to moderate, the robustness of the approach is substantially higher than that of a variety of reference comparison methods that address various challenges in using relevance feedback.
ROBUST METHODOLOGY FOR DETECTION OF SPIKES IN MULTIBEAM ECHO SOUNDER DATA

Currently, during the operation in shallow waters, scanning systems, such as multibeam systems, are capable of collecting thousands of points in a short time, promoting a greater coverage of the submerged bottom, with consequent increase in the detection capacity of objects. Although there has been an improvement in the accuracy of the depths collected, traditional processing, that is, manual, is still required. However, mainly due to the increased mass of data collected, manual processing has become extremely time-consuming and subjective, especially in the detection and elimination of spikes. Several algorithms are used to perform this task, but most of them are based on statistical assumptions hardly met and/or verified, such as spatial independence and normality. In this sense, the goal of this study is to present the SODA (Spatial Outlier Detection Algorithm) methodology, a new method for detection of spikes designed to treat bathymetric data collected through swath bathymetry systems. From computational simulation, promising results were obtained. SODA, in some cases, was capable to identify even 90% of spikes inserted on simulation, showing that the methodology is efficient and substantial to the bathymetric data treatment.
Semantic Relatedness as an Inter-facet Metric for Facet Selection over Knowledge Graphs

Faceted Browsing is a wide-spread approach for exploratory search. Without requiring an in-depth knowledge of the domain, users can narrow down a resource set until it fits their need. An increasing amount of data is published either directly as Linked Data or is at least annotated using concepts from the Linked Data Cloud. This allows identifying commonalities and differences among resources beyond the comparison of mere string representations of metadata. As the size of data repositories increases, so does the range of covered domains and the number of properties that can provide the basis for a new facet. Manually predefining suitable facet collections becomes impractical. We present our initial work on automatically creating suitable facets for a semantically annotated set of resources. In particular, we address two problems arising with automatic facet generation: (1) Which facets are applicable to the current set of resources and (2) which reasonably sized subset provides the best support to users?
A Tagger for Glossary of Terms Extraction from Ontology Competency Questions

Competency Questions (CQs) are questions expressed in natural language aimed to indicate ontology’s scope, which are later formalized according to the language used to represent the ontology. One intermediate step that facilitates formalizing CQs, proposed in ontology engineering methodologies, is to extract so-called Glossary of Terms from them, which is so far a manual process. To automate this intermediate step, we propose a tagger, which for the given sequence of words, in a CQ, decides whether it should be considered as a suggestion of vocabulary (a class, an instance or a property) in the created ontology, and in this way being a good candidate entry to the Glossary of Terms. We also report about preliminary evaluation of the tagger.