Reviewerly/exHarmony huggingface dataset:
Epistemic logic without closure

Abstract All standard epistemic logics legitimate something akin to the principle of closure, according to which knowledge is closed under competent deductive inference. And yet the principle of closure, particularly in its multiple premise guise, has a somewhat ambivalent status within epistemology. One might think that serious concerns about closure point us away from epistemic logic altogether—away from the very idea that the knowledge relation could be fruitfully treated as a kind of modal operator. This, however, need not be so. The abandonment of closure may yet leave in place plenty of formal structure amenable to systematic logical treatment. In this paper we describe a family of weak epistemic logics in which closure fails, and describe two alternative semantic frameworks in which these logics can be modelled. One of these—which we term plurality semantics —is relatively unfamiliar. We explore under what conditions plurality frames validate certain much-discussed principles of epistemic logic. It turns out that plurality frames can be interpreted in a very natural way in light of one motivation for rejecting closure, adding to the significance of our technical work. The second framework that we employ—neighbourhood semantics—is much better known. But we show that it too can be interpreted in a way that comports with a certain motivation for rejecting closure.
Haploid Versus Diploid Genetic Algorithms. A Comparative Study

Genetic algorithms (GAs) are powerful tools for solving complex optimization problems, usually using a haploid representation. In the past decades, there has been a growing interest concerning the diploid genetic algorithms. Even though this area seems to be attractive, it lacks wider coverage and research in the Evolutionary Computation community. The scope of this paper is to provide some reasons why this situation happens and in order to fulfill this aim, we present experimental results using a conventional haploid GA and a developed diploid GA tested on some major benchmark functions used for performance evaluation of genetic algorithms. The obtained results show the superiority of the diploid GA over the conventional haploid GA in the case of the considered benchmark functions.
Neural Machine Translation

The emergence of neural machine translation (NMT) has revolutionized the filed of machine translation. In the first section, we introduce the fundamental of NMT models. Then we study the advantages of NMT over traditional statistical machine translations (SMT), some of its existing challenges and recent research efforts. Finally, we summarize our four works that address several existing problems in NMT. In the second section, we follow attentional neural machine translation to use mathematical formula to define the overall procedure for NMT.
What Happened in CLEF $$\ldots $$ For a While?

2019 marks the 20 $$^\text {th}$$ birthday for CLEF, an evaluation campaign activity which has applied the Cranfield evaluation paradigm to the testing of multilingual and multimodal information access systems in Europe. This paper provides a summary of the motivations which led to the establishment of CLEF, and a description of how it has evolved over the years, the major achievements, and what we see as the next challenges.
Two-Stage Unsupervised Learning Method for Affine and Deformable Medical Image Registration

Conventional medical image registration relies on time-consuming iterative optimization. We propose a two-stage unsupervised learning method for 3D medical image registration. In the first stage, we learn a global image-wise affine map by a deep network. In the second stage, we learn a local voxel-wise deformation vector field by an encoder-decoder architecture. The final registered image is acquired by applying the local deformation field to the moved image of the first stage. The two networks are trained in an unsupervised manner by maximizing global and local normalized cross-correlations between the fixed image and moved images in the two stages respectively. The well-trained networks can be directly adopted to register images through forward propagation without iteration. We do not need ground-truth in training stage and aligning images in preprocess step. Experiments on four brain MRI datasets demonstrate that the proposed approach outperforms several state-of-the-art methods in terms of accuracy and efficiency.
ARTitser

The authors implemented an Augment Reality (AR) to develop a mobile application called ARTitser that was utilized for Junior High School students as a supportive tool to learn Biological Science. The said application runs in iOS that integrates AR technology education which may help the teachers in facilitating delivery of the daily lessons using a realistic representation of objects for a better learning experience. It may also assist the teachers to monitor the performance of the students when using AR lesson. Moreover, the students may enjoy ARTitser application because they will see 3D objects associated to biology lesson. Based on the result of the survey, the respondents highly recommended the ARTitser with a grand mean of 4.52 which is highly acceptable. Thus, the said tool can be implemented in the Junior High School students which may motivate as well as improve the performance of the students.
Generational Feature Selection using Random Forest Approach

Feature selection process is crucial step in many different knowledge discovery experiments. Here, some initial attempt to Generational Feature Selection using random forest were presented. This approach devotes to application of random forest algorithm to estimate importance of attributes with recursive application of generational feature selection. This method apply removing of selected features from dataset and then creates next generation of important feature set. This process goes until the most important feature will be a random value. Implemented method were applied on three artificial and real, medical datasets and the results of selection and classification are presented. The results were significantly better for limited datasets.
Indic SentiReview: Natural Language Processing based Sentiment Analysis on major Indian Languages

Sentiment Analysis(SA) can be a natural language processing(NLP) task that extracts opinions from the given text and classifies them as a negative or positive. Research works in SA is mostly conducted in English. Nowadays, the web indexes and other websites related to reviews also support non-English languages. It is therefore necessary to perform SA for these languages as well. There are numerous works found in the literature for SA in other languages worldwide. However, SA for Indian languages needs exploration. In this paper, we discuss various available lexicon resources and often used SA techniques in some Indian languages. Moreover, we present the theoretical parametric evaluation of our studied techniques and we also discuss challenges, which were identified during SA in Indian Languages.
Recommendations for Car Selection System Using Item-Based Collaborative Filtering (CF)

Car is a four or more wheels transportation that have many benefits for humanity, one of which can carry passengers and stuffs. The technology that has been developed brings a lot of information, this is aligned with information related to the car. It often happens when someone who wants to choose a car becomes confused because so many cars information are available on the internet. Therefore, we need a system that can help provide information about cars that are in accordance with the user's wishes, namely the recommendation system. The recommendation system requires the right recommendation In this research will focus on the problem of recommending the car selection system by building a recommendation system through an item-based Collaborative Filtering approach. To help provide solutions to the above problems, this recommendation system has 9 parameters. The application of item-based Collaborative Filtering algorithm produces a recommendation system that has a Mean Absolute Error (MAE) of 0.202 and has an accuracy rate of 95.955%.
Learning to Upsample Smoke Images via a Deep Convolutional Network

The flare, which aims to burn all kinds of waste gas produced by petrochemical enterprises, is an important facility for plants to keep safe and prevent harming the environment. However, when the exhaust gas is not sufficiently burned, black smoke will be produced in the flare system and endanger air quality and human health. So, as a crucial task, reducing the emission of flare black smoke is attracting a growing amount of research attention. In a typical flare smoke reduction system, a high-resolution smoke image is greatly beneficial to smoke recognition and analysis. But the smoke image usually encounters the low-resolution problem. Accordingly, in this paper we propose a super-resolution method specific for smoke images, which is called smoke images upsampling method (SIUM). Considering the texture and edge characteristics of the smoke images, the proposed SIUM learns a mapping between the low-resolution images and the associated high-resolution images. Experimental results demonstrate that our proposed SIUM is prominently superior to relevant state-of-the-art technologies when applied to upsample low-resolution smoke images.
Introduction to “Environmental Remote Sensing and GIS in Iraq”

This chapter presents the main technical features of the book titled “Environmental Remote Sensing and GIS in Iraq”. The book consisted of seven parts including the introduction (this chapter) and the conclusions (the closing chapter). The main body of the book consisted of five themes to cover Soil Characterization, Modelling, and Mapping in three chapters, Proximal Soil Sensing in two chapters, remote sensing (RS) and Geographical Information Systems (GIS) for Land Cover/Land Use Change Monitoring in three chapters, Land Degradation, Drought, and Dust Storms in six chapters, and RS and GIS for Natural Resources. The main technical elements of each chapter are presented under its relevant theme.
UserDEV

The increasing availability of user data constitutes new opportunities in various applications ranging from behavioral analytics to recommendations. A common way of analyzing user data is through "user group analytics" whose purpose is to breakdown users into groups to gain a more focused understanding of their collective behavior. The process consists of group discovery, group exploration, and group visualization. To date, user group analytics is done using separate tools which makes it fragmented and burdensome for analysts. In this paper, we describe UserDEV, a full-fledged user group analytics pipeline which combines discovery, exploration, and visualization of user groups, in a fully-connected fashion. UserDEV contributes a star-like architecture as well as a common data exchange model to tighten connections between the analytics components. We provide a realistic use case to show how UserDEV helps analysts perform analytical tasks on user groups. While we report a preliminary user study, we also discuss opportunities for an end-to-end evaluation of a group analytics framework.
On the Application of Ontological Patterns for Conceptual Modeling in Multidimensional Models

Data warehouses (DW) play a decisive role in providing analytical information for decision making. Multidimensional modeling is a special approach to modeling data, considered the foundation for building data warehouses. With the explosive growth in the amount of heterogeneous data (most of which external to the organization) in the latest years, the DW has been impacted by the need to interoperate and deal with the complexity of this new type of information, such as big data, data lakes and cognitive computing platforms, becoming evident the need to improve the semantic expressiveness of the DW. Research has shown that ontological theories can play a fundamental role in improving the quality of conceptual models, reinforcing their potential to support semantic interoperability in its various manifestations. In this paper we propose the application of ontological patterns, grounded in the Unified Foundational Ontology (UFO), for conceptual modeling in multidimensional models, in order to improve the semantic expressiveness of the models used to represent analytical data in a DW.
Fuzzy Controller Inference via Gradient Descent to Model the Longitudinal Behavior on Real Drivers

This paper introduces a method to represent Takagi-Sugeno Fuzzy Control Systems (FCSs) as computational graphs, so they can be adjusted through a supervised training process based on gradient descent. It has been tested both with artificial (i.e. a known fuzzy controller) and naturalistic (i.e. driver's data extracted from the vehicle and the environment) data. The results achieved show high conformance to synthetic data, and seem to describe a car-following behavior with quite good precision, which suggests that it is possible to model the driver's behavior in a longitudinal model based on if-then type rules.
Using a Database of Multiword Expressions in Dependency Parsing

Identifying and correctly handling multiword expressions is critical for understanding a language system and for properly functioning NLP tools. This paper presents a database of multiword expressions (MWE) we build for the Czech language which currently contains more than 7,000 entries. It contains detailed information about the properties of MWEs, e.g. about their idiomaticity and variability. The database also contains manually verified dependency structures of MWEs. We show one of the possible uses of the database: identification and correction of parsing errors in sentences containing MWEs.
Pedestrian Attribute Recognition Based on Mtcnn with Online Batch Weighted Loss

Due to the large number and huge diversity of attributes, pedestrian attribute recognition in video surveillance scenarios is a challenging task in the field of computer vision. Different from most previous works which only focus on extremely imbalanced attribute distribution problem, a new grouping way of attributes based multi-task convolutional neural network (MTCNN) is put forward, which exploits the spatial correlations among attributes and guarantees some independence of each attribute as well. Meanwhile, we propose a novel online batch weighted loss to narrow the performance differences among attributes and boost the model to gain a higher average recognition accuracy. The whole network can be trained end to end, and experimental results on PETA and RAP datasets show that our method achieves significant performance, comparing with those state-of-the-art methods.
Learning to Focus and Discriminate for Fine-Grained Classification

Existing state-of-the-art fine-grained classification methods usually use separated networks for discriminative region localization and feature learning/classification, and are thus complicated to implement and optimize. In this paper, we aim to provide a compact solution by deepening the collaboration between the region localization, feature learning and classification modules during the learning process of fine-grained classification. We thus propose a method that can learn to simultaneously localize discriminative regions and extract discriminative features by exploring the localization ability of classification convolutional neural networks and joint optimization of different modules. Our method, while being built upon a single backbone network and trained with only softmax losses, achieves state-of-the-art performance on three benchmark fine-grained datasets, which proves that our method is simple but effective for fine-grained classification.
Ideas on Improving Software Artifact Reuse via Traceability and Self-Awareness

We describe our vision towards automatic software and system development and argue that reusing knowledge from existing projects as well as traceability between corresponding artifacts are important steps towards this vision. We furthermore list barriers that are currently experienced with software artifact reuse and traceability in industry and suggest some ideas to overcome these barriers.
Digital Library Development and Evaluation to Improve Students’ Digital Literacy

Abstract The existence of digital libraries support education in the evolution of generation 4.0 education, conceptual design of digital libraries has become important as a reference in the development of digital libraries. Many studies deals with digital libraries, however, they have not accommodated the needs and behavior of different users, this research develops digital libraries by adding an initial interface to digital libraries and developing sharing digital content in learning. Evaluation of system development is carried out by involving two experimental classes and a control class. The results of the qualitative evaluation also showed that 80.9% gave positive comments which showed that the category was very suitable to be used for the learning process. From the results of the evaluation of the experimental class control class showed that the experimental class was better than the control class, this shows that in the experimental class the students’ information technology literacy skills were better than the control class.
More General Evaluation of a Client-Centered Counseling Agent

A lot of people in Japan suffer from bad conditions of their mental health with an increasing tendency, in particular jobseekers and elderly persons. The classical way to solve their problem is to consult a counselor, who treats these people in a way to become aware of the core of their problem and to solve it. However, the number of well qualified counselors is limited. For this purpose, we developed a VCA (Virtual Counseling Agent) as a further evolution of a formerly developed CRECA (Context Respectful Counseling Agent). CRECA had a text interface. To much more imitate a human counselor, VCA has an image avatar and a voice conversation using the Google Cloud audio API. Further, VCA is made independent of counseling content fields. Thus, it is more generalized than CRECA or ELIZA in order for every people to easily use everywhere in every situation. Here, VCA, CRECA, and ELIZA are comparatively evaluated by questionnaire after the use of 10 college students having career problems as well as elderly persons struggling with modern IT. As a result, VCA significantly exceeded the average value of ELIZA along with the significant difference at the level of 5%. Moreover, average of the evaluation value is not worse than CRECA. Compared to CRECA, VCA does not limit the content and field of consultation. Especially, elderly persons struggling with modern IT could not use CRECA that have only text interface. It can be used generally at any time in everyday natural conversation. It can easily be used by elderly people and the digital divided due to voice conversion.
On Combining Case Adaptation Rules

The case adaptation process in case-based reasoning is often modeled as having two steps: enumerating differences between a new problem and the problem part of a retrieved case and then applying an adaptation rule for each difference. This model is sufficient when (1) predefined adaptation rules exist for all differences the system encounters, and (2) adaptation rules are sufficiently independent that interactions are not a major issue. This paper presents an approach to handling case adaptation when these assumptions fail. It proposes an approach, RObust ADaptation (ROAD), that uses heuristics to guide multi-step adaptations, with each adaptation chosen in the context of adaptations applied previously. To reduce the potential for accumulated degradation of solution quality from long adaptation chains, it performs incremental retrieval of new source cases along the adaptation path, resetting the partially modified case to the "ground truth" of existing cases when an existing case is nearby. An evaluation supports the benefits of the model and illuminates some tradeoffs.
Critical analysis of a higher education benchmark via fuzzy logic

A graduate profile benchmark developed by a higher education organisation is subject to scrutiny. This occurs through the numerical parameterisation of text collected through surveys and the use of basic concepts drawn from Information Technology. The process uses data processing, analysis and evaluation to show the degree to which established an academic practice at the host institution correlates to pre-conceived professional outputs. The paper discusses standard practices including traditional and innovative teaching methods, in the light of achieved results. To conclude that postgraduate students require further engagement with the industry and exposure to the public whilst showing that the scientific and technical components of the subject courses are highly rated by stakeholders. Mitigation measures for the identified gaps include further curriculum development and inter-disciplinary work, reinforced with industrial liaison plus engagement with learned organisations and the public, via educational enhancement activities.
Improving Multi Object Tracking-By-Detection Model Using a Temporal Interlaced Encoding and a Specialized Deep Detector

Tracking-by-detection have become a hot topic for intelligent vehicle applications in recent year. Generally, the existing tracking-by-detection frameworks have difficulties with congestion, occlusion, and inaccurate detection in crowded scenes. In this paper, we propose a new framework for Multi-Object Tracking-by-detection (MOT-TbD) based on an temporal interlaced encoding video model and a specialized Deep Convolutional Neural Network (DCNN) detector. Spatio-temporal variation of objects between images are encoded into “interlaced images”. A specialized “interlaced object” deep detector is trained on a interlaced dataset. The detected objects are associated with a classical data association algorithm. Since interlaced objects are built to increase overlap during the association step, the performance of the MOT-TbD increases related to the same detector/association algorithm applied on non-interlaced images. The effectiveness of the method is demonstrated by experiments on popular tracking-by-detection datasets such as the PETS 2009 and TUD. Experimental results show that the proposed framework outperforms several state-of-the-art MOT-TbD methods.
Prnu Pattern Alignment for Images and Videos Based on Scene Content

This paper proposes a novel approach for registering the PRNU pattern between different camera acquisition modes by relying on the imaged scene content. First, images are aligned by establishing correspondences between local descriptors: The result can then optionally be refined by maximizing the PRNU correlation. Comparative evaluations show that this approach outperforms those based on brute-force and particle swarm optimization in terms of reliability, accuracy and speed. The proposed scene-based approach for PRNU pattern alignment is suitable for video source identification in multimedia forensics applications.