Reviewerly/exHarmony huggingface dataset:
A Deep Blind Image Quality Assessment with Visual Importance Based Patch Score

Convolutional neural networks (CNNs)-based no-reference image quality assessment (NR-IQA) suffers from insufficient training data. The conventional solution is splitting the training image into patches, assigning each patch the quality score, while the assignment of patch score is not consistent with the human visual system (HVS) well. To address the problem, we propose a patch quality assignment strategy, introducing the weighting map to describe the degree of visual importance of each distorted pixel, integrating the weighting map and the feature map to pool the quality score of each patch. With the patch quality, a CNNs-based NR-IQA model is trained. Experimental results demonstrate that proposed method, named as blind image quality metric with improved patch score (BIQIPS), improves the performance on most of the distortion types, especially on the types of local distortions, and achieves state-of-the-art prediction accuracy among the NR-IQA metrics.
An Improved Visual Background Extraction Algorithm Combining Depth Information

Abstract ViBe is one of the most commonly used background subtraction method, which conducts foreground detection on each frame pixel-by-pixel. When the algorithm is applied to video sequence with large depth differences such as forest fire surveillance, the problem of traditional ViBe is appearing. If the algorithm parameters of different depth are the same, it will inevitably lead to two situations: the nearby shaking of trees is easy to cause false detection because of the large pixel area, and the distant smoke is easy to miss as it occupies small pixels of the image. To solve this problem, an improved visual background extraction algorithm combining depth information is proposed. The method calculates the dark channel information as depth information, and a conversion function is designed to adjust sensitivity to accommodate moving targets with huge depth difference. The experimental results demonstrate that the improved ViBe algorithm has better performance than traditional ViBe algorithm in forest fire surveillance. In addition, the improvement of the algorithm does not excessively increase the computational time-consuming.
Research on Core Data Protection Based on Cloud Computing

The rise of cloud computing has brought new challenges and opportunities to data protection technology. On the one hand, large-scale cloud storage systems contain huge amounts of data. Their system complexity and service characteristics make data reliability severely challenged, requiring stronger data protection technologies. At the same time, cloud host surfaces with important business data are stored. Against the intrusion threat of hackers around the world, the common data protection mechanism in the host can not resist the hacker’s destruction behavior, and needs to adopt new data protection technology to deal with it. To this end, a cloud-based user core data protection model is designed to decompose user data into core data and general data. The core data will be stored in a user-controllable private cloud. The general data is filtered and stored in the public service provider. In the cloud, the protection and security of user core data is effectively solved. By utilizing the existing network security products and combining the designed data classification algorithms, the cloud-based enterprise core data protection system is constructed according to the model. The validity of the model is verified by the actual use, and the core data of the user in the cloud computing is used. Protection explores new ways.
Need Analysis on The Inclusive English Materials for The Primary Graders: Voices from Practitioners

This study is aimed at conducting a need analysis on the existence of English materials for the students of elementary level in an inclusive classroom. The data is obtained from inclusive teacher who has ELT undergraduate educational background and the students of pre-service training of PGMI at IAIN Jember. The result shows that teacher takes a very important role in conducting an inclusive classroom in primary level due to the fact that they have to prepare materials and activities specifically for the students who have social, emotional, and behavioral difficulties (SEBD). Listening and speaking skill (oracy cycle) are considered as the first skills to be taught and introduced to the students of primary level in order to be able to make interrelationship with others more easily. English materials that compatible with the students of primary level in an inclusive classroom are the materials that encourage their multiple intelligences and recognize their learning style. The teaching procedure is emphasized on the role play in the form of guided instruction flowing from fully guided, partly guided, to fully free learning so the scaffolding of presenting materials which emphasized on touching their feeling can improve their sense of empathy and sense of taking care of others as well as building their social skill.
A formal framework for reasoning about opportunistic propensity in multi-agent systems

Opportunism is an intentional behavior that takes advantage of knowledge asymmetry and results in promoting agents’ own value and demoting others’ value. It is important to eliminate such selfish behavior in multi-agent systems, as it has undesirable results for the participating agents. In order for monitoring and eliminating mechanisms to be put in the right place, it is needed to know in which context agents are likely to perform opportunistic behavior. In this paper, we develop a formal framework to reason about agents’ opportunistic propensity. Opportunistic propensity refers to the potential for an agent to perform opportunistic behavior. Agents in the system are assumed to have their own value systems and knowledge. With value systems, we define agents’ state preferences. Based on their value systems and incomplete knowledge about the state, they choose one of their rational alternatives to perform, which might be opportunistic behavior. We then characterize the situation where agents are likely to perform opportunistic behavior and the contexts where opportunism is impossible to occur, and prove the computational complexity of predicting opportunism.
Cellular Transport Systems Improved: Achieving Efficient Operations with Answer Set Programming

Autonomous vehicles for in-plant transportation are widely accepted in the industry nowadays. If applied in order picking, efficiency of their operations is essential for the performance of the overall system. Even though the developed systems are tailored to work in highly volatile environments their procedures are programmed in a comparably old-fashioned and inflexible manner. Answer set programming is experiencing a revival due to advances in the development of solving algorithms and computer hardware. This paper is an invited, extended version of Schieweck et al. (Proceedings of the 9th international joint conference on computational intelligence, 2007, [1]), in which several approaches for the application of answer set programming in order picking systems with autonomous vehicles are discussed. In this paper, theSchieweck, Steffen provided information on the approachesis extended, theKern-Isberner, Gabriele discussion expanded and the paradigm of reactive answer set programmingTen Hompel, Michael is additionally taken into account.
Multi-Feature Fusion Network for Salient Region Detection

Salient region detection is a fundamental problem in computer vision and image processing. Deep learning models perform better than traditional approaches but suffer from their huge parameters and slow speeds. To handle these problems, in this paper we propose the multi-feature fusion network (MFFN) - a efficient salient region detection architecture based on Convolution Neural Network (CNN). A novel feature extraction structure is designed to obtain feature maps from CNN. A fusion dense block is used to fuse all low-level and high-level feature maps to derive salient region results. MFFN is an end-to-end architecture which does not need any post-processing procedures. Experiments on the benchmark datasets demonstrate that MFFN achieves the state-of-the-art performance on salient region detection and requires much less parameters and computation time. Ablation experiments demonstrate the effectiveness of each module in MFFN.
Light Corner Based Object Detector with Stacked-ENet Backbones

Abstract Object detection algorithms have made great progress in the past few years because of the development of deep learning. From region selection to anchor box regression, the accuracy of the algorithms becoming more and more accurate, but they’re also far away from vision mechanism of human. Corner based is a new approach for object detection, but methods proposed nowadays are in huge wastage of time and resource, leading them not available in vast bulk of jobs. In this paper, we proposed a method which makes corner-based object detector could inference in real-time. We analyze the disadvantages of existing methods, found out the most consuming parts and the solution for improvements, which finally makes the algorithm efficiency and get a result which is competitive with other existing real-time methods.
Witness-Based Searchable Encryption with Aggregative Trapdoor

The well-known open problem in public key encryption with keyword search is how to avoid internal adversaries as the server. Implicitly, the internal attack is implemented as follows. Upon receiving a trapdoor, the probability polynomial time internal adversary can always act as a sender to produce each ciphertext for each keyword if keyword space is bounded by a polynomial of the security parameter. Then, the adversary runs the test algorithm for the trapdoor and all produced ciphertext, and then infer the correct keyword. To overcome this problem, the original framework must be changed slightly. A fundamental goal is creates a secure bridge between the sender and receiver. It not only keeps testability of the server, but also avoids imitating a sender. Witness-based searchable encryption (WBSE) is a manner to realize the design goal. In this paper, we formalize an abstracted notion, witness-based searchable encryption with aggregative trapdoor. Under the notion, we present a nearly optimal solution for WBSE under the barrier with trapdoor size proportional to n (the number of senders). Comparing with the existing scheme with trapdoor size O(n), the proposed scheme is based on bilinear map, and offers size only in n.
Artificial Neural Networks Methodologies to Optimize Engine Performance Parameters Using MATLAB

This work is concerned with the use of artificial neural network as a simulation tool for optimizing the performance of four-stroke single-cylinder diesel engine, operating at various conditions for this performance, test on a four-stroke diesel engines is conducted and the performance parameters are calculated with standard formulae. The output values obtained from the conventional method are used as input for training artificial neural networks in combination with backpropagation algorithm has been performed using MATLAB. The results obtained from the practical networks are compared with the conventional values and the errors are estimated for each parameter. The error deviation obtained against each parameter indicates the net variation of engine output, and accordingly the corrective actions may be initiated with the engine for the improvement of performance parameters.
What Is a Musical Act? Understanding Improvisation Through Artefact and Performance

The purpose of this article is to address the process/product dichotomy focusing on improvisational phenomena, in which most of musical ontology’s assumptions—the duality between work and performance, creation and rendition, essence and context—seem to be flawed. I will focus on the notions of artefact and performance, seeing them as complementary counterparts in what I would call musical act. The idea is to consider improvisation as the ideal paradigm in which performance conditions enter directly into the constitution of an aesthetic artefact, and, hence, a musical act takes place.
Bipolar Log-Intensity-Variance Histogram Method for Local Image Patch Intensity Change Measurements

For a fixed-position camera, the intensity changes of an image pixel are often caused by object movement or illumination change. This paper focuses on such a problem: given two adjacent local image patches, how can the causes of intensity change be determined? A bipolar log-intensity-variance histogram is proposed to describe the intensity variations on the chaos phase plot subspace. This is combined with two sigmoid functions to construct a probabilistic measure function. Experimental results show that the proposed measurements are more effective and robust than conventional methods to the cause of variation in image intensity.
Implementation and Evaluation of a Multimodel Filter for Aircraft Ground Operations

During aircraft ground operations, sensors and technologies such as stereovision systems can be used to locate other aircraft on the airfield in order to improve crew situational awareness. Unfortunately, their positional accuracy is deteriorated by sensor noise. This paper proposes tracking algorithms as a means of filtering this noise in order to improve accuracy. A simulation environment based on the dynamics of a passenger aircraft on the ground is set up and used to tune and evaluate various combinations of state transition models and Kalman filters. The models and filters are evaluated at different airports and in different scenarios, including the aircraft being parked, the aircraft moving straight ahead, and the aircraft making a turn. The best performing Kalman filters in different scenarios are integrated into two interacting multiple model filters. Through evaluation, the best interacting multiple model filter is selected and its performance is compared with that of the individual filters. Results show that the accuracy of the selected IMM filter is superior to that of the individual Kalman filters.
Yazılım Hata Tahmini için Sıralı Sınıflandırma Yaklaşımı

Software bug prediction is the process of utilizing classification and/or regression algorithms to predict the presence of possible errors (or defects) in a source code. However, current classification studies in the literature assume that the target attribute values in the datasets are binary (i.e. buggy or non-buggy) or unordered, so they lose inherent order between the class values such as zero, less and more bug levels. To overcome this drawback, this study proposes a novel approach which suggests ordinal classification methods as a solution for software bug prediction problem. This article compares ordinal and nominal versions of various classification algorithms (random forest, support vector machine, Naive Bayes and k-nearest neighbor) in terms of classification performance on real-world 38 software engineering datasets. The results indicate that ordinal classification approach achieves better classification accuracy on average than the traditional (nominal) solutions.
ChalkboARd

Augmented Reality (AR) devices and applications are gaining in popularity, and - with recent trends such as Pokemon Go - are venturing into public spaces where they become more and more pervasive. In consequence, public AR displays might soon be part of our cityscapes and may impact on our everyday view of the world. In this work, we present ChalkboARd, a prototype of an AR-enabled public display that seamlessly integrates into its environment. We investigate the influence of our system on the attention of bystanders in a field study (N=20). The field deployment of ChalkboARd provides evidence that AR for public displays needs to be interactive and adaptive to their surroundings, while at the same time taking privacy issues into account. Nevertheless, ChalkboARd was received positively by the participants, which points out the (hidden) potential of public AR displays.
Preliminary Multi-lingual Evaluation of a Question Answering System Based on the Node of Knowledge Method

The Node of Knowledge (NOK) method is a method for knowledge representation. It is used as a basis for development of formalism for textual knowledge representation (FNOK). FNOK was used for development of a Question Answering (QA) System whose research results are presented in this paper. The QA system is based on storing text in relational databases without losing semantics and it is implemented in Oracle. In this paper, we present the results of preliminary evaluation of the QA system for its independency on the language used. The system was tested using the same set of sentences and questions translated in English, German, Italian and Croatian language. Knowledge in the system was based on 26 simple sentences. 44 questions were set, and the system offered 62 answers for each language (some questions had more than one answer). Research results have shown that this QA system can be used for any of the four languages without the need to change functions and algorithms. It is only necessary to set the dictionary data for each language, implement synonyms and recognize different word forms.
Dynamic visualisation of features and contexts for context-oriented programmers

Context-oriented programming languages allow programmers to develop context-aware systems that can adapt their behaviour dynamically upon changing contexts. Due to the highly dynamic nature of such systems and the many possible combinations of contexts to which such systems may adapt, developing such systems is hard. Feature-based context-oriented programming helps tackle part of this complexity by modelling the possible contexts, and the different behavioural adaptations they can trigger, as separate feature models. Tools can also help developers address the underlying complexity of this approach. This paper presents a visualisation tool that is intricately related to the underlying architecture of a feature-based context-oriented programming language, and the context and feature models it uses. The visualisation confronts two hierarchical models (a context model and a feature model) and highlights the dependencies between them. An initial user study of the visualisation tool is performed to assess its usefulness and usability.
Clustering Functional MRI Patterns with Fuzzy and Competitive Algorithms

We used model-free methods to explore the brain’s functional properties adopting a partitioning procedure based on cross-clustering. We selected Fuzzy C-Means (FCM) and Neural Gas (NG) algorithms to find spatial patterns with temporal features and temporal patterns with spatial features. We applied these algorithms to a shared fMRI repository of face recognition tasks. We matched the classes found and our results of functional connectivity analysis with partitioning of BOLD signal signatures. We compared the outcomes using the just acquired model-based knowledge as likely ground truth, confirming the role of Fusiform Brain Regions. In general, partitioning results show a better spatial clustering than temporal clustering for both algorithms. In the case of temporal clustering, FCM outperforms Neural Gas. The relevance of brain subregions related to face recognition were correctly distinguished by the algorithms and the results are in agreement with the current neuroscientific literature.
An Argumentation-Based Approach for Computing Inconsistency Degree in Possibilistic Lightweight Ontologies

Abstract Reasoning with inconsistent ontologies plays an important role in Semantic Web applications. An important feature of argument theory is that it is able to naturally handle inconsistencies in ontologies and allows a user to represent information in the form of an argument. In argumentation, given an inconsistent knowledge base, arguments compete to decide which are the accepted consequences. In this paper, we are interested in using the argumentation for the inconsistency degree of uncertain knowledge bases expressed in possibilistic DL-Lite (the key notion in reasoning from a possibilistic DL-Lite knowledge base) without going through the negative closure. In the present work, the terminological base is assumed to be fully certain and the uncertainty is only considered on the assertion based. We proved that it is coherent and feasible to use Dung’s abstract argumentation theory to compute the inconsistency degree and how argumentation semantics relate to the state of the art of handling inconsistency.
P‐35: Human Visual System Inspired Artifact Reduction in Projector Compensation

Projection onto an arbitrary surface requires radiometric compensation to correct for distortions caused by the colors and texture of the underlying surface. Radiometric compensation modifies the projector input image with respect to the projection surface characteristics. Due to the limited dynamic range of any projector, the modified target image can easily go beyond the projector intensity limits, causing saturation artifacts. In this paper, a novel locally‐adaptive, spatial luminance modification framework is presented to reduce brightness/contrast demands on the projector during radiometric compensation.
Cloud storage platform for efficient RDF compression

In current web environment, a large of RDF datasets are producing. Thus, the storage of RDF data is becoming an important part of Semantic Web development. Due to the triple structure of RDF data, how to efficiently compress RDF data as much as possible without breaking the integrity of data is become one of the important issues. by Comparing previous research, we found that people had made many efforts in the compression of static, large RDF datasets. In this paper, we not only propose a cloud-based compression approach based on previous research, but also made efforts in data security. Due to the openness of the internet, anyone can publish their Linked Data. At this time, the security of personal data will become very important. Our cloud storage platform not only provides publishers with efficient compression services but also maximizes the security of data. To improve the openness of the platform, we adopt REST (REpresentational State Transfer) architecture to exchange data between publishers and cloud.
An Ontological Approach to Specify Conflicts among Non-Functional Requirements

It is a usual practice for a user to narrate the Non-Functional Requirements (NFRs) in natural language and the requirements engineers manually try to express the same, using semi-formal or formal language notations. However, inaccurate and the laborious manual approach may fail to detect all potential NFRs and conflicts among them. Existing solutions for specifying NFRs are based on a graphical representation that requires manual efforts. Furthermore, they do not take into account the classification of the types of conflicting NFRs that helps to prioritize NFRs. In addition, these approaches are not used in industrial practice due to three main reasons viz. 1) High manual inference 2) Sharing and reusing work can be difficult and 3) No support for machine understanding. Therefore, the aim of our research is to formally specify conflicting NFRs from available natural language NFRs by means of ontological representation that helps requirements analysts prioritize the NFRs at an early stage of requirements engineering.