Reviewerly/exHarmony huggingface dataset:
Joint Neighboring Coding with a Low-Rank Constraint for Multi-Atlas Based Image Segmentation

Multi-atlas methods have been successful for solving many medical image segmentation problems. Under the multi-atlas segmentation framework, labels of atlases are first propagated to the target image space with the deformation fields generated by registering atlas images onto a target image, and then these labels are fused to obtain the final segmentation. While many label fusion strategies have been developed, weighting based label fusion methods have attracted considerable attention. In this paper, we first present a unified framework for weighting based label fusion methods. Under this unified framework, we find that most of recent developed weighting based label fusion methods jointly consider the pair-wise dependency between atlases. However, they independently label voxels to be segmented, ignoring their neighboring spatial structure that might be informative for obtaining robust segmentation results for noisy images. Taking into consideration of potential correlation among neighboring voxels to be segmented, we propose a joint coding method (JCM) with a low-rank constraint for the multi-atlas based image segmentation in a general framework that unifies existing weighting based label fusion methods. The method has been validated for segmenting hippocampus from MR images. It is demonstrated that our method can achieve competitive segmentation performance as the state-of-the-art methods, especially when the quality of images is poor.
Zero-Knowledge Proofs for Improved Lattice-Based Group Signature Scheme with Verifier-Local Revocation

The first lattice-based group signature scheme with verifier-local revocation (GS-VLR) was introduced by Langlois et al. in PKC 2014, and subsequently, a full and corrected version was designed by Ling et al. in TCS 2018. However, zero-knowledge proofs in both schemes are within a structure of Bonsai Tree, and have bit-sizes of the group public-key and the member secret-key proportional to $$\log N$$ , where N is the group size. On the other hand, the revocation tokens in both schemes are related to some public matrix and the group member secret-key, and thus only obtain a weaker security, selfless-anonymity. For the tracing algorithms in both schemes, they just run in the linear time of N. Therefore, for a large group, zero-knowledge proofs in lattice-based GS-VLR schemes are not that secure and efficient. In this work, we firstly utilize an efficient and compact identity-encoding technique which only needs a constant number of public matrices to encode the member’s identity information and it saves a $$\mathcal {O}(\log N)$$ factor in both bit-sizes for the group public-key and the group member secret-key. Secondly, separating from the member secret-key, we generate revocation token within some secret Gaussian vector and thus obtain a stronger security, almost-full anonymity. Moreover, the explicit traceability, to trace the signer’s identity in a constant time, independent of N, for the tracing authority is also satisfied. In particular, a new Stern-type statistical zero-knowledge proofs protocol for an improved lattice-based GS-VLR scheme enjoying the above three advantages is proposed.
Domain specific concept ontologies and text summarization as hierarchical fuzzy logic ranking indicator on malay text corpus

&lt;span&gt;Ranking function is a predictive algorithm that is used to establish a simple ordering of documents according to its relevance. This step is critical because the results’ quality of a Domain Specific Information Retrieval (IR) such as Hadith Information Retrieval is fundamentally dependent of the ranking function. A Hierarchical Fuzzy Logic Controller of &lt;em&gt;Mamdani&lt;/em&gt;-type Fuzzy Inference System has been built to define the ranking function, based on the Malay Information retrieval’s BM25 Model. The model examines three-inputs (Ontology BM25 Score, Fabrication Rate of Hadith and Shia Rate of Hadith) and four-output values of Final Ranking Score which consist of three triangular membership functions. The proposed system has outperformed the BM25 original score and the Vector Space Model (VM) on 16 queries, while the BM25 original score and Vector Space Model only yield better result in 9 and 2 queries respectively on the P@10, %no measures and MAP. P@10 represent the values of Precision at Rank 10 P@10), %no measures represent the percentage of queries with no relevant documents in the top ten retrieved and MAP represents Mean Average Precision of the queries. The results show the proposed system have capability to demote negative documents and move up the relevant documents in the ranking list and its capability to recall unseen document with the application of ontology in text retrieval. For the future works, the researcher would like to apply the usage of other Malay Semantic elements and another corpus for positive ranking indicator.&lt;/span&gt;
Manifestations of Transphobia in Computer-Mediated Communication. A Case Study of Language Discrimination in English and Polish Internet-Mediated Discourse

Studies in Polish Linguistics » Volume 14 » Issue 3 » Manifestations of Transphobia in Computer-Mediated Communication. A Case Study of Language Discrimination in English and Polish Internet-Mediated Discourse A A A
Recognition of Handwritten Images Using Multilayer Neural Networks

The architecture and training algorithms of multilayer neural networks are considered. A description of the implemented NeuralNet program and the results of research on recognition of handwritten digits of the MNIST database is given.
A SoC-based acceleration method for UAV runway detection image pre-processing algorithm

Runway detection algorithm is the foundation for ensuring the effectiveness of vision based Unmanned Aerial Vehicle (UAV) autonomous landing, which accomplishes landing site perception through large number of images. Considering the real-time demand of fixed-wing UAV landing and the onboard constraints, the image pre-processing system designed in this paper is built on Zynq-7000 platform. After optimizing image scaling algorithm and saliency analysis algorithm based on frequency domain residuals, IP cores are generated via Xilinx's Vivado HLS tool for specific modules of the algorithm and added to the image pre-processing system under the SoC collaborative architecture. This architecture combines both advantages of both FPGA and ARM, with parallel processing and subsystem control capabilities. The experimental results verify the SoC collaborative acceleration based onboard runway detection system for UAV landing can process up to 20 frames per second, which is nearly 20 times faster than ARM.
Avatars: The Other Side of Proteus’s Mirror

The trend for online interactions, can be regarded as being ‘anti-socially social’, meaning that a great deal of time is spent playing, working and socializing with the internet serving as the communication conduit. Within that Virtual Social Environment very deep relationships are formed and maintained without the parties ever having met each other face-to-face. Raising the question how much does the physical appearance of an avatar influence the perception of the person behind it? Are relationships informed by appearance even in the virtual world and what implications does that have for second language acquisition? This paper leads to a small-scale research project where a selection of avatars with various racially identifiable characteristics were used to identify which avatars a second language speaker would feel more at ease interacting with in the target language. The resultant research aims to test three hypotheses regarding preferred avatar choice for second language users based solely on perceptions.
Designing Serious Mobile Location-Based Games

The technical requirements of mobile location-based games have been met sufficiently well to make location-based mobile games an everyday object. Games like Ingress or Pokémon GO have experienced a huge popularity. Hence, the question arises how the obvious attraction of these games can be used to achieve goals other than entertainment, such as learning. This article describes the basic scientific background of a workshop of the ICEC-JCSG 2019 for the development of design principles for serious mobile location-based games. First the game types involved are defined, then relevant design principles are identified and presented providing a basis to substantiate design guidelines in the workshop and contribute to the purposeful design of mobile location-based games.
Using an Extended Attack Defense Graph Model to Estimate the Risk of a Successful Attack on an IT Infrastructure

Nowadays, securing the IT infrastructure is an ongoing task in every company and organization.For small and medium-sized enterprises, this task is challenging because of its complexity and the related costs.Especially the risk assessment of threats and the choice of appropriate countermeasures is hard to handle by this kind of enterprises.Using the example of a ransomware attack, this paper describes how to use a method for risk assessment on the basis of attack defence graphs and Monte Carlo simulations.The details of the simulation algorithm are explained and formal aspects are considered.
Generating Formality-Tuned Summaries Using Input-Dependent Rewards

Abstractive text summarization aims at generating human-like summaries by understanding and paraphrasing the given input content.Recent efforts based on sequence-to-sequence networks only allow the generation of a single summary.However, it is often desirable to accommodate the psycho-linguistic preferences of the intended audience while generating the summaries.In this work, we present a reinforcement learning based approach to generate formality-tailored summaries for an input article.Our novel input-dependent reward function aids in training the model with stylistic feedback on sampled and ground-truth summaries together.Once trained, the same model can generate formal and informal summary variants.Our automated and qualitative evaluations show the viability of the proposed framework.
A rule based sentiment analysis in Telugu

Sentimental Analysis in the English language is a relatively easier task to perform as it has a predefined set of rules followed and accepted universally. But, when it comes to Indian languages, there isn’t a benchmark dataset. Moreover, if a dataset exists, it cannot be validated as a similar sentence may differ in the meaning as the regional languages are very unpredictable and have no proper rules. In this paper, we used a Rule-Based Approach for Telugu sentiment analysis using Telugu SentiWordNet. Here, we obtained the sentiment using SentiWordNet and validated the results using ACTSA which is an annotated corpus data set.
A Robust and High Capacity Data Hiding Method for H.265/HEVC Compressed Videos with Block Roughness Measure and Error Correcting Techniques

Recently, the H.265/HEVC video coding has been standardised by the ITU-T VCEG and the ISO/IEC MPEG. The improvements in H.265/HEVC video coding structure (CTU, motion compensation, inter- and intra-prediction, etc.) open up new possibilities to realise better data hiding algorithms in terms of capacity and robustness. In this paper, we propose a new data hiding method for HEVC videos. The proposed method embeds data in 4 × 4 and some selected larger transform units. As theory of Human Visual System suggests that human vision is less sensitive to change in uneven areas, relatively coarser blocks among the 8 × 8 and 16 × 16 blocks are selected as embedding destinations based on the proposed Jensen-Shannon Divergence and Second Moment (JSD-SM) block coarseness measure. In addition, the SME(1,3,7) embedding technique is able to embed three bits of message by modifying only one coefficient and therefore exhibits superior distortion performance. Furthermore, to achieve better robustness against re-compression attacks, BCH and Turbo error correcting codes have been used. Comparative studies of BCH and Turbo codes show the effectiveness of Turbo codes. Experimental results show that the proposed method achieves greater payload capacity and robustness than many existing state-of-the-art techniques without compromising on the visual quality.
Methodological Issues in Observational Studies

Background: Starting from the 1960s, practitioners and researchers have looked for ways to empirically investigate new technologies such as inspecting the effectiveness of new methods, tools, or practices. With this purpose, the empirical software engineering domain started to identify different empirical methods, borrowing them from various domains such as medicine, biology, and psychology. Nowadays, a variety of empirical methods are commonly applied in software engineering, ranging from controlled and quasi-controlled experiments to case studies, from systematic literature reviews to the newly introduced multivocal literature reviews. However, to date, the only available method for proving any cause-effect relationship are controlled experiments. Objectives: The goal of the thesis is introducing new methodologies for studying causality in empirical software engineering. Methods: Other fields use observational studies for proving causality. They allow observing the effect of a risk factor and testing this without trying to change who is or is not exposed to it. As an example, with an observational study it is possible to observe the effect of pollution on the growth of a forest or the effect of different factors on development productivity without the need of waiting years for the forest to grow or exposing developers to a specific treatment. Conclusion: In this thesis, we aim at defining a methodology for applying observational studies in empirical software engineering, providing guidelines on how to conduct such studies, how to analyze the data, and how to report the studies themselves.
Input distinguishability of linear dynamic control systems

When observabilities of hybrid dynamic systems are considered, the distinguishability of subsystems takes a very important role. Necessary and sufficient conditions for distinguishability of linear dynamic systems are obtained. Some illustrative examples are presented.
Multiple particle filtering for tracking wireless agents via Monte Carlo likelihood approximation

Abstract The localization of multiple wireless agents via, for example, distance and/or bearing measurements is challenging, particularly if relying on beacon-to-agent measurements alone is insufficient to guarantee accurate localization. In these cases, agent-to-agent measurements also need to be considered to improve the localization quality. In the context of particle filtering, the computational complexity of tracking many wireless agents is high when relying on conventional schemes. This is because in such schemes, all agents’ states are estimated simultaneously using a single filter. To overcome this problem, the concept of multiple particle filtering (MPF), in which an individual filter is used for each agent, has been proposed in the literature. However, due to the necessity of considering agent-to-agent measurements, additional effort is required to derive information on each individual filter from the available likelihoods. This is necessary because the distance and bearing measurements naturally depend on the states of two agents, which, in MPF, are estimated by two separate filters. Because the required likelihood cannot be analytically derived in general, an approximation is needed. To this end, this work extends current state-of-the-art likelihood approximation techniques based on Gaussian approximation under the assumption that the number of agents to be tracked is fixed and known. Moreover, a novel likelihood approximation method is proposed that enables efficient and accurate tracking. The simulations show that the proposed method achieves up to 22% higher accuracy with the same computational complexity as that of existing methods. Thus, efficient and accurate tracking of wireless agents is achieved.
Give MEANinGS to Robots with Kitchen Clash: A VR Human Computation Serious Game for World Knowledge Accumulation

In this paper, we introduce the framework of MEANinGS for the semi-autonomous accumulation of world knowledge for robots. Where manual aggregation is inefficient and prone to incompleteness and autonomous approaches suffer from underspecified information, we deploy the human computation game Kitchen Clash and give evidence of its efficiency, completeness and motivation potential.
Effects of Age and Motivation for Visiting on AR Museum Experiences

Augmented reality(AR) provides a unique viewing experience at museums where people understand abstract history through physical artifacts. Although AR usage in museum settings has been increasing, it is not well understood how AR viewing experience differs in different groups of visitors, which can be problematic considering that museums are places visited by diverse groups of people. In this study, we evaluate the differences in AR experiences according to the characteristics of the visitors. The results show the effect of AR usage in museum settings with visitors’ different age groups and motivations for visiting.
PENGEMBANGAN BAHAN AJAR EVALUASI PEMBELAJARAN UNTUK PENDIDIKAN GURU SEKOLAH DASAR UNIVERSITAS KUNINGAN

Learning Evaluation is one of the compulsory subjects that must be taken by students at PGSD FKIP Kuningan University in order to obtain assessment and evaluation skills. However, there is no teaching evaluation material for PGSD that explicitly discusses the elementary school learning evaluation process. The purpose of this study is to develop instructional evaluation teaching materials on PGSD learning evaluation courses which are expected to equip students to be applied in primary schools when they become teachers. This research method is a research and development study by taking the object of research at the Kuningan University PGSD students (UNIKU). The results of the validation of learning evaluation material experts obtained results with a total score of 74 of the 20 learning indicators assessed with a range of scores 1-5 for the assessment of development products, so that a score of 3.7 from all assessment indicators in the learning evaluation instrument can be said with GOOD category. The results of student responses to the feasibility of developing teaching materials obtained by 90.27% of students on the response Very good and good. Based on the results obtained from the response to the readability of the product development, it was obtained that the response of students was 93.65% in the Very Good and Good categories. Based on the categorization of the gain score analysis in the Hake calculation table, the value of 0.59 is categorized as Medium. Then it can be concluded that the Learning evaluation teaching materials for Kuningan University PGSD students can be declared eligible for use
Reading Chinese Scene Text with Arbitrary Arrangement Based on Character Spotting

Chinese characters are always arranged in arbitrary orders and forms in scene images which are difficult to handle for text line based methods. Recently approaches adopt irregular text line reading to solve this problem but a text line in Chinese is often ambiguous. In this paper, we propose a character based framework to spotting Chinese text from another view. Specifically, the framework consists of three components for character detection, character recognition and character grouping respectively. It is worth mentioning that a novel Conditional Random Field(CRF) based character grouping algorithm is presented for arbitrary arrangement in Chinese text. Experiments on ReCTS-ARB549 dataset demonstrate that our framework achieves superior performance comparing with state of the art text line based approaches.
Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.
Intelligent 3D Analysis for Detection and Classification of Breast Cancer

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. Breast cancer computer aided diagnosis (CAD) systems can provide such help and they are important and necessary for breast cancer control. Micro calcifications and masses are the two most important indicators of malignancy, and their automated detection is very valuable for early breast cancer diagnosis. Since masses are often indistinguishable from the surrounding parenchymal, automated mass detection and classification is even more challenging. This research presents algorithms for building a classification system or CAD, especially to obtain the different characteristics of mass and micro calcification using association technique based on classification. Starting with an individual-speciﬁc deformable of 3D breast model, this modelling framework will be useful for tracking visible tumors between mammogram images, as well as for registering breast images taken from diﬀerent imaging modalities. From the results, the classifier developed able to perform well by successfully classifying the cancer and non-cancer (normal) images with the accuracy of 97%. Apart from that, by applying color map to the final results of segmentation provides a more interesting display of information and gives more direction to the purpose of image processing, which distinguishes between cancerous and non-cancerous tissues.
Visualization Effect of Changing Climatic Trends on Natural Calamities:Hurricanes

In the coming decades, the world will see many changes in its environment which are liable and threatening to all the life on the planet. In this paper, we will try to visualize and form a connection between one of the most dangerous form of natural calamity, hurricanes and the climatic changes over the past century by collecting all the data available to us regarding the same. We have used the RStudio software for all the visualizations done in this paper.