Miracle-dz/UniFAR-FTUs huggingface dataset:
UDHAV BHOSALE. IMAGE FUSION AND IMAGE QUALITY ASSESSMENT OF FUSED IMAGES

Accurate diagnosis of tumor extent is important in radiotherapy. This paper presents the use of image fusion of PET and MRI image. Multi-sensor image fusion is the process of combining information from two or more images into a single image. The resulting image contains more information as compared to individual images. PET delivers high-resolution molecular imaging with a resolution down to 2.5 mm full width at half maximum (FWHM), which allows us to observe the brain's molecular changes using the specific reporter genes and probes. On the other hand, the 7.0 T-MRI, with sub-millimeter resolution images of the cortical areas down to 250 m, allows us to visualize the fine details of the brainstem areas as well as the many cortical and sub-cortical areas. The PETMRI fusion imaging system provides complete information on neurological diseases as well as cognitive neurosciences. The paper presents PCA based image fusion and also focuses on image fusion algorithm based on wavelet transform to improve resolution of the images in which two images to be fused are firstly decomposed into sub-images with different frequency and then the information fusion is performed and finally these sub-images are reconstructed into result image with plentiful information. We also propose image fusion in Radon space.This paper presents assessment of image fusion by measuring the quantity of enhanced information in fused images. We use entropy, mean, standard deviation and Fusion Mutual Information, cross correlation, Mutual Information Root Mean Square Error, Universal Image Quality Index and Relative shift in mean to compare fused image quality. Comparative evaluation of fused images is a critical step to evaluate the relative performance of different image fusion algorithms. In this paper, we also propose image quality metric based on the human vision system (HVS).
Hands-free Text Entry using Inverse Arithmetic Coding

We have inverted an efficient method for text compression, arithmetic coding (Witten et al., 1987), to create an efficient method for text entry. Our text-entry interface, Dasher, is driven by continuous two-dimensional gestures, using for example a mouse, touch screen, or eyetracker. The user writes by steering through a continuously expanding two-dimensional world in which the alternative continuations of the text are displayed in alphabetical order. Dasher’s language model predicts which letters might come next and makes the more probable letters easier to write by giving them more space on the screen. The language model can be trained on example documents, and adapts to the user’s language as he writes.
Automatic Age Estimation System for Face Images

Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications.
Extracting technical domain knowledge to improve software architecture

This thesis advances the state of the art in terms of our understanding of the influence the technical implementation details have upon software architecture. We propose the concept of the technical domain to capture the implementation concerns of a specific domain as well as viable solutions known as domain tactics. Rather than relying on experience or rediscovering domain specific architectural knowledge, we capture this information in a technical domain model. This model encourages reuse of domain specific patterns, is suitable for tradeo analysis at the requirements stage, and acts as a teaching aid for new developers to the domain. Furthermore, we present a method for defining a technical domain and mining architectural knowledge. Using this method developers can enhance their understanding of a technical domain. To evaluate our approach we developed a prototype toolchain, RAPPT, to realise an implementation based on the principles of the technical domain in the context of mobile apps. RAPPT is a model based tool that generates architectural sca olding that developers then extend to finish o their mobile apps. Finally, we evaluated our approach and RAPPT with a user evaluation, commercial and open source case studies, and expert interviews. All of the evaluations focused on the technical domain of mobile apps with initial work for the technical domains of smart homes and AI solutions available in the Appendix. We found that our approach captures architectural knowledge that can benefit novice developers and a toolchain that can be used in the development of commercial apps. However, the domain specific architectural knowledge needs to be presented in a way for professionals to quickly find the information they need and should include working code snippets as examples.
Patrixa: a unification-based parser for Basque and its application to the automatic analysis of verbs

In this chapter we describe a computational grammar for Basque, and the first results obtained using it in the process of automatically acquiring subcategorization information about verbs and their associated sentence elements (arguments and adjuncts). In section 1 we describe the Basque syntax and the grammar we have developed for its treatment. The grammar is partial in the sense that it cannot recognize every sentence in real texts, but it is capable of describing the main syntactic elements, such as noun-phrases (NPs), prepositional phrases (PPs), and subordinate and simple sentences. This can be useful for several applications. In section 2 we explain the syntactic analyzer (or parser) used to automatically acquire information on verbal subcategorization from texts. The results will later be used by a linguist or processed by statistical filters. This work has been done by the IXA Natural Language Processing research group, centered on the application of automatic methods to the analysis of Basque.
Network Analysis with Stochastic Grammars

Abstract : Digital forensics requires significant manual effort to identify items of evidentiary interest from the ever-increasing volume of data in modern computing systems. One of the tasks digital forensic examiners conduct is mentally extracting and constructing insights from un structured sequences of events. This research assists examiners with the association and individualization analysis processes that make up this task with the development of a Stochastic Context -Free Grammars (SCFG) knowledge representation for digital forensics analysis of computer network traffic. SCFG is leveraged to provide context to the low-level data collected as evidence and to build behavior profiles. Upon discovering patterns, the analyst can begin the association or individualization process to answer criminal investigative questions. Three contributions resulted from this research. First , domain characteristics suitable for SCFG representation were identified and a step -by- step approach to adapt SCFG to novel domains was developed. Second, a novel iterative graph-based method of identifying similarities in context-free grammars was developed to compare behavior patterns represented as grammars. Finally, the SCFG capabilities were demonstrated in performing association and individualization in reducing the suspect pool and reducing the volume of evidence to examine in a computer network traffic analysis use case .
Specific Device Discovery Method for D2D Communication as an Underlay to LTE-Advanced Networks

Device discovery for D2D (device-to-device) communication enables a device to discover other devices in order to initiate communication with them. Devices should perform the discovery phase using a small quantity of radio resource in a short time and be able to reduce the load of the base station. Legacy device discovery schemes have focused on discovering as many target devices as possible. However, it is not appropriate for peer-to-peer D2D communication scenario. Further, synchronization problems are an important issue for discovery signal transmission. This paper proposes a discovery method that one requesting device discovers a specific target for communication. Multiple antenna beamforming is employed for the synchronization between the base station and a target device. The proposal can reduce the load of the base station using the information that it already maintains and improve the reliability of the device discovery because two times of synchronizations using beamforming among the base station and devices can make the exact discovery of a target device with mobility possible.
Delay-Based Issues in IP Networks

Many solutions have been proposed these last years to improve the Quality of Service (QoS) in the Internet. These include protocols to avoid congestion, mechanisms for traffic smoothing, flow classification and resource reservation scheme. Most of these solutions impose restriction on users traffic in order to offer QoS guarantee. In addition, they are based on the shortest path routing and so they do not try to improve the traffic balance in the network. We believe this is an important issue. Underloaded areas of the network may present a good alternative if they are taken into account by the routing protocol during path calculation. We present in this paper a new routing protocol based on a route discovery mechanism, which takes into account the instantaneous load of the network to determine the shortest end-to-end delay paths
Edge detection as a starting point for remote sensing scene interpretation

We propose an edge-constrained smoothing filter to reduce within-class variance prior to spectral classification of remote sensing imagery. This is motivated by the scale mismatch between certain image resolutions and the landcover objects represented, leading to violation of an assumption on which pixelwise spectral classification is based. The first step of our procedure is edge detection within the scene, and then smoothing takes place largely within the confines of those edges, thus introducing spatial context to the smoothed value of any pixel. Our filter is adaptive to local variance so that the high variance areas undergo a high level of smoothing, while other areas are modified less. The smoothed image is processed with a conventional supervised classification, and the results compared to thematic maps obtained without the smoothing step. Comparisons using SPOT imagery of a complex urban and peri-urban environment show that our method decreases within-class variance and increases class separability, leading to landcover polygons that are relatively clean and useful as GIS objects.
Building automatic customer complaints filtering application based on Twitter in Bahasa Indonesia

Twitter has become a media to provide communication between a company with its customers. The average number of Twitter active users monthly is 330 million. A lot of companies realize the potential of Twitter to establish good relationship with their customers. Therefore, they usually have one official Twitter account to act as customer care division. In Indonesia, one of the company that utilizes the potential of Twitter to reach their customers is PT Telkom. PT Telkom has an official customer service account (called @TelkomCare) to receive customers’ problem. However, because of this account is open for public, Twitter users might post all kind of messages (not only complaints) to Telkom Care account. This leads to a problem that the Telkom Care account contains not only the customer complaints but also compliment and ordinary message. Furthermore, the complaints should be distributed to relevant division such as “Indihome”, “Telkomsel”, “UseeTV”, and “Telepon” based on the content of the message. This research built the application that automatically filter twitter post messages into several pre-defined categories (based on existing divisions) using Naïve Bayes algorithm. This research is done by collecting Twitter message, data cleaning, data pre-processing, training and testing data, and evaluate the classification result. This research yields 97% accuracy to classify Twitter message into the categories mentioned earlier.
Fountain Codes for GNSS

The dissemination of large messages in a fast and robust way through the GNSS signal in space is relevant for new services such as high-accuracy and authentication, as well as for existing services. Large messages cannot be enclosed into a single word or page, hence without an appropriate strategy of dissemination, losing one portion of the message can imply losing the full message till it is rebroadcast again. Fountain codes are ideal for erasure channels, providing high-robustness against chunk losses. They allow encoding a message into small packets so that the original message can be reconstructed when receiving any subset of the packets when this subset is slightly higher than the size of the original message. This paper analyses the applicability of fountain codes to the GNSS domain and its singularities and explores different implementations described in the literature. A simple implementation based on Random Linear Fountain Codes has been developed and evaluated, including transmission performance and computational resources. A comparison against traditional strategies based on offsetting and rebroadcasting is also presented for different messages such as ECDSA signature dissemination, almanac broadcasting and post-quantum signatures, in different reception conditions.
Teachers' Conceptions of the Internet and the World Wide Web: A Representational Toolkit as a Model of Expertise

Given rapid changes in the use of network computer technologies in education, it is increasingly important to better understand teachers' conceptions of these technologies, especially as used in teaching and learning. This is particularly true regarding teachers' ways of thinking about the Internet and the World Wide Web. Two questions are addressed in this study: First, does a teacher's conceptual representation of a network technology have an impact on how he or she uses it? Second, do experts think about network technologies such as the Internet and the World Wide Web in ways different than novices? To examine the role of teachers' conceptual representations in detail, a survey and ten case studies were conducted among pre- and in-service teachers enrolled in university courses. The survey elicited the representations that teachers had of the Internet and the World Wide Web. The case studies consisted of network problem-solving tasks using think-aloud protocols. The results show a surprisingly diverse set of plausible representations of the Internet and the Web, not related to the level of technical expertise. The case studies reveal relationships between representations and navigation strategies, in which experts employ multiple structurally and epistemically different representations at different stages in network tasks. This suggests that experts have “representational toolkit” containing many different representations. Experts not only know how to use each representational tool, but they know which representational tool to use for which purpose and how to sequence the selection and use of multiple representational tools. This reconceptualization of the nature of expertise has implications for the design of educational applications of technologies, for teacher training, and for learning and teaching more generally.
A new learning controller based on neural networks for robot trajectories tracking

This paper presents a new learning control method for robot trajectory tracking of unknown model system based on both the inverse dynamics neural network identifier and feedforward compensation network controller. Basic control configurations are briefly presented and new online training schemes are proposed. Some measurements to accelerate the learning and to improve the stability and convergence are discussed. It is also shown that the method can follow and arbitrarily prescribe trajectories. Simulation was performed to show the feasibility and effectiveness of the proposed scheme. <<ETX>>
Team Incentives for Education in Developing Countries A Field Randomized Experiment in Benin ∗

This paper combines theory and a randomized field experiment to test whether individual and team incentives can improve students learning and performance. I analyze a simple model where students have different abilities, and choose effort in response to an incentive. The model predicts that incentives, when given individually, work only on students with an intermediate level of ability. I analyze similar incentives when they are given to teams of students instead of individually. I find that team tournament predicts higher effect. Using a field experiment to test the predictions, I randomly assign 10th grade students from 100 Beninese secondary schools to three treatment groups and a control group. In the first group called Individual Target, each participant is offered a monetary promise to be paid to her based on her performance on the secondary school certification examination (BEPC). In the second group called Team Target, I randomly assign participants to teams of four students. Each team is offered a monetary promise to be paid to the team based on its average performance on the BEPC. In the third group called Team Tournament, I randomly assign participants to 84 teams of four students across the country. Each of the three teams with the greatest average performance on the BEPC win a prize. On average, students assigned to treatment groups are 9% more successful than students assigned to the control group. Honors are awarded to 7% of students in the control group, to 13% in the Individual Target and Team Target groups, and 18% in the Team Tournament treatment group. The average treatment effect on the overall test score is 0.27 standard deviations in the Individual Target, 0.26 standard deviations (significant at the 10% level) in the Team Target, and 0.31 standard deviations in the Team Tournament. Evidence from simultaneous quantile regressions indicates the presence of heterogeneity in the treatment effect – as is predicted theoretically.
Linear reinforcement learning with options

The thesis deals with linear approaches to the Markov Decision Process (MDP). In particular, we describe Policy Evaluation (PE) methods and Value Iteration (VI) methods that work with representations of MDPs that are compressed using a linear operator. We then use these methods in the context of the options framework, which is way of employing temporal abstraction to speed up MDP solving. The main novel contributions are: the analysis of convergence of the linear compression framework, a condition for when a linear compression framework is optimal, an in-depth analysis of the LSTD algorithm, the formulation of value iteration with options in the linear framework and the combination of linear state aggregation and options.
If Local Weather Was Our Only Indicator

It is understandably difficult for individuals and communities to recognize the effect of gradual climate change when it occurs in the context of local weather patterns, which normally vary from year to year. This recognition difficulty delays discussion of the causes of climate change and forestalls adjustments in policy and action. In this article, the authors estimate the length of time it would take a majority of localities to simultaneously acknowledge climate change if the only source of information about climate change was local weather. They run computer simulations using U.S. weather station data from 1946 to 2005. Local weather is allowed to vary randomly around a constant mean (models assuming no climate change) or a rising mean (models assuming climate change). They run separate models for annual average temperature, annual maximum temperature, and annual variation in monthly precipitation, varying the definition of unusual weather from 0.5 to 2.5 standard deviations from the historic average and varying the number of consecutive years of unusual weather required to reject the belief of normal variation and accept the belief of climate change from 1 year to 5 years. When it is assumed that acknowledgment of climate change requires three consecutive years of weather, a full standard deviation or more above the historic mean, it requires, depending on which weather event is being modeled, an average of 21 years, 86 years, or 82 years for a majority of localities to be in a state of climate change belief.
Can your paper evade the editors axe? Towards an AI assisted peer review system

This work is an exploratory study of how we could progress a step towards an AI assisted peer- review system. The proposed approach is an ambitious attempt to automate the Desk-Rejection phenomenon prevalent in academic peer review. In this investigation we first attempt to decipher the possible reasons of rejection of a scientific manuscript from the editors desk. To seek a solution to those causes, we combine a flair of information extraction techniques, clustering, citation analysis to finally formulate a supervised solution to the identified problems. The projected approach integrates two important aspects of rejection: i) a paper being rejected because of out of scope and ii) a paper rejected due to poor quality. We extract several features to quantify the quality of a paper and the degree of in-scope exploring keyword search, citation analysis, reputations of authors and affiliations, similarity with respect to accepted papers. The features are then fed to standard machine learning based classifiers to develop an automated system. On a decent set of test data our generic approach yields promising results across 3 different journals. The study inherently exhibits the possibility of a redefined interest of the research community on the study of rejected papers and inculcates a drive towards an automated peer review system.
Occurrence of the hairy ark cockle ( Anadara gubernaculum , Reeve 1844 ) in Mayangan coastal waters , Subang – Province West Java : new distribution record of Indonesia

Thirty six specimens of the hairy ark cockle (Anadara gubernaculum Reeve, 1844) were collected from Mayangan waters, Subang of Province West Java, during marine biodiversity survey along northern coast of Java Island, Indonesia. Previous records of the cockle showed its occurrence in Cilincing, northern coast of Jakarta, Semarang coastal waters of Province Central Java, and Sidoarjo coastal waters located in northern part of Province East Java. The identification of the species was performed by morphological characters and is supported by molecular analysis. This finding represents a new distribution record of the species in Indonesia.