Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Two CNN Demosaicking Algorithms

Most cameras capture the information of only one color for a given pixel. This results in a mosaicked image that must be interpolated to get three colors at each pixel. The step going from a mosaicked image to a regular RGB image is called demosaicking. This paper studies two recent demosaicking methods based on convolutional neural networks that achieve artifact-free state-of-the-art results: Deep joint demosaicking and denoising by Gharbi et al. and Color image demosaicking via deep residual learning by Tan et al. We show that these methods beat by almost two decibels the best human-crafted methods, while being faster by one order of magnitude. This, arguably, seals the destiny of human-crafted methods on this subject.
Cross-lingual plagiarism detection techniques for English-Hindi language pairs

Abstract Plagiarism is defined as stealing of another author’s language, thoughts, or ideas as one’s own original work. Most of the work in the literature has been emphasized on monolingual plagiarism detection. Given text in two different languages, the objective of Cross-Lingual Plagiarism Detection (CLPD) model is to determine if the text in one language is plagiarized with the text written in another language. The cross-lingual plagiarism detection is mainly an unexplored area and is very important to improve the ethical practice, academic integrity and honesty in academics. The problem of CLPD has emerged as very important yet difficult problem because it is very easy to steal the content of a document written in another language through translation softwares. In this paper, we present different cross-language plagiarism detection approaches in context of Indian languages pairs such as Hindi-English, English-Hindi language pairs. The research in cross-language plagiarism detection is very limited but has opened up opportunities due to development in recent advancement in distributed word representation techniques in monolingual and bi-lingual context.
Acta Polytechnica

The paper deals with the possibilities and limitations of the image processing of digitalized terrestrial photographs from analogue metric cameras. By the end of the 20th century, analogue metric cameras such as Zeiss UMK were used for documentation purposes in the fields of industrial and investment construction, nature and landscape preservation, heritage preservation, etc. Currently, the collections of photographs are stored at specialized archives of many different institutions, such as libraries, museums, universities, etc. These sets of photographs provide a material background for a 3D digital reconstruction of a subject of interest at the time of taking the photographs. The digital image processing of old photographs may be more difficult due to unknown parameters of the used camera, such as the focal length, image coordinates of the fiducial marks and distortion parameters of the lens system, etc. In this case, it may be difficult to process these photographs in a photogrammetric software. The paper presents a methodology for the digital photogrammetric processing of analogue terrestrial photographs. The data processing is based on the parameters of the used metric cameras, which are described in their calibration reports. The image processing was tested in two commercial photogrammetric software tools that utilize the technology of Structure-from-Motion (SfM) or multiimage intersection photogrammetry to process image datasets.
A Pilot Randomized Controlled Crossover Study Comparing Regional Heparinization to Regional Citrate Anticoagulation for Continuous Venovenous Hemofiltration

Objective To evaluate the efficacy and safety of a regional heparinization and a regional citrate method of anticoagulation in CVVH. Design Randomized controlled cross-over study Subjects Ten critically ill patients with acute renal failure Setting ICU of tertiary hospital Intervention CVVH was performed with pre-filter fluid replacement at 2000ml/h and a blood flow rate of 150ml/min. Regional heparinization was by the administration of heparin pre-filter at 1500IU/h and protamine post-filter at 15mg/h. Regional citrate anticoagulation was by means of a citrate-based replacement fluid (14 mmol/L) administered pre-dilution. Results We studied nine males and one female. The mean age and APACHE II score were 70.5 and 17 respectively. Median circuit life was 13 hours (IQR 9.28) for the regional heparinization method compared to 17 hours (IQR 12,19.5) for the regional citrate method (p=0.77). There were no episodes of bleeding in either group. Conclusion Regional heparinization and regional citrate anticoagulation achieve similar circuit life in critically ill patients receiving CVVH.
Adaptive Near Duplicate Image Retrieval Using SURF and CNN Features

: In this paper, we present an adaptive approach in order to match and retrieve near duplicate images at different scales. Matching only local Features does not necessarily identify visually similar images. Global features are fast at matching but less accurate. Many existing methods either use local features or CNN features for image or video retrieval task. In this paper, we combined the use of SURF local points and CNN features extracted around SURF points in order to match near duplicate image pairs. Image pairs are segmented into blocks and CNN features of the image block containing matched SURF features are extracted and matched. Regions around matched image blocks are grown adaptively and matching is carried out until CNN mismatch is observed. To verify our proposed approach, experiments are carried out on benchmarking California-ND and Holiday dataset. Compared to traditional approaches for image retrieval, our approach not only retrieves relevant images but also provides detail of localized matched patch. For California-ND dataset and Holiday dataset, we achieve remarkable mAP (mean average precision) score up to 0.86 and 0.74 respectively.
Algorithms for computing Maximally Redundant Trees for IP/LDP Fast- Reroute

A complete solution for IP and LDP Fast-Reroute using Maximally Redundant Trees is presented in [I-D.ietf-rtgwg-mrt-frr- architecture]. This document defines the associated MRT Lowpoint algorithm that is used in the default MRT profile to compute both the necessary Maximally Redundant Trees with their associated next-hops and the alternates to select for MRT-FRR.
Set to Ordered Text: Generating Discharge Instructions from Medical Billing Codes

We present set to ordered text, a natural language generation task applied to automatically generating discharge instructions from admission ICD (International Classification of Diseases) codes. This task differs from other natural language generation tasks in the following ways: (1) The input is a set of identifiable entities (ICD codes) where the relations between individual entity are not explicitly specified. (2) The output text is not a narrative description (e.g. news articles) composed from the input. Rather, inferences are made from the input (symptoms specified in ICD codes) to generate the output (instructions). (3) There is an optimal order in which each sentence (instruction) should appear in the output. Unlike most other tasks, neither the input (ICD codes) nor their corresponding symptoms appear in the output, so the ordering of the output instructions needs to be learned in an unsupervised fashion. Based on clinical intuition, we hypothesize that each instruction in the output is mapped to a subset of ICD codes specified in the input. We propose a neural architecture that jointly models (a) subset selection: choosing relevant subsets from a set of input entities; (b) content ordering: learning the order of instructions; and (c) text generation: representing the instructions corresponding to the selected subsets in natural language. In addition, we penalize redundancy during beam search to improve tractability for long text generation. Our model outperforms baseline models in BLEU scores and human evaluation. We plan to extend this work to other tasks such as recipe generation from ingredients.
Studying Political Decision Making With Automatic Text Analysis

Analyzing political text can answer many pressing questions in political science, from understanding political ideology to mapping the effects of censorship in authoritarian states. This makes the study of political text and speech an important part of the political science methodological toolbox. The confluence of increasing availability of large digital text collections, plentiful computational power, and methodological innovations has led to many researchers adopting techniques of automatic text analysis for coding and analyzing textual data. In what is sometimes termed the “text as data” approach, texts are converted to a numerical representation, and various techniques such as dictionary analysis, automatic scaling, topic modeling, and machine learning are used to find patterns in and test hypotheses on these data. These methods all make certain assumptions and need to be validated to assess their fitness for any particular task and domain.
Sinkhole+CloneID: A hybrid attack on RPL performance and detection method

ABSTRACT Demands for the deployment of large-scale Low power and Lossy Networks (LLN) are growing these days. Internet of Things (IoT) refers to networks that communicate over Internet Protocols (IPs). Routing Protocol for Low power and Lossy Networks (RPL) is a standard routing protocol for networks which has been exposed to a variety of attacks. Previous works have addressed those attacks; however, each study has focused on a single attack. In this work, we propose the hybrid of Sinkhole and CloneID attacks named “Sink-Clone” attack. The paper demonstrates the effect of this hybrid attack on RPL performance and evaluates it against a detection method which is inspired by previous detection methods. The hybrid attack and the Intrusion Detection System (IDS) have been simulated in Cooja simulator where the results show the impacts of the hybrid attack compared to the standalone attacks as well as the capability of the proposed IDS in detecting malicious nodes in a hybrid scenario.
Hierarchy-Based Partition Models: Using Classification Hierarchies to Improve the Statistical Estimation of Bigrams

We propose a novel machine learning technique that can be used to estimate probability distributions for categorical random variables that are equipped with a natural set of classiﬁcation hierarchies, such as words equipped with word class hierarchies, wordnet hierarchies, and suf-ﬁx and afﬁx hierarchies. We evaluate the estimator on bigram language modelling with a hierarchy based on word sufﬁxes, using English, Danish, and Finnish data from the Europarl corpus with training sets of up to 1–1.5 million words. The results show that the proposed estimator outperforms modiﬁed Kneser-Ney smoothing in terms of perplexity on unseen data. This suggests that important information is hidden in the classiﬁcation hierarchies that we routinely use in computational linguistics, but that we are unable to utilize this information fully because our current statistical techniques are either based on simple counting models or designed for sample spaces with a distance metric, rather than sample spaces with a non-metric topology given by a classiﬁcation hierarchy.
Message Security Implementation by Using a Combination of Hill Cipher Method and Pixel Value Differencing Method in Mozilla Thunderbird Email Client

One of the problems in this field of communication is the insecurity of messages sent across computer networks. Even though sometimes the contents of a message can be something that is sensitive and prone to abuse. One method that can be used to overcome this problem is by using a combination of cryptography and steganography. In this research, an add-on menu for Mozilla application was developed to secure messages to be sent via e-mail using Hill Cipher’s cryptographic method on messages and insert them using steganographic pixel value differencing method on image files to be extracted and returned to the form originally. The results of this study indicate that with experiments using messages with length and image file extensions that are also different, namely. bmp, . jpg, . png, and . gif can insert the initial message into the image file and be returned to its original form with a maximum length limit message 1000 characters and no manipulation of the image file from the insertion message.
Data-Anonymous Encoding for Text-to-SQL Generation

On text-to-SQL generation, the input utterance usually contains lots of tokens that are related to column names or cells in the table, called table-related tokens. These table-related tokens are troublesome for the downstream neural semantic parser because it brings complex semantics and hinders the sharing across the training examples. However, existing approaches either ignore handling these tokens before the semantic parser or simply use deterministic approaches based on string-match or word embedding similarity. In this work, we propose a more efficient approach to handle table-related tokens before the semantic parser. First, we formulate it as a sequential tagging problem and propose a two-stage anonymization model to learn the semantic relationship between tables and input utterances. Then, we leverage the implicit supervision from SQL queries by policy gradient to guide the training. Experiments demonstrate that our approach consistently improves performances of different neural semantic parsers and significantly outperforms deterministic approaches.
UMA REVISÃO SOBRE ACESSIBILIDADE NO DESENVOLVIMENTO DE INTERNET DAS COISAS: OPORTUNIDADES E TENDÊNCIAS

Internet of Things (IoT) has become a significant topic of interest in recent years. Billions of everyday devices will be connected and new forms of interaction grow with this new paradigm. However, the future role of IoT in regarding to inclusion in smart environments is not well defined yet. Further studies are still needed to investigate whether these technologies will be an enabler or whether it will introduce more barriers. In this paper, we conducted a systematic mapping to provide an overview of how accessibility has been considered in the development of IoT systems. The results show that the community has presented Assistive Technology solutions based on IoT resources and a lack of studies addressing accessibility and user studies in IoT projects. Therefore, the findings provide an overview of current research in IoT, as well as give implications for new research opportunities.
Topical Expressivity in Short Texts

With each passing minute, online data is growing exponentially. A bulk of such data is generated from short text social media platforms such as Twitter. Such platforms are fundamental in social media knowledge-based applications like recommender systems. Twitter, for example, provides rich real-time streaming information. Extracting knowledge from such short texts without automated support is not feasible due to Twitter's platform streaming nature. Therefore, an automated method for comprehending patterns in such text is a need for many knowledge systems. This paper provides solutions to generate topics from Twitter data. We present several techniques related to topical modelling to identify topics of interest in short texts. Topic modelling is inherently problematic in shorter texts with very sparse vocabulary in addition to the informal language used in their dissemination. Such findings are informative in knowledge extraction for social media-based recommender systems as well as in understanding tweeters over time.
Energy-Efficiency Optimization of UAV-Based Cognitive Radio System

Unmanned aerial vehicles (UAVs) equipped with data transmission and sensing facilities are gaining more popularity in different applications due to its miniaturization and mobility. In this paper, a UAV-based overlay cognitive radio (CR) network is investigated in which the UAV is used as a secondary user (SU). This paper proposes an efficient energy management solution to improve the performance of the UAV. When SUs opportunistically utilize the licensed spectrum of the primary network, spectrum sensing is needed to determine whether to transmit data or not, so the sensing time and secondary transmission power should be jointly optimized. We formulate this non-convex optimization problem subject to multiple constraints, which seeks to investigate on the effect of the sensing time and transmission power on the performance of the system. The problem is difficult to tackle, then we propose an algorithm applying the techniques of alternating optimization and dichotomy method. In addition, we compare the proposed algorithm with the particle swarm optimization (PSO) algorithm to verify its performance. Numerical results show that our proposed algorithm outperforms the PSO algorithm and significantly enhances the energy efficiency of the UAV-based CR system.
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems

Stochastic Proximal Gradient (SPG) methods have been widely used for solving optimization problems with a simple (possibly non-smooth) regularizer in machine learning and statistics. However, to the best of our knowledge no non-asymptotic convergence analysis of SPG exists for non-convex optimization with a non-smooth and non-convex regularizer. All existing non-asymptotic analysis of SPG for solving non-smooth non-convex problems require the non-smooth regularizer to be a convex function, and hence are not applicable to a non-smooth non-convex regularized problem. This work initiates the analysis to bridge this gap and opens the door to non-asymptotic convergence analysis of non-smooth non-convex regularized problems. We analyze several variants of mini-batch SPG methods for minimizing a non-convex objective that consists of a smooth non-convex loss and a non-smooth non-convex regularizer. Our contributions are two-fold: (i) we show that they enjoy the same complexities as their counterparts for solving convex regularized non-convex problems in terms of finding an approximate stationary point; (ii) we develop more practical variants using dynamic mini-batch size instead of a fixed mini-batch size without requiring the target accuracy level of solution. The significance of our results is that they improve upon the-state-of-art results for solving non-smooth non-convex regularized problems. We also empirically demonstrate the effectiveness of the considered SPG methods in comparison with other peer stochastic methods.
Improving Question Generation With to the Point Context

Question generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence. A major challenge in QG is to identify answer-relevant context words to finish the declarative-to-interrogative sentence transformation. Existing sequence-to-sequence neural models achieve this goal by proximity-based answer position encoding under the intuition that neighboring words of answers are of high possibility to be answer-relevant. However, such intuition may not apply to all cases especially for sentences with complex answer-relevant relations. Consequently, the performance of these models drops sharply when the relative distance between the answer fragment and other non-stop sentence words that also appear in the ground truth question increases. To address this issue, we propose a method to jointly model the unstructured sentence and the structured answer-relevant relation (extracted from the sentence in advance) for question generation. Specifically, the structured answer-relevant relation acts as the to the point context and it thus naturally helps keep the generated question to the point, while the unstructured sentence provides the full information. Extensive experiments show that to the point context helps our question generation model achieve significant improvements on several automatic evaluation metrics. Furthermore, our model is capable of generating diverse questions for a sentence which conveys multiple relations of its answer fragment.
A Distributed Consensus Protocol Based on Neighbor Selection Strategies for Multi-Agent Systems Convergence

Multi-agent systems (MASs), which consist of numerous mobile agents, is a promising research area in artificial intelligence that has been profusely applied to Engineering. A consensus problem of discrete time MASs with switching topology is investigated in this paper. First, a new distributed consensus protocol based on different neighbor selection strategies is proposed. In order to reach system consensus, the protocol requires each agent to intelligently refer to two neighbors for calculating and updating state. Compared with traditional protocol, the new protocol with different strategies can significantly reduce the cost of comparison, data storage and computation during MASs evolution. Next, three concrete neighbor selection strategies and an optimized strategy are designed. Then, we analyze and prove the stability of the protocol by Lyapunov theorem and Gerschgorin Theorem. The range of parameters, that affect reaching a consensus and equilibrium state in each strategy, is given in the proof. Finally, the experimental results demonstrate the effectiveness of the new protocol and the convergence performance of MASs under different neighbor selection strategies and parameter settings.
30 GHz Broadband Bow-tie Printed Ridge Gap Waveguide Antennas

The development of wireless and satellite communication systems has led to high demand for microwave and millimeter wave application components, which play an essential role in the upcoming 5G communication. The coverage area of such systems is control by transmitted power as well as the antenna gain of the system. Hence, it is essential to design a high gain antenna that can mitigate the losses and extend the system coverage area. Higher frequencies lead to smaller sizes of RF components including antennas. However, the implementation of passive components and guiding structure becomes difficult based on traditional guiding structures such as microstrip lines, and waveguides at millimeter wave bands. Microstrip line suffers from cavity modes, which leads to surface waves and has more losses at higher frequencies. The rectangular waveguide has high power handling capability, low losses, and high Q-factors which makes it very attractive for high-frequency applications. On the other hand, at high frequencies, the wavelengths become challenging to construct with current machining techniques as ensuring good electrical contact become very challenging. In this thesis, planar high gain antennas are designed based on Printed Ridge Gap Waveguide (PRGW). The primary objective of this work is to develop high gain, wideband antennas that can support the future demand for high data transmission. Therefore, a detailed analysis for PRGW has been introduced as well as featured designs of the high gain antenna. This antenna array can perform for future 5G communication purpose, and it fulfills all the requirements of mm-wave bands. In this work, a groove-based wideband bow-tie slot antenna array is designed at 30 GHz based on printed ridge gap waveguide technology (PRGW). A two-section T-shaped ridge is designed to feed a bow tie slot placed on the upper ground of PRGW. The gain of the proposed slot antenna is enhanced by using a horn-like groove. Then, the proposed high gain element is deployed to build up a 1 x 4 bow-tie slot antenna array loaded with three-layer groove antenna. The proposed antenna array is fabricated and measured, where the measured results show a -10 dB impedance bandwidth from 29.5 to 37 GHz (22%). The fabricated prototype achieves a high gain of 15.5 dBi and a radiation efficiency higher than 80% over the operating frequency bandwidth. Besides, to reduce the edge diffraction in the E-plane, an artificial corrugation ring is deployed with a certain depth so that it can improve the overall antenna performance.