Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Expectation-Based Spatial Scan Statistic

OBJECTIVE This paper describes a new expectation-based scan statistic that is robust to outliers (individual anomalies at the store level that are not indicative of outbreaks). We apply this method to prospective monitoring of over-the-counter (OTC) drug sales data, and demonstrate that the robust statistic improves timeliness and specificity of outbreak detection. BACKGROUND The expectation-based scan statistic [1-2] is a variant of the spatial and space-time scan statistics [3-4] that enables timely and accurate detection of disease outbreaks by accounting for spatial and temporal variation in baseline disease rates. The expectation-based method first infers the expected count for each spatial location by time series analysis, and then finds spatial regions with counts that are significantly higher than expected. Our SSS system [5] is currently using this method for daily, nationwide monitoring of OTC sales data from the National Retail Data Monitor [6].
Hybrid wireless indoor positioning with ibeacon and WI-FI

The fingerprint of location can reach high precision and high accuracy with the assistance of complex positioning algorithms. However, the indoor environment is complex so that the accuracy of indoor positioning is affected easily and seriously by several kinds of signal interference. In this paper, we use iBeacon as anchor method to help WiFi positioning algorithms. In the hybrid algorithms, iBeacon divides the space into different parts, with the combined iBeacon and Wi-Fi offline fingerprint data, and then it locates partly. Compared to the single Wi-Fi indoor positioning algorithms, it can significantly reduce the amount of calculation and the error of positioning.
A vitória de Donald Trump na imprensa Portuguesa – a (im)parcialidade em perspetiva

A mediatizacao associada as eleicoes presidenciais norte-americanas constitui o ponto de partida para analisarmos a forma como a imprensa generalista portuguesa reagiu a vitoria de Donald Trump nas eleicoes de 8 de novembro de 2016. O impacto das referidas eleicoes resulta da configuracao da ordem mundial, ainda dependente de um unilateralismo associado ao papel dos Estados Unidos da America em diversos conflitos e da especificidade das referidas eleicoes e seus candidatos. Em termos metodologicos recorremos a tecnica da analise de conteudo, em simultaneo com a abordagem descritiva, com o objetivo de tracar um quadro geral do impacto da vi toria de Donald Trump na imprensa portuguesa, bem como a aplicabilidade do principio da isencao ou da parcialidade na transmissao da informacao, considerando a tradicao portuguesa de uma nao partidarizacao dos meios de comunicacao social. De acordo com os propositos da investigacao, os resultados esperados permitirao aprofundar o conhecimento sobre a existencia ou nao de um posicionamento ideologico face ao candidato eleito. A analise basear-se-a no conteudo das capas de jornais posteriores as eleicoes norte-americanas e reforcara o estudo da comunicacao politica associada ao papel dos meios de comunicacao social em Portugal.
Investigating Citation Linkage Between Research Articles

In recent years, there has been a dramatic increase in scientific publications across the globe. To help navigate this overabundance of information, methods have been devised to find papers with related content, but they are lacking in the ability to provide specific information that a researcher may need without having to read hundreds of linked papers. The search and browsing capabilities of online domain specific scientific repositories are limited to finding a paper citing other papers, but do not point to the specific text that is being cited. Providing this capability to the research community will be beneficial in terms of the time required to acquire the amount of background information they need to undertake their research. In this thesis, we present our effort to develop a citation linkage framework for finding those sentences in a cited article that are the focus of a citation in a citing paper. This undertaking has involved the construction of datasets and corpora that are required to build models for focused information extraction, text classification and information retrieval. As the first part of this thesis, two preprocessing steps that are deemed to assist with the citation linkage task are explored: method mention extraction and rhetorical categorization of scientific discourse. In the second part of this thesis, two methodologies for achieving the citation linkage goal are investigated. Firstly, regression techniques have been used to predict the degree of similarity between citation sentences and their equivalent target sentences with medium Pearson correlation score between predicted and expected values. The resulting learning models are then used to rank sentences in the cited paper based on their predicted scores. Secondly, search engine-like retrieval techniques have been used to rank sentences in the cited paper based on the words contained in the citation sentence. Our experiments show that it is possible to find the set of sentences that a citation refers to in a cited paper with reasonable performance. Possible applications of this work include: creation of better science paper repository navigation tools, development of scientific argumentation across research articles, and multi-document summarization of science articles.
Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing

Most modern Natural Language Processing (NLP) systems are subject to the well known problem of lack of portability to new domains and genres: there is a substantial drop in their performance when tested on data from a new domain, i.e., when test data is drawn from a related but different distribution from training data. This problem is inherent in the assumption of independent and identically distributed (i.i.d.) variables for machine learning systems, but has started to get attention only in recent years. The need for domain adaptation arises in almost all NLP tasks --- the goal of this workshop is to provide a meeting point for research that approaches the problem of adaptation from the varied perspectives of machine learning and a variety of NLP tasks. We believe there is much to gain by treating domain adaptation as a general learning strategy that utilizes prior knowledge of a specific or a general domain in learning about a new domain. Sharing insights, methodologies and successes across tasks will contribute towards a better understanding of this problem. To this end, this workshop presents original research in areas such as parsing, machine translation, dialog act tagging, entity recognition, summarization, etc. with the common theme of domain adaptation. We received sixteen submissions in all, out of which eight were selected for inclusion in the workshop.
Overcome the Screen Limitations of Smartwatch

Nowadays, given the popularity of wearable devices, The smartwatch is considered as the leading product category and taking the main share of wearable device market. So far smartwatch is only used as an accessorial device of smart phones due to its screen size. The objective of this study is to review literatures and find solutions to overcome the screen limitations. In the literature review, several methods are introduced including using other devices as output devices, other object as screens by projecting technologies
Realtime C&C Zeus Packet Detection Based on RC4 Decryption of Packet Length Field

Zeus is one of the will-published malwares. It infects PC's via emails or vulnerable applications and extracts private information such as user ID, encryption key, etc., causing considerable damages. Zeus, when it was first found in 2006, started as a C&C (Command and Control) style botnet and is now moving to P2P (Peer-to-Peer) style. This paper describes techniques to detect and classify C&C Zeus packets, however the suggested technique should be able to be adapted to P2P Zeus packets also.
Doing Corpus Linguistics

Part I: Introduction to doing corpus linguistics and register analysis 1. Linguistics, corpus linguistics, and language variation 2. Register analysis Part II: Searches in available corpora 3. Searching a corpus 4. Projects using publically-available corpora Part III: Building your own corpus, analyzing your quantitative results, and making sense of data 5. Building your own corpus 6. Basic statistics 7. Statistical tests 8. Doing corpus linguistics 9. A way forward
Parallel Algorithms and Systolic Architectures for 1-and 2-D Interpolation Using Discrete Hartley Transform

Abstract In this paper, we have presented an architecture for VLSI implementation of interpolation of 1-D real-valued data using a highly parallel Hartley-based interpolation algorithm. The proposed structure is fully pipelined, and neither reordering of data nor any delay cell is required within the computing blocks. For complete interpolation of an TV-point sequence into a sequence of length (ATL), the structure requires N/2 computational cycles, the duration of a computational cycle being equal to the time required to perform a multiply-accumulation operation. Due to the massive parallelism inherent with the DHT-based fast interpolation algorithm, the time-complexity of interpolation has been independent of the interpolation factor L. The proposed structure would, therefore, be very useful for fast interpolation in high-speed applications. In addition, a novel two-stage approach is suggested for 2-D interpolation, in which the rows and the columns of the 2-D data matrix are interpolated in two distinct stages using the proposed 1-D interpolation modules. A 2-D interpolation of (N x 7V)-point data to (NL x ATL)-point data can be computed by NL(L + 1) cuncurrent iV-point DHTs, which makes it convenient for implementation in a shared memory parallel machine. Using N(L + 1) numbers of 1-D interpolation modules, a VLSI will perform a 2-D interpolation in every (JV +1)/ 2 computational-cycles. Moreover, the proposed 2-D scheme requires a simpler program code and offers significant computational savings over the existing FFT-based fast 2-D interpolation algorithm for implementation in a general-purpose computer.
High-performance decimal floating point units

The main subject of this PhD. thesis is the research and development of new algorithms and high-performance hardware architectures for decimal fixed and floating-point arithmetic. Current financial, e-commerce and user-oriented applications make an intensive use of integer and fractional numbers represented in decimal radix. This makes an attractive opportunity for the microprocessor manufactures to provide a dedicated DFU (decimal floating-point unit) in their new high-end microprocessors. This interest is supported by the recent approval of the revision of the IEEE 754 floating-point standard (IEEE 754-2008), which incorporates a specification for decimal arithmetic. In this context, this Ph.D. thesis presents the research and design of new algorithms and high-performance architectures for DFX (decimal fixed-point) and DFP (decimal floating-point) units to evaluate in hardware the basic arithmetic operations, that is, add/sub, multiplication (and fused multiply-add) and division. The objective is to obtain efficient hardware implementations, being competitive with other proposals from both the academia and the industry. We have studied the use of new algorithms, decimal encodings and methods to improve the performance and efficiency of the resultant architectures. With the applications of these concepts, the resulting units have a reduced latency. Moreover, we have opened new paths that could serve as a design guide design for future commercial processors. A further contribution was focused on improving the reliability of computations. We have proposed a new method for sum error checking applied to BCD addition/subtraction, which presents a reduced hardware complexity when compared with other solutions used in current microprocessors.
Features based approach for indexation and representation of unstructured Arabic documents

The increase of textual information published in Arabic language on the internet, public libraries and administrations requires implementing effective techniques for the extraction of relevant information contained in large corpus of texts. The purpose of indexing is to create a document representation that easily find and identify the relevant information in a set of documents. However, mining textual data is becoming a complicated task, especially when taking semantic into consideration. In this paper, we will present an indexation system based on contextual representation that will take the advantage of semantic links given in a document. Our approach is based on the extraction of keyphrases. Then, each document is represented by its relevant keyphrases instead of its simple keywords. The experimental results confirms the effectiveness of our approach.
Learning Semantic Concepts from Noisy Media Collection for Automatic Image Annotation

Along with the explosive growth of images, automatic image annotation has attracted great interest of various research communities. However, despite the great progress achieved in the past two decades, automatic annotation is still an important open problem in computer vision, and can hardly achieve satisfactory performance in real-world environment. In this paper, we address the problem of annotation when noise is interfering with the dataset. A semantic neighborhood learning model on noisy media collection is proposed. Missing labels are replenished, and semantic balanced neighborhood is construct. The model allows the integration of multiple label metric learning and local nonnegative sparse coding. We construct semantic consistent neighborhood for each sample, thus corresponding neighbors have higher global similarity, partial correlation, conceptual similarity along with semantic balance. Meanwhile, an iterative denoising method is also proposed. The method proposed makes a marked improvement as compared to the current state-of-the-art.
Electric polarization induced by strain gradient

Electric polarization induced by a strain gradient-flexoelectricity-is studied theoretically on the system of parallel chains of harmonic oscillators. Instead of the strain gradient, the external forces are chosen as an external parameter. They are applied on the boundary of a sample the shape of which ensures nonhomogeneous deformation to be created.
Implementing an Automatic Functional Test Pattern Generation for Mixed-Signal Boards in a Maintenance Context

In this paper, we present in the context of mixed-signal board maintenance testing, language and implementation aspects of an automatic functional test pattern generation approach. The goal is to help maintenance test engineers. Our modeling method for mixed-signal boards and their components is presented and the languages proposed for modeling are described. The implementation of our test data generation process based on constraint logic programming is discussed. The application to a simple board is shown and the tool Copernicia developed in the context of our work is presented.
Study on the Countermeasure of Security Problems of Big Data Caused by Data Fragmentation

The analysis and application of big data has become the frontier science at this era. People make universal attentions on the values that are existing or potential brought by the application of big data, but they make little attentions on the security threats behind the application of big data. At present, some researches have realized the security threats generated by the data leakage, its significance is not less than the application of big data. In this article, we put forward the security problems of data leakage caused by data fragmentation of big data, we also put forward some corresponding solutions and mathematical models, and finally, we conduct theory verification through the actual cases, which can effectively prevent the security problems of data leakage caused by data fragmentation.
Proper Noun Extracting Algorithm for Arabic Language

Many of Natural Language Processing (NLP) techniques have been used in Information Retrieval, the results is not encouraging. Proper names are problematic for cross language information retrieval (CLIR), detecting and extracting proper noun in Arabic language is a primary key for improving the effectiveness of the system. The value of information in the text usually is determined by proper nouns of people, places, and organizations, to collect this information it should be detected first. The proper nouns in Arabic language do not start with capital letter as in many other languages such as English language so special treatment is required to find them in a text. Little research has been conducted in this area; most efforts have been based on a number of heuristic rules used to find proper nouns in the text. In this research we use a new technique to retrieve proper nouns from the Arabic text by using set of keywords and particular rules to represent the words that might form a proper noun and the relationships between them. To extract proper nouns from the retrieved document, we need some information about it and where it was found. First, we mark the phrases that might include proper nouns; second, we apply rules to find the proper noun and we use simple methods (stop wording and stemming) usually yield significant improvements. To test the system we have used 20 articles extracted from the Al-Raya newspaper published in Qatar and Alrai newspaper published in Jordan.
Network Security Incident Detection Based on Network Topology Patterns

In this work, we explore the option of using graph topology patterns for security incident detection in NetFlow data. NetFlow data sets in which data flows related to attacks are specially marked are analyzed using graph visualization techniques in combination with manual methods to identify prospective network topology patterns related to attacks. These patterns are subsequently validated and their merit for incident detection assessed. The current research shows that while such pattern based approach is unlikely to provide a highly reliable incident detection method on its own, it can well complement other methods and can detect attacks that remain unnoticed by statistical analysis of network traffic. Keywords—Network security; Data visualization; Graph topology patterns.
Energy Efficient Cooperative Communication with Grouped Relay in MB-OFDM for UWB Systems

Ultra Wide Band (UWB) technology is a fast emerging technology for short distance wireless communication. UWB plays a preeminent role in the short range wireless systems, due to the advantages of high speed, low power consumption and very high security. Methods/Statistical Analysis: With cooperative communication as a decisive member in its side this paper proposes cooperative MB-OFDM communication using effective grouped relays in multichannel with use of Dual Carrier Modulation (DCM) for UWB systems. We can attain maximum diversity by using simple Maximum Likelihood (ML) receiver without reduction of data rate as its found using almounti coding which is most used to provide diversity in cooperative communication. Findings: The Proposed method’s resulting diversity is observed using outage probability as a basic parameter and also how the outage probability varies in accordance to threshold power. Application/Improvements: The results shows an improved BER versus SNR with effect of CFO is considered and the improvement in channel capacity and data rate of various different modulation schemes is obtained by varying SNR and distance.
Topical Structure Analysis as an Alternative Learning Strategy for Coherent Writing

In consideration of the need for a heuristic tool which could help learners to handle coherence problems, this study focuses on evaluating the efficacy of instruction in Topical Structure Analysis (TSA) as a revision strategy for EFL learners. The findings show that students receiving instruction in TSA performed better on topical progressions. More proportions and better quality parallel progressions, sequential progressions, and extended parallel progressions were found to contribute to better overall coherence. In addition, students reported positive feedback in terms of learning and applying TSA. Many of them reported that TSA helped them to reorganize their essays and to examine coherence between sentences. In terms of the guidance given by this strategy, TSA allowed students to become more aware of the relations between sentence topics and overall discourse topics. In addition to the positive changes in students' information organization, the results indicate that instruction in TSA contributes to students' awareness of coherent writing as well as motivating them to become more responsible writers. Instead of merely depending on teachers' comments and guidance, students participated in the revising process more actively and were inspired to interpret coherence using a new perspective.
Static Dalvik Bytecode Optimization for Android Applications

Since just‐in‐time (JIT) has considerable overhead to detect hot spots and compile them at runtime, using sophisticated optimization techniques for embedded devices means that any resulting performance improvements will be limited. In this paper, we introduce a novel static Dalvik bytecode optimization framework, as a complementary compilation of the Dalvik virtual machine, to improve the performance of Android applications. Our system generates optimized Dalvik bytecodes by using Low Level Virtual Machine (LLVM). A major obstacle in using LLVM for optimizing Dalvik bytecodes is determining how to handle the high‐level language features of the Dalvik bytecode in LLVM IR and how to optimize LLVM IR conforming to the language information of the Dalvik bytecode. To this end, we annotate the high‐level language features of Dalvik bytecode to LLVM IR and successfully optimize Dalvik bytecodes through instruction selection processes. Our experimental results show that our system with JIT improves the performance of Android applications by up to 6.08 times, and surpasses JIT by up to 4.34 times.
Designing educational simulation for impact test machine

We report the simulation models for enhancing the metacognitive skills. Mechanical Engineering students of Babylon Institute in Iraq are chosen for case study. The design of model and Impact Test Simulations (ITS) for Metallurgy subject is addressed. The simulation software developed using Microsoft Studio 2010 includes tutorials to explain all necessary theoretical background and formulas. Step-by-step computerized procedures and numerical examples are rendered in a simple user-friendly, visual and interactive environment to enhance metacognitive skills and students' learning. Before starting the design process, objectives and the salient features of simulation are clearly identified. Subsequently, real laboratory experiments are conducted for collecting data and coding them in computer language. Despite the existence of numerous module embedded graphical simulation, the developed software package is expected to provide valuable tool for both students and instructors. Furthermore, the same package in the form of a bi-product can also be used as a "research tool" together with the application for engineering education.