Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Networks for Document Image and Text Processing

Nowadays, the main libraries and document archives are investing a considerable effort on digitizing their collections. Indeed, most of them are scanning the documents and publishing the resulting images without their corresponding transcriptions. This seriously limits the document exploitation possibilities. When the transcription is necessary, it is manually performed by human experts, which is a very expensive and error-prone task. Obtaining transcriptions to the level of required quality demands the intervention of human experts to review and correct the resulting output of the recognition engines. To this end, it is extremely useful to provide interactive tools to obtain and edit the transcription. Although text recognition is the final goal, several previous steps (known as preprocessing) are necessary in order to get a fine transcription from a digitized image. Document cleaning, enhancement, and binarization (if they are needed) are the first stages of the recognition pipeline. Historical Handwritten Documents, in addition, show several degradations, stains, ink-trough and other artifacts. Therefore, more sophisticated and elaborate methods are required when dealing with these kind of documents, even expert supervision in some cases is needed. Once images have been cleaned, main zones of the image have to be detected: those that contain text and other parts such as images, decorations, versal letters. Moreover, the relations among them and the final text have to be detected. Those preprocessing steps are critical for the final performance of the system since an error at this point will be propagated during the rest of the transcription process. The ultimate goal of the Document Image Analysis pipeline is to receive the transcription of the text (Optical Character Recognition and Handwritten Text Recognition). During this thesis we aimed to improve the main stages of the recognition pipeline, from the scanned documents as input to the final transcription. We focused our effort on applying Neural Networks and deep learning techniques directly on the document images to extract suitable features that will be used by the different tasks dealt during the following work: Image Cleaning and Enhancement (Document Image Binarization), Layout Extraction, Text Line Extraction, Text Line Normalization and finally decoding (or text line recognition). As one can see, the following work focuses on small improvements through the several Document Image Analysis stages, but also deals with some of the real challenges: historical manuscripts and documents without clear layouts or very degraded documents. Neural Networks are a central topic for the whole work collected in this document. Different convolutional models have been applied for document image cleaning and enhancement. Connectionist models have been used, as well, for text line extraction: first, for detecting interest points and combining them in text segments and, finally, extracting the lines by means of aggregation techniques; and second, for pixel labeling to…
International Journal of Modern Trends in Engineering and Research

: Basically object detection and object tracking are two important and challenging aspects in many computer vision applications like surveillance system, vehicle navigation, autonomous robot navigation, compression of video etc. Object detection is first low level important task for any video surveillance application. To detection of moving object is a challenging task. Tracking is required in higher level applications that required the location and shape of object. There are three key steps in video analysis: detection of interesting moving objects, tracking of such objects from frame to frame, and analysis of object tracks to recognize their behavior. Object detection and tracking especially for human and vehicle is currently most active research topic. A lot of research has been undergoing ranging from applications to noble algorithms. The main objective of this paper is to review (survey) of various moving object detection and object tracking methodologies.
Sustainable and ICT-Enabled Development in Developing Areas: An E-Heritage E-Commerce Service for Handicraft Marketing

Human-induced threats serve as potential hazards to cultural heritage assets, especially in developing areas where the local community, in general, is a deprived class. Sustainable tourism development is acknowledged as an economic activity to ensure careful management of assets along with local community empowerment and participation. As such, ICT-enabled development is applied in rural development projects to promote sustainable rural livelihood, but success is still limited due to a lack of community involvement and sharing in the economic gains of tourism. With this perspective in mind, the present study focuses on emerging marketing models (e-commerce) that can provide new business ventures for local communities by identifying critical online marketing elements driven by local residents.
General, Scalable Path-Sensitive Fault Detection

Recent research has shown that program paths are important in static analysis for detecting and reporting faults. However, developing a scalable path-sensitive analysis is challenging. Often the techniques only address one particular type of fault and require much manual effort to tune for the desirable scalability and precision. In this paper, we present a novel framework that automatically generates scalable, interprocedural, path-sensitive analyses to detect user specified faults. The framework consists of a specification technique for expressing program properties related to faults, a scalable path-sensitive algorithm, and a generator that unifies the two. The generated analysis identifies not only faults but also the path segments that are relevant to the faults. The generality of the framework is accomplished for both data and control centric faults, so that the detection of multiple types of faults can be unified, which enables the exploitation of fault interactions for diagnosis and efficiency. We implemented our framework and generated fault detectors for identifying buffer overflow, integer truncation and signedness errors, and null-pointer dereference. We experimentally demonstrated that the generated analysis scales up to at least half a million lines of code, and its detection capability is comparable to manually produced analyses. In our experiment, a total of 53 faults of the three types from 9 benchmarks are detected, among which 37 have not been reported previously. The results show that we are able to identify faults deeply embedded in the code, and the average length of faulty path segments is 1–4 procedures, which provides a focus for diagnosis.
Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS)

BICT 2015 aims to provide a world-leading and multidisciplinary venue for researchers and practitioners in diverse disciplines that seek the understanding of key principles, processes and mechanisms in biological systems and leverage those understandings to develop novel information and communications technologies (ICT). BICT 2015 targets two thrusts: THRUST 1: Indirect Bioinspiration (ICT designed after biological principles, processes and mechanisms). Examples include evolutionary computation, artificial gene regulatory networks, neural networks/computation, swarm intelligence, cellular automata, artificial immune systems, amorphous computing, artificial life, artificial chemistry, reaction-diffusion computing, self-organization, chaotic systems, social networks, game theory, computational epidemics, agent-based modeling, and nature-inspired models and calculi. THRUST 2: Direct Bioinspiration (ICT utilizing biological materials and systems). Examples include biosensing, molecular assembly, cellular computing, molecular computing/communication, membrane computing, bacterial computing/communication, DNA computing and memory, Physarum computing, quantum computing, biometric security/authentication, brain-computer interfaces and energy harvesting from biological sources.
Customer Satisfaction in Service Companies: the Case of the Restaurant Industry

The services industry has experienced a change during the past few years to become an important line of business in the economy of Latin American countries. This is due not only to the performance of industries in this sector, but also to the fact that all sectors in these economies began to add different services to their products aiming at increasing their product value and setting themselves apart from their competitors. When it comes to loyalty to and preference for restaurant services, customer satisfaction makes a difference. It is something that the industry requires from businesses for them to be successful. It goes without saying that offering an excellent quality product is also essential to be able to stand out in this industry. The purpose of this research study is to establish the determining factors of consumer satisfaction in the restaurant industry and to provide them with a tool that allows identifying the most important factors for achieving customer satisfaction.
Autoregressive processes

In this article, the definition, properties, and applications of linear autoregressive processes (or autoregressions) are reviewed. These form an important subset of the class of autoregressive moving‐average (ARMA) processes which are widely used as stationary models for time series data. Particular attention is paid to the problem of selecting and estimating appropriate autoregressions to describe empirically observed time series. WIREs Comp Stat 2011 3 316–331 DOI: 10.1002/wics.163
Effective resource management using machine learning in medicine: an applied example

Background The field of medicine is rapidly becoming digitised, and in the process passively amassing large volumes of healthcare data. Machine learning and data analytics are advancing rapidly, but these have been slow to be taken up in the day-to-day delivery of healthcare. We present an application of machine learning to optimise a laboratory testing programme as an example of benefiting from these tools. Methods Canterbury District Health Board has recently implemented a system for urgent lab sample processing in the community, reducing unnecessary emergency presentations to hospital. Samples are transported from primary care facilities to a central laboratory. To improve the efficiency of this service, our team built a prototype transport scheduling platform using machine learning techniques and simulated the efficiency and cost impact of the platform using historical data. Results Our simulation demonstrated procedural efficiency and potential for annual savings between 5% and 14% from implementing a real-time lab sample transport scheduling platform. Advantages included providing a forward job list to the laboratory, an expected time to result and a streamlined transport request process. Conclusion There are a range of opportunities in healthcare to use large datasets for improved delivery of care. We have described an applied example of using machine learning techniques to improve the efficiency of community patient lab sample processing at scale. This is with a view to demonstrating practical avenues for collaboration between clinicians and machine learning engineers.
The Use of Colorful Semantics to Improve Sentence Construction in Writing Sentences Among Year Four Pupils

This action research was set out to develop pupils’ writing skill using colorful semantics. The main two objectives established from the outset were to develop pupils’ sentence construction and improve my teaching practice in sentence construction teaching respectively. As the researcher, I conducted this classroom action research to 31 pupils which comprised 17 males and 14 females of Year 4J of SJK(C) Cheng Siu 2 in Malaysia. The data was gathered through pre- writing test and post- writing test, pupils’ work as well as teacher’s reflective journal. Over the course of four weeks, I introduced colorful semantics to my pupils in accordance to the sequence of ‘who’, ‘what doing’, ‘what’ and ‘where’. As can be induced from the findings, colorful semantics was statistically significant to pupils’ sentence construction through the enhancement of pupils’ ability to understand words of each sentence part and also to write sentence in the correct order. The findings also depicted that utilization of colorful semantics improved my teaching practice to become a reflective, passionate, and motivational teacher. Therefore, the positive results warrant further research into colorful semantics with suggestions to integrate Information, Communication, and Technology (ICT) into lessons and expose pupils to more sentence types.
Efficient and scalable graph view maintenance for deductive graph databases based on generalized discrimination networks

Graph databases provide a natural way of storing and querying graph data. In contrast to relational databases, queries over graph databases enable to refer directly to the graph structure of such graph data. For example, graph pattern matching can be employed to formulate queries over graph data. However, as for relational databases running complex queries can be very time-consuming and ruin the interactivity with the database. One possible approach to deal with this performance issue is to employ database views that consist of pre-computed answers to common and often stated queries. But to ensure that database views yield consistent query results in comparison with the data from which they are derived, these database views must be updated before queries make use of these database views. Such a maintenance of database views must be performed efficiently, otherwise the effort to create and maintain views may not pay off in comparison to processing the queries directly on the data from which the database views are derived. At the time of writing, graph databases do not support database views and are limited to graph indexes that index nodes and edges of the graph data for fast query evaluation, but do not enable to maintain pre-computed answers of complex queries over graph data. Moreover, the maintenance of database views in graph databases becomes even more challenging when negation and recursion have to be supported as in deductive relational databases. In this technical report, we present an approach for the efficient and scalable incremental graph view maintenance for deductive graph databases. The main concept of our approach is a generalized discrimination network that enables to model nested graph conditions including negative application conditions and recursion, which specify the content of graph views derived from graph data stored by graph databases. The discrimination network enables to automatically derive generic maintenance rules using graph transformations for maintaining graph views in case the graph data from which the graph views are derived change. We evaluate our approach in terms of a case study using multiple data sets derived from open source projects.
Non-destructive Testing of Duralumin and Titanium Alloys

The article presents the structure of a subminiaturized eddy-current transducer (EST) of a transformer type with the exciting, measuring and compensation windings, which is based on the pyramid-shaped core providing the localization of the magnetic field on the area of 0.0025 mm2. It also presents the main transducer parameters that allow providing this localization of the magnetic field. A measurement procedure that allows one to perform high-accuracy monitoring of flaws in different alloys is described. The eddy-current transducer was successfully tested on several objects, e.g., a 5.5 μm thick duralumin alloy and welded seams of 5 mm thick titanium plates. The penetration depth of the field of eddy currents into the investigated object is determined and the dependences, describing the response of the ECT at different depths of the defect, are outlined. The dependences of the ECT signal on the flaws in these structures are given. Keywords—eddy-current transducer; titanium alloys; duralumin; measuring system; filter
Large Scale Knowledge Extraction from Biomedical Literature Based on Semantic Role Labeling (Wissensextraktion im Großmaßstab aus biomedizinischen Texten mittels Semantic Role Labeling)

This doctorate aimed at the development of a broad scale text mining approach covering a multitude of relation types (e.g. activation, inhibition, phosphorylation and others) as well as entity types (e.g. genes, metabolites, diseases and others). The resulting text mining system EXCERBT was developed, optimized and evaluated in hindsight on practical usability for systems biology. The system is characterized in technical hindsight by high processing speed and easy extensibility. EXCERBT is a semantic search engine for biomedical texts additionally comprising a new approach for automatically generating biomedical lexica.
Assessment of knowledge and attitude towards alternative to animal experimentation in research and education among interns and post graduate medical students in a teaching hospital, Tamilnadu, India

Background: Use of animals for various purposes like food, transportation, pets, sports, recreation and companionship is as old as the human beings itself. Animals also serve as a tool for education, research, medical procedures, toxicological screening, for several decades. Since pain, distress and death of animals occur commonly during scientific experiments, various guidelines have been proposed and posed many restrictions over the experimental use of animals.To assess the knowledge and attitude towards alternative to animal experimentation in research and education among interns and postgraduate medical students in a teaching hospital.Methods: A cross sectional study was carried out by self-administered questionnaire among interns (92) and postgraduate medical students (53) in a teaching hospital in July 2018. The data was analysed by descriptive statistics and expressed in percentage.Results: Among 145 participants, interns 92 (63.4%), postgraduates 53 (36.6%). 70.2% interns and 68.8% postgraduates had adequate knowledge about alternative animal experimentation and 67% and 67.1% of interns and postgraduates had knowledge about animal experiments. 69.4% interns and 68.8% postgraduates had positive attitude towards alternative animal experimentation.83. 8% interns and 70.9% postgraduates were agreed to have various barriers to alternative animal experimentation.Conclusions: Majority of interns and postgraduates have appreciable knowledge of alternative to animal experimentation, but their attitude is scarce. It is imperative to incorporate continuous training through workshops for budding medical professionals to provide innovative scientific knowledge in research and education towards alternative to animal experimentation.
Extending Vulnerability Discovery with Fuzzing and Symbolic Execution to Realistic Applications

In 2016, DARPA held the Cyber Grand Challenge (CGC) using a special execution and evaluation environment to compare the results of di erent techniques in automated vulnerability discovery. However, this special execution environment simplifies many of the complexities seen in real binaries on a desktop Linux system. In this paper, we augment the top-scoring, open source, vulnerability discovery component from the CGC by providing additional functionality with respect to files, file systems, and library function summaries to more e ectively operate on realistic Linux binaries. We begin by transforming the CGC challenge binaries to resemble more realistic Linux binaries by way of dynamically linked standard C library functions and compiling for a 64-bit system. We then look at examples of popular Linux applications to evaluate our solution. We find that support for files is important and the lack of function summaries for C library functions and system calls limits the e ective use of symbolic execution in a real Linux environment as compared with the CGC.
Modeling Strategies to Improve the Dependability of Cloud Infrastructures

Cloud computing presents some challenges that need to be overcome, such as planning infrastructures that maintain availability when failure events and repair activities occur. Cloud infrastructure planning that addresses the dependability aspects is an essential activity because it ensures business continuityand client satisfaction. Redundancy mechanisms cold standby, warm standby and hot standby can be allocated to components of the cloud infrastructure to maintain the availability levels agreed in service level agree-ment (SLAs). Mathematical formalisms based on state space such as stochastic Petri nets and based on combinatorial as reliability block diagrams can be adopted to evaluate the dependability of cloud infrastructures considering the allocation of different redundancy mechanisms to its components. This chapter shows the adoption of the mathematical formalisms stochastic Petri nets and reliability block diagrams to dependability evalua- tion of cloud infrastructures with different redundancy mechanisms.
Design of CMOS integrated phase-locked loops for multi-gigabits serial data links

Design of CMOS integrated phase-locked loops for multi-gigabits serial data links (Dec 2006) Shanfeng Cheng Chair of Advisory Committee: Jose Silva-Martinez High-speed serial data links are quickly gaining in popularity and replacing the conventional parallel data links in recent years when the data rate of communication exceeds one gigabits per second. Compared with parallel data links, serial data links are able to achieve higher data rate and longer transfer distance. This dissertation is focused on the design of CMOS integrated phase-locked loops (PLLs) and relevant building blocks used in multi-gigabits serial data link transceivers. Firstly, binary phase-locked loops (BPLLs, i.e., PLLs based on binary phase detectors) are modeled and analyzed. The steady-state behavior of BPLLs is derived with combined discrete-time and continuous-time analysis. The jitter performance characteristics of BPLLs are analyzed. Secondly, a 10 Gbps clock and data recovery (CDR) chip for SONET OC192, the mainstream standard for optical serial data links, is presented. The CDR is based on a novel referenceless dual-loop half-rate architecture. It includes a binary phase-locked loop based on a quad-level phase detector and a linear frequency-locked loop based on a linear frequency detector. The proposed architecture enables the CDR to achieve large locking range and small jitter generation at the same time. The prototype is implemented in 0.18 ÃŽÂ1⁄4m CMOS technology and consumes 250 mW under 1.8 V supply. The jitter generation is 0.5 ps-rms and 4.8 ps-pp. The jitter peaking and jitter tolerance performance exceeds the specifications defined by SONET OC-192 standard. Thirdly, a fully-differential divideby-eight injection-locked frequency divider with low power dissipation is presented. The frequency divider consists of a four-stage ring of CML (current mode logic) latches. It has a maximum operating frequency of 18 GHz. The ratio of locking range over center frequency is up to 50%. The prototype chip is implemented in 0.18 ÃŽÂ1⁄4m CMOS technology and consumes 3.6 mW under 1.8 V supply. Lastly, the design and optimization techniques of fully differential charge pumps are discussed. Techniques are proposed to minimize the nonidealities associated with a fully differential charge pump, including differential mismatch, output current variation, low-speed glitches and highspeed glitches. The performance improvement brought by the techniques is verified with simulations of schematics designed in 0.35 ÃŽÂ1⁄4m CMOS technology.