RDNF Oriented Analytics to Random Boolean Functions

Field programmable gate arrays (FPGA) and other complex programmable devices (CPLD) require new logic minimization techniques since the cost functions used for conventional target implementations are no longer valid. Until now, existing tools are only adapted to the new requirements. However, the underlying approaches for logic minimization and technology mapping remained the same. We present logic minimization techniques that extend classical approaches. Basic function properties as e.g. linearity, monotony, and symmetry of its variables are detected. They are used in decomposition and partial collapsing steps to group variables with common properties within a multi-level Boolean network. The Boolean functions are stored as decision diagrams. Three different Boolean normal forms are used: disjunctive normal form, Reed-Muller expansion, and equivalence polynomial. Therefore, three types of decision diagrams are needed: binary decision diagrams (BDD), functional decision diagrams (FDD), and equivalence decision diagrams (EDD). A multiple domain minimization approach based on decomposition, domain selection, variable ordering, and variable polarity optimization is introduced.

This work represents the final phase in establishing a firm relationship between the Boolean minimization problem (BMP) and the abduction problem (AP). Specifically, it demonstrates how the BMP could be solved using an algorithm based on the parsimonious covering model, which in turn models a class of APs. The implementation of the algorithm that solves the BMP in an abductive framework provides a much needed empirical validation of the theoretical properties of the algorithm, in addition to providing a different method for computing the minimized form for a Boolean function.

Past years have experienced increasing mortality rate due to lung cancer and thus it becomes crucial to predict whether the tumor has transformed to cancer or not, if the prediction is made at an early stage then many lives can be saved and accurate prediction also can help the doctors start their treatment. Computed tomography plays a vital role in ensuring the condition of tumor that by checking the size of tumor, location of tumor, etc. In this paper, we have proposed a framework for prediction of cancer at an early stage so that many lives that are in an endangered situation could be revived. Basically, our focus is on two domains of computer science that is Digital Image Processing acronymed DIP and Machine Learning. Digital image processing is well-known for the phase of preprocessing the image. In the further stage, the pre-processed image is exposed to segmentation phase and then the segmented image is passed for feature extraction and finally the extracted features are trained using machine learning classification algorithms like SVM (Support Vector Machines), Random Forest, ANN (Artificial Neural Network) . Based on the classification results obtained, prediction is made whether the tumor is benign or malignant. The inevitable parameters such as accuracy, Recall and precision are calculated for determining which algorithm has the highest predictive accuracy.

Revealing the canalizing structure of Boolean functions: Algorithms and applications

Machine Learning (ML) has been categorized as a branch of Artificial Intelligence (AI) under the Computer Science domain wherein programmable machines imitate human learning behavior with the help of statistical methods and data. The Healthcare industry is one of the largest and busiest sectors in the world, functioning with an extensive amount of manual moderation at every stage. Most of the clinical documents concerning patient care are hand-written by experts, selective reports are machine-generated. This process elevates the chances of misdiagnosis thereby, imposing a risk to a patient's life. Recent technological adoptions for automating manual operations have witnessed extensive use of ML in its applications. The paper surveys the applicability of ML approaches in automating medical systems. The paper discusses most of the optimized statistical ML frameworks that encourage better service delivery in clinical aspects. The universal adoption of various Deep Learning (DL) and ML techniques as the underlying systems for a variety of wellness applications, is delineated by challenges and elevated by myriads of security. This work tries to recognize a variety of vulnerabilities occurring in medical procurement, admitting the concerns over its predictive performance from a privacy point of view. Finally providing possible risk delimiting facts and directions for active challenges in the future.

The computer science education research community has thought deeply about how students learn computational thinking (CT) as it relates to other domains of computer science (CS; e.g. programming) and core content areas (STEM, humanities), but less work has examined the role of CT in pathways to computer science for K-5 teachers. This panel examines the experiences of practitioners - educators, administrators, and curriculum designers--who have both experienced and supported others in incorporating CT in elementary school settings as a pathway to or component of computer science education. All panelists have worked with teachers not previously trained to teach CS and have encountered the many opportunities and difficulties of bringing CS to in-service teachers. They will reflect on the multiple ways educators grapple with CT: as an entry point to computer science, as a way to enrich core disciplines, and as a way to support equitable practice - for example, several of the panelists have experiences leveraging CT and other domains of CS to support the expression and development of emergent bilingual students. The panel will explore ways in which CT and its associated language and strategies for problem solving may provide a particularly helpful onramp to CS generally, including integration with other disciplines and with language about academic skills more generally.

The problem of synthesis of combinational circuits of code converters designed to reduce the length of words from a given set of encoded binary words is considered. The encoding assumes that different binary words will be encoded by different binary codes of shorter length. Code converters of this type are designed to reduce the length of binary words transmitted in digital systems over data buses when the bit depth of the transmitted words exceeds the bit depth of the data bus. For example, 18-bit or 17-bit words need to be transmitted over a 16-bit data bus. Each such word can be transmitted in two cycles of operation of a digital system, however, this approach reduces the overall performance of the system. One of the approaches to solve such problems is the development of combinational circuits that convert long binary encoded words into shorter ones. The proposed methods for solving the problem of synthesizing circuits of code converters are based on the compilation and logical minimization of such forms of systems of incompletely specified Boolean functions as disjunc­tive normal forms (DNF) and binary decision diagrams (BDD). Using BDD to minimize representations of k-valued functions that depend on Boolean variables is also proposed. Technologically independent logical minimization of functional descriptions of the designed code converters is proposed to be performed by programs for minimizing systems of Boolean functions in the DNF class and programs for joint minimization of BDD representations of systems of completely specified Boolean functions. Minimization of functional descriptions is aimed at reducing the hardware complexity of combinational circuits in the basis of library elements or programmable FPGA elements implementing code converters of the class in question.

The results of experiments on the circuit implementation of modular multipliers in the design library of ASIC (Application-Specific Integrated Circuits) and FPGA (Field-Programmable Gate Array) are presented. The initial descriptions of modular multiplier projects were given by systems of not fully defined (partial) Boolean functions and algorithmic VHDL descriptions. Logical optimization was carried out in the class of disjunctive normal forms (DNF) and representations of Boolean function systems by BDD (Binary Decision Diagrams). The synthesized circuits were evaluated by area and time delay. It is established that the use of partial Boolean function models and preliminary logi­cal BDD optimization allows one to improve the parameters of synthesized ASIC and FPGA blocks for small module values, however, the best solutions for large module values can be obtained using algorithmic VHDL descriptions of modular multipliers. In the synthesis of modular multiplier circuits as part of an FPGA and the use of ISE and Vivado design systems it is advisable to use synthesized VHDL operations (a*b) mod p.

With regard to road safety issues, a deep understanding of the driver as a logic system is crucial to predict the most probable behavior according to the contextual elements. Knowledge and data about human functional abilities exist. But the problem is to organize and structure them. The development of a computational approach in driver modelization is addressed. In the first part, a brief historical overview is presented of available driver models in ergonomics and psychological areas, and the distinction between predictive and explicative models in an implementation perspective is the focus. In the second part, the computational aspect of the work is described, along with the software concepts, the cognitive modeling needs, and the implementation choices. Object-oriented techniques were chosen because they provide a modular overview of the general system and offer a convenient representation of cognitive processes. Object-oriented formalism, in particular object modeling technique diagrams, acts as a bridge between the two domains of computer science and the human sciences. The objective is to determine whether it is possible to implement reliably a driver model using the techniques from artificial intelligence and based on the theoretical knowledge from cognitive sciences research. This attempt to establish links between different scientific domains, requiring a common tool, is a challenge. A first step of a work that will have to be developed in a long-term time scale, taking into account its quite ambitious objective, is described.

A comparison of the complexity of the longest and shortest disjunctive normal forms and a lower bound for the number of prime disjunctive normal forms for almost all Boolean functions

Along with the rise of available astronomical data, captured from numerous facilities from around the world, a need for faster and more sophisticated data analysis methods emerges. Data captures from numerous observation of large quantities of object in the sky can reach large volumes very quickly, making it impossible for scientist to analyze by hand. This rises the need for fast and reliable automated methods of data processing, which can be found in computer science research. Leveraging algorithms used in different areas of research is crucial for processing information about celestial bodies. In this work, we apply machine learning methods from computer science domain into an astronomy problem. We lay out three different machine learning algorithms, along with their inner workings, and show how they can be applied to astronomy problems. We show how those algorithms can be used to speed up processing of large volumes of data, and how they can help scientists in classification of celestial bodies. We investigate how each algorithm performs and try to find the best performing one in the problem of classification of different objects, based on their characteristics.

Nowadays information security and in particular web security becomes a key knowledge domain in computer science and engineering education. Because of the rapid change and development of web and network technologies, social network sites, which support social interactions between course instructor and students can be a good venue supporting the teaching and learning of information security. This paper examines an online social network formed over Facebook as a course discussion forum of course for web security. A number of interesting patterns and findings were identified from social network analyses, which offer reflections and suggestions to engineering educators through a practical implementation of online learning community.

Chapter 12 - Causes and Effects

Gaze estimation is one of the recent popular research topics in the domain of computer science/ engineering for vision-based human-computer-interaction. It has huge potential for application in assessment of physical condition of drivers while driving, controlling robots and prosthetics, surveillance etc. For successful gaze estimation the eyes have to be detected perfectly and this will lead to efficient eye centre localization. Several approaches have been used by researchers for eye and eye centre detection. The present work uses Haar-like cascade classifier for effective face and eye detection. This method has the advantage of lower computational load, faster processing due to the associated AdaBoost algorithm. High success rate can be easily achieved in several environments using proper training sets. However, for eye centre localization an improved version of Hough transform in two dimensional parametric space has been used as it is very simple to use and implement practically. This hybrid approach has been successfully tested using low-cost webcams in different lighting conditions with and without spectacles.

Machine translation is an important research area in natural language processing. In this paper, we propose an innovative fusion translation model that combines the slicing method and the alignment method and select articles from different domains for experiments to explore the translation effects of the proposed method. The experiments show that the proposed CRF sequence annotation cut-scoring method performs excellently, with F-values of 86.7%, 89.0%, and 87.4% for the three domains of computer science, civil engineering, and medicine, respectively. The hybrid alignment method based on the length and lexical information also performs well, both in terms of correctness and recall and the hybrid alignment method is able to obtain better results than the length or lexical information methods alone. Putting the proposed method into the translation accuracy test, the BLEU score of the cut-and-align fusion translation model is improved to 11.08, while the NIST score is improved to 3.6468, which is a significant improvement in the translation quality of English.