Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical programming with total functions

Functional programming offers an advantage over imperative programming: functional programs are easier to reason about and understand, which makes certain classes of errors less common. Yet, the two disciplines have some pitfalls in common: any computation, functional or not, may be non-terminating, or may terminate in a run-time error. Turner describes a discipline called “Total Functional Programming” (TFP) in which these pitfalls are impossible, due to some easily-checked rules which require all recursion to be done structurally. [30] In this report, I detail my findings about the practical benefits and limitations of total functional programming, as well as the interactions which arise between TFP and the design and implementation of a rich functional programming language. These findings are the result of implementing a TFP compiler as a modification of the Glasgow Haskell Compiler (GHC), as well as a total standard library and a variety of total example programs.
The construction of oracles for software testing

Software testing is important throughout the software life cycle. Testing is the part of the software development process where a computer program is subject to specific conditions to show that the problem meets its intended design. Building a testing oracle is one part of software testing. An oracle is an external mechanism which can be used to check test output for correctness. The characteristics of available oracles have a dominating influence on the cost and quality of software testing. In this thesis, methods of constructing oracles are investigated and classified. There are three kinds of method of constructing oracles: the pseudo-oracle approach, oracles using attributed grammars and oracles based on formal specification. This thesis develops a method for constructing an oracle, based on the Z specification language. A specification language can describe the correct syntax and semantics of software. The contextual part of a specification describes all the legal input to the program and the semantics part describes the meaning of the given input data. Based on this idea, an oracle is constructed and a prototype is implemented according to the method proposed in the thesis.
Hash algorithm design for a non-uniformly distributed database

Computer networks have continued to make substantial advances in the past couple of decades, through better technologies and methodologies employed. As the usage of the networks continues to increase exponentially, high throughput of the networks has to be maintained with various performance-efficient network algorithms. IP address lookup is one of the processes, the performance of which dearly affects the overall network performance. Hashing has been widely used for fast IP address lookup due to its simplicity, but hashing has commonly assumed an address set with uniformly distributed key values. Performance from these known hashing techniques is far from optimal due to the high non-uniformity in actual IP address distribution. In this dissertation, we present a new methodology to deal with hashing on non-uniform distributed database. The new methodology is based on determining the "bit-wise" distribution" of database bits. With the knowledge of the bit-wise distribution hashing can be improved by being able to calculate how an XOR operator will alter the randomness of the hashing algorithm. We show how our methodology can be applied to an ad-hoc hashing algorithm or a predefined XOR folding hashing algorithm. Bit-duplication techniques are also proposed to further increase the hash randomness. An extensive analysis on correlation among hash keys is also performed to look for the best hash combinations. Simulation results show a very significant improvement in performance on randomly generated addresses and several sample IP address databases over the standard classical approaches to hashing.
Estudio comparativo sobre la capacidad de atrapamiento del catiónradical abts+. por los aceites esenciales de especies aromáticas con alto contenido de trans-anetol y estragol.

Essential oils and extracts of six species with high content of trans -anethole and estragole (tarragon, french tarragon, star anise, “anisillo”, fennel and Ocimum spp.) were obtained by microwave-assisted hydrodistillation (MWHD) and simultaneous distillation-solvent extraction (SDE). Compound separation and identification were performed by GC-MS (EI,70 eV), using Kovats indices and comparisons with MS from different libraries (Adams, NIST02 and Wiley138). The antioxidant activity of essential oils was evaluated as their radical scavenging capacity using the ABTS +. (2.2'-azinobis-(3-ethyl
CULTURE OF INTEGRATION: LITERACY TOOLS FOR THE MASSES

A wide variety of online mechanisms now exist to facilitate learning. Whilst much research has looked at various aspects of these tools, little research has focused upon how these tools interact and what the pedagogic value of them is. The purpose of this research is to assess the impact of wikis, blogs and discussion forums on teaching and learning in order to determine the factors that might be inhibiting a realisation of their potential. The survey data suggests that discussion forums are used a lot more in teaching and learning than existing literature suggest; additionally tutors and students use more than one tool for their teaching and learning, and overall students and tutors have a very positive view of their uses in teaching and learning. The results show that there is a lack of tutorial support for both teachers and students: over half of the student population and over three quarters of the tutor population state that they have not received any training on the technical and pedagogical uses and understanding of literacy tools because there is no training available, or that there is training available but they have not been made aware of such training. More work needs to be completed assessing the suitability and effectiveness of existing student and tutor tutorials, integrating tutorial support for tutors within formal teaching and professional development programs, and to monitor the effectiveness of all tutorials to ensure that they are technically and pedagogically appropriate.
Inference Machines: Parsing Scenes via Iterated Predictions

Extracting a rich representation of an environment from visual sensor readings can benefit many tasks in robotics, e.g., path planning, mapping, and object manipulation. While important progress has been made, it remains a difficult problem to effectively parse entire scenes, i.e., to recognize semantic objects, man-made structures, and landforms. This process requires not only recognizing individual entities but also understanding the contextual relations among them. The prevalent approach to encode such relationships is to use a joint probabilistic or energy-based model which enables one to naturally write down these interactions. Unfortunately, performing exact inference over these expressive models is often intractable and instead we can only approximate the solutions. While there exists a set of sophisticated approximate inference techniques to choose from, the combination of learning and approximate inference for these expressive models is still poorly understood in theory and limited in practice. Furthermore, using approximate inference on any learned model often leads to suboptimal predictions due to the inherent approximations. As we ultimately care about predicting the correct labeling of a scene, and not necessarily learning a joint model of the data, this work proposes to instead view the approximate inference process as a modular procedure that is directly trained in order to produce a correct labeling of the scene. Inspired by early hierarchical models in the computer vision literature for scene parsing, the proposed inference procedure is structured to incorporate both feature descriptors and contextual cues computed at multiple resolutions within the scene. We demonstrate that this inference machine framework for parsing scenes via iterated predictions offers the best of both worlds: state-of-the-art classification accuracy and computational efficiency when processing images and/or unorganized 3-D point clouds. Additionally, we address critical problems that arise in practice when parsing scenes on board real-world systems: integrating data from multiple sensor modalities and efficiently processing data that is continuously streaming from the sensors.
Inducing criteria for lexicalization parts of speech using the Cyc KB

Abstract We present an approach for learning criteria forpart-of-speech classiﬁcation by induction over thelexicon contained within the Cyc knowledge base.This produces good results (73.3%) using a deci-sion tree that incorporates semantic features (e.g.,Cyc ontological types), as well as syntactic fea-tures (e.g., headword morphology). Accurate re-sults (90.5%) are achieved for the special caseof deciding whether lexical mappings should usecount noun or mass noun headwords. For this spe-cial case, comparable results are also obtained us-ing OpenCyc (86.9%), the publicly available ver-sion of Cyc, and the Cyc-to-WordNet translation ofthe semantic speech part criteria (86.3%). 1 Introduction We use the term lexical mapping to describe the relation be-tween a word and its syntactic and semantic features in a se-mantic lexicon. The term lexicalize will refer to the processof producingthese mappings,which are referredto as lexical-izations. 1 Selecting the part of speech for the lexical mappingis required so that proper inﬂectional variations can be recog-nized and generated for the term. Although often a straight-forward task, there are special cases that can pose problems,especially when ﬁne-grained speech part categories are used.In particular, deciding whether the headword in a phraseshould be lexicalized as a mass noun is not as straightforwardas it might seem. There are guidelines available in traditionalgrammar texts, as well as the more technical linguistics lit-erature. But these mainly cover high level categories, suchas substances, the prototypical category for mass nouns, andconcrete objects, the prototypical category for count nouns.However, for lower-level categories the distinctions are notso clear, especially when the same headword occurs in differ-ent types of contexts. For example, “source code” is a massnoun usage, whereas “postal code” is a count noun usage.In addition, sometimes the same word will be a mass nounin some contexts and a count noun in others, depending on
Motion planning with localization and mapping uncertainties for a mobile manipulator in exploration and inspection tasks

In this research, we address the motion planning (MP) problem in real world robotic exploration and inspection tasks, where robot localization and mapping uncertainties have to be incorporated into the planned motions. The robot considered in this work is a mobile manipulator system, which combines the mobility of the base with the dexterousness of the manipulator, and therefore is suitable for the exploration and inspection task in both open spaces and locally cluttered environments. To explore the environment, a laser range scanner is mounted in the front of the mobile base. The first part of this work considers localization and mapping uncertainties in the motion planning problem. We propose RRT-SLAM, which uses a rapidly-exploring random tree (RRT) in conjunction with a simulated particle based simultaneous localization and mapping (SLAM) algorithm to expand the tree. The simulated SLAM explicitly accounts for localization and mapping uncertainties in the planning stage. Moreover, the RRT itself is represented in the uncertainty-configuration space (UC-space), which is an augmented configuration space with an extra dimension of uncertainty. The collision probability along a planned path is explicitly calculated and is used to select a planned path. Since the standard Euclidean metric does not reflect the structure of the UC-space well, we address the efficiency of the RRT in the UC-space. We treat the issue from a data clustering point of view and propose a fractal dimension (FD) based checking criterion for efficient node generation. The advantages of our FD based approach are illustrated and discussed, and we demonstrate the positive results in simulations. The second part of this research addresses planning motions for the manipulator to accomplish an inspection task, with the base staying stationary. Therefore, no sensor observation is available during the motion. We extend the probabilistic roadmap (PRM) algorithm to plan motions. Since the base pose of the manipulator is not precisely known
Computational Intelligence: The Experts Speak

From the Publisher: * Computational intelligence is the term used by computer scientists to describe the overlapping technical interest areas that would be more readily identified by nontechnologists as AI * Includes past, present, and future examples of applications developed from computational intelligence studies * The editors and contributors are the leaders in the CI community writing in their respective fields of expertise
Automated planning in very large, uncertain, partially observable environments

Automated planning is investigated for very large, uncertain, partially observable environments. Experimental analysis is conducted on two widely different applications : the classic RISK game and a simulation of an Urban Search and Rescue Operation (US&RO). Many aspects of automated planning are explored simultaneously to limit assumptions, problem domain requirements, and subject matter expertise. Challenges addressed are in search capability, implementation strategy (scalability, modularity and extensibility), and machine learning for continual improvement. A general architecture is designed for future applications, and includes a hierarchical framework, a well-defined set of functions, and a nine- step implementation strategy. Metrics are designed and implemented at seven levels of the hierarchy to address scalability, modularity and extensibility. Experimental results are analyzed for efficiency and effectiveness at three levels of simultaneous machine learning: plan generation (plan search and selection), planning cycle operation (incorporates plan-execution and -assessment as an iterative process), and planners' utility (measure of planners ability to accomplish a mission or win a game)
The Fiasco Kernel: Requirements Definition

Fiasco is a new implementation of the L4 microkernel interfa ce for the x86 architecture. It is intended to be a drop-in replacement for L4/x86. Fiasco is intended to have good real-time properties, and it should be small, fast, embeddable, maintainable, and well-documented. It d oes not need to be portable, but it should be able to serve as a model for future L 4 implementations. It will be distributed under a freeware (open-source) licen se. This document summarizes the preparations for the Fiasco im ple entation. It contains a definition of the problem which triggered the cons truction of Fiasco, a requirements definition, and pointers to documents contain i g further implementation prerequisites. Dresden University of Technology, Dept. of Computer Scienc e, Operating Systems Group, D-01099 Dresden, Germany; Email: hohmuth@innocent.com
A New Technique to Hide Encrypted Data in QR Codes

In the present paper, the authors have introduced a new data-hiding algorithm, where a secret message is encrypted with combined cryptographic method and then hide the encrypted data in a QR Code (Quick Respond Codes). The embedded QR code may be sent to destination or may be saved for future use. QR Codes are mainly used to convey or store messages because they have higher or large storage capacity than any other normal conventional ‘barcodes’. In the present work the authors have introduced the encryption technique developed by Nath et.al to encrypt any secret message and then embed inside QR Code. In the encryption method Nath et al. have used bit-manipulation, byte-reshuffling and generalized modified Vernam Cipher method. The cryptography method used here has been tested on different plain texts and it was found that the method is unbreakable using normal cryptanalysis like frequency analysis, plain-text attack, Differential attack, Brute-force attack, etc. The data is encrypted using a symmetric key, then inserted in QR code, so that data can not be easily retrievable without adequate authorization / permission.
Matting-based restoration and enhancement for high performance panoramic imaging

It is known that images seen by human eyes and those captured by camera lens are evidently different. In general, a human eye can see the majority of the view field. However, a lens has limited visual range due to various specifications and uses. We can obtain a panoramic image that has a wider view field using stitching techniques from input images captured by putting the lens in different view angles. However, the panorama image may not be clear enough due to various reasons. In this study, we address a challenging problem where each input image is focused to a certain view point with a different distance. Since the lens has a finite depth-of-field, part of the image must contain some out-of-focus blur. To synthesize a clear panorama image from such kind of blurred input images, it is necessary to de-blur the input images first. In this paper, we assume that the spatially variant out-of-focus blur in an input image is approximately uniform, and study a matting-based method for restoring the image via de-convolving the input image.
Use of Microsoft Kinect in a dual camera setup for action recognition applications

Conventional human action recognition methods use a single light camera to extract all the necessary information needed to perform the recognition. However, the use of a single light camera poses limitations which can not be addressed without a hardware change. In this thesis, we propose a novel approach to the multi camera setup. Our approach utilizes the skeletal pose estimation capabilities of the Microsoft Kinect camera, and uses this estimated pose on the image of the non-depth camera. The approach aims at improving performance of image analysis of multiple camera, which would not be as easy in a typical multiple camera setup. The depth information sharing between the camera is in the form of pose projection, which depends on location awareness between them, where the locations can be found using chessboard pattern calibration techniques. Due to the limitations of pattern calibration, we propose a novel calibration refinement approach to increase the detection distance, and simplify the long calibration process. The two tests performed demonstrate that the pose projection process performs with good accuracy with a successful calibration and good Kinect pose estimation, however not so with a failed one. Three tests were performed to determine the calibration performance. Distance calculations were prone to error with a mean accuracy of 96% under 60cm difference, and dropping drastically beyond that, and a stable orientation calculation with mean accuracy of 97%. Last test also proves that our new refinement approach improves the outcome of the projection significantly with a failed pattern calibration, and allows for almost double the camera difference detection of about 120cm. While the orientation mean calculation accuracy achieved similar results to pattern calibration, the distance was less so at around 92%, however, it did maintain a stable standard deviation, while the pattern calibration increased as distance increased.
A study of the construction of a mobile learning oriented mathematics learning activity

This research integrated mathematics education theory and the spirit of mobile learning into authentic context. Researchers designed and implemented the mathematics learning activities takes mathematical ability of K-12 students as the criterion, and combined with wireless network and mobile devices as learning tools to construct a mathematical learning campus that would be helpful for the mathematics learning. The learning activity would also base on constructive learning model and the RME (Realistic Mathematics Education) theory, and consist of interesting competitive game. The mobile learning tools can make the mathematics knowledge to be restructured and present the connection between mathematics concept and authentic context by responding to learning demand immediately, retrieving knowledge actively, providing mobility with learning context, interactivity of learning process. Learners will have drastic change in mathematics learning. Mathematics learning within real-world context would be linked to real life more instead of abstract thinking. Using the teaching material easy to get on campus, students can get rid of merely dull calculation and formulas in mathematics learning, and they can use mathematical concept in real-life context and find mathematics leaning more meaningful. In the process of constructing mathematics knowledge, students can find the mathematics again from "question context" to "mathematics question solved in the context. " They can utilize mathematics tools to reconstructure knowledge, to solve problems of real life, and to experience that mathematics is from life and used in life by the new advantage of mobile learning.
Locating Features with a Camera in Hand

This paper presents a procedure to obtain the 3D localization of straight edges from images taken by a camera-in-hand of a robot. Although location of polyhedral objects can be obtained from points, there are some advantages when using edges. We present two methods to compute the localization of edges, directly using the information of their projections in the image. One of them requires the identification of a tip and five degrees of freedom of the object are determined. The other do not require point identification, but only four degrees of freedom are obtained.
Knowledge Management in Support of Education

Abstract This paper is about the application of knowledge management to supporting education. Knowledge management is about leveraging what an organization knows and improving connectivity between knowledge sources and/or knowledge users. New knowledge manage­ment methods and technologies greatly improve the ability of faculty and students to improve their connectivity, find knowledge, and to visualize knowledge. It is expected that this will lead to improved learning. However, issues such as cost, organizational politics, and learning culture may prevent this from happening. Keywords : Knowledge Management, Education, Wiki, Knowledge Transfer, Community of Practice
Acquisition of Hypernyms and Hyponyms from the WWW

Recently research in automatic ontology construction has become a hot topic, because of the vision that ontology will be the core component to realize the semantic web. This paper presents a method to automatically construct ontology by mining the web. We introduce an algorithm to automatically acquire hypernyms and hyponyms for any given lexical term using search engine and natural language processing techniques. First, query phrase is constructed using the frame “X is a/an Y”. Then corpora sentences is obtained from the result from search engine. Natural language processing techniques is then employed to discover hypernym/hyponym lexical terms. The methodologies proposed here in this paper can be used to automatically augment natural language ontology, such as WordNet, domain specific knowledge.