Miracle-dz/UniFAR-FTUs huggingface dataset:
Bidirectional reasoning of horn clause programs : transformation and compilation

A compilative approach for forward reasoning of horn rules in Prolog is presented. Pure horn rules - given as Prolog clauses - are to be used for forward and backward reasoning. These rules are translated into Prolog clauses, denoting one forward reasoning step. Forward chaining is triggered by an initial fact, from which the consequences are derived. Premises of forward rules are verified by Prolog's backward proof procedure using the original clauses. Thus, without any changes to the Prolog interpreter integrated bidirectional reasoning of the original horn rules is possible. Breadth-first and depth-first reasoning strategies with enumeration and collection of conclusions are implemented. In order to translate forward clauses into WAM operations several improvements are introduced. To avoid inefficient changes of program code derived facts are recorded in a special storage area called retain stack. Subsumption of a new conclusion by previously derived facts is tested by a built-in procedure. As a reasonable application of this kind of forward reasoning its use is demonstrated for integrity constraint checking.
Games used in teaching information literacy skills

The files in this Jorum site are the games and activities developed by Adam Edwards and Vanessa Hill to support teaching information literacy skills at Middlesex University. Some of the games will need laminating and cutting up for use in multiple sets. You can use these for your own teaching. We just ask that you tell us if you make new versions as we like to build on new ideas.
Improving energy efficiency and impairing environmental impacts oncloud centers by transforming virtual machine into self-adaptive resource container

Enterprises are seeking on-demand computing models that can be employed with better utilization and reduced operational cost by remitting up to the users' needs; this brings up zero charges as zero demand exhibits because user demands are vary drastically over time. To reinforce dynamic resource provision, service providers have to maintain more computational resources than needed. Meanwhile, the IT sector has more apprehensions about the impact on the environment due to an increase in carbon dioxide emissions, higher electricity consumption and a growth in the electronic wastes from electronic components. Most of the research works focus primarily on the expertise required for providing the needed resources and not care on resource utilized which brings unsustainability. To achieve sustainable computing, unwanted installation of contemporary computational resources should be rolled up and better sharing options should be made available. This paper proposes new virtualization techniques which engage cloud services exclusively between host and guest operating environments. By doing so, this mechanism stands as the best crossover with other working engines and provide open service to execute any type of applications on it. Finally, the combination of cloud service and virtualization enables container features with efficient utilization factor. Most probably, a proper combination of these resources solves any computational issues so these two resource mechanism’s always standing on the top of the change. This experiment analysis aims to compare the performance of container with virtual machine based container in an adopted infrastructure via cloud simulator. And the result of better efficiency metrics attained by virtual based container were explored and plotted.
Exploring States Of Mind: Emotion Visualization With Bio-Feedback Sensors In A 3D Environment

Current studies unveil the potential of using new technologies for emotion recognition. However, bio-feedback sensors, which can be a fruitful source of data are not explored in relation to additional stimuli. The following demo explores the opportunity of how dynamic visualization stimuli and bio-feedback sensors might provide insight into mental states that the user is not aware of. The system design is the result of a project undertaken by the authors to employ the potential of this type of sensors in the context of therapies. Early results suggest a correlation between the visualization and the emotions of users, as well as users' interest and engagement with the system. Further research can be beneficial for creating a greater therapy experience, as well as generating new user testing methods and developing game interactions that would make use of those variables.
Widening the scope of FabLearn Research: Integrating Computational Thinking, Design and Making

FabLearn has primarily been concerned with studies of digital fabrication technologies in education, however, we witness an increased interest in integrating other related topics such as computational thinking, digital design and empowerment as an integrated whole. In this paper, we present a five years design research program for digital fabrication, computational thinking and design, to highlight why the FabLearn community should embrace this wider agenda to accomplish its ultimate goal to encourage a new generation to critically and constructively engage in the design of digital technology. The contribution of this paper is a number of open questions and considerations regarding the scope of European FabLearn research that we hope the community will consider and that might give rise to further discussions.
MixedUX: A Mixed Prototyping Framework for Usability Testing in Augmented Reality

In this paper, we rethink and challenge conventional ways of prototyping. We present a new perspective on how usability testings of digital products may benefit from emerging augmented reality (AR) technologies. We demonstrate a conceptual prototype of an innovative framework that makes it possible to combine 3D models of complex devices, represented by holograms, with 2D user interfaces (UIs) opened in a web browser on a physical touch display. The framework aims to facilitate the processes of UI design and interactions with complex, costly, or even not-yet-existing systems. For demonstration purposes we use the Microsoft HoloLens Development Edition and a conventional touch display of a smartphone.
Ambivalence: Simultaneous Tactile Experiences of Using and Recycling Smartphones

This demonstration is built on the postcolonial scholar, Homi Bhabha's idea of Ambivalence and exhibits experiences of "smooth" and "rough" tactile feelings simultaneously to convey to a typical smartphone user the struggles of electronic waste (e-waste) workers when they dismantle, test, and recycle broken electronic devices. The demonstration consists of two components: 1) a mobile game that imitates e-waste workers' routine tasks, and 2) a smart glove that reacts correspondingly to the player's moves in the game by simulating unpleasant feelings. This demonstration introduces the harsh and inconvenient experiences that e-waste workers face in recycling practices, along with the familiar smooth tactile experiences in touch-based interactive devices. The co-presence and concurrent experiences of "smooth" and "rough" create an 'ambivalence' and allow the user to reflect on the stark differences between the two worlds of interactions with mobile phones. This demonstration is aimed to later develop empathetic connections between people with different privileges and backgrounds.
Performance analysis of the IEEE 802.11 MAC protocol for wireless LANs

Wireless local area networks (WLANs) are extremely popular being almost everywhere including business, office and home deployments. The IEEE 802.11 protocol is the dominating standard for WLANs. The essential medium access control (MAC) mechanism of 802.11 is called distributed co‐ordination function (DCF). This paper provides a simple and accurate analysis using Markov chain modelling to compute IEEE 802.11 DCF performance, in the absence of hidden stations and transmission errors. This mathematical analysis calculates in addition to the throughput efficiency, the average packet delay, the packet drop probability and the average time to drop a packet for both basic access and RTS/CTS medium access schemes. The derived analysis, which takes into account packet retry limits, is validated by comparison with OPNET simulation results. We demonstrate that a Markov chain model presented in the literature, which also calculates throughput and packet delay by introducing an additional transition state to the Markov chain model, does not appear to model IEEE 802.11 correctly, leading to ambiguous conclusions for its performance. We also carry out an extensive and detailed study on the influence on performance of the initial contention window size (CW), maximum CW size and data rate. Performance results are presented to identify the dependence on the backoff procedure parameters and to give insights on the issues affecting IEEE 802.11 DCF performance. Copyright © 2005 John Wiley & Sons, Ltd.
Modelling and analysis of a semantic sensor service provider ontology

The realization of Internet of Things has gained a huge amount of momentum in the past few years. It’s vision is to interconnect devices from all over the world. These devices are heterogeneous and produce data that is multi modal and diverse in nature. The heterogeneity of the devices and data makes interoperability an issue in IoT. In this paper we are presenting the modelling of a semantic sensor service provider and its ontology i.e., the Sensor Service Provider (SSP) ontology. The semantic sensor service provider is a module which is a part of a larger system i.e., a semantic IoT system based on context aggregation of an indoor environment. To provide interoperability between the devices used by the system, we have developed ontologies for each domain of the system. The modelling of the ontology presented in this paper reuses the SSN ontology to define the basic concepts and observations of a sensor, and has been extended to define concepts related to the module itself. Simple Protocol and Resource Description Framework (RDF) Query Language (SPARQL) queries are used to retrieve data from the ontology as well as manipulate the data stored to it.
Word and subword modelling in a segment-based HMM word spotter using a data analytic approach

In this work we focus on methods for representing acoustic-phonetic knowledge in a speech recognizer and for analyzing the system's behavior in detail. The testbed for developing these methods is a segment-based hidden Markov model (HMM) recognizer. The HMM framework is used to model the segmenter's deviations from the ideal behavior of one segment per phone. We employ an HMM topology that allows a phone to be associated with more than one segment. Biphone HMM's model instances in which a segment is associated with more than one phone. We compared the effectiveness of various segment measurement sets on a phonetic recognition task. The measurements consisted of short-time spectral representations measured at particular positions relative to segment boundaries. The key result was that the addition of spectra measured outside the segment to those measured inside led to a significant improvement in performance. In the course of investigation methods for representing knowledge in the measurement sets, we built linear regression models to estimate $F\sb1$ and $F\sb2$ from a set of mel-frequency spectral coefficients (MFSC's). We show that such a model is inadequate for predicting formant values at the ends of their observed ranges. However, by adding nonlinear transformations of the MFSC's to the regressor set, highly-accurate models $(R\sp2 > .96)$ valid for more than 80% of observed formant frequencies could be built. We also developed a technique we term grouped multiple discriminant analysis to address the fact that within-class covariance varies greatly among phones, contrary to the assumptions of conventional multiple discriminant analysis. We used the segment-based HMM to investigate word modelling issues as well. Models were compared using a word spotting task. The models varied along three dimensions: training method, type of pronunciation network, and measurement set. In the course of this investigation, we developed novel algorithms for word spotter scoring and performance evaluation. The scoring algorithm determines the beginning and end points of a presumed keyword and computes an estimate of the probability that the keyword occurred between those points. Finally, we outline the philosophy of exploratory data analysis and discuss how the methodology can be employed in the design of speech recognizers. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.) (Abstract shortened by UMI.)
Meanings in Digital Fabrication

The aim of this study is to investigate the drivers of physical realization of meaningful objects, in particular, how these drivers can contribute to prototyping of products. An intensive digital fabrication workshop, a part of a summer school, is used as a case study. We discuss the results of the workshop in terms of the background of the participants and their motivation in the perspective of the rationale of their prototypes. We identify how the background and motivation of teams of the participants influenced the meaning making in digitally fabricated prototypes.
Intrusion detection system using fuzzy rough set feature selection and modified KNN classifier

Intrusion detection systems are used to detect and prevent the attacks in networks and databases. However, the increase in the dimension of the network dataset has become a major problem nowadays. Feature selection is used to reduce the dimension of the attributes present in those huge data sets. Classical Feature selection algorithms are based on Rough set theory, neighborhood rough set theory and fuzzy sets. Rough Set Attribute Reduction Algorithm is one of the major theories used for successfully reducing the attributes by removing redundancies. In this algorithm, significant features are selected data are extracted. In this paper, a new feature selection algorithm is proposed using the Maximum dependence Maximum Significance algorithm. This algorithm is used for selecting the minimal number of attributes of knowledge Discovery and Data (KDD) data set. Moreover, a new K-Nearest Neighborhood based algorithm proposed for classifying data set. This proposed feature selection algorithm considerably reduces the unwanted attributes or features and the classification algorithm finds the type of intrusion effectively. The proposed feature selection and classification algorithms are very efficient in detecting attacks and effectively reduce the false alarm rate.
Texture segmentation from non-textural background using enhanced MTC

In image processing, segmentation of textural regions from non-textural background has not been given a significant attention, however, considered to be an important problem in texture analysis and segmentation task. In this paper, we have proposed a new method, which fits under the framework of mathematical morphology. The entire procedure is based on recently developed textural descriptor termed as Morphological Texture Contrast (MTC). In this work authors have employed the bright and dark top-hat transformations to handle the bright and dark features separately. Both bright and dark features so extracted are subjected to MTC operator for identification of the texture components which in turn are used to enhance the textured parts of the original input image. Subsequently, our method is employed to segment the bright and dark textured regions separately from the two enhanced versions of the input image. Finally, the partial segmentation results so obtained are combined to constitute the final segmentation result. The method has been formulated, implemented and tested on benchmark textured images. The experimental results along with the performance measures have established the efficacy of the proposed method.
Enterprise gamification systems and employment legislation: a systematic literature review

A recent innovation in employee motivation systems is the introduction of ‘gamification’, which refers to the use of game design mechanics and principles to influence behaviour to enhance staff motivation and engagement. Enterprise gamification systems aggravate the differences in information availability between employers and employees, and employees who may be forced to adopt such systems may be placed under stress, worsening employment relationships in the workplace. Therefore, this research examines the potential legal implications of gamified employee motivation systems. This study undertook a systematic review of enterprise gamification and then used thematic analysis coupled with a review of legislation to examine whether gamification in workplaces meets the legal obligations of employers under their ‘duty of good faith’ in the New Zealand context. We find that carefully designed enterprise gamification systems should provide sufficient information and clarity for employees and support positive employment relationships. Deployments of enterprise gamification systems should be carefully planned with employee consultation and feedback supporting the introduction of an enterprise gamification system. Future research should look beyond the ‘good faith’ obligation and examine the relationship between gamification systems and the law on personal grievances.
Automatic screening of retinal lesions for grading diabetic retinopathy

Diabetic Retinopathy (DR) is a diabetical retinal syndrom. Large number of patients have been suffered from blindness due to DR as compared to other diseases. Priliminary detection of DR is a critical quest of medical image processing. Retinal Biomarkers are termed as Microaneurysms (MAs), Haemorrhages (HMAs) and Exudates (EXs) that are helpful to grade Non-Proliferative DR (NPDR) at different stages. This research work contributes an automatic design for the retinal lesions screening to grade DR system. The system is comprised of unique preprocessing determination of biomarkers and formulation of profile set for classification. During preprocessing, Contrast Limited Adaptive Histogram Equalization (CLAHE) is utilized and Independent Component Analysis (ICA) is extended with Curve Fitting Technique (CFT) to eliminate blood vessels and optic disc as well as to detect biomarkers from the digital retinal image. Subsequent, NPDR lesions based distinct eleven features are deduced for the purpose of classification. Experiments are performed using a fundus image database. The proposed method is appropriate for initial grading of DR.
A CVS-Server Security Architecture — Concepts and Formal Analysis

We present a secure architecture of a CVS-server, its implementation (i.e. mainly its configuration) and its formal analysis. Our CVS-server is uses cvsauth, that provides protection of passwords and protection of some internal data of the CVS repository. In contrast to other (security oriented) CVS-architectures, our approach allows the CVS-server run on an open filesystem, i.e. a filesystem where users can have direct access both by CVS-commands and by standard UNIX/POSIX commands such as mv. For our secure architecture of the CVS-server, we provide a formal specification and security analysys. The latter is based on a refinement mapping high-level security requirements on the architecture on low-level security mechanisms on the UNIX/POSIX filesystem level. The purpose of the formal analysis of the secure CVS-server architecture is twofold: First, it is the bases for the specification of mutual security properties such as non-repudiation, authentication and access control for this architecture. Second, the mapping of the architecture on standard security implementation technology is described. Thus, our approach can be seen as a method to give a formal underpinning for the usually tricky business of system administrators.
Age-related changes in regiospecific expression of Lipolysis Stimulated Receptor (LSR) in mice brain

The regulation of cholesterol, an essential brain lipid, ensures proper neuronal development and function, as demonstrated by links between perturbations of cholesterol metabolism and neurodegenerative diseases, including Alzheimer’s disease. The central nervous system (CNS) acquires cholesterol via de novo synthesis, where glial cells provide cholesterol to neurons. Both lipoproteins and lipoprotein receptors are key elements in this intercellular transport, where the latter recognize, bind and endocytose cholesterol containing glia-produced lipoproteins. CNS lipoprotein receptors are like those in the periphery, among which include the ApoB, E binding lipolysis stimulated lipoprotein receptor (LSR). LSR is a multimeric protein complex that has multiple isoforms including α and α’, which are seen as a doublet at 68 kDa, and β at 56 kDa. While complete inactivation of murine lsr gene is embryonic lethal, studies on lsr +/- mice revealed altered brain cholesterol distribution and cognitive functions. In the present study, LSR profiling in different CNS regions revealed regiospecific expression of LSR at both RNA and protein levels. At the RNA level, the hippocampus, hypothalamus, cerebellum, and olfactory bulb, all showed high levels of total lsr compared to whole brain tissues, whereas at the protein level, only the hypothalamus, olfactory bulb, and retina showed the highest levels of total LSR. Interestingly, major regional changes in LSR expression were observed in aged mice which suggests changes in cholesterol homeostasis in specific structures in the aging brain. Immunocytostaining of primary cultures of mature murine neurons and glial cells isolated from different CNS regions showed that LSR is expressed in both neurons and glial cells. However, lsr RNA expression in the cerebellum was predominantly higher in glial cells, which was confirmed by the immunocytostaining profile of cerebellar neurons and glia. Based on this observation, we would propose that LSR in glial cells may play a key role in glia-neuron cross talk, particularly in the feedback control of cholesterol synthesis to avoid cholesterol overload in neurons and to maintain proper functioning of the brain throughout life.
Topology-dependent, bifurcated mitochondrial quality control under starvation

ABSTRACT Selective elimination of mitochondria by autophagy is a critical strategy for a variety of physiological processes, including development, cell-fate determination and stress response. Although several mechanisms have been identified as responsible for selective degradation of mitochondria, such as the PINK1-PRKN/PARKIN- and receptor-dependent pathways, aspects of the mechanisms and particularly the principles underlying the selection process of mitochondria remain obscure. Here, we addressed a new selection strategy in which the selective elimination of mitochondria is dependent on organellar topology. We found that populations of mitochondria undergo different topological transformations under serum starvation, either swelling or forming donut shapes. Swollen mitochondria are associated with mitochondrial membrane potential dissipation and PRKN recruitment, which promote their selective elimination, while the donut topology maintains mitochondrial membrane potential and helps mitochondria resist autophagy. Mechanistic studies show that donuts resist autophagy even after depolarization through preventing recruitment of autophagosome receptors CALCOCO2/NDP52 and OPTN even after PRKN recruitment. Our results demonstrate topology-dependent, bifurcated mitochondrial recycling under starvation, that is swollen mitochondria undergo removal by autophagy, while donut mitochondria undergo fission and fusion cycles for reintegration. This study reveals a novel morphological selection for control of mitochondrial quality and quantity under starvation.