Miracle-dz/UniFAR-FTUs huggingface dataset:
A Semantic Protocol-Based Approach for Developing Business Processes ∗

A (business) protocol is a modular, public specification of an interaction among different roles that achieves a desired purpose. We model protocols in terms of the commitments of the participating roles. Commitments enable reasoning about actions, thus allowing the participants to comply with protocols while acting flexibly to exploit opportunities and handle exceptions. A policy is a (typically private) rule-based description of a participant’s business logic that controls how it participates in a protocol. We propose that a business process be conceptualized as a cohesive set of protocols, and be enacted by agents playing specified roles in the protocols in which they participate. The agents would respect the given protocols while adhering to their local policies. We propose OWL-P, a language for specifying protocols, and implement it using a multiagent architecture. We compile OWL-P specifications of protocols into skeletons for each role. Each skeleton corresponds to a set of rules with place-holders for policies. Developing an agent involves using the rules for its intended roles and supplying the necessary policies. The key benefits of this approach are (1) a separation of concerns between protocols and policies in contrast to traditional monolithic approaches; (2) reusability of protocol specifications based on design abstractions such as specialization and aggregation; and (3) flexibility of enactment of processes in a manner that respects local policies while adapting continually. This paper develops further results on a programming methodology through which agents can be implemented to realize desired processes. This methodology includes design patterns that ensure that agents built according to those patterns will be guaranteed to be compliant to the stated protocols.
The effects of social supports on changes in students’ perceived instrumentality of schoolwork for future goal attainment

Research on perceived instrumentality of students’ academic work for attaining life goals has shown to have positive effects on academic achievement and motivation The purpose of the study was to examine the changes in perceived instrumentality over time and to identify how significant others such as parents, teachers and peers affect changes in perceived instrumentality. The variables of gender, prior achievement levels, academic pressure and socioeconomic status, which had significant influences on perceived instrumentality according to previous research, were used as control variables. Longitudinal data were collected for four years of 6908 students and analysed using the Growth Curve Modelling technique. Parents’ and teachers’ academic expectations and their career guidance, and social support from peers were used as predictors for changes in perceived instrumentality. Our results revealed that perceived instrumentality increased over time but in a decelerated pattern. In addition, parents and peers had significant influences on perceived instrumentality and its change, but teachers did not. The results suggest that support from parents and peers helps students to understand and internalise why they have to study hard in school to achieve their future long-term life goals.
Augmenting the Zachman Enterprise Architecture Framework with a Systemic Conceptualization

The Zachman Framework offers a classification of the models created in an enterprise architecture project. These models form a holistic representation of the organization. Despite the prominent position of the Framework, there is little information publicly available to help designers create exact models that fit each other. In this paper, we propose a conceptualization based on General Systems Thinking. Our conceptualization provides concrete guidelines for creating the models required by the Framework. The proposed conceptualization establishes a better understanding of the models and of their relationships. This facilitates the creation and interpretation of the models. It also improves the traceability between them. We illustrate our approach with the results of a case study.
Research Paper: Consumer Health Concepts That Do Not Map to the UMLS: Where Do They Fit?

OBJECTIVE This study has two objectives: first, to identify and characterize consumer health terms not found in the Unified Medical Language System (UMLS) Metathesaurus (2007 AB); second, to describe the procedure for creating new concepts in the process of building a consumer health vocabulary. How do the unmapped consumer health concepts relate to the existing UMLS concepts? What is the place of these new concepts in professional medical discourse? DESIGN The consumer health terms were extracted from two large corpora derived in the process of Open Access Collaboratory Consumer Health Vocabulary (OAC CHV) building. Terms that could not be mapped to existing UMLS concepts via machine and manual methods prompted creation of new concepts, which were then ascribed semantic types, related to existing UMLS concepts, and coded according to specified criteria. RESULTS This approach identified 64 unmapped concepts, 17 of which were labeled as uniquely "lay" and not feasible for inclusion in professional health terminologies. The remaining terms constituted potential candidates for inclusion in professional vocabularies, or could be constructed by post-coordinating existing UMLS terms. The relationship between new and existing concepts differed depending on the corpora from which they were extracted. CONCLUSION Non-mapping concepts constitute a small proportion of consumer health terms, but a proportion that is likely to affect the process of consumer health vocabulary building. We have identified a novel approach for identifying such concepts.
Understanding Organizational Memory System for Managing Knowledge

Information System (IS), has been extended to Knowledge Management System (KMS) focusing on knowledge as main contents instead of information. While KMS evolving rapidly, with the awareness of treating knowledge as an organizational memory, evolved Organizational Memory System (OMS). The three tools (IS, KMS and OMS) are to manage data, information and knowledge in organization, have stimulated confusion to the beginners of Information Technology (IT) practitioners. The aim of the paper is to provide a document that able to stimulate rich understanding of OMS from KMS and IS concept. This was done by identified eight descriptions of OMS and comparing twenty characteristics of IS, KMS, OMS extracted from previous studies. Finally we use the results to identify necessary consideration for developing OMS. The identification was presented in the form of OMS development model. Future study can be done to verify the model.
Experimental Evaluation of Occupancy Grid Map Improvement by Sonar Data Corrections

This paper addresses the creation of a consistent occupancy grid map from local sensors data collected during mobile robot movement. The map building problem, together with the mobile robot localization problem, is one of the major problems of mobile robots navigation. It can be defined as an environment modelling problem using different algorithms for sensor data integration. Bayesian and Dempster-Shafer algorithm combined with sonar sensors were used in our approach. A simple solution to deal with irregular disposition of sonar sensors on mobile robot body has been proposed. It increases the robustness of mapping, which is experimentally evaluated for both Bayesian and Dempster-Shafer on-line mapping algorithms. Experiments were conducted on Pioneer 2 DX mobile robot
A novel Cdk9 inhibitor preferentially targets tumor cells and synergizes with fludarabine

Cdk9 is a key elongation factor for RNA transcription and functions by phosphorylating the C-terminal domain of RNA polymerase II. Here we present direct evidence that cdk9 is important for cancer cell survival and describe the characterization of the potent cdk9 inhibitor CDKI-73 in primary human leukemia cells. CDKI-73 induced caspase-dependent apoptosis that was preceded by dephosphorylation of cdk9 and serine 2 of RNA polymerase II. CDKI-73 was more potent than the pan-cdk inhibitor flavopiridol and showed >200-fold selectivity against primary leukemia cells when compared with normal CD34+ cells. Furthermore, CDKI-73 was equipotent in poor prognostic sub-groups of leukemia patients and showed cytotoxic synergy with the nucleoside analog fludarabine. The Mechanism of synergy was associated with CDKI-73-mediated transcriptional inhibition of MCL1 and XIAP that was maintained when used in combination with fludarabine. Our data present a strong rationale for the development of cdk9 inhibitors such as CDKI-73 as anticancer therapeutics.
The use of english swear words among chinese youths / Tian Huey Ni Fanny

This study was aimed to investigate the usage of English swear words among Chinese youths as well as to compare the use of swear words between male and female youths. The framework of this study is made up of the works of Battistella (2005), Lakoff (1995) and Talbot (1998). The participants consisted of seventy four (74) students studying at a private secondary school in the state of Selangor. Instrumentation used for the survey included a 26 item questionnaire and semi-structured interviews with focus groups. Two categories of focus groups consisted of randomly selected participants from the same sample and English language teachers from the school of the participants. Permission to conduct the study was obtained from the school principal, teachers and participants. The analysis was based on Battistella (2005) for taboo language and Lakoff (1995) and Talbot (1998) for gender and language. The results showed that the most common swear words among Chinese male and female youths belong to the obscenity category. The study infers that anger was the main reason for swearing among both genders. However, male youths also cited that the need of self- identity and the use of swear words were also due to peer influence. Female youths, on the other hand, indicated that swearing was linked to emotions. The study also found that male youths tend to swear more in the same gender group while female youths tend to swear more in mixed gender group. The study also found that the presence of different genders in the environment largely influences male language choice but not the female speakers. In conclusion, it is hoped that the findings of this study will be able to shed some light on the choice use of language among Chinese youths.
A score based indexing scheme for palmprint databases

This paper proposes an efficient retrieval technique which uses a new indexing scheme for a large palmprint database. For a given palmprint query image, the proposed indexing scheme makes use of match score vectors to determine an index and uses this index to reduce the search space of the database. Finally, the retrieval technique uses palmprint texture to find the top t best matches from the reduced search space. The proposed technique has been tested on publicly available palmprint database viz PolyU [1] of 7752 images and is found to be an efficient technique based on score vector indexing scheme. The test reveals that hitrate of 98.01% is acheived for the bin-success rate of 98.28%. The proposed indexing scheme is found to be more powerful than any other known indexing scheme based on match score vectors.
Opioid antagonists and antisera to endogenous opioids increase the nociceptive response to formalin: demonstration of an opioid kappa and delta inhibitory tone.

The present experiments explored the role of endogenous opioids in the behavioral response to a formalin-induced nociceptive stimulus in the rat. Flinching was taken as a measure of the intensity of the nociceptive stimulus after the administration of formalin into the dorsal surface of the paw of control animals, or in animals receiving i.p. administration of receptor-selective doses of opioid antagonists including naloxone, naltrindole (delta opioid antagonist), nor-binaltorphimine (kappa opioid antagonist) or beta-funaltrexamine (mu opioid antagonist). Additionally, antisera to [Leu5]enkephalin, [Met5]enkephalin and dynorphin A (1-13) (dynorphin) were administered intrathecally before formalin to explore the contribution of endogenous opioids in modulation of the flinching response. Formalin-induced flinching was increased significantly by naloxone, and receptor selective doses of naltrindole and nor-binaltorphimine, but not beta-funaltrexamine. Additionally, antisera to [Leu5]enkephalin and dynorphin also resulted in a significant increase in formalin-induced flinching, whereas antisera to [Met5]enkephalin had no effect. On the basis of significant increases in formalin-induced flinching produced by 1) receptor-selective doses of delta and kappa, but not mu, opioid antagonists and 2) antisera to [Leu5]enkephalin and dynorphin A, but not [Met5]enkephalin, these data suggest the presence of an opioid inhibitory tone which acts to limit the intensity of the pain signal. This tone appears to be mediated via activation of delta and kappa receptors, possibly by a [Leu5]enkephalin- and dynorphin-like substance, respectively.
On applications of graph/network theory to problems in communication systems

Graph/network theory results are applicable to problems in communications. As a representative example, the node coloring problem in graph theory is applicable to the channel assignment problem in cellular mobile communication systems. The node coloring problem is NP-complete, meaning that optimally solving it is very difficult. Therefore, we use heuristic algorithms for the channel assignment problem. In this case, the graph theory results show the legitimacy of using heuristic techniques. On the other hand, we can directly apply graph theory to communication problems. For example, on contents delivery services in the Internet, we place mirror servers that provide the same contents on the network. Location problems on flow networks are applicable to mirror server allocation problems. In a simple case, we can efficiently solve the problem. In this paper, we concentrate on multi-hop wireless networks and consider the relationship between their problems and the results of graph/network theory.
Near-Regular BTF Texture Model

In this paper we present a method for seamless enlargement and editing of intricate near-regular type of bidirectional texture function (BTF) which contains simultaneously both regular periodic and stochastic components. Such BTF textures cannot be convincingly synthesised using neither simple tiling nor using purely stochastic models. However these textures are ubiquitous in many man-made environments and also in some natural scenes. Thus they are required for their realistic appearance visualisation. The principle of the presented BTF-NR synthesis and editing method is to automatically separate periodic and random components from one or more input textures. Each of these components is subsequently independently modelled using its corresponding optimal method. The regular texture part is modelled using our roller method, while the random part is synthesised from its estimated exceptionally efficient Markov random field based representation. Both independently enlarged texture components from the original measured textures representing one (enlargement) or several (editing) materials are combined in the resulting synthetic near-regular texture.
Buffer-aided relay selection with interference cancellation and secondary power minimization for cognitive radio networks

In this paper, we consider a cooperative underlay cognitive radio network with K half-duplex decode-and-forward relays that are used by the source-destination pairs of both the primary network (PN) and secondary network (SN). The SN exploits buffers with size of L data elements at relays to store the incoming secondary information and retransmit it later. We propose a novel buffer-aided relay selection scheme with successive interference cancellation and optimal SN power allocation that allows to mitigate the mutual interference between PN and SN. Using extensive simulations, we investigate performance of the proposed scheme in terms of the secondary throughput and power expenditure. Our results show that the proposed policy manages the interference between PN and SN efficiently, minimizes secondary power consumption and achieves higher secondary throughput compared to the recently proposed policy.
Sistema de Navega\c{c}\~ao Aut\^onomo Baseado em Vis\~ao Computacional

Autonomous robots are used as the tool to solve many kinds of problems, such as environmental mapping and monitoring. Either for adverse conditions related to the human presence or even for the need to reduce costs, it is certain that many efforts have been made to develop robots with an increasingly high level of autonomy. They must be capable of locomotion through dynamic environments, without human operators or assistant systems' help. It is noted, thus, that the form of perception and modeling of the environment becomes significantly relevant to navigation. Among the main sensing methods are those based on vision. Through this, it is possible to create highly-detailed models about the environment, since many characteristics can be measured, such as texture, color, and illumination. However, the most accurate vision-based navigation techniques are computationally expensive to run on low-cost mobile platforms. Therefore, the goal of this work was to develop a low-cost robot, controlled by a Raspberry Pi, whose navigation system is based on vision. For this purpose, the strategy used consisted in identifying obstacles via optical flow pattern recognition. Through this signal, it is possible to infer the relative displacement between the robot and other elements in the environment. Its estimation was done using the Lucas-Kanade algorithm, which can be executed by the Raspberry Pi without harming its performance. Finally, an SVM based classifier was used to identify patterns of this signal associated with obstacles movement. The developed system was evaluated considering its execution over an optical flow pattern dataset extracted from a real navigation environment. In the end, it was verified that the processing frequency of the system was superior to the others. Furthermore, its accuracy and acquisition cost were, respectively, higher and lower than most of the cited works.
Left Ventricle Segmentation from Heart ECHO Images Using Image Processing Techniques

 Abstract— Cardiovascular diseases are the major causes of mortality in the world. The most important and widely used tool for accessing the heart state is echocardiography(also abbreviated as ECHO). ECHO images are used e.g. for location of any damage of heart tissues, in calculation of cardiac tissue displacement at any arbitrary point and to derive useful heart parameters like size, shape, cardiac output and ejection fraction. In this paper, an algorithm for Left Ventricle (LV) segmentation of heart in ECHO images is presented. The left ventricle delivers the requirement of blood to all the parts of body and so it is important to consider the condition of left ventricle. It is based on the level set method. The main idea of the level set method is to get the ROI (Region of Interest), using velocity term F, speed term D, gradient value of ϕ, first order derivative, mean curvature K and second order derivative. Then, the segmentation of the LV is obtained.
A Computational Model of Inferior Colliculus Responses to Amplitude Modulated Sounds in Young and Aged Rats

The inferior colliculus (IC) receives ascending excitatory and inhibitory inputs from multiple sources, but how these auditory inputs converge to generate IC spike patterns is poorly understood. Simulating patterns of in vivo spike train data from cellular and synaptic models creates a powerful framework to identify factors that contribute to changes in IC responses, such as those resulting in age-related loss of temporal processing. A conductance-based single neuron IC model was constructed, and its responses were compared to those observed during in vivo IC recordings in rats. IC spike patterns were evoked using amplitude-modulated tone or noise carriers at 20–40 dB above threshold and were classified as low-pass, band-pass, band-reject, all-pass, or complex based on their rate modulation transfer function tuning shape. Their temporal modulation transfer functions were also measured. These spike patterns provided experimental measures of rate, vector strength, and firing pattern for comparison with model outputs. Patterns of excitatory and inhibitory synaptic convergence to IC neurons were based on anatomical studies and generalized input tuning for modulation frequency. Responses of modeled ascending inputs were derived from experimental data from previous studies. Adapting and sustained IC intrinsic models were created, with adaptation created via calcium-activated potassium currents. Short-term synaptic plasticity was incorporated into the model in the form of synaptic depression, which was shown to have a substantial effect on the magnitude and time course of the IC response. The most commonly observed IC response sub-types were recreated and enabled dissociation of inherited response properties from those that were generated in IC. Furthermore, the model was used to make predictions about the consequences of reduction in inhibition for age-related loss of temporal processing due to a reduction in GABA seen anatomically with age.
SparseCodePicking: feature extraction in mass spectrometry using sparse coding algorithms

Mass spectrometry (MS) is an important technique for chemical profiling which calculates for a sample a high dimensional histogram-like spectrum. A crucial step of MS data processing is the peak picking which selects peaks containing information about molecules with high concentrations which are of interest in an MS investigation. We present a new procedure of the peak picking based on a sparse coding algorithm. Given a set of spectra of different classes, i.e. with different positions and heights of the peaks, this procedure can extract peaks by means of unsupervised learning. Instead of an $l_1$-regularization penalty term used in the original sparse coding algorithm we propose using an elastic-net penalty term for better regularization. The evaluation is done by means of simulation. We show that for a large region of parameters the proposed peak picking method based on the sparse coding features outperforms a mean spectrum-based method. Moreover, we demonstrate the procedure applying it to two real-life datasets.