Miracle-dz/UniFAR-FTUs huggingface dataset:
A Digital Signature Scheme based on Elliptic Curve Cryptosystem

People develop science very fast today and the network communication is grown very rapidly. The signature of digitization has extended already to apply on the network communication and the digital signature designing scheme of all kinds are proposed for this thing. Chung et al. proposed an ID-based digital signature scheme on elliptic curve cryptosystem in 2007. In 2008, Yang et al. proposed an article of weakness of Chung et al.’s scheme. Yang et al. use the linear Diophantine equation successfully to avoid touching the elliptic curve discrete logarithm problem (ECDLP) in Chung et al.’s scheme. As the result, the Chung’s scheme becomes insecure. Hence, we propose a new scheme to delete the weakness and our scheme is designed by using elliptic curve cryptosystem (ECC). We use the elliptic curve discrete logarithm problem for the security of system in order to prevent the outside attack such that we can set up a secure digital signature system. Keywords— Elliptic curve, Information security, Digital signature, Hash function.
Respiratory motoneuronal activity is altered by injections of picomoles of glutamate into cat brain stem

The local neural circuitry underlying the control of breathing was studied by injecting nanoliter volumes of excitatory amino acids into discrete regions of cat brain stem. Experiments were performed on chloralose-urethane anesthetized, vagotomized, paralyzed, and artificially ventilated cats. Phrenic, intercostal, and recurrent laryngeal nerve discharges were recorded. Multibarrel pipettes were used for recording and pressure ejection of drugs or a dye for marking recording and ejection sites. Ejected volumes were directly monitored for every injection. Injections, proximal to neurons discharging with a respiratory periodicity, of as little of 200 fmol of L-glutamate in 200 pl of saline elicited marked, site-specific increases or decreases in respiratory motoneuronal discharge. N-Methyl-D-aspartic acid and homocysteic acid elicited similar site-specific alterations in respiratory motor output, although some details of the response could differ qualitatively. Responses to all the excitatory agents used were attenuated by concurrent injection of kynurenic acid, DL-2-amino-4- phosphonobutyric acid, or glutamic acid diethyl ester. There was no change in spontaneous phrenic nerve discharge in response to injections of equivalent or larger volumes of saline or lidocaine. These results indicate a heterogeneity in the spatial organization of the brain-stem neural circuitry underlying respiratory control, which has not been described previously. This injection technique may provide a mechanism for probing the neural circuitry underlying other behaviors.
Latent Semantic Analysis for Dialogue Act Classification

This paper presents our experiments in applying Latent Semantic Analysis (LSA) to dialogue act classification. We employ both LSA proper and LSA augmented in two ways. We report results on DIAG, our own corpus of tutoring dialogues, and on the CallHome Spanish corpus. Our work has the theoretical goal of assessing whether LSA, an approach based only on raw text, can be improved by using additional features of the text.
My Name Is Khan: The Use of Twitter in the Campaign for 2013 Pakistan General Election

The 2013 Pakistan General Election was as a democratic milestone in the nation's troubled political history which also witnessed the rise of Pakistan Tehreek-i-Insaf (PTI, under its leader Imran Khan), from its marginal status to a formidable political status. This study focuses on the first ever Twitter campaign by Pakistani political parties to mobilize, inform and engage voters during the elections. Through a content analysis of tweets (n =10140) from top four parties, our study reveals significant differences in their usage patterns. PTI's twitter usage was the most distinctive as it involved greater interaction with the public, more campaign updates and greater mobilization of citizens to vote. Through triangulation of our findings with public ally available election data we also find that PTI's success (at a provincial level) was largely driven by a unique combination of Twitter communication and face-to-face campaigning aimed at increasing voter turnout, especially among the youth population.
Pay-per-use concept in healthcare: a grounded theory perspective

Healthcare organizations worldwide are faced with the growing challenge of introducing structural and technological reforms and cost-effective IT solutions that will transform the traditional structure of healthcare provision. Current technological developments have shed optimistic light on the future of IT in healthcare. This paper introduces a 'Pay-per-use' concept in healthcare delivery services, which could prove appropriate for a small country like the State of Kuwait. The research question is directed towards the appropriateness and usefulness of such a concept. The need for effective and efficient healthcare delivery services in health organizations implies the need to establish a healthcare paradigm that is investment, communication, technology, platform, database, application-independent. The grounded theory (GT) forms the basis of the new concept, using a social context and referring to a typical clinical scenario. The social process fundamental to GT provides a specific and systematic approach to understand medical work and a network of relationships among healthcare entities. Interpretations and analysis of data in the social context facilitates clearer understanding of concepts and categories and enables the formulation of a theory and a model architecture. The State of Kuwait is a typical example of a small country with rapidly advancing IT, where the new concept can be put to the test within the required infrastructure for the development of a beneficial healthcare delivery system in the country.
Flip or Flop?

P rocedural education in medicine has been an evolving process. Within pulmonary medicine, the recent advances and significant improvements in diagnostic and therapeutic technology have increased the pressure to discover and adopt efficient and effective teaching techniques. The general public is increasingly intolerant of the traditional medical apprenticeship model, where the risks and burdens of training are disproportionately borne by patients and there is a growing demand for objective measures to determine procedural competency. In response, specialty societies have been remodeling their training and certification processes over the last several years. In 2015, a consensus statement by an American College of Chest Physicians expert panel on adult bronchoscopy training suggested that bronchoscopy training should incorporate multiple training tools and that certification by professional societies should move from volume-based certification to a skill acquisition and knowledge-based competency assessment.1 As there is no clear consensus on exactly what constitutes a clinically meaningful skill competency metric, procedural training in practice today involves variable combinations of didactic lectures, low-fidelity and highfidelity simulators, animal models, and supervised practice in patients. Although this education typically follows traditional styles of teaching (teacher-centered models), alternative styles of teaching (learner-centered models) such as the flipped classroom and problem-based learning are gradually being adopted in training programs and conferences throughout the country with the hope of improving knowledge-based competency. The difference between traditional and nontraditional models of teaching is striking. In the traditional model, the instructor is the primary source of knowledge and provides a structured lecture on the topic, leaving limited time for in-depth discussion and questions. Nontraditional models, colloquially referred to as a flipped classroom, require the students to review the instructional content before the class, and class time is utilized to probe the depth of understanding in structured group discussions under the guidance of the instructor. The flipped classroom model has shown some benefits in other fields,2 prompting calls for wider implementation in medical education.3 In procedural training, the flipped classroom model translates to reviewing lectures on the details of the procedures outside the classroom and then conducting hands on training using either models or simulators under the guidance experts. There are theoretical advantages to this model, as the learner can review the lectures at their own pace and repeatedly if needed, devoting more class time to practice, technique refinement, and instructor interaction. However, there is scant available evidence to support the superiority of one approach over another.
Online Cognitive Behavioral Therapy for Bulimic Type Disorders, Delivered in the Community by a Nonclinician: Qualitative Study

Background Cognitive behavioral therapy is recommended in the National Institute for Clinical Excellence guidelines for the treatment of bulimia nervosa. In order to make this treatment option more accessible to patients, interactive online CBT programs have been developed that can be used in the user’s own home, in privacy, and at their convenience. Studies investigating online CBT for bulimic type eating disorders have provided promising results and indicate that, with regular support from a clinician or trained support worker, online CBT can be effective in reducing bulimic symptoms. Two main factors distinguish this study from previous research in this area. First, the current study recruited a wide range of adults with bulimic type symptoms from the community. Second, the participants in the current study had used cCBT with support from a nonclinical support worker rather than a specialist eating disorder clinician. Objective To investigate participants’ experiences of using an online self-help cognitive behavioral therapy (CBT) package (Overcoming Bulimia Online) for bulimia nervosa (BN) and eating disorders not otherwise specified (EDNOS). Methods Eight participants with a mean age of 33.9 years took part in semi-structured interviews. Interviews were transcribed and analyzed using a 6-step thematic analysis process. Results Saturation was achieved, and 7 themes were identified in the dataset. These were: (1) conceptualizing eating disorders, (2) help-seeking behavior, (3) aspects of the intervention, (4) motivation to use the online package, (5) privacy and secrecy with regard to their eating problems, (6) recovery and the future, and (7) participant engagement describing individuals’ thoughts on taking part in the online research study. Conclusions Participants suggested that online CBT self-help represented a generally desirable and acceptable treatment option for those with bulimic type eating problems, despite some difficulties with motivation and implementation of some elements of the package. Trial Registration International Standard Randomized Controlled Trial Number of the original RCT that this study is based on: ISRCTN41034162; http://www.controlled-trials.com/ISRCTN41034162 (Archived by WebCite at http://www.webcitation.org/6Ey9sBWTV)
Theory and Algorithms for Information Extraction and Classification in Textual Data Mining

Regular expressions can be used as patterns to extract features from semi-structured and narrative text [8]. For example, in police reports a suspect’s height might be recorded as “{CD} feet {CD} inches tall”, where {CD} is the part of speech tag for a numeric value. The result in [1] shows us that regular expressions could have higher performance than explicit expressions in some applications such as Posting Act Tagging. Although much work has been done in the field of information extraction, relatively little has focused on the automatic discovery of regular expressions. Therefore, my Ph.D. research will focus on the automatic generation of reduced regular expressions (RREs) (defined in [8]) used in Information Extraction (IE). The reduced regular expressions learned can be directly used to extract features from free text, or they can be used to fill in templates in Eric Brill’s Transformation-Based Learning (TBL) [2] frameworks. The original templates in TBL are explicit expressions, which are weaker than reduced regular expressions. I propose an innovative enhancement to TBL termed “Error-Driven Boolean-Logic-Rule-Based Learning” (BLogRBL) [9], which is strictly more powerful than TBL [2]. Similar to Brill’s method, rules are automatically derived from templates during learning. It differs from Brill’s technique in that rules take the form of complex expressions of combinational logic. Therefore, my final contribution in my PhD thesis will be a framework that combines regular expression discovery with BLogRBL. A necessary component of this research is a study of various biases inherent in the use of reduced regular expressions in IE. The purpose of this work is to determine the language biases, search biases, and overfitting biases in the RRE discovery and BLogRBL algorithms.
Model-checking Driven Black-box Testing Algorithms for Systems with Unspecified Components

Component-based software development has posed a serious challenge to system verification since externally-obtained components could be a new source of system failures. This issue can not be completely solved by either model-checking or traditional software testing techniques alone due to several reasons: 1) externally obtained components are usually unspecified/partially specified; 2)it is generally difficult to establish an adequacy criteria for testing a component; 3)components may be used to dynamically upgrade a system. This paper introduces a new approach (called {\em model-checking driven black-box testing}) that combines model-checking with traditional black-box software testing to tackle the problem in a complete, sound, and automatic way. The idea is to, with respect to some requirement (expressed in CTL or LTL) about the system, use model-checking techniques to derive a condition (expressed in communication graphs) for an unspecified component such that the system satisfies the requirement iff the condition is satisfied by the component, and which can be established by testing the component with test cases generated from the condition on-the-fly. In this paper, we present model-checking driven black-box testing algorithms to handle both CTL and LTL requirements. We also illustrate the idea through some examples.
Intelligent Automated Agents for Flight Training Simulators

Abstract : Training in flight simulators will be more effective if the agents involved in the simulation behave realistically. Accomplishing this requires that the automated agents be under autonomous, intelligent control. We are using the Soar cognitive architecture to implement intelligent agents that behave as much like humans as possible. In order to approximate human behavior, the agents must integrate planning and reaction in real time, adapt to new and unexpected situations, learn with experience, and exhibit the cognitive limitations and strengths of humans. This paper describes two simple tactical flight scenarios and the knowledge required for an agent to complete them. In addition, the paper describes an implemented agent model that performs in limited tactical scenarios on three different flight simulators. Artificial intelligence, Believable agents, Flexible behaviors, Realistic training environments, Soar.
Advertisement Data Management and Application Design in WBCs

This paper describes the advertisement data management and the corresponding intelligent application design implementation for wireless billboard channel (WBC) in the emerging ubiquitous consumer wireless world (UCWW). The algorithms and application runs at the application enabler sub-layer of a WBC service provider (WBC-SP)'s node for broadcasting wireless services to mobile terminals (MTs). The advertisement data is formatted by abstract syntax notation one (ASN.l) and organized by segments with WBC algorithms to reduce the mobile user (MU)'s access time. The intelligent application was used to run the data management algorithms, which is implemented with three tiers: a service discovery and maintenance tier acting as a client-server distributed system for data collection and organization; an intelligent application tier holding all business logic and common application programming interfaces (APIs); and a multi-agent systems (MAS) tier maintaining the advertisement, discovery and association (ADA) agents' lifecycle, and supplying directory facilitator services and message transport services. The performance evaluation of the proposed data management and application architecture details are provided.
Automatic Thesaurus Generation from Raw Text using Knowledge-Poor Techniques

In addition to showing how lexical units are related within a eld, domain-speciic thesauri give an idea of what subjects are important to that eld and are thus useful at many points in an information system. The major impediment to creation of thesauri has been the cost of their manual creation. We present here a number of automatic techniques that jointly produce a rst draft of a thesaurus from any domain-deening collection of text. The techniques are knowledge-poor in that no domain knowledge is required for their use. We have successfully applied these techniques to over twenty corpora ranging from 1 to 6 megabytes. Results from the thesaurus produced from a collection of medical abstracts will also be presented here.
A Panel of Overexpressed Proteins for Prognosis in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is a common cancer with poor prognosis. In order to identify useful biomarkers for accurately classifying prognostic risks for ESCC patients, we examined the expression of six proteins by immunohistochemistry (IHC) in 590 paraffin-embedded ESCC samples. The candidate proteins include p53, EGFR, c-KIT, TIMP1 and PI3K-p110α reported to be altered in ESCC tissues as well as another important component of PI3K, PI3K-p85α. Of the six proteins tested, p53, EGFR, c-KIT, TIMP1 and PI3K-p85α were detected with high expression in 43.0%, 36.6%, 55.9%, 70.7% and 57.1% of tumors, respectively. Significant associations were found between high expression of PI3K-p85α, EGFR and p53 and poor prognosis (P = 0.00111; 0.00001; 0.00426). Applying these three proteins as an IHC panel could divide patients into different subgroups (P<0.000001). Multivariate cox regression analysis indicated that the three-protein panel was an independent prognostic factor with very high statistical significance (HR = 2.090, 95% CI: 1.621–2.696, P = 0.00000001). The data suggest that the three-protein panel of PI3K-p85α, EGFR and p53 is an important candidate biomarker for the prognosis of patients with ESCC.
Adaptive Gaussian mixture filter based on statistical linearization

Gaussian mixtures are a common density representation in nonlinear, non-Gaussian Bayesian state estimation. Selecting an appropriate number of Gaussian components, however, is difficult as one has to trade of computational complexity against estimation accuracy. In this paper, an adaptive Gaussian mixture filter based on statistical linearization is proposed. Depending on the nonlinearity of the considered estimation problem, this filter dynamically increases the number of components via splitting. For this purpose, a measure is introduced that allows for quantifying the locally induced linearization error at each Gaussian mixture component. The deviation between the nonlinear and the linearized state space model is evaluated for determining the splitting direction. The proposed approach is not restricted to a specific statistical linearization method. Simulations show the superior estimation performance compared to related approaches and common filtering algorithms.
Investigation of Parameters Influencing Reverse Fault Rupture Propagation to the Ground Surface

Surface fault rupture poses a serious threat to infrastructure in many seismically active regions, but knowledge about the factors which control the likelihood of surface displacement is limited. Current probabilistic frameworks rely only on fault mechanism and moment magnitude to predict the probability of rupture to the ground surface. However, recent work has shown that there may be other parameters which also deserve consideration. For example, statistical analyses have demonstrated that variation in near surface material stiffness may significantly affect the probability of surface rupture over reverse faults. In addition, numerical investigations indicate that the rupture history of native soil deposits also greatly influences the nature of rupture propagation. Given that evidence exists which suggests multiple variables are at work, this study aimed to improve our understanding of which are most critical for predicting surface fault rupture hazard. We sought to generate physical evidence concerning the impact of near surface soil stiffness, soil type, and rupture history on fault rupture propagation. A 3 meter long by 1 meter wide fault box apparatus was constructed to simulate idealized reverse fault rupture oriented at 45° beneath 60cm of soil. Relatively large dimensions were chosen so that shear wave velocity measurements could be taken directly without interference from the walls of the apparatus. Experiments were conducted on loose sand, dense sand, stiff clay, and soft clay. The same sand was used for both the dense and loose sand v experiments and is identified as Monterey #2/16. The clay was a scale model mixture of San Francisco Bay Mud and consisted of kaolinite, bentonite, class C fly ash, and water. Separate batches of clay were mixed with differing final water contents for the stiff and soft clay experiments. In each case, the fault box was filled to 60 cm and rupture was driven to the surface in two phases. The first phase represented an undisturbed native soil deposit with no existing shear band. The second simulated repeat rupture along a pre-existing shear band. The results indicate that increasing material stiffness promotes rupture propagation in both sand and clay. When disturbed soil is re-ruptured, surface rupture occurred much more readily in all materials. Overall, the presence of a pre-existing shear band was shown to have the greatest impact on the likelihood of surface rupture, though both material stiffness and type were also found to have a strong influence as well. The fault box experiments support …
Keypoint-based binocular distance measurement for pedestrian detection system

Pedestrian Detection System (PDS) has become a significant research area designed to protect road-users. Despite the huge number of research works, many PDSs are designed to detect pedestrians without knowing their precise distances from vehicles. In fact, a priori knowledge of the distance between the car and pedestrian allows taking the appropriate decision to avoid collisions. In this context, the distance estimation problem is investigated in this paper. For that purpose, we use HOG-SVM method to detect pedestrians, and keypoints-based feature extraction method to generate the parallax in a binocular vision system. In addition, we design a crossover re-detection method to reinforce the robustness of the system. Our approach is not only applicable to measure the distance between vehicles and pedestrians, but, it can be used efficiently to measure the distance to other objects such as traffic signs or animals. Through real word experiments, the system shows a margin of error of 7.5 % in outdoor and long-range conditions.