Miracle-dz/UniFAR-FTUs huggingface dataset:
Class-wise information gain

This paper proposes a new feature-goodness criterion named class-wise information gain (CIG). The CIG is able to measure the goodness of a feature for recognizing a specific class, and further helps to select the features with the highest information content for a specific class. In order to confirm the effectiveness of the CIG, a CIG-based malware detection method is proposed. Eight groups of experiments on three public malware datasets are carried out to evaluate the performance of the proposed CIG-based malware detection method through cross-validation. Comprehensive experimental results suggest that the CIG is an effective feature-goodness criterion, and the proposed CIG-based malware detection method is effective to detect malware loaders and infected executables. This method outperforms the information gain (IG)-based malware detection method for about 26% in detecting infected executables, without decrease in detecting malware loaders, while its memory requirement is about 60% less than that of the IG-based malware detection method empirically.
Scene Reconstruction from Multiple Uncalibrated Views

We describe a factorization-based method to reconstruct Euclidean shape and motion from multiple perspective views with uncalibrated cameras. The method rst performs a projective reconstruction using a bilinear factorization algorithm, and then converts the projective solution to a Euclidean one by enforcing metric constraints. We present three factorization-based normalization algorithms to generate the Euclidean reconstruction and the intrinsic parameters, assuming zero skews. The rst two algorithms are linear, one for dealing with the case that only the focal lengths are unknown, and another for the case that the focal lengths and the constant principal point are unknown. The third algorithm is bilinear, dealing with the case that the focal lengths, the principal points and the aspect ratios are all unknown. Experimental results show that out method is e cient and reliable. @2000 Carnegie Mellon University
An Intelligent System for Question Answering

Sanda M. Harabagiu Dan I. Moldovan University of Southern California Southern Methodist University Los Angeles, CA 90089-2562 Dallas, Texas 75275-0122 harabagi@usc.edu moldovan@seas.smu. edu Abstract This paper introduces a system intended for question answering based on abductive inference. The system uses a large knowledge base structured around WordNet. Texts and questions are uniformly processed and semantic paths between concepts are established using a marker propagation method. The paths bring forward inferences and implicatures that are otherwise di cult to extract. Implicit inferences contribute to the information provided by explicit inferences, producing answers for a variety of question types. 1 Basic Idea An approach for building intelligent systems is to implement an explicit declarative knowledge base on which operates a formal reasoning mechanism. Thus, of central importance are: (1) knowledge representation and structuring, (2) design of inference rules, and (3) control of inference process. Since the entire system revolves around the knowledge base, for many, intelligent systems are synonymous with knowledgebased systems. Experience has shown that there are major problems with this approach; only modest size systems working in restricted domains were successfully implemented. Since knowledge is unlimited, no matter how large the knowledge base is, it is not possible to capture all the knowledge in the world. Knowledge based systems may work well in some domain, but quickly fail when the domain changes slightly. Lenat [6] called this the brittleness problem. Text-based Intelligent Systems Another possible solution for intelligent systems, that becomes more and more evident and feasible, is to use the expressive power of natural language to represent knowledge. Jacobs [5] called this text-based intelligent systems ( TBIS ) and was among the rst to suggest that this approach may replace or augment traditional knowledge-based systems. In essence, a TBIS may work like this: a text that represents domain, context or any other form of information is transformed by a natural language processing system into a knowledge representation. The sophistication of natural language processing may vary from simple tagging to \deep understanding", and this determines the power of such a system. Then, the user provides information also in the form of natural language. This information may be a query, or some other form. The user's information needs to be transformed by the same natural language processing system in a form of knowledge. Then, the text and user representations are compared, and inferential information is extracted.
Event-Based Recursive Distributed Filtering Over Wireless Sensor Networks

In this technical note, the distributed filtering problem is investigated for a class of discrete time-varying systems with an event-based communication mechanism. Each intelligent sensor node transmits the data to its neighbors only when the local innovation violates a predetermined Send-on-Delta (SoD) data transmission condition. The aim of the proposed problem is to construct a distributed filter for each sensor node subject to sporadic communications over wireless networks. In terms of an event indicator variable, the triggering information is utilized so as to reduce the conservatism in the filter analysis. An upper bound for the filtering error covariance is obtained in form of Riccati-like difference equations by utilizing the inductive method. Subsequently, such an upper bound is minimized by appropriately designing the filter parameters iteratively, where a novel matrix simplification technique is developed to handle the challenges resulting from the sparseness of the sensor network topology and filter structure preserving issues. The effectiveness of the proposed strategy is illustrated by a numerical simulation.
How students learn in a problem-based curriculum

Problem-based learning (PBL) emphasizes active generation of learning issues by students. Both students and teachers, however, tend to worry that not all important knowledge will be acquired. To explore this question, problem effectiveness (i.e., for each problem, the degree of correspondence between student-generated learning issues and pre-set faculty objectives) was examined in three interdependent studies. The three studies used the same participants: about 120 second year students and 12 faculty tutors in a 6-week course on normal pregnancy, delivery, and child development at the Medical Faculty of the University of Limburg in the Netherlands, 1990-1991. The participants were randomly assigned to 12 tutorial groups that were each given the same 12 problems; the problems were based on 51 faculty objectives; the tutors were asked to record all learning issues generated by their groups. Study 1 addressed the question: To what degree are faculty objectives reflected by student-generated learning issues? Study 2 addressed the question: To what extent do students miss certain objectives, and are these objectives classifiable? Study 3 addressed the question: Do students generate learning issues not expected by faculty, and are these issues relevant to the course content, and finally, why do students generate these issues? To help answer these questions, the studies employed expert raters and a teacher familiar with the course content. The results of Study 1 showed 1 Parts of this chapter were presented at the Annual Meeting of the American Educational Research Association, April 1992, San Francisco, California. (ERIC Document Reproduction Service No. ED 343 955)
High-level primitives for recursive maximum likelihood estimation

This paper proposes a high-level language constituted of a small number of primitives and macros for describing recursive maximum likelihood (ML) estimation algorithms. This language is applicable to estimation problems involving linear Gaussian models or processes taking values in a finite set. The use of high-level primitives allows the development of highly modular ML estimation algorithms based on simple numerical building blocks. The primitives, which correspond to the combination of different measurements, the extraction of sufficient statistics, and the conversion of the status of a variable from unknown to observed, or vice versa, are first defined for linear Gaussian relations specifying mixed deterministic/stochastic information about the system variables. These primitives are used to define other macros and are illustrated by deriving new filtering and smoothing algorithms for linear descriptor systems. The primitives are then extended to finite state processes and used to implement the Viterbi ML state sequence estimator for a hidden Markov model.
Phytofabrication of bioinduced silver nanoparticles for biomedical applications

Synthesis of nanomaterials holds infinite possibilities as nanotechnology is revolutionizing the field of medicine by its myriad applications. Green synthesis of nanoparticles has become the need of the hour because of its eco-friendly, nontoxic, and economic nature. In this study, leaf extract of Rosa damascena was used as a bioreductant to reduce silver nitrate, leading to synthesis of silver nanoparticles (AgNPs) in a single step, without the use of any additional reducing or capping agents. The synthesized nanoparticles were characterized by the use of UV-visible spectroscopy, fourier transform infrared spectroscopy, dynamic light scattering, transmission electron microscopy, and field emission scanning electron microscopy. Time-dependent synthesis of AgNPs was studied spectrophotometrically. Synthesized AgNPs were found to possess flower-like spherical structure where individual nanoparticles were of 16 nm in diameter, whereas the agglomerated AgNPs were in the range of 60–80 nm. These biologically synthesized AgNPs exhibited significant antibacterial activity against Gram-negative bacterial species but not against Gram-positive ones (Escherichia coli and Bacillus cereus). Anti-inflammatory and analgesic activities were studied on a Wistar rat model to gauge the impact of AgNPs for a probable role in these applications. AgNPs tested positive for both these activities, although the potency was less as compared to the standard drugs.
Hemodynamic Predictors of Myocardial Oxygen Consumption During Static and Dynamic Exercise

Hemodynamic predictors of myocardial oxygen consumption (MVO2) during static and dynamic exercise were examined in ten normal subjects. Studies were done under the following circumstances: 1) during upright bicycle exercise at an average heart rate of 147 beats/min, 2) during static exercise with an isometric load in the left hand equal to 17% of the maximal voluntary contraction (MVC), and 3) during combined dynamic exercise (average heart rate 147 beats/min) and static exercise using 17% MVC of the left hand. Mean myocardial blood flow (MBF) was 181 ml/100 gm LV/min during dynamic exercise, 98 ml/100 gm LV/min during static exercise, and 201 ml/100 gm LV/min during combined static and dynamic exercise. Addition of a static load to the dynamic load resulted in a higher blood pressure (average 12 mm Hg), MVO2 and MBF than during dynamic exercise alone. MVO2 correlated best with products of heart rate and blood pressure regardless of whether the blood pressure was obtained by a central aortic catheter (r = 0.88) or by a blood pressure cuff (r = 0.85). When the current data were combined with previous data, 82 determinations of MVO2 and MBF in 29 normal subjects during several levels of upright exercise were available for analysis. Forty-four determinations were done during dynamic upright exercise, 18 during exercise after propranolol, ten during combined static and dynamic work, and ten during static work alone. MVO2 correlated best with the product of heart rate and blood pressure (r = 0.86). Heart rate alone correlated better with MVO2 (r = 0.82) than did the tension time index (r = 0.65) or the product of systolic blood pressure, heart rate, and ejection time (r = 0.68). The readily measured variables of heart rate and of heart rate × blood pressure correlated well with MVO2 in normal young men during exercise under a wide variety of circumstances.
Ximelagatran: Direct Thrombin Inhibitor

Warfarin sodium is an effective oral anticoagulant drug. However, warfarin has a narrow therapeutic window with significant risks of hemorrhage at therapeutic concentrations. Dosing is difficult and requires frequent monitoring. New oral anticoagulant agents are required to improve current anticoagulant therapy. Furthermore, while warfarin is effective in venous disease, it does not provide more than 60% risk reduction compared with placebo in venous thrombosis prophylaxis and considerably lower risk reduction in terms of arterial thrombosis. Ximelagatran is an oral pro-drug of melagatran, a synthetic small peptidomimetic with direct thrombin inhibitory actions and anticoagulant activity. As an oral agent, ximelagatran has a number of desirable properties including a rapid onset of action, fixed dosing, stable absorption, apparent low potential for medication interactions, and no requirement for monitoring of drug levels or dose adjustment. It has a short plasma elimination half-life of about 4 hours in cases of unexpected hemorrhage or need for reversal. Its main toxicity relates to the development of abnormal liver biochemistry and/or liver dysfunction with “long-term” use of the drug. This usually occurs within the first 6 months of commencing therapy, with a small percentage of patients developing jaundice. The biochemical abnormality usually resolves despite continuation of the drug. The cause of this toxicity remains unknown. Clinical studies to date have shown that ximelagatran is noninferior to warfarin in stroke prevention in patients with nonvalvular atrial fibrillation, noninferior to standard therapy as acute and extended therapy of deep vein thrombosis (DVT), and superior to warfarin for the prevention of venous thromboembolism post-major orthopedic surgery. It has also been shown to be more effective than aspirin alone for prevention of recurrent major cardiovascular events in patients with recent myocardial infarction.
Are property rights institutions and financial development complements or substitutes? The case of private investment

This article explores whether the quality of the financial sector has an impact on the relationship between property rights institutions and private investment. Using dynamic panel techniques, we show that strong property rights promote increased private investment and that this positive effect is stronger in countries with poorer financial systems, suggesting that institutions and finance act as substitutes in their effect on investment.
Locally Linear Embedding and its Variants for Feature Extraction

* The author was supported by the János Bolyai fellowship of the Hungarian Academy of Sciences. Abstract – Many problems in machine learning are hard to manage without applying some pre-processing or feature extraction method. Two popular forms of dimensionality reduction are the methods of principal component analysis (PCA) [2] and multidimensional scaling (MDS) [18]. In this paper we examine Locally Linear Embedding (LLE), which is an unsupervised, non-linear dimension reduction method that was originally proposed for visualisation. We will show that LLE is capable of feature extraction if we choose the right parameter values. In addition, we extend the original algorithm for more efficient classification. Afterwards we apply the methods to several databases that are available at the UCI repository, and then show that there is a significant improvement in classification performance.
An agent for the prospect presentation problem

Evaluating complex propositions that are composed of several lotteries is a difficult task for humans. Presentation styles can affect the acceptance rate of such proposals. We introduce an agent that chooses between two presentation methods, while aspiring to maximize proposal acceptance. Our agent uses decision theory in order to model human behavior and uses the model to select the presentation which maximizes its expected outcome. We examine several decision theories, and use machine learning to adapt them to our domain. We perform an extensive evaluation of our agent in comparison to other baseline agents and show that presentation can indeed affect the acceptance rate of propositions and that the agent we propose succeeds in selecting beneficial presentations.
Route Discovery Protocol for Optimizing the Power Consumption in Wireless Ad-hoc Network

Ad-hoc networks are becoming increasingly popular due to their wide range of uses. Mobile ad-hoc network have Nodes for at no cost to move and arrange them in random order. Ad-hoc networks are suitable for make use of in situation where an infrastructure would not unavailable or to deploy one is not cost effective. In ad-hoc networks MAC protocols are responsible for coordinating access from active nodes. Different MAC protocols with various objectives have been projected for wireless ad hoc networks. Almost of the wired networks based on the symmetric links, which are always unchanging. But this is not a crate with ad-hoc networks as the nodes are movable and continuously varying its position within network. maximize the network life span, is regular purpose of ad hoc network, since ad hoc network nodes are understood to be dead when they are without of battery. Another main objective is increasing the capacity of the network. However we have shown that some schemes degrade the network throughput as well as bring unfairness in the network in order to save energy and to increase the network throughput as compared to the standard protocol. The proposed methods give a very high energy saving and better throughput which increases the battery life of the ad hoc nodes making the system cost effective. Also the proposed schemes bring fairness in the pair wise network throughput.
A Recommendation Algorithm Using Hybrid Clustering

Recommendation algorithms apply knowledge discovery techniques to the problem of making product recommendation during a live interaction and achieving widespread success in Ecommerce nowadays. With the magnitudes of users and items grow rapidly, resulting in the extreme sparsity of user rating data and the decreasing of real time performance. To solve the problem, the paper proposed a personalized recommendation algorithm using hybrid clustering technology. Users are clustered based on users’ ratings on items. Then, the proposed approach utilizes the item clustering collaborative filtering to produce the recommendations. The recommendation joining user clustering and item clustering collaborative filtering is more scalable than the traditional one.
The Security Council as a Legal Hegemon

This article will examine the United Nations Security Council’s efforts to implement, preserve, and universalize the obligations of the 1968 Nuclear Nonproliferation Treaty. This discussion will lead to questions regarding the Security Council’s role and authority in the international legal system, and ultimately to a consideration of how the international legal system can better guarantee that the Security Council does not exercise an unwarranted degree of legal power at the expense of the member states of the United Nations.
Agent-based negotiation of service level agreements for web service compositions

This paper presents an agent-based negotiation framework to support the autonomous establishment and maintenance of Service Level Agreements for QoS aware web service compositions. It uses a two-layered architecture for the coordinated negotiation of QoS constraints, in order to ensure collective functionality, endto-end QoS and the stateful coordination of complex web service compositions. It forms part of the adaptive Agent based Negotiation of SLAs for Web Service Compositions 2 service composition management framework which is being developed by the ASAPM Project. We present a prototype implementation of the framework. It is built on top of the JADE agent framework and also uses the WS2JADE plug-in which enables the integration of web services and JADE agents.
The Performance and Usability of a Factory-Calibrated Flash Glucose Monitoring System

Abstract Introduction: The purpose of the study was to evaluate the performance and usability of the FreeStyle® Libre™ Flash glucose monitoring system (Abbott Diabetes Care, Alameda, CA) for interstitial glucose results compared with capillary blood glucose results. Materials and Methods: Seventy-two study participants with type 1 or type 2 diabetes were enrolled by four U.S. clinical sites. A sensor was inserted on the back of each upper arm for up to 14 days. Three factory-only calibrated sensor lots were used in the study. Sensor glucose measurements were compared with capillary blood glucose (BG) results (approximately eight per day) obtained using the BG meter built into the reader (BG reference) and with the YSI analyzer (Yellow Springs Instrument, Yellow Springs, OH) reference tests at three clinic visits (32 samples per visit). Sensor readings were masked to the participants. Results: The accuracy of the results was demonstrated against capillary BG reference values, with 86.7% of sensor results within Consensus Error Grid Zone A. The percentage of readings within Consensus Error Grid Zone A on Days 2, 7, and 14 was 88.4%, 89.2%, and 85.2%, respectively. The overall mean absolute relative difference was 11.4%. The mean lag time between sensor and YSI reference values was 4.5±4.8 min. Sensor accuracy was not affected by factors such as body mass index, age, type of diabetes, clinical site, insulin administration, or hemoglobin A1c. Conclusions: Interstitial glucose measurements with the FreeStyle Libre system were found to be accurate compared with capillary BG reference values, with accuracy remaining stable over 14 days of wear and unaffected by patient characteristics.
Grounding planning operators by affordances

An apparatus and method for the controlled delivery of vaporized chemical precursor to an LPCVD reactor is disclosed. A liquid flow controller is closely coupled with a highly efficient liquid vaporizer. The liquid flow controller, which contains no moving parts or polymeric seals, employs as a basic element an efficient gas-liquid separator downstream of the flow control element which assures that an uninterrupted constant velocity flow of liquid enters the high-temperature zone of the vaporizer. When the output of the vaporized-precursor delivery system is linked with the gas inlet of an LPCVD reactor, the very precise vapor delivery rates obtained translate into very precisely controlled thin film deposition rates.