Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining android applications for discovering API call usage patterns and trends

Software libraries and frameworks, consisting of a collection of Class and Interface definitions, provide a mechanism for code reuse by providing methods, APIs, components (generic functionality) and a support structure for developers to build applications, products and solutions. KitKat, Jelly Bean, Ice Cream Sandwich, Honeycomb and Gingerbread are different versions (open-source) of Android, one of the most popular mobile platforms in the world. In this thesis, we present the results of our large-scale (consisting of 1, 120 open-source applications and 17.4 million lines of code) API usage analysis of Android applications. Our work is motivated by the need to mine actual Android API usage, frequent API call usage patterns and trends to understand and generate empirical data on how developers are using the mobile platform in their applications. Extracting popular and frequently-invoked methods, API packages and API call-usage patterns is useful to both the API Producers and API Consumers. For example, API Producers can view the quantitative data on API usage as a feedback from users on the relevance, usability and applicability of the respective APIs. We conduct a series of experiments on analysing the Android platform API usage (usage of different packages, usage of methods, usage across categories) and present the results of our analysis using graphs such as Bubble Chart, Radar Chart, Heat-Map for effective visualization of the results and for extraction of actionable information.
Optimal polarimetric decomposition variables-non-linear dimensionality reduction

Polarimetric SAR image analysis often depends upon proper identification of the relevant degrees of freedom for the problem at hand. Employing physical models of particular scattering processes simplifies identification of the appropriate polarimetric variables. Determining how well variables chosen on the basis of a particular model describe the region of applicability of that model is difficult. Here we attempt in a model independent manner to identify "optimal" variables to both segment an image and highlight the variation within each segment. The method presently employed is non-linear dimensionality reduction.
Throughput-Delay Analysis for Multicast with Inter Session Network Coding

Here, we characterize the throughput of a broad cast network with receivers using rate less codes with block size. We characterize the system throughput asymptotically. Specifically, we explicitly show how the throughput behaves for different values of the coding block size as a function. We are able to provide a lower bound on the maximum achievable throughput. Using simulations, we show the tightness of the bound with respect to system parameters and find that its performance is significantly better than the previously known lower bounds. The packets are not decidable if any deviation is occurred.
Implementation of the Software for Research of Objects Detections of Passive Detectors of Movement

Abstract – In this paper, by results of the lead probings a data domain, the software for research of objects detections of passive detectors of movement is offered. It is lead his successful embodying and testing. In the offered software singularities of a data storage of builders of model on a phisical layer in a DBMS have been considered. Data entry forms are developed and implemented. Export of a shaped database of standard signals to external files for data exchange with other software is stipulated. Keywords – Implementation software, research the objects detection, passive motion detectors. I. I NTRODUCTION Owing to impetuous evolution of information technologies there are many software products which allow to lift on qualitatively new level scientific researches in various areas of a science and engineering [1-7]. To such software products should refer first of all: Axiom, Derive, Maсsyma, Maple, MatLab, MathCAD, Mathematica, LabVIEW and many other things. Among all diversity of available software products is not present such which to the full would ensure needs of the scientist or the contributor. First of all it touches highly specialized researches. Such yields contain the powerful mathematical apparatus allowing to lead the most complicated evaluations but do not include a database in view of all necessary singularities proper in concrete researches [8]. One of such researches is the researches of objects detections of passive detectors of movement [9,10]. Therefore development infware and the software for researches of objects detections of passive detectors of movement in view of all delicacies of research experiment is actual. II. C
Iggy Roca (ed.) (1997). Derivations and constraints in phonology. Oxford: Clarendon Press. Pp. xii+601.

In Optimality Theory (OT), a grammar is a language-particular ranking of universal constraints (Prince & Smolensky 1993). This work was supported by the National Science Foundation under grant SBR- 9420424. I am grateful for comments received from Juliette Blevins, Mike Hammond, Paul de Lacy, Ania Łubowicz, Alan Prince, Doug Pulleyblank, Iggy Roca, Nick Sherrard and Jen Smith. Of course, I alone am responsible for the contents of this review. There are two types of constraints: markedness constraints prohibit (or require) certain output configurations; and faithfulness constraints demand identity in input → output mappings. Phonological generalisations are expressed by the interaction, through ranking, of these constraints. In classical generative phonology (CGP), a grammar is a language-particular ordering of rules (Chomsky & Halle 1968). Though the rules are also language- particular, they are constructed using universal abbreviatory devices subject to an evaluation metric. Phonological generalisations are expressed by the rules and their ordering.
Querying big graphs within bounded resources

This paper studies the problem of querying graphs within bounded resources. Given a query Q, a graph G and a small ratio α, it aims to answer Q in G by accessing only a fraction GQ of G of size |GQ| ≤ α |G|. The need for this is evident when G is big while our available resources are limited, as indicated by α. We propose resource-bounded query answering via a dynamic scheme that reduces big G to GQ. We investigate when we can find the exact answers Q(G) from GQ, and if GQ cannot accommodate enough information, how accurate the approximate answers Q(GQ) are. To verify the effectiveness of the approach, we study two types of queries. One consists of pattern queries that have data locality, such as subgraph isomorphism and strong simulation. The other is the class of reachability queries, without data locality. We show that it is hard to get resource-bounded algorithms with 100% accuracy: NP-hard for pattern queries, and non-existing for reachability when α ≠ 1. Despite these, we develop resource-bounded algorithms for answering these queries. Using real-life and synthetic data, we experimentally evaluate the performance of the algorithms. We find that they scale well for both types of queries, and our approximate answers are accurate, even 100% for small α.
A Case Study for NoSQL Applications and Performance Benefits: CouchDB vs. Postgres

As HPC continues driving towards exascale, there is an increasing need to access and interface with massive data sets. As such interactions become more commonplace, developers need to readdress how data is stored and rethink the infrastructure to enable efficient access and analysis of data. It is becoming evident that traditional, relational databases are not always the most appropriate solution to allow users on-demand access to big data sets. In this study we show that using non-relational, ”NoSQL” databases, such as document stores or key-value stores, can offer large benefits in performance, accessibility and availability. We present a use case from the TeraGrid User Portal that demonstrates a NoSQL solution for auditing and processing batch job data efficiently in order to provide users rapid access to this data. We also present an analysis of benchmarking data to identify usage patterns and data sets for which each architecture is best suited.
A new polynomial robust controller design for nonlinear uncertain systems

The problem of stabilizing a class of nonlinear uncertain systems is considered. The system possesses uncertainty which is time-varying; the uncertainty is unknown but lies within a prescribed set. No statistical information on the uncertainty is imposed. A new controller design which invokes a polynomial state dependent gain is proposed. The coefficients of the polynomial provide design flexibility. If the order of the polynomial is chosen high enough, then practical stability, is assured regardless of the choice of the coefficients. Two design methods, pole-placement and linear quadratic, are proposed for the coefficients. <<ETX>>
State-of-art on PLS Path Modeling through the available software

The purpose of this paper is to present PLS Path Modeling, to describe the various options of LVPLS 1.8 and PLS-Graph 3.0 for carrying out a path model, and to comment the output of both software. PLS-Graph 3.0 is actually based on LVPLS 1.8. As an added value, PLS-Graph has a very friendly graphical interface for drawing the model and a resampling module (jackknife and bootstrap). The presentation is illustrated by data which have been used to construct the European Consumer Satisfaction Index (ECSI) for a mobile phone provider.
Variable structure control of a rodless pneumatic servoactuator with discontinuous sliding surfaces

A rodless pneumatic servoactuator undergoes disturbances that do not satisfy the matching condition. Standard variable structure control approach leads to a disturbance-contaminated sliding surface and loses the disturbance rejection attribute. A two-fold sliding modes approach is employed to resolve this problem. Integration of the two layers of sliding surfaces yields a single discontinuous surface, on which the unmatched disturbances can be rejected. Set point regulation and sinusoidal tracking experiments using both approaches as well as PID control are conducted and compared to demonstrate the effectiveness of the proposed method.
A unified approach to teaching feedback in electronic circuits courses

A generalized negative feedback model is proposed as a vehicle for teaching feedback amplifiers in engineering schools. The model applies to both discrete and operational amplifier configurations and does not dictate the use of specific gain definitions (such as transconductance) for analyzing a given negative feedback amplifier. The main features of the suggested methodology and examples for quick evaluation of closed-loop gain, frequency response, and input and output impedances of negative feedback amplifiers are presented. The proposed method has been successfully tested in the class room over the past two years. >
Optimal low power XOR gate decomposition

With the remarkable growth of portable application and the increasing frequency and integration density, power is being given comparable weight to speed and area in IC designs. For the problem of low power decomposition of an XOR gate, if the implementation technology is static CMOS logic, previous research gave an O(n) log (n) time algorithm which assumes that the inputs have both polarities available. But that approach can not be used in dynamic logic. In this paper, we analyze the properties of optimal XOR decompositions in dynamic logic. Based on these optimality properties, we design an optimal algorithm to solve the low power XOR decomposition problem in dynamic logic. We also point out that the previous solution for static logic is not optimal, and give an optimal algorithm which does not even change the input polarities.
Customer-centered reliability methodology

This paper proposes a series of reliability measures for multistate reliability models. As quality and reliability should be defined from the viewpoint of the customer, so should reliability measures. The circumstances under which each measure approximates customer satisfaction are discussed, and a new measure based on lifetime weighting functions is introduced in an attempt to make the multistate reliability model more responsive to time-varying customer demands. Examples are given of the calculation of these measures for a typical continuous-time Markov model, and the extension of these measures to continuous and mixed models is discussed.
Understanding the Efficiency of Multi-Server Service Systems

In the design and operation of service systems, it is important to determine an appropriate level of server utilization the proportion of time each server should be working. In a multi-server queue with unlimited waiting space, the appropriate server utilization typically increases as the number of servers and the arrival rate increases. We explain this economy of scale and give a rough quantitative characterization. We also show how increased variability in the arrival and service processes tends to reduce server utilization with a given grade of service. As part of this analysis, we develop simple approximations for the mean steady-state waiting time and the full steady-state waiting-time distribution. These approximations exploit an infinite-server approximation for the probability of delay and a single-server approximation for the conditional waiting-time distribution given that waiting occurs. The emphasis is on simple formulas that directly convey understanding.
A 1.06- $\mu$ W Smart ECG Processor in 65-nm CMOS for Real-Time Biometric Authentication and Personal Cardiac Monitoring

Many wearable devices employ the sensors for physiological signals (e.g., electrocardiogram or ECG) to continuously monitor personal health (e.g., cardiac monitoring). Considering private medical data storage, secure access to such wearable devices becomes a crucial necessity. Exploiting the ECG sensors present on wearable devices, we investigate the possibility of using ECG as the individually unique source for device authentication. In particular, we propose to use ECG features toward both cardiac monitoring and neural-network-based biometric authentication. For such complex functionalities to be seamlessly integrated in wearable devices, an accurate algorithm must be implemented with ultralow power and a small form factor. In this paper, a smart ECG processor is presented for ECG-based authentication as well as cardiac monitoring. Data-driven Lasso regression and low-precision techniques are developed to compress neural networks for feature extraction by 24.4 $\times $ . The 65-nm testchip consumes 1.06 $\mu \text{W}$ at 0.55 V for real-time ECG authentication. For authentication, equal error rates of 1.70%/2.18%/2.48% (best/average/worst) are achieved on the in-house 645-subject database. For cardiac monitoring, 93.13% arrhythmia detection sensitivity and 89.78% specificity are achieved for 42 subjects in the MIT-BIH arrhythmia database.
Energy Consumption for Video Transmission in Mobile Ad Hoc Networks

In the mobile ad hoc network the energy consumed by the transmitter and receiver is more significant due the more energy consumption in the transmitting and receiving of the data. Therefore energy consumption in the devices is needed to optimize. In this paper we analysis the Doppler shift effect on the energy consumption of the transmitter in video transmission over the mobile ad hoc network. In this work we analyses the energy consumption of the transmitter device over the MQAM modulation and vary the average velocity of the devices.
The Supreme Court and Information Privacy

The US Supreme Court has written a great deal about ‘privacy’ in a wide variety of contexts. Between 1970 and 2011, the Court used the term ‘privacy’ in 631 opinions. In just over half (328 opinions) did an opinion significantly address some aspect of privacy. The breakdown of those 328 opinions offers insight into the contexts in which the Supreme Court concerns itself with privacy (Figure 1). Not surprisingly, most of these opinions addressed privacy rights that the Court has found are protected by the US Constitution. The largest single category by far was cases involving a ‘reasonable expectation of privacy’ under the Fourth Amendment to the Constitution (210, or 64 per cent of opinions), including the extent to which privacy rights are implicated, or violated, by searches or seizures that are initiated or conducted by private parties with some degree of government involvement. Opinions involving the First Amendment and freedom of expression and association issues accounted for 18 per cent (59), a large portion of which involved reviews of obscenity regulations and prosecutions. Fourteen opinions (4 per cent) involved what the Court has come to call ‘decisional privacy’, namely the privacy rights the Court has found implied in the Constitution that protect the rights of adults to make decisions about activities such as reproduction, contraception, the education of their children, and sexual intimacy. A handful of opinions (16, or 5 per cent) involve the privacy-related characteristics of the Fifth Amendment’s protection against self-incrimination. Aside from constitutional privacy issues, 20 (6 per cent) of the Court’s opinions involving privacy addressed the application of the two privacy exemptions to the Freedom of Information Act. A significant number of opinions (69, or 21 per cent) did not fit neatly within any single discrete category, but reflect a
Greedy Interaction Elements Coverage Analysis for AI-Based T-Way Strategies

Recently, many researchers have started to adopt Artificial Intelligence AI-based strategies for t-way testing. Here, each interaction is covered at most once whenever possible. In many AI-based strategies, sampling for the most optimal test cases is given utmost priority, but measuring of the interaction coverage metric per test case is often neglected. In the situation where not all test cases can be executed due to constraints on project deadline, the availability of interaction coverage metric per test case can be a useful indicator on how greedy each AI-based strategy of interests is. In this manner, test engineers can make informed decision on the selection of suitable strategies for use. In this paper, this study presents a systematic analysis of existing AIbased strategies including that of Hill Climbing HC, Simulated Annealing SA, Tabu Search TS, Great Flood GF, Particle Swarm Optimization PSTG and Harmonic Search Strategy HSS as far as its rate of coverage per test case is concerned. In doing so, this paper demonstrates that HSS, in most cases, gives competitive interaction coverage rate as compared to competing AI-based strategies but with less number of iterations.
Radio Implementation of a Testbed For Cognitive Radio Source Localization Using USRPS and GNU Radio

Abstract : The shift from wired to fully wireless communication is causing an increasing demand on the frequency spectrum. The cognitive radio was introduced to solve spectrum scarcity by allowing spectrum sharing between licensed and unlicensed users. This approach presents a challenge to source localization because of the cognitive radio's capability to shift its spatial, frequency and temporal parameters. The extended semi-range-based (ESRB) and cooperative-received-signal-strength-based (CRSSB) localization schemes are proposed to overcome the challenge of identifying and locating a cognitive radio over time using a wireless sensor network. The objective of this thesis was to set up a testbed using GNU Radio and Universal Software Radio Peripherals (USRPs) to estimate the position of a cognitive radio device using the ESRB and CRSSB localization schemes. The ESRB algorithm does not provide accurate position estimates but the estimates are observed to be concentrated in the vicinity and converging toward the true position of the secondary user. The errors are believed to be caused by three factors: a limited number of sensor nodes used (four), an insufficient number of spectral scans per superframe (55), and the lack of synchronization among sensor nodes. The CRSSB localization scheme gave a more accurate position estimation.
QUESTION CLASSIFICATION USING STATISTICAL APPROACH: A COMPLETE REVIEW

In recent days, question classification is growing in popularity as it has an important role in question answering systems, information retrieval and it can be used in a wide range of other domains. The main aim of question classification is to accurately assign labels to questions based on expected answer type. Past research works have relied on matching questions against hand-crafted rules. However, rules require enormous effort to create and often suffer from being too specific. A great deal of current research works on question classification is based on statistical approach to overcome these issues by employing machine learning techniques such as Support Vector Machine and Artificial Neural Network. This paper presents an updated literature survey of current methods or approaches for question classification in the areas of question answering systems, information retrieval and educational environment. Question classification involving other languages besides English has also been examined.
Information-theoretic sum capacity of reverse link CDMA systems

The information-theoretic sum capacity of reverse link CDMA systems is investigated in this paper. As the reverse link CDMA systems are interference-limited, for given channel and noise conditions, the information-theoretic system capacity can be achieved by optimally managing the interference from mobile stations. Optimal interference management means optimal allocation of the transmit powers of the mobile stations. The exhaustive search of the optimal power allocation policy that maximizes the aggregate system capacity, requires O(N/sup M/) computations, where M is the number of the mobile station in the system and N is the granularity of the transmit power of a mobile station. In this paper, we prove that for a single cell, the number of computations to find the optimal power allocation solution can be reduced to O(NM), without sacrificing the optimality. We also show that an upper and a lower bound can be computed with O(M) computations, and the bounds are tight for small signal to noise ratio.
Topic Correlation Analysis for Bearing Fault Diagnosis Under Variable Operating Conditions

This paper presents a Topic Correlation Analysis (TCA) based approach for bearing fault diagnosis. In TCA, Joint Mixture Model (JMM), a model which adapts Probability Latent Semantic Analysis (PLSA), is constructed first. Then, JMM models the shared and domain-specific topics using “fault vocabulary” . After that, the correlations between two kinds of topics are computed and used to build a mapping matrix. Furthermore, a new shared space spanned by the shared and mapped domain-specific topics is set up where the distribution gap between different domains is reduced. Finally, a classifier is trained with mapped features which follow a different distribution and then the trained classifier is tested on target bearing data. Experimental results justify the superiority of the proposed approach over the stat-of-the-art baselines and it can diagnose bearing fault efficiently and effectively under variable operating conditions.