Miracle-dz/UniFAR-FTUs huggingface dataset:
CIEVS: A cloud-based framework to modernize the Indian election voting system

In India, Information and Communication technologies (ICT) are being increasingly deployed in almost each sector of economy including media, health, agriculture, transportation, and education. Upon an analysis of the existing Indian Election Voting System, it is felt that the ICT remains to be exploited to its fullest potential. Although, the Election Commission of India (ECI) has recently taken up several ICT-enabled initiatives, but the application of advanced ICTs still needs to be promoted in Indian Election Voting System. In this paper, we propose an I-Voting System to provide for an additional channel of voting to the Indian electorate. This paper evaluates the utility of cloud computing technology in I-Voting and specifies ancillary benefits arising out of its deployment in I-Voting. Further, this paper proposes a cloud deployment model “Cloud-based Integrated Election Voting System (CIEVS)” that leverages advanced Information and Communication technologies including cloud computing, web services and mobile phone services to integrate the existing Electronic Voting Machine (EVM) System with the I-Voting System to provide a solution to the problem of prevailing low voting percentages in the Indian elections. Additionally, the paper elaborates on the components of the proposed model besides discussing the advantages offered by the proposed model.
Selective Placement Data Cache for Low Energy Embedded System

This paper proposes a process aware selective placement scheme for N-way set associative cache with the help of a victim set. In this work, we consider the data cache subsystem, as it is one of the most power consuming microarchitectural parts of an embedded system and set associative cache, being one of the popular mapping schemes for data cache. We propose a selective placement scheme to reduce tag comparison and power consumption using victim set. We show that, this scheme has reduced the tag comparison by 70% and power saving by 72% as compared to conventional caching scheme. Experimental results are obtained using Simplescalar 2.0 cache simulator for SPEC95benchmark suite.
Using Category and Keyword for Personalized Recommendation: A Scalable Collaborative Filtering Algorithm

Scalability is another major issue for recommender systems except data sparsity and prediction quality. However, it has still not been well solved while many social recommendation models have been propose to improve the latter two problems. In this paper, we propose a scalable collaborative filtering algorithm based matrix factorization that introduce two common context factors: category and keyword besides social information. In the proposed model, we make prediction together using two preference matrices:user-category and user-keyword instead of only using the user-item rating matrix. This has the advantage that for new items, our model can make use of the two factors to make prediction, although they do not exist in the rating matrix. Experimental results on real dataset show that our model has a good scalability for new items, while still performing better than other state-of-art models.
Graphical debugging of distributed applications: Using UML object diagrams to visualize the state of distributed applications at runtime

While debugging is one of the most time consuming tasks software developers perform, the tool support, especially for distributed applications, is lacking according to many professional developers. The Visual Debugger is an approach to provide an easy-to-use tool which gives software developers an abstract view on the current state of their application in debugging sessions. While similar tools for non distributed applications already exist, the Visual Debugger addresses the more complex debugging scenarios in distributed applications. Therefore, several views with different levels of abstraction of the current state of an application are provided. Although the approach may be adapted to other object oriented programming languages, the current target of the Visual Debugger is languages running on the Java Virtual Machine with the focus on Java.
Techniques for Programmable Logic Array Folding

The optimal PLA folding problem is presented and discussed in its different forms. In particular, new algorithms for row folding in unconstrained architectures and in AND-OR-AND architectures are presented and their complexity examined. The problem of finding an optimal row folding after a column folding has been performed, is described and an algorithm for its solution given. Finally, the organization of an APL package for row and column folding of PLA's is introduced and experimental results reported.
Inclusive Prekindergarten Classrooms in a New Era: Exploring the Perspectives of Teachers in the United States.

The purpose of this article is to share the results of a qualitative research study designed to shed light on the perspectives of inclusive prekindergarten classroom teachers in the United States. This study used surveys, interviews, and classroom observations to explore understandings of recently adopted learning standards and accountability reforms, as well as perceptions of the benefits and challenges of today’s inclusive prekindergarten settings. Data revealed four primary themes: early childhood teachers in inclusive prekindergarten settings value collaboration; they believe inclusive settings have the potential to benefit all children; inclusive prekindergarten teachers are facing significant new challenges; and, they need additional resources to address the challenges and realize the benefits. Building on these data, the authors provide an argument for the field of early childhood education in the United States to help build the capacity of inclusive prekindergarten settings to meet existing challenges and new demands.
PSIPOP : Planning with Sensing over Partially Closed Worlds

We present a new partial order planner called PSIPOP, which builds on SNLP. We drop the closed world assumption, add sensing actions, add a class of propositions about the agent's knowledge, and add a class of universally quantiied propositions. This latter class of propositions, which we call-forms, distinguishes this research. -forms represent partially closed worlds, such as \Block A is clear", or \x:ps is the only postscript le in directory =tex. " We present our theory of planning with sensing and show how partial order planning is performed using-forms. Noteworthy are the facts that lack of information can be represented precisely and all quantiied reasoning has polynomial complexity. Thus, in nite domains where the maximum plan length is bounded, PSIPOP is NP-complete. Several researchers have examined planning with sensing in an open world, where the agent must take action both to acquire knowledge and to change the But how does the agent represent a partially closed world (PCW){i.e., the fact that either (A) it knows everything about a particular question , or (B) it knows precisely what it does not know about that question. For example, how does it represent that \(1) a:ps and b:ps are the only postscript les in directory =tex" (an example of A) or that \(2) c:ps is in =home and d:ps may or may not be in =home, but there are no other postscript les in =home" (an example of B). Such representations are needed for numerous actions as: preconditions: E.g., when removing a directory in Unix, the directory must be empty. 1 This paper is a slight expansion of our paper presented at the 1999 AAAI Spring Symposium on Search Techniques for Problem Solving under Uncertainty and Incomplete Information. 1 Note that the blocks world trick of using Clear(x) when a block has nothing on it does not easily generalize if a block can have numerous blocks on top of it. eeects of sensing new information: E.g., when performing a Unix \ls", one learns something about all les, namely, those in the the directory and those not in it. In contrast, SADL (Golden & Weld 1996) uses conditional eeects to represent the eeects of sensing. Some works (e.g., (Scherl & Levesque 1997)) use rst-order logic (FOL), which easily represents PCWs but which appears to preclude practical planning algorithms due to the undecidability of entailment in FOL. The other works build upon the partial order planning (POP) …
Factors influencing the electoral participation of Asian immigrants in New Zealand

ABSTRACT New Zealand electoral data show that those identifying with an Asian ethnicity have the lowest electoral turnout of any of the broad ‘ethnic’ categories used by government statisticians. What accounts for this finding? Given a paucity of quantitative data with which to answer this question, we employ a qualitative, focus group-based research design to examine the electoral participation of first-generation Asian immigrants.1 We ask, first, what are the main factors that lead Asian immigrants in New Zealand to participate or not participate in parliamentary elections and, second, do these reasons vary among immigrants from different national backgrounds? Our findings suggest that the low electoral turnout recorded among New Zealand Asians is likely to be at least in part an artefact of the recentness of much immigration from non-English-speaking countries such as South Korea and China. The paper also identifies the role government agencies, political parties and the ethnic media could play in communicating information about New Zealand’s electoral politics, with potentially significant dividends for future voting rates among Asian immigrants in New Zealand.
CRX controls retinal expression of the X-linked juvenile retinoschisis (RS1) gene

X-linked juvenile retinoschisis is a heritable condition of the retina in males caused by mutations in the RS1 gene. Still, the cellular function and retina-specific expression of RS1 are poorly understood. To address the latter issue, we characterized the minimal promoter driving expression of RS1 in the retina. Binding site prediction, site-directed mutagenesis, and reporter assays suggest an essential role of two nearby cone-rod homeobox (CRX)-responsive elements (CRE) in the proximal −177/+32 RS1 promoter. Chromatin immunoprecipitation associates the RS1 promoter in vivo with CRX, the coactivators CBP, P300, GCN5 and acetylated histone H3. Transgenic Xenopus laevis expressing a green fluorescent protein (GFP) reporter under the control of RS1 promoter sequences show that the −177/+32 fragment drives GFP expression in photoreceptors and bipolar cells. Mutating either of the two conserved CRX binding sites results in strongly decreased RS1 expression. Despite the presence of sequence motifs in the promoter, NRL and NR2E3 appear not to be essential for RS1 expression. Together, our in vitro and in vivo results indicate that two CRE sites in the minimal RS1 promoter region control retinal RS1 expression and establish CRX as a key factor driving this expression.
Push and Pull in Sensor Network Query Processing

Sensor networks provide new opportunities for data collection. This in turn creates opportunities to apply database techniques for analyzing this data. Such efforts are complicated because data is spread over wireless nodes that transmit messages via radio. Nodes have limited battery life, and using the radio is expensive. Therefore, rather than gathering all data at a base station, it is more efficient to construct query plans that transmit as little data as necessary to produce a solution. In this paper, we present an overview of our work on sensor network query processing and characterize it in the context of push versus pull techniques for data extraction. Push means to install conditions within the network which, when met, trigger data transmission. Pull means to have the base station request particular data. We believe this is promising approach to generalizing processing techniques for the purpose of developing a methodology for designing query algorithms.
Automatic Design Space Exploration of Approximate Algorithms for Big Data Applications

Nowadays, Big Data represents a new era in data exploration, manipulation and utilization as it requires an effective rethinking of platforms, computing paradigms, architectures and algorithms. Indeed, performance constraints cannot be sustained by traditionally designed platforms and new design methodologies need to be defined. Approximate computing is a promising design strategy, able to deal with these issues, as it trades off accuracy and performance parameters, such as energy, time and required hardware resources. It allows designers to exploit the inherent resiliency of algorithms, i.e. when the application tolerates an approximate result, and develop a platform with lower power consumption, smaller area footprints and lower elaboration time. In this paper, we present a new design exploration methodology whereby generic algorithms, described by means of C/C++, are implemented in hardware by automatically tuning the performance with respect to a given maximum accuracy loss, illustrating a preliminary experimental result on some algorithms.
Clicking for business English success

In this paper an online platform for learning business English vocabulary is described, which is created in Subotica Tech - College of Applied Sciences and which came to be as a result of the collaborative work of professors and students. Aspects of English course content are depicted in means of different previous knowledge of the students. Solution of the problems arising from such a situation are given by “opening” the classroom and using the concept of the Internet of things. The structure of the e-learning system is briefly outlined, as well as the evaluation method of students' learning outcomes. Possibilities of education enhancement offered by the learning system integrated in massive open online courses are discussed. Further development of the present learning system in direction of m-learning system are represented.
Executive share ownership, experience and basic salaries: the influence on IPO share option schemes and performance

Corporate governance research often focuses on two theoretical stands, agency theory and resource dependence theory. Whist both provide distinct theoretical roles, this paper combines them to argue that executive stock option plans (ESOs) can serve a dual role, that of re-alignment of managers’ and shareholders’ interests, and board stability, by ‘locking’ the executive to reward and thus retaining managerial talent. The paper focuses on a unique sample of 311 entrepreneurial initial public offerings. It examines their choice of schemes prior to and at the initial public offering (IPO). It gives consideration to ESO choices being associated with board ownership, executive wealth and cognitive characteristics of the IPO firm’s management team. Finally, it examines performance in line with signalling theory, showing that IPO underpricing is reduced by the presence of executive stock options and that high growth positively moderates the link between underpricing and conditional ESO plans.
FTC design for polytopic LPV systems subject to actuator saturations

In this paper, a Fault Tolerant Control (FTC) scheme using virtual actuators for discrete-time polytopic Linear Parameter Varying (LPV) systems subject to actuator saturations is proposed. The plant with the faulty actuator is augmented to take into account the saturations and modified adding the virtual actuator block that masks the fault. The elements of the FTC control loop are designed using Linear Matrix Inequalities (LMIs) in order to achieve the Pole Placement and the H2 norm specifications. To assess the performance of the proposed approach an aeronautical application is used.
James: A Humanoid Robot Acting over an Unstructured World

The recent trend of humanoid robotics research has been deeply influenced by concepts such as embodiment, embodied interaction and emergence. In our view, these concepts, beside shaping the controlling intelligence, should guide the very design process of the modern humanoid robotic platforms. In this paper, we discuss how these principles have been applied to the design of a humanoid robot called James. James has been designed by considering an object manipulation scenario and by explicitly taking into account embodiment, interaction, and the exploitation of smart design solutions. The robot is equipped with moving eyes, neck, arm and hand, and a rich set of sensors, enabling proprioceptive, kinesthetic, tactile and visual sensing. A great deal of effort has been devoted to the design of the hand and touch sensors. Experiments, e.g., tactile object classification, have been performed, to validate the quality of the robot perceptual capabilities
Markovian State and Action Abstractions for MDPs via Hierarchical MCTS

State abstraction is an important technique for scaling MDP algorithms. As is well known, however, it introduces difficulties due to the non-Markovian nature of state-abstracted models. Whereas prior approaches rely upon ad hoc fixes for this issue, we propose instead to view the state-abstracted model as a POMDP and show that we can thereby take advantage of state abstraction without sacrificing the Markov property. We further exploit the hierarchical structure introduced by state abstraction by extending the theory of options to a POMDP setting. In this context we propose a hierarchical Monte Carlo tree search algorithm and show that it converges to a recursively optimal hierarchical policy. Both theoretical and empirical results suggest that abstracting an MDP into a POMDP yields a scalable solution approach.
Effective Selectional Restrictions for Unsupervised Relation Extraction

Unsupervised Relation Extraction (URE) methods automatically discover semantic relations in text corpora of unknown content and extract for each discovered relation a set of relation instances. Due to the sparsity of the feature space, URE is vulnerable to ambiguities and underspecification in patterns. In this paper, we propose to increase the discriminative power of patterns in URE using selectional restrictions (SR). We propose a method that utilizes a Web-derived soft clustering of n-grams to model selectional restrictions in the open domain. We comparatively evaluate our method against a baseline without SR, a setup in which standard 7class Named Entity types are used as SR and a setup that models SR using a finegrained entity type system. Our results indicate that modeling SR into patterns significantly improves the ability of URE to discover relations and enables the discovery of more fine-granular relations.
The Regression-calibration Method for Fitting Generalized Linear Models with Additive Measurement Error

This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). Discussion will include specified measurement error, measurement error estimated by replicate error-prone proxies, and measurement error estimated by instrumental variables. The discussion focuses on software developed as part of a small business innovation research (SBIR) grant from the National Institutes of Health (NIH).
Online person orientation estimation based on classifier update

Person orientation estimation is valuable for intelligent video surveillance. Although much progress has been made in recent years, it still faces challenges such as varying poses, illuminations and viewpoints. Most existing approaches merely use appearance information or combine it with motion information. Appearance-based classifiers are trained offline without updating in real time, which can not adapt to unknown scenes. To fix it, a novel orientation estimation approach based on online appearance-based classifier update is proposed. Reliable motion direction is determined acting as pre-estimated person orientation to update the appearance-based classifier. Moreover, a novel criterion based on motion reliability is proposed to determine the motion direction. Experimental results show that the proposed approach achieves more competitive performances especially for unknown scenes.
Methodology for Behavioral-based Malware Analysis and Detection Using Random Projections and K-Nearest Neighbors Classifiers

In this paper, a two-stage methodology to analyze and detect behavioral-based malware is presented. In the first stage, a random projection is decreasing the variable dimensionality of the problem and is simultaneously reducing the computational time of the classification task by several orders of magnitude. In the second stage, a modified K-Nearest Neighbors classifier is used with Virus Total labeling of the file samples. This methodology is applied to a large number of file samples provided by F-Secure Corporation, for which a dynamic feature has been extracted during Deep Guard sandbox execution. As a result, the files classified as false negatives are used to detect possible malware that were not detected in the first place by Virus Total. The reduced number of selected false negatives allows the manual inspection by a human expert.
A Novel Heuristic Usage of Helpful Actions for Conformant-FF System

Conformant planning is usually transformed into a search problem in the space of belief states, where the combinatorial explosion of search space has been one of the most intractable problems. In this paper, we present a novel usage of the helpful action pruning technique in the Conformant-FF planner. The key idea is to change the way it deals with helpful actions and first consider actions from the so-called implication path which was used by Conformant-FF for concluding which subgoal would be considered known to be true in the relaxed planning graph. We first point out the semantics of solving by cases, indicated by the implication paths of the relaxed planning process. In line with the semantics, we then propose our heuristic idea of using these implication paths further by attempting to collect certain groups of helpful actions such that executing all actions within a group can achieve some subgoal while executing an individual action in the group can not due to incomplete information. This technique usually leads to the goal faster and cuts down the search space dramatically. We evaluate the idea experimentally. In a number of conformant benchmarks, our heuristic pruning technique outperforms helpful actions pruning in both planning efficiency and the size of search space.