Miracle-dz/UniFAR-FTUs huggingface dataset:
A common framework for aspect mining based on crosscutting concern sorts

The increasing number of aspect mining techniques proposed in literature calls for a methodological way of comparing and combining them in order to assess, and improve on, their quality. This paper addresses this situation by proposing a common framework based on crosscutting concern sorts which allows for consistent assessment, comparison and combination of aspect mining techniques. The framework identifies a set of requirements that ensure homogeneity in formulating the mining goals, presenting the results and assessing their quality. We demonstrate feasibility of the approach by retrofitting an existing aspect mining technique to the framework, and by using it to design and implement two new mining techniques. We apply the three techniques to a known aspect mining benchmark and show how they can be consistently assessed and combined to increase the quality of the results. The techniques and combinations are implemented in FINT, our publicly available free aspect mining tool
Association Between Menstrual Hygiene Practice and Exposure to Risk of RH Problems among the Female Students of College Level in Surkhet

The goal of this cross-sectional study, carried out in two constituent campuses of Surkhet, was to explore an association between menstrual hygiene practice and exposure to RH risk among female students. Stratified sampling was applied to recruiting 282 female students for this study. For data collection, a self-administrative questionnaire was conducted. The majority of the respondents in this study were between the ages of 19 and 24, the upper-caste Hindus, married and unemployed. The study found that the average age of the menarche among rural and urban female students of the selected campuses was 13.73 years. It was found that many girls in rural campuses faced restrictions during menstruation. Disposable sanitary pads were commonly utilized by them. Reproductive health problems were more common among the female students in the urban campuses, including genital itching, urinary tract infections, and menstrual problems. The computed relative risk of restricting the practice, bathing once a day, and utilizing a sanitary disposal pad was less than 1, indicating that using a sanitary disposal pad minimizes the likelihood of RH problems. The results of the study indicated that those who change absorbents as needed and use homemade pads may have experienced RH problems. Deriving on the results of this study, it is suggested that research and advocacy regarding this issue be raised at campuses.
Evaluating contextual information for wearable computing

Does the use of a wearable context-aware computer increase task proficiency? User trials have been carried out in an attempt to answer this question. The study involves WECAPC software developed to ran on the /spl chi//sup 3/ wearable computer. The trials demonstrate that a wearable computer with simple context-awareness yields superior performance to either Internet search or realworld activity. Thus, we conclude that the use of context does indeed improve user task proficiency.
Efficient Structure Learning in Factored-State MDPs

We consider the problem of reinforcement learning in factored-state MDPs in the setting in which learning is conducted in one long trial with no resets allowed. We show how to extend existing efficient algorithms that learn the conditional probability tables of dynamic Bayesian networks (DBNs) given their structure to the case in which DBN structure is not known in advance. Our method learns the DBN structures as part of the reinforcement-learning process and provably provides an efficient learning algorithm when combined with factored Rmax.
Multi-Document Biography Summarization

In this paper we describe a biography summarization system using sentence classification and ideas from information retrieval. Although the individual techniques are not new, assembling and applying them to generate multi-document biographies is new. Our system was evaluated in DUC2004. It is among the top performers in task 5–short summaries focused by person questions.
On the Weight Enumerator and the Maximum Likelihood Performance of Linear Product Codes

Product codes are widely used in data-storage, optical and wireless applications. Their analytical performance evaluation usually relies on the truncated union bound, which provides a low error rate approximation based on the minimum distance term only. In fact, the complete weight enumerator of most product codes remains unknown. In this paper, concatenated representations are introduced and applied to compute the complete average enumerators of arbitrary product codes over a field Fq. The split weight enumerators of some important constituent codes (Hamming, Reed-Solomon) are studied and used in the analysis. The average binary weight enumerators of Reed Solomon product codes are also derived. Numerical results showing the enumerator behavior are presented. By using the complete enumerators, Poltyrev bounds on the maximum likelihood performance, holding at both high and low error rates, are finally shown and compared against truncated union bounds and simulation results.
Evaluating Recommendation Algorithms by Graph Analysis

We present a novel framework for evaluating recommendation algorithms in terms of the `jumps' that they make to connect people to artifacts. This approach emphasizes reachability via an algorithm within the implicit graph structure underlying a recommender dataset, and serves as a complement to evaluation in terms of predictive accuracy. The framework allows us to consider questions relating algorithmic parameters to properties of the datasets. For instance, given a particular algorithm `jump,' what is the average path length from a person to an artifact? Or, what choices of minimum ratings and jumps maintain a connected graph? We illustrate the approach with a common jump called the `hammock' using movie recommender datasets.
Verifiable Computation with Massively Parallel Interactive Proofs

As the cloud computing paradigm has gained prominence, the need for verifiable computation has grown increasingly urgent. The concept of verifiable computation enables a weak client to outsource difficult computations to a powerful, but untrusted, server. Protocols for verifiable computation aim to provide the client with a guarantee that the server performed the requested computations correctly, without requiring the client to perform the computations herself. By design, these protocols impose a minimal computational burden on the client. However, existing protocols require the server to perform a large amount of extra bookkeeping in order to enable a client to easily verify the results. Verifiable computation has thus remained a theoretical curiosity, and protocols for it have not been implemented in real cloud computing systems. Our goal is to leverage GPUs to reduce the server-side slowdown for verifiable computation. To this end, we identify abundant data parallelism in a state-of-the-art general-purpose protocol for verifiable computation, originally due to Goldwasser, Kalai, and Rothblum, and recently extended by Cormode, Mitzenmacher, and Thaler. We implement this protocol on the GPU, obtaining 40-120x server-side speedups relative to a state-of-the-art sequential implementation. For benchmark problems, our implementation reduces the slowdown of the server to factors of 100-500x relative to the original computations requested by the client. Furthermore, we reduce the already small runtime of the client by 100x. Similarly, we obtain 20-50x server-side and client-side speedups for related protocols targeted at specific streaming problems. We believe our results demonstrate the immediate practicality of using GPUs for verifiable computation, and more generally that protocols for verifiable computation have become sufficiently mature to deploy in real cloud computing systems.
The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning

A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi- dimensional feature space (also known as attribute-value descriptions). A teacher partitions a set of training examples into a finite number of classes. The task of the learning algorithm is to induce a concept from the training examples. In this paper, we formally distinguish three types of features: primary, contextual, and irrelevant features. We also formally define what it means for one feature to be context-sensitive to another feature. Context-sensitive features complicate the task of the learner and potentially impair the learner's performance. Our formal definitions make it possible for a learner to automatically identify context-sensitive features. After context-sensitive features have been identified, there are several strategies that the learner can employ for managing the features; however, a discussion of these strategies is outside of the scope of this paper. The formal definitions presented here correct a flaw in previously proposed definitions. We discuss the relationship between our work and a formal definition of relevance.
Tit-for-Tat Reconfiguration Rule for Unstructured P2Ps and Its Evaluation

In this paper, we propose a clustering scheme for unstructured P2Ps based on the notion of ``tit-for-tat'' strategy. The proposed scheme is a combination of an identification of critical links, local reconfiguration of incident links, and a retaliation rule. By adopting such a tit-for-tat strategy in selecting incident links to be disconnected while reconfiguring the underlying P2P overlay, it emerges a cooperative behavior of the participant peers. The performance of the proposed scheme is evaluated by simulation. The result of simulations indicates that under the proposed scheme, each peer can collect a large number of peers sharing similar interest, and it improves the performance of conventional clustering schemes.
The Self-Organizing Symbiotic Agent

In [N. A. Baas, Emergence, Hierarchies, and Hyper-structures, in C.G. Langton ed., Artificial Life III, Addison Wesley, 1994. ] a general framework for the study of Emergence and hyper-structure was presented. This approach is mostly concerned with the description of such systems. In this paper we will try to bring forth a different aspect of this model we feel will be useful in the engineering of agent based solutions, namely the symbiotic approach. In this approach a self-organizing method of dividing the more complex "main-problem" to a hyper-structure of "sub-problems" with the aim of reducing complexity is desired. A description of the general problem will be given along with some instances of related work. This paper is intended to serve as an introductory challenge for general solutions to the described problem.
When are recommender systems useful?

Recommender systems are crucial tools to overcome the information overload brought about by the Internet. Rigorous tests are needed to establish to what extent sophisticated methods can improve the quality of the predictions. Here we analyse a refined correlation-based collaborative filtering algorithm and compare it with a novel spectral method for recommending. We test them on two databases that bear different statistical properties (MovieLens and Jester) without filtering out the less active users and ordering the opinions in time, whenever possible. We find that, when the distribution of user-user correlations is narrow, simple averages work nearly as well as advanced methods. Recommender systems can, on the other hand, exploit a great deal of additional information in systems where external influence is negligible and peoples' tastes emerge entirely. These findings are validated by simulations with artificially generated data.
"Squeaky Wheel" Optimization

We describe a general approach to optimization which we term "Squeaky Wheel" Optimization (swo). In swo, a greedy algorithm is used to construct a solution which is then analyzed to find the trouble spots, i.e., those elements, that, if improved, are likely to improve the objective function score. That analysis is used to generate new priorities that determine the order in which the greedy algorithm constructs the next solution. This Construct/Analyze/Prioritize cycle continues until some limit is reached, or an acceptable solution is found.SWO can be viewed as operating on two search spaces: solutions and prioritizations. Successive solutions are only indirectly related, via the re-prioritization that results from analyzing the prior solution. Similarly, successive prioritizations are generated by constructing and analyzing solutions. This "coupled search" has some interesting properties, which we discuss.We report encouraging experimental results on two domains, scheduling problems that arise in fiber-optic cable manufacturing, and graph coloring problems. The fact that these domains are very different supports our claim that swo is a general technique for optimization.
Deriving Abstract Semantics for Forward Analysis of Normal Logic Programs

The problem of forward abstract interpretation of {\em normal} logic programs has not been formally addressed in the literature although negation as failure is dealt with through the built-in predicate ! in the way it is implemented in Prolog. This paper proposes a solution to this problem by deriving two generic fixed-point abstract semantics $F^b and $F^\diamond for forward abstract interpretation of {\em normal} logic programs. $F^b$ is intended for inferring data descriptions for edges in the program graph where an edge denotes the possibility that the control of execution transfers from its source program point to its destination program point. $F^\diamond$ is derived from $F^b$ and is intended for inferring data descriptions for textual program points.
The Calán-Yale Deep Extragalactic Research (CYDER) Survey: Optical Properties and Deep Spectroscopy of Serendipitous X-Ray Sources

We present the first results from the Calán-Yale Deep Extragalactic Research (CYDER) survey. The main goal of this survey is to study serendipitous X-ray sources detected by Chandra in an intermediate flux range (10-15 to 10-12 ergs s-1) that comprises most of the X-ray background. A total of 267 X-ray sources spread over five archived fields were detected. The log N- log S distribution obtained for this sample is consistent with the results of other surveys. Deep V and I images were taken of these fields in order to calculate X-ray-to-optical flux ratios. Identifications and redshifts were obtained for 106 sources using optical spectroscopy from 8 m class telescopes to reach the optically faintest sources, to the same level as deeper X-ray fields like the Chandra Deep Fields, showing that the nature of sources detected depends mostly on the optical limit for spectroscopy. In general, sources optically classified as obscured active galactic nuclei (AGNs) have redder optical colors than unobscured AGNs. A rough correlation between fX/fopt and hard X-ray luminosity was found for obscured AGNs, confirming the prediction by existing models that in obscured AGNs the optical light is completely dominated by the host galaxy. The previously claimed decrease of the obscured-to-unobscured AGN ratio with increasing X-ray luminosity is observed. However, this correlation can be explained as a selection effect caused by the lower optical flux of obscured AGNs. Comparison between the observed NH distribution and predictions by existing models shows that the sample appears complete up to NH < 3 × 1022 cm-2, while for more obscured sources incompleteness plays an important role in the observed obscured-to-unobscured AGN ratio.
FORMULATION, ANALYSIS AND VALIDATION OF NANOSUSPENSIONS-LOADED VORICONAZOLE TO ENHANCE SOLUBILITY

Objective: This study aimed to enhance the solubility of voriconazole (VRZ) via loading to nanosuspensions using solvent/anti-solvent technique. The optimisation of independent variables (polymer concentrations) was carried out to achieve the desired particle size and maximise the percentage of entrapment efficiency (EE %) and drug loading (DL %) using design-expert®software. Methods: Design-Expert® software, version 13, was used to design and optimise nanosuspensions-loaded VRZ using 23 factorial designs. Concentrations of polyvinylpyrrolidone, hydroxypropyl methylcellulose and poloxamers were selected as independent variables to achieve ideal particle size, polydispersity index (PDI), entrapment efficacy (EE %) and drug loading (DL %). Atomic force microscopy (AFM), differential scanning calorimetry (DSC) and saturated solubility were used to assess the lyophilized nanoparticles. The compatibility between the drug and the polymers was studied using Fourier transform infrared spectroscopy (FTIR). Results: The particle size, PDI, EE %, and DL % were in the range of 15.6–145.6 nm, 0.010-0.120, 55.9 %-91.9 %, and 6.68-36.76 %, respectively. The saturated solubility of nanosuspensions-loaded VRZ (NS-VRZ) relative to free VRZ was increased tenfold in DW and twelvefold in PBS (pH 7.4). DSC thermogram confirmed the incorporation of VRZ in the nanosuspensions. The AFM of NS-VRZ validated spherical tiny particle size with a smooth surface. There is no chemical interaction between VRZ and the polymers, according to an FTIR investigation. Conclusion: The solubility of VRZ was successfully enhanced by loading to nanosuspensions. The solvent/anti-solvent technique was proven to be cost-effective, easy to operate and suitable for the preparation of NS-VRZ using Design-Expert®software.
Mixed Integer Linear Programming for Exact Finite-Horizon Planning in Decentralized Pomdps

We consider the problem of finding an n-agent joint-policy for the optimal finite-horizon control of a decentralized Pomdp (Dec-Pomdp). This is a problem of very high complexity (NEXP-hard in n ≥ 2). In this paper, we propose a new mathematical programming approach for the problem. Our approach is based on two ideas: First, we represent each agent's policy in the sequence-form and not in the tree-form, thereby obtaining a very compact representation of the set of joint-policies. Second, using this compact representation, we solve this problem as an instance of combinatorial optimization for which we formulate a mixed integer linear program (MILP). The optimal solution of the MILP directly yields an optimal joint-policy for the Dec-Pomdp. Computational experience shows that formulating and solving the MILP requires significantly less time to solve benchmark Dec-Pomdp problems than existing algorithms. For example, the multi-agent tiger problem for horizon 4 is solved in 72 secs with the MILP whereas existing algorithms require several hours to solve it.
JIRiSS - an Eclipse plug-in for Source Code Exploration

JIRiSS (information retrieval based software search for Java) is a software exploration tool that uses an indexing engine based on an information retrieval method. JIRiSS is implemented as a plug-in for Eclipse and it allows the user to search Java source code for the implementation of concepts formulated as natural language queries. The results of the query are presented as a ranked list of software methods or classes, ordered by the similarity to the user query. In addition to that, JIRiSS includes other advanced features like automatically generated software vocabulary, advanced query formulation options including spell-checking as well as fragment-based search
Evolutionary design of artificial neural networks with different nodes

Evolutionary design of artificial neural networks (ANNs) offers a very promising and automatic alternative to designing ANNs manually. The advantage of evolutionary design over the manual design is their adaptability to a dynamic environment. Most research in evolving ANNs only deals with the topological structure of ANNs and little has been done on the evolution of both topological structures and node transfer functions. The paper presents a new automatic method to design general neural networks (GNNs) with different nodes. GNNs combine generalisation capabilities of distributed neural networks (DNNs) and computational efficiency of local neural networks (LNNs). We use an evolutionary programming (EP) algorithm with new mutation operators which are very effective for evolving GNN architectures and weights simultaneously. Our EP algorithm allows GNNs to grow as well as shrink during the evolutionary process. Our experiment results show the effectiveness and accuracy of evolved GNNs.
The Generalized Area Theorem and Some of its Consequences

There is a fundamental relationship between belief propagation (BP) and maximum a posteriori decoding. The case of transmission over the binary erasure channel was investigated in detail in a companion paper (C. MEacuteasson, A. Montanari, and R. Urbanke, "Maxwell's construction: The hidden bridge between iterative and maximum a posteriori decoding," IEEE Transactions on Information Theory, submitted for publication). This paper investigates the extension to general memoryless channels (paying special attention to the binary case). An area theorem for transmission over general memoryless channels is introduced and some of its many consequences are discussed. We show that this area theorem gives rise to an upper bound on the maximum a posteriori threshold for sparse graph codes. In situations where this bound is tight, the extrinsic soft bit estimates delivered by the BP decoder coincide with the correct a posteriori probabilities above the maximum a posteriori threshold. More generally, it is conjectured that the fundamental relationship between the maximum a posteriori probability (MAP) and the BP decoder which was observed for transmission over the binary erasure channel carries over to the general case. We finally demonstrate that in order for the design rate of an ensemble to approach the capacity under BP decoding the component codes have to be perfectly matched, a statement which is well known for the special case of transmission over the binary erasure channel.