Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Mining Methodological Weaknesses and Suggested Fixes

Predictive accuracy claims should give explicit descriptions of the steps followed, with access to the code used. This allows referees and readers to check for common traps, and to repeat the same steps on other data. Feature selection and/or model selection and/or tuning must be independent of the test data. For use of cross-validation, such steps must be repeated at each fold. Even then, such accuracy assessments have the limitation that the target population, to which results will be applied, is commonly different from the source population. Commonly, it is shifted forward in time, and it may differ in other respects also. A consequence of source/target differences is that highly sophisticated modeling may be pointless or even counter-productive. At best, model effects in the target population may be broadly similar. Investigation of the pattern of changes over time is required. Such studies are unusual in the data mining literature, in part because relevant data have not been available. Several recent investigations are noted that shed interesting light on the comparison between observational and experimental studies, with particular relevance when there is an interest in giving parameter estimates a causal interpretation. Data mining activity would benefit from wider co-operation in the development and deployment of computing tools, and from better integration of those tools into the publication process.
Regression Discontinuity Designs With Multiple Rating-Score Variables

Abstract In the absence of a randomized control trial, regression discontinuity (RD) designs can produce plausible estimates of the treatment effect on an outcome for individuals near a cutoff score. In the standard RD design, individuals with rating scores higher than some exogenously determined cutoff score are assigned to one treatment condition; those with rating scores below the cutoff score are assigned to an alternate treatment condition. Many education policies, however, assign treatment status on the basis of more than one rating-score dimension. We refer to this class of RD designs as “multiple rating score regression discontinuity” (MRSRD) designs. In this paper, we discuss five different approaches to estimating treatment effects using MRSRD designs (response surface RD; frontier RD; fuzzy frontier RD; distance-based RD; and binding-score RD). We discuss differences among them in terms of their estimands, applications, statistical power, and potential extensions for studying heterogeneity of treatment effects.
Text-independent cross-language voice conversion

So far, cross-language voice conversion requires at least one bilingual speaker and parallel speech data to perform the training. This paper shows how these obstacles can be overcome by means of a recently presented text-independent training method based on unit selection. The new method is evaluated in the framework of the European speech-to-speech translation project TC-Star and achieves a performance similar to that of text-dependent intralingual voice conversion.
Arbitrated IP Core Based Dynamic Task Mapping Algorithm for Networks-on-Chip

Mapping of the tasks onto the core is the main aim of the paper. So there is need to design such an algorithm that the tasks should not wait in the queue for long time. Our main focus is to design an algorithm that should concentrate on the load balancing, such that the problem of overheating of the cores is reduced. For this purpose different researchers have given different mapping algorithms. It would be the best solution if the designed algorithm could balance the load dynamically over the grid of cores. In this paper, we have mapped the tasks onto the cores such that performance of the network improves along with balanced load distribution. In experimental results, the proposed AICM approach is compared with sequential mapping considering 5X5 mesh topology.
In vitro activity of methanolic extract from Lagerstroemia speciosa (Linn. ex. Murray) bark against pathogenic bacteria

Crude methanolic extract of Lagerstroemia speciosa barks (Linn. ex. Murray) was subjected to antimicrobial screening including six Gram-positive and eight Gram-negative bacteria. The extract demonstrated significance antibacterial activities on 5 tested bacteria with the inhibition zone ranging from 10-15 mm. The minimum inhibitory concentration (MIC) of the extract was assessed by microdilution method. MIC value of Bacillus spizizenii ATCC6633, Bacillus cereus and Streptococcus coagulase-negative (SCN) was 0.25 mg/ml whilst Bacillus licheniformis and Acinetobacter anitratus showed MIC value of 0.50 mg/ml. However, only B. spizizenii ATCC6633 and B. licheniformis showed the minimum bactericidal concentration (MBC) value of 2.00 mg/ml and 1.00 mg/ml, respectively. The morphological changes of B.spizizenii ATCC6633 and A. anitratus with the treatment of the extract were observed under Scanning Electron Microscope (SEM). The results suggested that the L. speciosa methanolic extract had caused membrane structural degenerations of the cells and finally leading to cell-lysis.
Backward-Bounded DSE: Targeting Infeasibility Questions on Obfuscated Codes

Software deobfuscation is a crucial activity in security analysis and especially in malware analysis. While standard static and dynamic approaches suffer from well-known shortcomings, Dynamic Symbolic Execution (DSE) has recently been proposed as an interesting alternative, more robust than staticanalysis and more complete than dynamic analysis. Yet, DSE addresses only certain kinds of questions encountered by a reverser, namely feasibility questions. Many issues arising during reverse, e.g., detecting protection schemes such as opaque predicates, fall into the category of infeasibility questions. We present Backward-Bounded DSE, a generic, precise, efficient and robust method for solving infeasibility questions. We demonstrate the benefit of the method for opaque predicates and call stack tampering, and give some insight for its usage for some other protection schemes. Especially, the technique has successfully been used on state-of-the-art packers as well as on the government-grade X-Tunnel malware – allowing its entire deobfuscation. Backward-Bounded DSE does not supersede existing DSE approaches, but rather complements them by addressing infeasibility questions in a scalable and precise manner. Following this line, we proposesparse disassembly, a combination of Backward-Bounded DSE and static disassembly able to enlarge dynamic disassembly in a guaranteed way, hence getting the best of dynamic and static disassembly. This work paves the way for robust, efficient and precise disassembly tools for heavily-obfuscated binaries.
Curcumin-loaded lipid nanocarrier for improving bioavailability, stability and cytotoxicity against malignant glioma cells

Abstract Objective: In the present study, Curcumin (CU)-loaded nanocarrier (NC) such as nanoemulsion (NE) was developed with the objective of increasing its cytotoxicity and bioavailability through lymphatic transport by enhancing its solubility and intestinal permeability. Materials and methods: Based on the area obtained in pseudoternary phase diagram, various % combination of Labrafac Lipophile WL 1349, Solutol HS 15, Transcutol HP and distilled water were selected. Formulations which passed physical stability studies were selected for further studies such as globule size, zeta potential, in vitro release, ex vivo permeation, in vitro lipolysis studies, bioavailability studies and cytotoxicity against glioblastoma cells (U-87). Result and discussion: The optimized NC (NE-SB1) had small average globule diameter of 67 ± 6 nm with zeta potential of −37 ± 2.5 mv which indicated long-term dispersion stability. During in vitro lipolysis study, the digestion rate of medium chain triglycerides increased with decreased globule diameter. Statistically significant difference was found in AUC0−inf of NC formulation (p < 0.05) compared to CU suspension. The relative bioavailability of NC was found 11.88 ± 0.47 with respect to CU suspension. During cytotoxicity studies, IC50 of CU solution on U87 cells was found 24.23 µM, while for the NE- SB1 it was 16.41 µM. The optimized formulation was found to be stable during 6 months of accelerated stability. Conclusion: The overall results revealed that the CU-loaded NC is a very effective approach for enhancing the oral absorption of poorly water-soluble drug CU and have great potential for future clinical application.
Ancient Gene Duplicates in Gossypium (Cotton) Exhibit Near-Complete Expression Divergence

Whole genome duplication (WGD) is widespread in flowering plants and is a driving force in angiosperm diversification. The redundancy introduced by WGD allows the evolution of novel gene interactions and functions, although the patterns and processes of diversification are poorly understood. We identified ∼2,000 pairs of paralogous genes in Gossypium raimondii (cotton) resulting from an approximately 60 My old 5- to 6-fold ploidy increase. Gene expression analyses revealed that, in G. raimondii, 99.4% of the gene pairs exhibit differential expression in at least one of the three tissues (petal, leaf, and seed), with 93% to 94% exhibiting differential expression on a per-tissue basis. For 1,666 (85%) pairs, differential expression was observed in all tissues. These observations were mirrored in a time series of G. raimondii seed, and separately in leaf, petal, and seed of G. arboreum, indicating expression level diversification before species divergence. A generalized linear model revealed 92.4% of the paralog pairs exhibited expression divergence, with most exhibiting significant gene and tissue interactions indicating complementary expression patterns in different tissues. These data indicate massive, near-complete expression level neo- and/or subfunctionalization among ancient gene duplicates, suggesting these processes are essential in their maintenance over ∼60 Ma.
Amino Acid Prodrugs: An Approach to Improve the Absorption of HIV-1 Protease Inhibitor, Lopinavir

Poor systemic concentrations of lopinavir (LPV) following oral administration occur due to high cellular efflux by P-glycoprotein (P-gp) and multidrug resistance-associated proteins (MRPs) and extensive metabolism by CYP3A4 enzymes. In this study, amino acid prodrugs of LPV were designed and investigated for their potential to circumvent efflux processes and first pass effects. Three amino acid prodrugs were synthesized by conjugating isoleucine, tryptophan and methionine to LPV. Prodrug formation was confirmed by the LCMS/MS and NMR technique. Interaction of LPV prodrugs with efflux proteins were carried out in P-gp (MDCK-MDR1) and MRP2 (MDCK-MRP2) transfected cells. Aqueous solubility studies demonstrated that prodrugs generate higher solubility relative to LPV. Prodrugs displayed higher stability under acidic conditions and degraded significantly with rise in pH. Uptake and transport data suggested that prodrugs carry significantly lower affinity towards P-gp and MRP2 relative to LPV. Moreover, prodrugs exhibited higher liver microsomal stability relative to LPV. Hence, amino acid prodrug modification might be a viable approach for enhancing LPV absorption across intestinal epithelial and brain endothelial cells which expresses high levels of P-gp and MRP2.
Multigrid solution of the Navier-Stokes equations on triangular meshes

A new Navier-Stokes algorithm for use on unstructured triangular meshes is presented. Spatial discretization of the governing equations is achieved using a finite-element Galerkin approximation, which can be shown to be equivalent to a finite-volume approximation for regular equilateral triangular meshes. Integration to steady state is performed using a multistage time-stepping scheme, and convergence is accelerated by means of implicit residual smoothing and an unstructured multigrid algorithm. Directional scaling of the artificial dissipation and the implicit residual smoothing operator is achieved for unstructured meshes by considering local mesh stretching vectors at each point. The accuracy of the scheme for highly stretched triangular meshes is validated by comparing computed flat-plate laminar boundary-layer results with the well known similarity solution and by comparing laminar airfoil results with those obtained from various well established structured, quadrilateralmesh codes. The convergence efficiency of the present method is also shown to be competitive with those demonstrated by structured quadrilateral-mesh algorithms.
Mining Projected Clusters in High-Dimensional Spaces

Clustering high-dimensional data has been a major challenge due to the inherent sparsity of the points. Most existing clustering algorithms become substantially inefficient if the required similarity measure is computed between data points in the full-dimensional space. To address this problem, a number of projected clustering algorithms have been proposed. However, most of them encounter difficulties when clusters hide in subspaces with very low dimensionality. These challenges motivate our effort to propose a robust partitional distance-based projected clustering algorithm. The algorithm consists of three phases. The first phase performs attribute relevance analysis by detecting dense and sparse regions and their location in each attribute. Starting from the results of the first phase, the goal of the second phase is to eliminate outliers, while the third phase aims to discover clusters in different subspaces. The clustering process is based on the k-means algorithm, with the computation of distance restricted to subsets of attributes where object values are dense. Our algorithm is capable of detecting projected clusters of low dimensionality embedded in a high-dimensional space and avoids the computation of the distance in the full-dimensional space. The suitability of our proposal has been demonstrated through an empirical study using synthetic and real datasets.
Di¤usion, Strategic Interaction, and Social Structure

We provide an overview and synthesis of the literature on how social networks in ‡uence behaviors, with a focus on di¤usion. We discuss some highlights from the empirical literature on the impact of networks on behaviors and di¤usion. We also discuss some of the more prominent models of network interactions, including recent advances regarding interdependent behaviors, modeled via games on networks.
A Survey on Various Approach used in Named Entity Recognition for Indian Languages

Named Entity Recognition (NER) is an application of Natural Language Processing (NLP). NER is a activity of Information Extraction. NER is a task used for automated text processing for various industries, key concept for academics, artificial intelligence, robotics, Bioinformatics and many more. NER is always essential when dealing with chief NLP activity such as machine translation, question-answering, document summarization etc. Most NER work has been done for other European languages. Among Indian constitutional languages, NER work has been done for few languages. Not enough work is possible due to some challenges such as lack of resources, ambiguity in language, morphologically rich and many more. In this paper, we found many challenges available in NER for Indian languages and compared by measuring standard evaluation metrics values of accuracy, precision, recall and F-measure.
Markov Dynamics as a Zooming Lens for Multiscale Community Detection: Non Clique-Like Communities and the Field-of-View Limit

In recent years, there has been a surge of interest in community detection algorithms for complex networks. A variety of computational heuristics, some with a long history, have been proposed for the identification of communities or, alternatively, of good graph partitions. In most cases, the algorithms maximize a particular objective function, thereby finding the ‘right’ split into communities. Although a thorough comparison of algorithms is still lacking, there has been an effort to design benchmarks, i.e., random graph models with known community structure against which algorithms can be evaluated. However, popular community detection methods and benchmarks normally assume an implicit notion of community based on clique-like subgraphs, a form of community structure that is not always characteristic of real networks. Specifically, networks that emerge from geometric constraints can have natural non clique-like substructures with large effective diameters, which can be interpreted as long-range communities. In this work, we show that long-range communities escape detection by popular methods, which are blinded by a restricted ‘field-of-view’ limit, an intrinsic upper scale on the communities they can detect. The field-of-view limit means that long-range communities tend to be overpartitioned. We show how by adopting a dynamical perspective towards community detection [1], [2], in which the evolution of a Markov process on the graph is used as a zooming lens over the structure of the network at all scales, one can detect both clique- or non clique-like communities without imposing an upper scale to the detection. Consequently, the performance of algorithms on inherently low-diameter, clique-like benchmarks may not always be indicative of equally good results in real networks with local, sparser connectivity. We illustrate our ideas with constructive examples and through the analysis of real-world networks from imaging, protein structures and the power grid, where a multiscale structure of non clique-like communities is revealed.
Information-Driven Adaptive Structured-Light Scanners

Sensor planning and active sensing, long studied in robotics, adapt sensor parameters to maximize a utility function while constraining resource expenditures. Here we consider information gain as the utility function. While these concepts are often used to reason about 3D sensors, these are usually treated as a predefined, black-box, component. In this paper we show how the same principles can be used as part of the 3D sensor. We describe the relevant generative model for structured-light 3D scanning and show how adaptive pattern selection can maximize information gain in an open-loop-feedback manner. We then demonstrate how different choices of relevant variable sets (corresponding to the subproblems of locatization and mapping) lead to different criteria for pattern selection and can be computed in an online fashion. We show results for both subproblems with several pattern dictionary choices and demonstrate their usefulness for pose estimation and depth acquisition.
Computational allergenicity prediction of transgenic proteins expressed in genetically modified crops

Development of genetically modified (GM) crops is on increase to improve food quality, increase harvest yields, and reduce the dependency on chemical pesticides. Before their release in marketplace, they should be scrutinized for their safety. Several guidelines of different regulatory agencies like ILSI, WHO Codex, OECD, and so on for allergenicity evaluation of transgenics are available and sequence homology analysis is the first test to determine the allergenic potential of inserted proteins. Therefore, to test and validate, 312 allergenic, 100 non-allergenic, and 48 inserted proteins were assessed for sequence similarity using 8-mer, 80-mer, and full FASTA search. On performing sequence homology studies, ~94% the allergenic proteins gave exact matches for 8-mer and 80-mer homology. However, 20 allergenic proteins showed non-allergenic behavior. Out of 100 non-allergenic proteins, seven qualified as allergens. None of the inserted proteins demonstrated allergenic behavior. In order to improve the predictability, proteins showing anomalous behavior were tested by Algpred and ADFS separately. Use of Algpred and ADFS softwares reduced the tendency of false prediction to a great extent (74–78%). In conclusion, routine sequence homology needs to be coupled with some other bioinformatic method like ADFS/Algpred to reduce false allergenicity prediction of novel proteins.
Dandelion

Bitcoin and other cryptocurrencies have surged in popularity over the last decade. Although Bitcoin does not claim to provide anonymity for its users, it enjoys a public perception of being a privacy preserving financial system. In reality, cryptocurrencies publish users' entire transaction histories in plaintext, albeit under a pseudonym; this is required for transaction validation. Therefore, if a user's pseudonym can be linked to their human identity, the privacy fallout can be significant. Recently, researchers have demonstrated deanonymization attacks that exploit weaknesses in the Bitcoin network's peer-to-peer (P2P) networking protocols. In particular, the P2P network currently forwards content in a structured way that allows observers to deanonymize users. In this work, we redesign the P2P network from first principles with the goal of providing strong, provable anonymity guarantees. We propose a simple networking policy called Dandelion which provides quasi-optimal, network-wide anonymity, with minimal cost to the network's utility. We also discuss practical implementation challenges and propose heuristic solutions.
Beyond Sights: Large Scale Study of Tourists' Behavior Using Foursquare Data

In this paper, we show how we can use Foursquare check-ins to understand the behavior of tourists that would be hard using traditional methods, such as surveys. For that, we analyze the behavior of tourists and residents in four popular cities around the world in four continents: London, New York, Rio de Janeiro, and Tokyo. We perform a spatio-temporal study of properties of the behavior of these two classes of users (tourists and residents). We have identified, for instance, that some locations have features that are more correlated with the tourists' behavior, and also that even in places frequented by tourists and residents there are clear distinction in the patterns of behavior of these groups of users. Our study also enables to identify which and when sights are popular. Our results could be useful in several cases, for example, to help in the development of new place recommendation systems for tourists, or to help city planners to better support tourists in their cities.
Macrophage apoptosis in rat skeletal muscle treated with bupivacaine hydrochloride: Possible role of MCP‐1

The fate of macrophages infiltrating damaged rat skeletal muscle fibers after intramuscular injection of the anesthetic bupivacaine hydrochloride (BPVC) and the possible roles of monocyte chemoattractant protein–1 (MCP‐1) were investigated. The number of macrophages reached a maximum level at 2 days after the injection and then gradually decreased. The number of apoptotic cells detected by terminal deoxynucleotidyl transferase–mediated dUTP nick‐end labeling (TUNEL) assay was elevated at 2–4 days and decreased thereafter. In serial sections, TUNEL‐positive cells were also immunopositive for RM‐4, an antibody specific for identification of macrophages. Electron microscopy further confirmed that it was the macrophages themselves that were undergoing apoptosis. As assessed by reverse transcriptase–polymerase chain reaction (RT‐PCR), high levels of MCP‐1 mRNA in BPVC‐treated muscles were observed and positively correlated with maximum macrophage infiltration. However, the levels of MCP‐1 mRNA returned to normal low values coincident with decrease of inflammation and healing of damaged muscle fiber. Local apoptosis of macrophages, under the control of MCP‐1, may be involved in healing of BPVC‐treated muscles. © 2002 Wiley Periodicals, Inc. Muscle Nerve 26: 79–86, 2002