Miracle-dz/UniFAR-FTUs huggingface dataset:
Inspection Points and Meta-Abduction in Logic Programs

In the context of abduction in Logic Programs, when finding an abductive solution for a query, one may want to check too whether some other literals become true (or false) as a consequence, strictly within the abductive solution found, that is without performing additional abductions, and without having to produce a complete model to do so. That is, such consequence literals may consume, but not produce, the abduced literals of the solution. We show how this type of reasoning requires a new mechanism, not provided by others already available. To achieve it, we present the concept of Inspection Point in Abductive Logic Programs, and show, by means of examples, how one can employ it to investigate side-effects of interest (the inspection points) in order to help choose among abductive solutions. The touchstone of enabling inspection points can be construed as meta-abduction, by (meta-)abducing an “abduction" to check (i.e. to passively verify) that a certain concrete abduction is indeed adopted in a purported abductive solution. We show how to implement inspection points on top of already existing abduction solving systems — ABDUAL and XSB-XASP — in a way that can be adopted by other systems too.
Dynamic Gap Selector: A Smith Waterman Sequence Alignment Algorithm with Affine Gap Model Optimization

Smith Waterman algorithm (S-W) is nowadays considered one of the best method to perform local alignments of biological sequences characterizing proteins, DNA and RNA molecules. Indeed, S-W is able to ensure better accuracy levels with respect to the heuristic alignment algorithms by extensively exploring all the possible alignment configurations between the sequences under examination. It has been proven that the first amino acid (AA) or nucleotide (NT) inserted/deleted (that identify a gap open) found during the alignment operations performed on sequences is more significant from a biological point of view than the subsequent ones (called gap extension), making the so called Affine Gap model a viable solution for biomolecules alignment. However, this version of S-W algorithm is expensive both in terms of computation as well as in terms of memory requirements with respect to others less demanding solutions such as the ones using a Linear Gap model. In order to overcome these drawbacks we have developed an optimised version of the S-Walgorithm based on Affine Gap model called Dynamic Gap Selector (DGS S-W). Differently from the standard S-W Affine Gap method, the proposed DGS S-W method reduces the memory requirements from 3*N*M to N*M where N and M represents the size of the compared sequences. In terms of computational costs, the proposed algorithm reduces by a factor of 2 the number of operations required by the standard Affine Gap model. DGS S-W method has been tested on two protein and one RNA sequences datasets, showing mapping scores very similar to those reached thanks to the classical S-W Affine Gap method and, at the same time, reduced computational costs and memory usage
Optimal Tiling Algorithms for Inter-frame Video Compression

A shearing machine for metal sheets having a pair of upper mobile and lower stationary cooperating blades, each blade having two segments mounted perpendicular to each other. The two segments at right angles of the mobile blade are movable independently either with respect to each other or with respect to the common support so that, in turn each segment is able to execute cut strokes of different lengths in order selectively to engage one and/or the other of the segments. Each of them is able to execute cuts longer than its own longitudinal extension.
A Trace-Based Framework for supporting Digital Object Memories

In this paper, we present a Trace Based framework for managing and transforming traces of observation and use of real life objects. Considering trace based systems as Digital Object Memories (DOMe), we describe how our frame-work can be used to manage DOMe, using trace models and transformations to raise the abstraction level of traces and infer useful knowledge. To demonstrate our approach, we present a simple example of smart home where the use and the state of some of everyday objects are observed, and the resulting traces of such observations are exploited as DOMe providing useful services.
Overexpression of the LexA-regulated tisAB RNA in E. coli inhibits SOS functions; implications for regulation of the SOS response

The DNA damage induced SOS response in Escherichia coli is initiated by cleavage of the LexA repressor through activation of RecA. Here we demonstrate that overexpression of the SOS-inducible tisAB gene inhibits several SOS functions in vivo. Wild-type E. coli overexpressing tisAB showed the same UV sensitivity as a lexA mutant carrying a noncleavable version of the LexA protein unable to induce the SOS response. Immunoblotting confirmed that tisAB overexpression leads to higher levels of LexA repressor and northern experiments demonstrated delayed and reduced induction of recA mRNA. In addition, induction of prophage λ and UV-induced filamentation was inhibited by tisAB overexpression. The tisAB gene contains antisense sequences to the SOS-inducible dinD gene (16 nt) and the uxaA gene (20 nt), the latter encoding a dehydratase essential for galacturonate catabolism. Cleavage of uxaA mRNA at the antisense sequence was dependent on tisAB RNA expression. We showed that overexpression of tisAB is less able to confer UV sensitivity to the uxaA dinD double mutant as compared to wild-type, indicating that the dinD and uxaA transcripts modulate the anti-SOS response of tisAB. These data shed new light on the complexity of SOS regulation in which the uxaA gene could link sugar metabolism to the SOS response via antisense regulation of the tisAB gene.
PENGEMBANGAN MODEL MANAJEMEN PELATIHAN DAN PENGEMBANGAN PENDIDIKAN KARAKTER BERLOKUS PADEPOKAN KARAKTER

This research develops a model of characteristic training and character education management to produce academic, profession, and vocational (potential teacher and teacher) teachers who have superior competence in disseminating noble character of the nation. By using Researh & Development approach, this research uses Padepokan Character locus PPKn FIS Unnes Department. The subjects of the study were the newly graduated Unnes students, the young teachers in Semarang City and Central Java Province. The factual model of training management and the development of character education that runs so far contains weaknesses in various aspects, from planning, implementation, to evaluation of activities. On the basis of the analysis of a number of weaknesses of the factual model is then carried out the reconstruction of the conceptual model departs from the review of ethical factors and emotic factors. Conceptual model is then refined again by accommodating the results of focus group discussion with the stake holders and utilize the potential and resources owned by the character padepokan Unnes Political Education and Citizenship. This model has incorporated various aspects of the training, from the refinement of the factual model greed, the consideration of ethical factors and the emotional factors, as well as the aspirations and needs of the stake holders, so it is feasible to experiment in the wider domain.
A First-Order Nonhomogeneous Markov Model for the Response of Spiking Neurons Stimulated by Small Phase-Continuous Signals

We present a first-order nonhomogeneous Markov model for the interspike-interval density of a continuously stimulated spiking neuron. The model allows the conditional interspike-interval density and the stationary interspike-interval density to be expressed as products of two separate functions, one of which describes only the neuron characteristics and the other of which describes only the signal characteristics. The approximation shows particularly clearly that signal autocorrelations and cross-correlations arise as natural features of the interspike-interval density and are particularly clear for small signals and moderate noise. We show that this model simplifies the design of spiking neuron cross-correlation systems and describe a four-neuron mutual inhibition network that generates a cross-correlation output for two input signals.
Study of Static Classification of Social Spam Profiles in MySpace

Reaching hundreds of millions of users, major social networks have become important target media for spammers. Although practical techniques such as collaborative filters and behavioral analysis are able to reduce spam, they have an inherent lag (to collect sufficient data on the spammer) that also limits their effectiveness. Through an experimental study of over 1.9 million MySpace profiles, we make a case for analysis of static user profile content, possibly as soon as such profiles are created. We compare several machine learning algorithms in their ability to distinguish spam profiles from legitimate profiles. We found that a C4.5 decision tree algorithm achieves the highest accuracy (99.4%) of finding rogue profiles, while naïve Bayes achieves a lower accuracy (92.6%). We also conducted a sensitivity analysis of the algorithms w.r. t. features which may be easily removed by spammers.
Automatic Bass Line Transcription from Streaming Polyphonic Audio

This paper proposes a method for the automatic transcription of the bass line in polyphonic music. The method uses a multiple-FO estimator as a front-end and this is followed by acoustic and musicological models. The acoustic modeling consists of separate models for bass notes and rests. The musicological model estimates the key and determines probabilities for the transitions between notes using a conventional bigram or a variable-order Markov model. The transcription is obtained with Viterbi decoding through the note and rest models. In addition, a causal algorithm is presented which allows transcription of streaming audio. The method was evaluated using 87 minutes of music from the RWC Popular Music Database. Recall and precision rates of 64% and 60%, respectively, were achieved for discrete note events.
Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences

With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems in computational biology is how to effectively formulate the sequence of a biological sample (such as DNA, RNA or protein) with a discrete model or a vector that can effectively reflect its sequence pattern information or capture its key features concerned. Although several web servers and stand-alone tools were developed to address this problem, all these tools, however, can only handle one type of samples. Furthermore, the number of their built-in properties is limited, and hence it is often difficult for users to formulate the biological sequences according to their desired features or properties. In this article, with a much larger number of built-in properties, we are to propose a much more flexible web server called Pse-in-One (http://bioinformatics.hitsz.edu.cn/Pse-in-One/), which can, through its 28 different modes, generate nearly all the possible feature vectors for DNA, RNA and protein sequences. Particularly, it can also generate those feature vectors with the properties defined by users themselves. These feature vectors can be easily combined with machine-learning algorithms to develop computational predictors and analysis methods for various tasks in bioinformatics and system biology. It is anticipated that the Pse-in-One web server will become a very useful tool in computational proteomics, genomics, as well as biological sequence analysis. Moreover, to maximize users’ convenience, its stand-alone version can also be downloaded from http://bioinformatics.hitsz. edu.cn/Pse-in-One/download/, and directly run on Windows, Linux, Unix and Mac OS.
Economic Theory and Statistical Learning

This dissertation presents three independent essays in microeconomic theory. Chapter 1 suggests an alternative to the common prior assumption, in which agents form beliefs by learning from data, possibly interpreting the data in different ways. In the limit as agents observe increasing quantities of data, the model returns strict solutions of a limiting complete information game, but predictions may diverge substantially for small quantities of data. Chapter 2 (with Jon Kleinberg and Sendhil Mullainathan) proposes use of machine learning algorithms to construct benchmarks for “achievable" predictive accuracy. The paper illustrates this approach for the problem of predicting human-generated random sequences. We find that leading models explain approximately 10-15% of predictable variation in the problem. Chapter 3 considers the problem of how to interpret inconsistent choice data, when the observed departures from the standard model (perfect maximization of a single preference) may emerge either from context-dependencies in preference or from stochastic choice error. I show that if preferences are “simple" in the sense that they consist only of a small number of context-dependencies, then the analyst can use a proposed optimization problem to recover the true number of underlying context-dependent preferences.
Decomposing the Web Graph into Parameterized Connected Components

SUMMARY We propose a novel method for Web page grouping based only on hyperlink information. Because of the explosive growth of the World Wide Web, page grouping is expected to provide a general grasp of the Web for effective information search and netsurfing. The Web can be regarded as a gigantic digraph where pages are vertices and links are arcs. Our grouping method is a generalization of decomposition into strongly connected components, in which each group is constructed as a subset of a strongly connected component. Moreover, group sizes can be controlled by adjusting a parameter, called the threshold parameter. We call the resulting groups parameterized connected components (PCCs). The algorithm is simple and admits parallelization. Notably, we apply Dijkstra’s shortest path algorithm in our grouping method. This paper also includes experimental results for 15 million Web pages, which show the contribution of our method to efficient Web surfer navigation. 1.1 Purposes In this paper, we propose a novel method for Web page grouping based only on hyperlink information. We also present the results of evaluative experiments, which show how the grouping method using only hyperlink information can contribute to improving the quality of Web surfer navigation. Because of the explosive growth of the World Wide Web, a general grasp of the Web provided by page grouping helps to gather information suiting users’ interests. Therefore, we invent a new grouping method as an effective subsystem which we can integrate with systems for Web search or Web surfer navigation. We assume that grouping is expected to serve two purposes.
Doa Estimation for Multiple Sparse Sources with Normalized Observation Vector Clustering

This paper presents a new method for estimating the direction of arrival (DOA) of source signals whose number N can exceed the number of sensors M. Subspace based methods, e.g., the MUSIC algorithm, have been widely studied, however, they are only applicable when M > N. Another conventional independent component analysis based method allows M ges N, however, it cannot be applied when M < N. By contrast, our new method can be applied where the sources outnumber the sensors (i.e., an underdetermined case M < N) by assuming source sparseness. Our method can cope with 2- or 3-dimensionally distributed sources with a 2- or 3-dimensional sensor array. We obtained promising experimental results for 3 times 4, 3 times 5 and 4 times 5 (#sensors times #speech sources) in a room (RT60= 120 ms)
Molecular Signature of Cancer at Gene Level or Pathway Level? Case Studies of Colorectal Cancer and Prostate Cancer Microarray Data

With recent advances in microarray technology, there has been a flourish in genome-scale identification of molecular signatures for cancer. However, the differentially expressed genes obtained by different laboratories are highly divergent. The present discrepancy at gene level indicates a need for a novel strategy to obtain more robust signatures for cancer. In this paper we hypothesize that (1) the expression signatures of different cancer microarray datasets are more similar at pathway level than at gene level; (2) the comparability of the cancer molecular mechanisms of different individuals is related to their genetic similarities. In support of the hypotheses, we summarized theoretical and experimental evidences, and conducted case studies on colorectal and prostate cancer microarray datasets. Based on the above assumption, we propose that reliable cancer signatures should be investigated in the context of biological pathways, within a cohort of genetically homogeneous population. It is hoped that the hypotheses can guide future research in cancer mechanism and signature discovery.
Home advantage in Home Nations, Five Nations and Six Nations rugby tournaments (1883-2011)

This study evaluates the home advantage (percentage of points obtained playing at home) in Home Nations (HN), Five Nations (FN) and Six Nations (SN) tournaments between 1883 and 2011 obtaining a global significant value (p<0.001) of 60.99±1.57. The home advantage (HA) was calculated during different periods of the competitions taking into account the tournaments and teams (between 4 and 6 participants, depending on the competition). The HA is significant in all the periods, except in the HN 1932-1999. The evolution of the values of the HA is irregular until the professionalism of rugby (1995) a moment from which the values stabilize. The comparison in the HA between the different types of competitions didn’t find any significant values. When the HA was compared between the teams significant differences were found (p<0.001) at a global level, in FN and SN tournaments. The classification of a team (-0.720) and the number of points won (0.729) has a significant association with the HA.
Learning Bayesian Belief Networks Based on the MDL Principle : An Efficient Algorithm Using the Branch and Bound Technique

In this paper, the computational issue in the problem of learning Bayesian belief networks (BBNs) based on the minimum description length (MDL) principle is addressed. Based on an asymptotic formula of description length, we apply the branch and bound technique to finding true network structures. The resulting algorithm searches considerably saves the computation yet successfully searches the network structure with the minimum value of the formula. Thus far, there has been no search algorithm that finds the optimal solution for examples of practical size and a set of network structures in the sense of the maximum posterior probability, and heuristic searches such as K2 and K3 trap in local optima due to the greedy nature even when the sample size is large. The proposed algorithm, since it minimizes the description length, eventually selects the true network structure as the sample size goes to infinity. key words: Bayesian belief networks, minimum description length (MDL) principle, branch and bound technique, Cooper and Herskovits procedure, MDL-based procedure, K2, K3
An internal model approach to (optimal) frequency regulation in power grids

The power grid can be regarded as a large interconnected network of different subsystems, called control area’s. In order to guarantee reliable operation the frequency is tightly regulated around its nominal value, e.g. 50 Hz. Automatic regulation of the frequency in power grid is traditionally achieved by primary proportional control (droop-control) and a secondary PI-control on the generators, where economic considerations are largely neglected. Recently we proposed a novel distributed controller in [1] that controls power production such that the frequency is regulated in an economic efficient way. Main advantage of our approach, based on passivity properties of the system, is that it has the potential to deal with more complex models of dynamical networks and fairly rich classes of external perturbations.
Declaratively codifying software architectures using virtual software classifications

Most current day software engineering tools and environments do not sufficiently support software engineers to declare or to enforce the intended software architecture. Architectures are typically described at a too low level, inhibiting their evolution and understanding. Furthermore, most tools provide little support to verify automatically whether the source code conforms to the architecture. Therefore, a formalism is needed in which architectures can be expressed at a sufficiently abstract level, without losing the ability to perform conformance checking automatically. We propose to codify declaratively software architectures using virtual software classifications and relationships among these classifications. We illustrate how software architectures can be expressed elegantly in terms of these virtual classifications and how to keep them synchronized with the source code.
Generation, visualization, and editing of 3D video

3D video is the ultimate image medium recording dynamic visual events in the real world as is. Recorded object behaviors can be observed from any viewpoint, because 3D video records the object's full 3D shape, motion, and precise surface properties (i.e. color and texture). In our last paper, we presented a method of reconstructing dynamic 3D object shape from multi-view video images, by which a temporal series of 3D voxel representations of the object behavior can be obtained in real-time. In this paper, following an overview of the real-time 3D shape reconstruction method, we present: 1) an algorithm of generating texture on the 3D object surface from the multi-view video images, and 2) an editing system for visualizing 3D video with an omnidirectional background image using versatile 3D camera works. This paper mainly discusses how we can generate high fidelity object images from arbitrary viewpoints based on the 3D object shape of limited accuracy. We propose a novel texture mapping algorithm which maps textures onto the 3D object surface depending on a viewpoint. Experimental results demonstrate its effectiveness in generating high fidelity object images from arbitrary viewpoints.
Database-driven GUI Programming

In a recent SIGMOD paper, Goyal et al. [7] asked whether GUI programming is a database research problem and answered it in the affirmative giving a solution based on logical programming. Here, we argue similarly in that GUI-related data should be removed from program code altogether and be placed into tables. To achieve the goal of shorter and more transparent GUI programs, we indicate how suitable schemas and methods implemented as scripts may be defined within an object-relational database system. At the core of this design is a navigational extension of Tcl which has a direct match to the cursor based interaction style of our visual database system.