Miracle-dz/UniFAR-FTUs huggingface dataset:
Dimension reduction in text document retrieval by Hebbian neural network and nonlinear activation functions

The paper deals with utilization of neural networks for information retrieval. It is focused on reduction of text document space by Hebbian neural networks. The Hebbian neural network with Oja learning rule with linear activation function reduces term space into much lower dimension and gives good results for text document dimension reduction and retrieval. The aim of this paper is to try to increase the retrieval evaluation by F-measure that applies different nonlinear activation functions to the output layer of network. Results show better F-measure when applying other nonlinear activation functions instead of applying classical linear activation function. The results were verified on the collection of 50 documents and 100 terms, where documents were clustered into five different clusters. For each dimension the Precision, Recall and F-measure were computed and the results were depicted graphically.
Image Encryption Based on the General Approach for Multiple Chaotic Systems

In the recent years, researchers developed image encryption methods based on chaotic systems. This paper proposed new image encryption technique based on new chaotic system by adding two chaotic systems: the Lorenz chaotic system and the Rossler chaotic system. The main advantage of this technique is stronger security, as is shown in the encryption tests.
Mutational Specificity of γ-Radiation-Induced Guanine−Thymine and Thymine−Guanine Intrastrand Cross-Links in Mammalian Cells and Translesion Synthesis Past the Guanine−Thymine Lesion by Human DNA Polymerase η

Comparative mutagenesis of γ- or X-ray-induced tandem DNA lesions G[8,5-Me]T and T[5-Me,8]G intrastrand cross-links was investigated in simian (COS-7) and human embryonic (293T) kidney cells. For G[8,5-Me]T in 293T cells, 5.8% of progeny contained targeted base substitutions, whereas 10.0% showed semitargeted single-base substitutions. Of the targeted mutations, the G → T mutation occurred with the highest frequency. The semitargeted mutations were detected up to two bases 5′ and three bases 3′ to the cross-link. The most prevalent semitargeted mutation was a C → T transition immediately 5′ to the G[8,5-Me]T cross-link. Frameshifts (4.6%) (mostly small deletions) and multiple-base substitutions (2.7%) also were detected. For the T[5-Me,8]G cross-link, a similar pattern of mutations was noted, but the mutational frequency was significantly higher than that of G[8,5-Me]T. Both targeted and semitargeted mutations occurred with a frequency of ∼16%, and both included a dominant G → T transversion. As in 293T cells, more than twice as many targeted mutations in COS cells occurred in T[5-Me,8]G (11.4%) as in G[8,5-Me]T (4.7%). Also, the level of semitargeted single-base substitutions 5′ to the lesion was increased and 3′ to the lesion decreased in T[5-Me,8]G relative to G[8,5-Me]T in COS cells. It appeared that the majority of the base substitutions at or near the cross-links resulted from incorporation of dAMP opposite the template base, in agreement with the so-called “A-rule”. To determine if human polymerase η (hpol η) might be involved in the mutagenic bypass, an in vitro bypass study of G[8,5-Me]T in the same sequence was carried out, which showed that hpol η can bypass the cross-link incorporating the correct dNMP opposite each cross-linked base. For G[8,5-Me]T, nucleotide incorporation by hpol η was significantly different from that by yeast pol η in that the latter was more error-prone opposite the cross-linked Gua. The incorporation of the correct nucleotide, dAMP, by hpol η opposite cross-linked T was 3−5-fold more efficient than that of a wrong nucleotide, whereas incorporation of dCMP opposite the cross-linked G was 10-fold more efficient than that with dTMP. Therefore, the nucleotide incorporation pattern by hpol η was not consistent with the observed cellular mutations. Nevertheless, at and near the lesion, hpol η was more error-prone compared to a control template. The in vitro data suggest that translesion synthesis by another Y-family DNA polymerase and/or flawed participation of an accessory protein is a more likely scenario in the mutagenesis of these lesions in mammalian cells. However, hpol η may play a role in correct bypass of the cross-links.
A novel cross layer TCP pacing protocol for multi-hop wireless networks

Some research indicates that the TCP congestion control mechanism may cause the burstiness of traffic flow. A group of TCP segments are delivered simultaneously, while an acknowledgement (ACK) of a retransmission is successfully received. This burstiness leads to a network with high level contention , which extremely increases the probability of packet loss on wireless networks. TCP pacing is one of the solutions for TCP burstiness on multi-hop network. The idea is to spread the TCP segment transmissions over the whole Round Trip Time (RTT). Most of the pacing protocols attempt to insert a delay interval into the TCP segment flows or ACK flows, without noticing a Cross Layer pacing algorithm on IEEE802.11 with deferring and backoff mechanism. In this paper, we measure and analyze the way contention window size (CW) affects TCP throughput in different scenarios; and, we propose a cross layer TCP pacing protocol by controlling the lower bound of contention window for the optimization of the overall TCP throughput on multi-hop topology. Finally, a comparison of simulations is presented in order to verify the improvement achieved by our protocol.
A 5.3mW, 2.4GHz ESD protected Low-Noise Amplifier in a 0.13μm RFCMOS technology

An Electrostatic Discharge (ESD) protected Low- Noise Amplifier (LNA) for the 2.4 GHz ISM band designed in a 0.13 mum standard RFCMOS technology is presented. The amplifier, including packaging effects, achieves 16.8 dB power gain, reflexion coefficients S11, S22 < -30 dB over the 2.4 GHz ISM band, a peak noise figure of 1.8 dB, and an IIP3 of 1 dBm, while drawing less than 4.5 mA dc biasing current from the 1.2 V power supply. Further, the LNA withstands a Human Body Model (HBM) ESD stress up to plusmn2.0 kV, by means of the additional custom protection circuitry.
An Efficient Hybrid Music Recommender System Using an Incrementally Trainable Probabilistic Generative Model

This paper presents a hybrid music recommender system that ranks musical pieces while efficiently maintaining collaborative and content-based data, i.e., rating scores given by users and acoustic features of audio signals. This hybrid approach overcomes the conventional tradeoff between recommendation accuracy and variety of recommended artists. Collaborative filtering, which is used on e-commerce sites, cannot recommend nonbrated pieces and provides a narrow variety of artists. Content-based filtering does not have satisfactory accuracy because it is based on the heuristics that the user's favorite pieces will have similar musical content despite there being exceptions. To attain a higher recommendation accuracy along with a wider variety of artists, we use a probabilistic generative model that unifies the collaborative and content-based data in a principled way. This model can explain the generative mechanism of the observed data in the probability theory. The probability distribution over users, pieces, and features is decomposed into three conditionally independent ones by introducing latent variables. This decomposition enables us to efficiently and incrementally adapt the model for increasing numbers of users and rating scores. We evaluated our system by using audio signals of commercial CDs and their corresponding rating scores obtained from an e-commerce site. The results revealed that our system accurately recommended pieces including nonrated ones from a wide variety of artists and maintained a high degree of accuracy even when new users and rating scores were added.
Architecture of cluster-based FPGAs with memory

Embedded memory has become an essential part of FPGAs. In this paper, we investigate how a particular FPGA architecture can be enhanced by including a single memory array in each logic cluster. It is shown that the best overall speed and density results when a cluster contains between 16 and 20 logic elements and one memory array with 512 or 1024 bits. It is also shown that 40% of the logic and memory element inputs should be available outside the cluster.
Consistency of Natural Relations on Sets

The natural relations for sets are those definable in terms of the emptiness of the subsets corresponding to Boolean combinations of the sets. For pairs of sets, there are just five natural relations of interest, namely, strict inclusion in each direction, disjointness, intersection with the universe being covered, or not. Let N denote {1, 2, …, n} and (N2) denote {(i, j)[mid ]i, j∈N and i<j}. A function μ on (N2) specifies one of these relations for each pair of indices. Then μ is said to be consistent on M⊆N if and only if there exists a collection of sets corresponding to indices in M such that the relations specified by μ hold between each associated pair of the sets. Firstly, it is proved that if μ is consistent on all subsets of N of size three then μ is consistent on N. Secondly, explicit conditions that make μ consistent on a subset of size three are given as generalized transitivity laws. Finally, it is shown that the result concerning binary natural relations can be generalized to r-ary natural relations for arbitrary r[ges ]2.
Privacy Aware Access Control in Web Services Compositions

In the online transaction era, multiple heterogeneous web services collaborate to provide required services to the users. In such scenarios, the risks associated with the inadvertent privacy disclosure and intentional privacy breaches from the collaborative composition of participating web services whose privacy policies are not compatible with data owner's privacy preferences, is becoming a major cause of concern for the users of these services. In this paper, we propose a novel privacy-preserving web service composition model based on binary search tree based user's privacy preferences prioritization approach which takes into consideration the owner's privacy requirements and preferences while composing these services.
Simulation: the correct approach to complex availability problem]

Recently, system owners and operators have increasingly emphasized the actual amount of time equipment is capable of performing its intended function. For military systems, added complexity, longer service life requirements, reduced periodic maintenance, and less frequent checkouts have increased system availability requirements. However, these factors compound the difficulty in estimating the system's true availability. With dormant or semi-dormant systems, the amount of time a system “appears” available may differ from the “real” availability. The difference in “real” and “apparent” availability is often the result of a transition from an operational but dormant state to an inoperational but dormant state. The major contributions of this research are: (1) the development of the concept of “complex” availability that applies to systems which combine two or more elements of instantaneous, mission system, or steady-state availability, and (2) the development of a modeling technique to estimate the “real” availability for a system which involves “complex” availability.
Dynamic consensus for secured vehicular ad hoc networks

Vehicular ad hoc networks provide vehicle-to-vehicle communications and safety-related applications to enhance the road safety. However, safety-related applications, like Local Danger Warning, need a high trust level in received messages. Indeed, decisions are made depending on these messages. To increase the trustworthiness, a consensus mechanism is used. With consensus, vehicles need to receive at least X times the same warning before making a decision. Because the consensus should meet real-time constraints of safety applications, a main issue is to set parameter X. In this paper, we investigate the problem of consensus and propose a generic model to define decision method involved in consensus. Then, we propose to dynamically set the consensus parameter according to the neighborhood density and the warning criticalness. The proposed mechanism enhances the “majority of freshest X with threshold” decision method [1] and is analytically modeled. This context-aware and data-centric security mechanism ensures a quick and correct decision. We present some simulation results that validate our model.
The Relationship Between Transformational Leadership and Engagement: Self‐Efficacy as a Mediator

The present study investigated whether followers’ self-efficacy mediates the relation-ship between a transformational leadership style and followers’ engagement. 307 Czech employees evaluated the transformational leadership of their superior and their own work-related self-efficacy and work engagement. Of the 4 compared structural equation models, the model in which self-efficacy partially mediates the relationship between transformational leadership and engagement fitted best. The relationship between trans-formational leadership and self-efficacy showed to be weak, whereas the relationship between self-efficacy and engagement appeared to be moderately strong. A mediation analysis reported weak indirect effect of transformational leadership on engagement through self-efficacy. However, direct effect of transformational leadership on engage-ment was strong. Self-efficacy explained only a small portion of the relationship be-tween transformational leadership and engagement. Current research findings expand theories explaining changes in engagement and the mechanism of how transformational leadership influences leadership outcomes.
Blind Quality Metric for Multidistortion Images Based on Cartoon and Texture Decomposition

In this letter, a no-reference (NR) hybrid image quality assessment (IQA) metric based on cartoon-texture decomposition (CTD) is presented. Focusing on images distorted by both blur and noise, the method takes properties of CTD to separate image into cartoon part with salient edges and texture part with noises. Then, the blur degree and noise level can be estimated separately from different image parts, combine with a joint effect prediction between blur and noise distortions, we present a cartoon-texture decomposition-based blind metric (CTDBBM). Comparative studies with classical full-reference IQA metrics and state-of-the-art NR metrics are conducted on multidistortion image database: LIVEMD. Experimental results show that the CTDBBM performs well and has a high consistency with the human opinions given in the database.
Optimizing a Distributional Semantic Model for the Prediction of German Particle Verb Compositionality

In the work presented here we assess the degree of compositionality of German Particle Verbs with a Distributional Semantics Model which only relies on word window information and has no access to syntactic information as such. Our method only takes the lexical distributional distance between the Particle Verb to its Base Verb as a predictor for compositionality. We show that the ranking of distributional similarity correlates significantly with the ranking of human judgements on semantic compositionality for a series of Particle Verbs and the Base Verbs they are derived from. We also investigate the influence of further linguistic factors, such as the ambiguity and the overall frequency of the verbs and a syntactically separate occurrences of verbs and particles that causes difficulties for the correct lemmatization of Particle Verbs. We analyse in how far these factors may influence the success with which the compositionality of the Particle Verbs may be predicted.
Combination of Ant Colony Optimization and Bayesian Classification for Feature Selection in a Bioinformatics Dataset

Feature selection is widely used as the first stage of classification task to reduce the dimension of problem, decrease noise, improve speed and relieve memory constraints by the elimination of irrelevant or redundant features. One approach in the feature selection area is employing population-based optimization algorithms such as particle swarm optimization (PSO)-based method and ant colony optimization (ACO)-based method. Ant colony optimization algorithm is inspired by observation on real ants in their search for the shortest paths to food sources. Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. This paper empowers the ant colony optimization algorithm by enabling the ACO to select features for a Bayesian classification method. The naive Bayesian classifier is a straightforward and frequently used method for supervised learning. It provides a flexible way for dealing with any number of features or classes, and is based on probability theory. This paper then compares the performance of the proposed ACO algorithm against the performance of a standard binary particle swarm optimization algorithm on the task of selecting features on Postsynaptic dataset. The criteria used for this comparison are maximizing predictive accuracy and finding the smallest subset of features. Simulation results on Postsynaptic dataset show that proposed method simplifies features effectively and obtains a higher classification accuracy compared to other feature selection methods.
Evaluating Software Quality by the Use of User Satisfaction Measurements

This paper presents a method used to measure users opinion about software, and to assure the correctness of such measurements, in order to relate the measurements to software quality. The technique to set-up questionnaires, to collect and analyse measurement results, to use safeguards for error identification and furthermore to prevent defected responses and to extract an overall product evaluation, form the method presented here in. Additionally, some sample safeguards and results from experiments applying this method are presented.
PRMT 3, a Type I Protein Arginine N-Methyltransferase That Differs from PRMT1 in Its Oligomerization, Subcellular Localization, Substrate Specificity, and Regulation*

Methylation is one of the many post-translational modifications that modulate protein function. Although asymmetricN G,N G-dimethylation of arginine residues in glycine-arginine-rich domains of eucaryotic proteins, catalyzed by type I protein arginineN-methyltransferases (PRMT), has been known for some time, members of this enzyme class have only recently been cloned. The first example of this type of enzyme, designated PRMT1, cloned because of its ability to interact with the mammalian TIS21 immediate-early protein, was then shown to have protein arginine methyltransferase activity. We have now isolated rat and human cDNA orthologues that encode proteins with substantial sequence similarity to PRMT1. A recombinant glutathione S-transferase (GST) fusion product of this new rat protein, named PRMT3, asymmetrically dimethylates arginine residues present both in the designed substrate GST-GAR and in substrate proteins present in hypomethylated extracts of a yeastrmt1 mutant that lacks type I arginine methyltransferase activity; PRMT3 is thus a functional type I protein arginineN-methyltransferase. However, rat PRMT1 and PRMT3 glutathione S-transferase fusion proteins have distinct enzyme specificities for substrates present in both hypomethylatedrmt1 yeast extract and hypomethylated RAT1 embryo cell extract. TIS21 protein modulates the enzymatic activity of recombinant GST-PRMT1 fusion protein but not the activity of GST-PRMT3. Western blot analysis of gel filtration fractions suggests that PRMT3 is present as a monomer in RAT1 cell extracts. In contrast, PRMT1 is present in an oligomeric complex. Immunofluorescence analysis localized PRMT1 predominantly to the nucleus of RAT1 cells. In contrast, PRMT3 is predominantly cytoplasmic.
Statement map: reducing web information credibility noise through opinion classification

On the Internet, users often encounter noise in the form of spelling errors or unknown words, however, dishonest, unreliable, or biased information also acts as noise that makes it difficult to find credible sources of information. As people come to rely on the Internet for more and more information, reducing this credibility noise grows ever more urgent. The STATEMENT MAP project's goal is to help Internet users evaluate the credibility of information sources by mining the Web for a variety of viewpoints on their topics of interest and presenting them to users together with supporting evidence in a way that makes it clear how they are related. In this paper, we show how a STATEMENT MAP system can be constructed by combining Information Retrieval (IR) and Natural Language Processing (NLP) technologies, focusing on the task of organizing statements retrieved from the Web by viewpoints. We frame this as a semantic relation classification task, and identify 4 semantic relations: [AGREEMENT], [CONFLICT], [CONFINEMENT], and [EVIDENCE]. The former two relations are identified by measuring semantic similarity through sentence alignment, while the latter two are identified through sentence-internal discourse processing. As a prelude to end-to-end user evaluation of STATEMENT MAP, we present a large-scale evaluation of semantic relation classification between user queries and Internet texts in Japanese and conduct detailed error analysis to identify the remaining areas of improvement.
Einstein-Cartan formulation of Chern-Simons Lorentz-violating gravity

We consider a modification of the standard Einstein theory in four dimensions, alternative to R. Jackiw and S. -Y. Pi, Phys. Rev. D 68, 104012 (2003), since it is based on the first-order (Einstein-Cartan) approach to general relativity, whose gauge structure is manifest. This is done by introducing an additional topological term in the action which becomes a Lorentz-violating term by virtue of the dependence of the coupling on the space-time point. We obtain a condition on the solutions of the Einstein equations, such that they persist in the deformed theory, and show that the solutions remarkably correspond to the classical solutions of a collection of independent 2+1-dimensional (topological) Chern-Simons gravities. Finally, we study the relation with the standard second-order approach and argue that they both coincide to leading order in the modulus of the Lorentz-violating vector field.
The Emotional Burden of Monotheism: Satan, Theodicy, and Relationship with God

The thesis of this research was that belief in Satan helps Christian believers attenuate ambivalent feelings about God in the face of painful life events. Two questions were examined. First, are robust notions of Satan associated with more positive feelings toward God? Second, are robust notions of Satan associated with blaming God less for the pain and suffering inherent in human existence? In study 1 robust notions of Satan did predict more positive experiences with God. In study 2 robust notions of Satan were manifested by participants who were less likely to blame God for the pain and suffering in the world. These trends were consistent with the experimental predictions suggesting that belief in Satan may be functioning as an attributional category to reduce theodic blame toward God.