Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Purchase Negotiations in a Dynac Electronic Marketplace

Nowadays, there is a surge of B2C and B2B e-commerce operated on the Internet. However, many of these systems are often nothing more than electronic product or service catalogues. Against this background, it is argued that new generation systems based on automatic negotiation will emerge. This paper covers a particular kind of automatic negotiation systems, where a number of participants in a mobile dynamic electronic marketplace automatically negotiate the purchase of products or services, by means of multiple automated one-to-one bargainings. In a dynamic e-marketplace, the number of buyers and sellers and their preferences may change over time. By mobile we mean that buyers in a commercial area may initiate simultaneous negotiations with several sellers using portable devices like cell phones, laptops or personal digital assistants, so these negotiations do not require participants to be colocated in space. We will show how an expressive approach to fuzzy constraint based agent purchase negotiations in competitive trading environments, is ideally suited to work on these kind of e-marketplaces. An example of mobile e-marketplace, and a comparison between an expressive and an inexpressive approach will be presented to show the efficiency of the proposed solution.
The Mediating Role of Workplace Attitudes on the Leader–Member Exchange—Turnover Intention Relationship

We analyzed the role of three organizational stressors, organizational change, perceptions of politics, and interpersonal conflict at work on the relationship between leader member exchange (LMX) and turnover intention. Our contention is that LMX reduces various organizational stressors and, through that reduction, reduces turnover intention. We tested our model using 281 respondents from a diverse set of organizations. Results indicated that each stressor partially mediated the LMX–turnover intention relationship. In light of the findings, we recommend managers take efforts to develop strong LMX relationships and reduce the level of organizational stressors through appropriate information sharing.
Cardiac diseases classification based on scalable pattern representations

This paper analyzes the characteristics of semantics implicity of electrocardiogram(ECG) data stream, and describes the research motivation of finding the potential semantic matter on data stream. Based on SP-Tree, this paper has proposed Pattern2vec method, it can realize the vector representations of scalable pattern and take advantage of the vector to discover the implicit semantics on data stream. This paper has carried out the typical application study, namely using the vectorization expression of scalable patterns for helping medical workers realize accurate classification of cardiac disease.
Warranty Cost Forecast Based on Car Failure Data

A failure and warranty cost model is gained from a failure database. The model is a combination of statistical components with a multi-layer perceptron and a cross-entropy based learning rule. The model is used for forecasting warranty costs in alternative warranty condition scenarios. The estimate of forecast variance considers both the individual vehicle risk and the overall manufacturing quality fluctuation risk.
The big two dictionaries: Capturing agency and communion in natural language

Four studies developed and validated two dictionaries to capture agentic and communal expressions in natural language. Their development followed the Linguistic Inquiry and Word Count (LIWC) approach (Study 1) and we tested their validity with frequency-based analyses and semantic similarity measures. The newly developed Agency and Communion dictionaries were aligned with LIWC categories related to agency and communion (Study 2), and corresponded with subjective ratings (Study 3), confirming their convergent validity. Very low or absent correspondence between proposed dictionaries and unrelated LIWC categories demonstrated their discriminant validity (Study 2). Finally, we applied both dictionaries to language used in advertisements. In correspondence to gender stereotypes, male-dominated jobs were advertised with more agentic than communal words, and female-dominated jobs with more communal than agentic words (Study 4). Both dictionaries represent reliable tools for quantifying agentic and communal content in natural language, and will improve and facilitate future research on agency and communion.
Research Article Cloning, Expression, and Characterization of a Novel Thermophilic Monofunctional Catalase from Geobacillus Sp. Chb1

Catalases are widely used in many scientific areas. A catalase gene (Kat) from Geobacillus sp. CHB1 encoding a monofunctional catalase was cloned and recombinant expressed in Escherichia coli (E. coli), which was the first time to clone and express this type of catalase of genus Geobacillus strains as far as we know. This Kat gene was 1,467 bp in length and encoded a catalase with 488 amino acid residuals, which is only 81% similar to the previously studied Bacillus sp. catalase in terms of amino acid sequence. Recombinant catalase was highly soluble in E. coli and made up 30% of the total E. coli protein. Fermentation broth of the recombinant E. coli showed a high catalase activity level up to 35,831 U/mL which was only lower than recombinant Bacillus sp. WSHDZ-01 among the reported catalase production strains. The purified recombinant catalase had a specific activity of 40,526 U/mg and í µí°¾ í µí± of 51.1 mM. The optimal reaction temperature of this recombinant enzyme was 60 ∘ C to 70 ∘ C, and it exhibited high activity over a wide range of reaction temperatures, ranging from 10 ∘ C to 90 ∘ C. The enzyme retained 94.7% of its residual activity after incubation at 60 ∘ C for 1 hour. High yield and excellent thermophilic properties are valuable features for this catalase in industrial applications.
Preoperative setup planning for robotic surgery based on a simulation platform and Gaussian process

For the robotically-assisted minimally invasive surgery, preoperative planning is essential towards assisting surgeons to prepare the intervention and to decide the best access to the surgical site. Many recent studies in preoperative planning cannot evaluate the performance of the multi-arm cooperation, and thus their applications are constrained in real practise with multi-arm surgical robots. In this paper, we establish a simulation platform of a three-arm surgical robot. We propose to use two objective functions, Global Isotropy Index (GII) and Cooperation Capability Index (CCI), to reflect the dexterity of a robot arm and the performance of the multi-arm cooperation respectively. We also propose to use Gaussian Process Regression (GPR) to locate the optimal port placement and robot positioning. Simulation experiments are carried out to validate our method. The results demonstrate that our proposed performance optimization strategy based on GP is capable to guide surgeons to plan an intervention with the multi-arm surgical robot.
Analysis of kernel method for surface curvature estimation

Surface curvature estimation is a common component of CT colonography computer-aided polyp detection algorithms. A commonly used method to compute such curvatures employs convolution kernels. We have observed situations where the kernel method produces inaccurate results that could lead to undesirable false negative and false positive polyp diagnoses. In this paper, we numerically examine this method of curvature estimation. We propose optimal choices for smoothing parameters intrinsic to the method. The proposed smoothing parameters achieve more accurate and reliable curvatures compared to those reported in the literature. Our results include responses of the system with respect to Gaussian smoothing and Gaussian noise, results on the accuracy of the curvature estimation as a function of the distance from the true surface, and examples of specific topologies of the colonic surface for which the kernel method yields inaccurate responses.
Design of Derivative-free State Estimators for a Three Phase Induction Motor – A Comparative Study

filters are an alternative to approximate the Kalman filter for nonlinear problems. This paper intends to assess the potential of Particle Filter (PF) and its variants in the context of the state estimation problem of a three phase induction motor. The conventional Particle Filter (SIR! PF), and particle filters that employ importance sampling through proposal distributions such as Particle Filter with Extended Kalman Filter (PF! EKF) and Particle Filter with Unscented Kalman Filter (PF! UK F), which are proposed in the literature within the particle filtering framework that takes into account of the latest observational information to reduce the risk of weight degeneracy is described and the error behaviour is analyzed through Monte Carlo simulations with regard to three scenarios Viz., low speed operation, step changes in load torque and reversal of speed. Simulation results demonstrate the superior tracking performance of PF! EKF at the expense of higher computational effort over the other approaches and can be determined to be a good substitute for the UKF in terms of accuracy of the state vector estimation.
Modeling gene expression data via positive Boolean functions

In this work we propose an artificial model for the generation of biologically plausible gene expression data to be used in the evaluation of the performance of gene selection and clustering methods. The model allows to fix in advance the set of relevant genes and the functional classes involved in the problem; the input-output relationship is constructed by synthesizing a positive Boolean function. Despite its simplicity, it is sufficiently rich to take account of the specific peculiarities of gene expression data, including biological variability. A Java code had been developed to allow the user choose the model parameters according to the characteristics of the experiment he want to simulate. This permits to insert the artificial model into a distributed system for microarray analysis, in particular one based on a Grid infras-
Experience-Dependent Gene Expression in the Rat Hippocampus after Spatial Learning: A Comparison of the Immediate-Early GenesArc, c-fos, and zif268

Neuronal immediate-early gene (IEG) expression is regulated by synaptic activity and plays an important role in the neuroplastic mechanisms critical to memory consolidation. IEGs can be divided into two functional classes: (1) regulatory transcription factors (RTFs), which can broadly influence cell function depending on the “downstream” genes they regulate, and (2) “effector” proteins, which may directly modulate specific cellular functions. The objective of the current study was to determine whether the expression of an effector IEG (Arc) was similar to, or different from, that of two well characterized RTF IEGs (c-fos andzif268) after learning. IEG RNA levels from rats trained in spatial and nonspatial water tasks were determined using RNase protection assays and in situ hybridization. Overall, the regulation of the three IEGs was similar in the hippocampus and the entorhinal and primary visual cortices. Consequently, IEG RNA levels were positively correlated within a structure. By contrast,Arc and zif268 RNA levels were not correlated or only weakly correlated across structures, although c-fos RNA levels were moderately correlated across structures. Arc RNA expression differed from that ofzif268 and c-fos in two regards: (1) hippocampal Arc RNA levels were correlated with learning of the hippocampal-dependent spatial, but not hippocampal-independent cued response, water task, and (2) Arc RNA levels in the hippocampus and entorhinal cortex increased after spatial reversal learning relative to an asymptotic performance group. Thus, although the expression of Arc, zif268, and c-fos exhibited many similarities, Arcwas most responsive to differences in behavioral task demands.
Cholesky Factorization-Based Adaptive BLAST DFE for Wideband MIMO Channels

Adaptive equalization of wireless systems operating over time-varying and frequency-selective multiple-input multiple-output (MIMO) channels is considered. A novel equalization structure is proposed, which comprises a cascade of decision feedback equalizer (DFE) stages, each one detecting a single stream. The equalizer filters, as well as the ordering by which the streams are extracted, are updated based on the minimization of a set of least squares (LS) cost functions in a BLAST-like fashion. To ensure numerically robust performance of the proposed algorithm, Cholesky factorization of the equalizer input autocorrelation matrix is applied. Moreover, after showing that the equalization problem possesses an order recursive structure, a computationally efficient scheme is developed. A variation of the method is also described, which is appropriate for slow time-varying conditions. Theoretical analysis of the equalization problem reveals an inherent numerical deficiency, thus justifying our choice of employing a numerically robust algebraic transformation. The performance of the proposed method in terms of convergence, tracking, and bit error rate (BER) is evaluated through extensive computer simulations for time-varying and wideband channels.
On the Tightness of Deterministic Identity-Based Signatures

Deterministic ID-based signatures are digital signatures where secret keys are generated with random numbers by a key generation center while the signatures are generated deterministically. Although the deterministic ID-based signatures are useful for both systematic and cryptographic applications, to the best of our knowledge, there is no scheme with a tight reduction proof. In this work, we show that the scheme by Selvi et al. (IWSEC 2011) whose original proof is loose in the random oracle model is tightly secure by our new proof framework. We furthermore shed light on mathematical features necessary for achieving a tight reduction, and improve the scheme by Herranz (The Comp. Jour., 2006) to proving tightly secure in the random oracle model. The schemes described above are motivated to construct aggregate signatures with partial aggregation, and our technique is able to prove the security without the loss of the original aggregation capability.
Nickel chloride (NiCl2) induces endoplasmic reticulum (ER) stress by activating UPR pathways in the kidney of broiler chickens

It has been known that overexposure to Ni can induce nephrotoxicity. However, the mechanisms of underlying Ni nephrotoxicity are still elusive, and also Ni- and Ni compound-induced ER stress has been not reported in vivo at present. Our aim was to use broiler chickens as animal model to test whether the ER stress was induced and UPR was activated by NiCl2 in the kidney using histopathology, immunohistochemistry and qRT-PCR. Two hundred and eighty one-day-old broiler chickens were divided into 4 groups and fed on a control diet and the same basal diet supplemented with 300 mg/kg, 600mg/kg and 900mg/kg of NiCl2 for 42 days. We found that dietary NiCl2 in excess of 300 mg/kg induced ER stress, which was characterized by increasing protein and mRNA expression of ER stress markers, e.g., GRP78 and GRP94. Concurrently, all the three UPR pathways were activated by dietary NiCl2. Firstly, the PERK pathway was activated by increasing eIF2a and ATF4 mRNA expression. Secondly, the IRE1 pathway was activated duo to increase in IRE1 and XBP1 mRNA expression. And thirdly, the increase of ATF6 mRNA expression suggested that ATF6 pathway was activated. The findings clearly demonstrate that NiCl2 induces the ER stress through activating PERK, IRE1 and ATF6 UPR pathways, which is proved to be a kind of molecular mechanism of Ni- or/and Ni compound-induced nephrotoxicity.
Previous civic experience and Asian adolescents’ expected participation in legal protest: mediating role of self-efficacy and interest

ABSTRACT Not yet citizens, adolescents are encouraged through civic education to consider an active role in the future political life of their societies. This study seeks to understand the mediating role of political self-efficacy and political interest in the association between previous civic experience and Asian adolescents’ expected participation in legal protest. Using data from the International Civic and Citizenship Education Study (ICCS) 2009, which included 23,654 students from South Korea, Chinese Taipei, Hong Kong, Indonesia, and Thailand, a multi-group multi-level path analysis based on Rasch measures was employed to investigate how adolescent civic experiences both in and out of school influence their expected participation in legal protest. The results showed the effect of political discussion and that of school participation were fully mediated by political interest and self-efficacy. The effect of community participation was partially mediated by political interest and self-efficacy in the five societies except South Korea. While the total effect of school participation is relatively low across the five societies, the total effect of political discussion is stronger for adolescents in East Asia (South Korea, Chinese Taipei, and Hong Kong) and that of community participation is stronger in South East Asia (Thailand and Indonesia).
Power spectra reveal the influence of stochasticity on nonlinear population dynamics

Stochasticity alters the nonlinear dynamics of inherently cycling populations. The power spectrum can describe and explain the impacts of stochasticity. We fitted models to short observed time series of flour beetle populations in the frequency domain, then used a well fitting stochastic mechanistic model to generate detailed predictions of population spectra. Some predicted spectral peaks represent periodic phenomena induced or modified by stochasticity and were experimentally confirmed. For one experimental treatment, linearization theory explained that these peaks represent overcompensatory decay of deviations from deterministic oscillation. In another treatment, stochasticity caused frequent directional phase shifting around a cyclic attractor. This directional phase shifting was not explained by linearization theory and modified the periodicity of the system. If field systems exhibit directional phase shifting, then changing the intensity of demographic or environmental noise while holding constant the structure of the noise can change the main frequency of population fluctuations.
Measuring Search Trees

The SAT and CSP communities make a great use of search effort comparisons to assess the validity of an algorithm or a heuristic. There exist different ways of counting the size of a search tree developed by a procedure. However, they are seldomly defined, and their similarities/differences are not clear. This note aims at clarifying this issue. We formally define (some of) the existing measures and characterize the properties that we can expect from them. We show some weaknesses of the existing measures with respect to those properties. We propose the number of wrong decisions as a new way of measuring the search effort that is more accurate to effectively represent the effort an algorithm has devoted to the search. We show that this new way of measuring the search space behaves well with respect to the desired properties.
Fuzzing: The State of the Art

Abstract : Fuzzing is an approach to software testing where the system being tested is bombarded with test cases generated by another program. The system is then monitored for any flaws exposed by the processing of this input. While the fundamental principles of fuzzing have not changed since the term was first coined, the complexity of the mechanisms used to drive the fuzzing process have undergone significant evolutionary advances. This paper is a survey of the history of fuzzing, which attempts to identify significant features of fuzzers and recent advances in their development, in order to discern the current state of the art in fuzzing technologies, and to extrapolate them into the future.
Heterosynaptic Long-Term Potentiation of Inhibitory Interneurons in the Lateral Amygdala

Long-term potentiation (LTP) of synaptic transmission in the lateral amygdala (LA) is believed to underlie the formation and retention of fear memories. To explore the role of inhibitory transmission in amygdala plasticity, we recorded from LA inhibitory interneurons in vitro before and after tetanization of the thalamo-LA pathway, one of the major inputs to LA involved in fear learning. Tetanization resulted in LTP of the EPSPs elicited in both the tetanized thalamic pathway and the untetanized cortical pathway to LA. This LTP was NMDA-dependent and associated with a decrease in paired-pulse facilitation in both pathways. In LA excitatory cells, LTP of interneurons resulted in an increase in the amplitude of GABAergic IPSPs in both input pathways. Finally, isolated GABAergic IPSPs between inhibitory and excitatory neurons could be potentiated as well. Plasticity of inhibitory transmission within the LA may therefore contribute significantly to LA-mediated functions, such as fear conditioning.
Neuro Fuzzy Model Predictive Control of AQM Networks Supporting TCP Flows

One of the challenges in designing computer networks is "queue management and congestion avoidance". There are several studies for congestion reduction and controlling such as random early detection (RED) and its variants. More recent works on developing congestion avoidance methods include modeling a TCP flow in an active queue management (AQM) of a bottlenecked network link. Rather than classical control theories, that are applied to improve performance and stability of network flows, some studies are developed based on new control tools such as neural networks. In this article a neuro-fuzzy controller for active queue management is proposed. In this model, the number of neurons are determined according to complexity of the model and instead of using random values for initializing networkpsilas weights, near to optional values are used. The proposed method decreases the network training time and ensures convergence with higher probability. The results of this method show superior performance over other previous controllers such as classical and neural networks methods.
Neurological speech deficits as plot devices in novels

Stroke is a leading cause of death worldwide and a common cause of disability in adults in developed countries.1 Most strokes are not fatal and, in contrast to coronary artery disease and cancer, the major burden of stroke is chronic disability rather than death.2 It is unsurprising, therefore, that characters with stroke appear frequently in literature, although sometimes the diagnosis is wrong. In George Eliot's Silas Marner, for example, the title character is thought to suffer ‘strokes’ whereas the diagnosis is probably narcolepsy. There are a number of common themes in literary descriptions of people with stroke; first, the stroke is often brought on by high emotion or shock, but before the days of adequate treatment of hypertension, cerebral haemorrhage is likely to have been more common. Second, they frequently result in complete or partial loss of speech, which prevents the protagonist from imparting vital information or exposing villains. Third, they are often treated with bleeding if there is a doctor handy. Notable examples of this treatment are Billy Bones in Robert Louis Stevenson's Treasure Island, Squire Dornell in Thomas Hardy's short story The First Countess of Wessex and Prince Bolkonsky in Tolstoy's War and Peace. In the first two cases the treatment is apparently effective (although Bones dies a little while later of a second massive stroke when he receives the pirates ‘black spot’), although in reality the hypotension induced by bleeding would be dangerous and likely to extend the stroke if it was a cerebral infarct. Stroke related speech deficits can be used by authors to influence a novel's plot. We discuss several prominent examples using well-known novels where this occurs.