Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Algorithms for Fuzzy Cognitive Maps—A Review Study

This study presents a survey on the most recent learning approaches and algorithms that are related to fuzzy cognitive maps (FCMs). FCMs are cognition fuzzy influence graphs, which are based on fuzzy logic and neural network aspects that inherit their main advantages. They gained momentum due to their dynamic characteristics and learning capabilities. These capabilities make them essential for modeling and decision-making tasks as they improve the performance of these tasks. An efficient number of learning algorithms for FCMs, by modifying the FCM weight matrix, have been developed in order to update the initial knowledge of human experts and/or include any knowledge from historical data in order to produce learned weights. The proposed learning techniques have mainly been concentrated on three directions: on the production of weight matrices on the basis of historical data, on adaptation of the cause-effect relationships of the FCM on the basis of experts' intervention, and on the production of weight matrices by combining experts' knowledge and data. The learning techniques could be categorized into three groups on the basis of the learning paradigm: Hebbian-based, population-based, and hybrid, which subsequently combine the main aspects of Hebbian-based- and population-based-type learning algorithms. These types of learning algorithms are the most efficient and widely used to train the FCMs, according to the existing literature. A survey on recent advances on learning methodologies and algorithms for FCMs that present their dynamic capabilities and application characteristics in diverse scientific fields is established here.
Towards Distortion-Predictable Embedding of Neural Networks

Current research in Computer Vision has shown that Convolutional Neural Networks (CNN) give state-of-the-art performance in many classification tasks and Computer Vision problems. The embedding of CNN, which is the internal representation produced by the last layer, can indirectly learn topological and relational properties. Moreover, by using a suitable loss function, CNN models can learn invariance to a wide range of non-linear distortions such as rotation, viewpoint angle or lighting condition. In this work, new insights are discovered about CNN embeddings and a new loss function is proposed, derived from the contrastive loss, that creates models with more predicable mappings and also quantifies distortions. In typical distortion-dependent methods, there is no simple relation between the features corresponding to one image and the features of this image distorted. Therefore, these methods require to feed-forward inputs under every distortions in order to find the corresponding features representations. Our contribution makes a step towards embeddings where features of distorted inputs are related and can be derived from each others by the intensity of the distortion.
Vlerick Leuven Gent Working Paper Series 2004/01 Information Seeking about the Psychological Contract: the Impact on Newcomers' Evaluations of Their Employment Relationship Information Seeking about the Psychological Contract: the Impact on Newcomers' Evaluations of Their Employment Relationship Ans

Both socialization and psychological contract literature demonstrate that the first months of employment are critical for the development of a positive psychological contract with organizational newcomers (e. For this reason, it is the objective of this study to explicate newcomers' psychological contract perceptions and evaluations during the socialization process, using information seeking as the central antecedent variable. Based upon socialization and psychological contract literature, hypotheses are formulated that address the relationship between newcomer information seeking and (1) changes in newcomers' perceptions of promises exchanged with their employer, and (2) newcomers' evaluations of their employment relationship one year after entry. To test our hypotheses, a four-wave longitudinal survey among 333 newcomers has been conducted, covering the first year of their new employment relationship. These newcomers, all white-collar level, belonged to six large organizations located in Belgium. Data collections took place at four moments: (T1) at entry, (T2) three months after entry, (T3) six months after entry, and (T4) one year after entry. Results suggest that during the socialization process newcomers change their perceptions of promises but that, contrary to our expectations, these changes are not related to their information-seeking behaviors. On the other hand, and in line with our hypotheses, the frequency of contract-related information seeking during the socialization process significantly affects newcomers' evaluations of their employment relationship one year after entry. Newcomers who engage more frequently in information seeking make up a more positive evaluation of psychological contract fulfillment and they are also more satisfied with their employment relationship in general. Our findings are discussed in view of the available literature on newcomer socialization and psychological contract development and implications for theories on psychological contract development are drawn.
Features extraction via wavelet kernel PCA for data classification

The performance of a kernel-based method is usually sensitive to a choice of the values of the hyper parameters of a kernel function. In this paper, we present a novel framework of using wavelet kernels in the kernel principal component analysis (KPCA) in order to better explain the nonlinear relationships among original multivariate data. We propose to introduce dilation and translation factors into a wavelet kernel function, in order to narrow down the search for kernel parameters required to calculate the kernel matrix. We tested the hypothesis of implementing a wavelet kernel PCA (WKPCA) to extract the feature information using a set of simulated multi-scale clustered data. We show that WKPCA is an effective feature extraction method for transforming a variety of multi-dimensional clustered data into data with a higher level of linearity among the data attributes. That brings to an improvement in the accuracy of linear classifiers.
A Therapeutic Chemical Chaperone Inhibits Cholera Intoxication and Unfolding/Translocation of the Cholera Toxin A1 Subunit

Cholera toxin (CT) travels as an intact AB5 protein toxin from the cell surface to the endoplasmic reticulum (ER) of an intoxicated cell. In the ER, the catalytic A1 subunit dissociates from the rest of the toxin. Translocation of CTA1 from the ER to the cytosol is then facilitated by the quality control mechanism of ER-associated degradation (ERAD). Thermal instability in the isolated CTA1 subunit generates an unfolded toxin conformation that acts as the trigger for ERAD-mediated translocation to the cytosol. In this work, we show by circular dichroism and fluorescence spectroscopy that exposure to 4-phenylbutyric acid (PBA) inhibited the thermal unfolding of CTA1. This, in turn, blocked the ER-to-cytosol export of CTA1 and productive intoxication of either cultured cells or rat ileal loops. In cell culture studies PBA did not affect CT trafficking to the ER, CTA1 dissociation from the holotoxin, or functioning of the ERAD system. PBA is currently used as a therapeutic agent to treat urea cycle disorders. Our data suggest PBA could also be used in a new application to prevent or possibly treat cholera.
Shortest Path Analysis in Social Graphs

The shortest path problem is among the most fundamental combinatorial optimization problems to answer reachability queries. It is hard to deter-mine which vertices or edges are visited during shortest path traversals. In this paper, we provide an empirical analysis on how traversal algorithms behave on social networks. First, we compute the shortest paths between set of vertices. Each shortest path is considered as one transaction. Second, we utilize the pat-tern mining approach to identify the frequency of occurrence of the vertices. We evaluate the results in terms of network properties, i.e. degree distribution, clustering coefficient.
Comparison of the Emission of a High-Pressure Sodium Lamp Working at 50 Hz and at High Frequency

A spectroscopic study of the behavior of a high-pressure sodium (Na) lamp is performed in this paper. The lamp works at 50 Hz and high frequencies. The study includes the analysis of the detected spectra from 400 to 700 nm, the determination of the lamp stabilization time, the Na vapor pressure, and the electronic excitation temperatures.
Statistical Analysis of ML-Based Paraphrase Detectors with Lexical Similarity Metrics

Paraphrase detection has several important applications in natural language processing. Examples of such applications include language translation, text summarization, question answering, plagiarism detection, and online information retrieval. A number of metrics have been proposed in the literature to quantify the textual similarity between two sentences. However, the accuracy of utilizing each similarity metric alone in detecting paraphrases is very low. Though some machine learning (ML) techniques have been deployed for paraphrase detection, there is no known study that intensively benchmarks their performance on this problem under similar conditions. In this paper, we evaluate the utility of integrating five lexical similarity metrics with three standard machine learning paradigms to detect paraphrases. We apply statistical tests to compare and benchmark the relative significance of the adopted ML-based paraphrase detectors on different datasets.
CRF2 Signaling Is a Novel Regulator of Cellular Adhesion and Migration in Colorectal Cancer Cells

Stress has been proposed to be a tumor promoting factor through the secretion of specific neuromediators, such as Urocortin2 and 3 (Ucn2/3), however its role in colorectal cancer (CRC) remains elusive. We observed that Ucn2/3 and their receptor the Corticotropin Releasing Factor receptor 2 (CRF2) were up-regulated in high grade and poorly differentiated CRC. This suggests a role for CRF2 in the loss of cellular organization and tumor progression. Using HT-29 and SW620 cells, two CRC cell lines differing in their abilities to perform cell-cell contacts, we found that CRF2 signals through Src/ERK pathway to induce the alteration of cell-cell junctions and the shuttle of p120ctn and Kaiso in the nucleus. In HT-29 cells, this signaling pathway also leads to the remodeling of cell adhesion by i) the phosphorylation of Focal Adhesion Kinase and ii) a modification of actin cytoskeleton and focal adhesion complexes. These events stimulate cell migration and invasion. In conclusion, our findings indicate that CRF2 signaling controls cellular organization and may promote metastatic potential of human CRC cells through an epithelial-mesenchymal transition like process. This contributes to the comprehension of the tumor-promoting effects of stress molecules and designates Ucn2/3-CRF2 tandem as a target to prevent CRC progression and aggressiveness.
Checking computations in polylogarithmic time

Motivated by Manuel Blum’s concept of inst ante checking, we consider new, very fast and generic mechanisms of checking computations. Our results exploit recent advances in interactive proof protocols [LFKN], [Sh], and especially the MIP = NEXP protocol from [BFL]. WJe show that every nondeterministic computational task S(Z, y), defined as a polynomial time relation between the instance x, representing the input and output combined, and the witness y can be modified to a task S such that: (i) the same instances remain accepted; (ii) each instance/witness pair becomes checkable in poly! ogariihmic Monte Carlo time; and (iii) a witness satisfying S’ can be computed in polynomial time from a witness satisfying S. Here the instance and the description of S have to be provided in error-correcting code (since the checker will not notice slight changes). A modification of the MIP proof was required to achieve polynomial time in (iii); the earlier technique yields N“(*OglOg’1 time only. This result becomes significant if software and hardware reliability are regarded aa a considerable cost factor. The polylogarithmic checker is the only part of the system that needs to be trusted; it can be hard wired. (We use just one Checker for all problems! ) The checker is tiny and so presumably can be optimized and checked off-line at a modest cost. In this setup, a single reliable PC can monitor the operation of a herd of supercomputers working with possibly extremely powerful but unreliable software and untested hardware. 1Research partially supported by NSF Grant CCR-871OO78. Email: laci@cs.uchicago. edu 2Rese~& partiay supported by NSF Grant CCR-SMXI$XEK5. E-mail: fortnow@cs.uc&cago. edu 3Supported by NSF grant CCR-SJC115276. E-mail: Lnd@cs.bu. edu 4111 cummington St., Boston MA 02215. 5E-mail: mario@cs.uchicago . edu ~1100 E 58th St, Chicago IL 60637. Permission to copy without fee all or part of this msterisl is granted provided that tie copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee snd/or specific permission. @ 1991 ACM 089791-397-31911000410021 $1.50 Leonid A. Levin 3
Groupsourcing: Team Competition Designs for Crowdsourcing

Many data processing tasks such as semantic annotation of images, translation of texts in foreign languages, and labeling of training data for machine learning models require human input, and, on a large scale, can only be accurately solved using crowd based online work. Recent work shows that frameworks where crowd workers compete against each other can drastically reduce crowdsourcing costs, and outperform conventional reward schemes where the payment of online workers is proportional to the number of accomplished tasks ("pay-per-task"). In this paper, we investigate how team mechanisms can be leveraged to further improve the cost efficiency of crowdsourcing competitions. To this end, we introduce strategies for team based crowdsourcing, ranging from team formation processes where workers are randomly assigned to competing teams, over strategies involving self-organization where workers actively participate in team building, to combinations of team and individual competitions. Our large-scale experimental evaluation with more than 1,100 participants and overall 5,400 hours of work spent by crowd workers demonstrates that our team based crowdsourcing mechanisms are well accepted by online workers and lead to substantial performance boosts.
Does Search Matter? : Using Online Click Stream Data to Examine the Relationship between Online Search and Purchase Behavior

DOES SEARCH MATTER? : USING ONLINE CLICK STREAM DATA TO EXAMINE THE RELATIONSHIP BETWEEN ONLINE SEARCH AND PURCHASE BEHAVIOR Neveen F. Awad, Joni L. Jones, Jian Zhang Introduction The ability to track consumer behavior online is considered a prime advantage that online retailing has over brick and mortar retailers. However, firms are still grappling with how to understand their consumers through click steam data. Initially, firms were concerned with visit volume, conjecturing that by increasing the number of visitors, firm revenue would also increase. In fact, visit measures continue to be among the most widely used and cited online metric (Demers and Lev 2001). However, visit measures clearly do not tell the full story (Moe and Fader 2004). Thus practitioners and researchers alike are presented with an important issue, namely firms are collecting myriads of click stream data online, but they do not know what the data is telling them about the relationship between consumer search and purchase behavior. Thus, in this study, we explore the relationship between search behavior and purchase behavior at two different levels of analysis; specifically, we examine search breadth and search depth in association with purchase behavior in the extended and session-level contexts. Prior literature has examined inter-firm search analytically (Bakos 1997), in relation to posted price (Brynjolfsson and Smith 2000), in an experimental setting (Lynch and Ariely 2000), and in relation to individual tendency to search over time (Johnson, Moe et al. 2004). In this study, we extend this prior research by examining the relationship of online search behavior to online purchase behavior across sites. Our goal is assess whether a relationship between search and purchase behavior exists, and how that relationship appears different at different levels of analysis.
The NFκB-inducing kinase is essential for the developmental programming of skin-resident and IL-17-producing γδ T cells

γδ T cells contribute to first line immune defense, particularly through their ability for rapid production of proinflammatory cytokines. The cytokine profile of γδ T cells is hard-wired already during thymic development. Yet, the molecular pathways underlying this phenomenon are incompletely understood. Here we show that signaling via the NFκB-inducing kinase (NIK) is essential for the formation of a fully functional γδ T cell compartment. In the absence of NIK, development of Vγ5+ dendritic epidermal T cells (DETCs) was halted in the embryonic thymus, and impaired NIK function caused a selective loss of IL-17 expression by γδ T cells. Using a novel conditional mutant of NIK, we could show in vivo that NIK signaling in thymic epithelial cells is essential for the thymic hardwiring of γδ T cell cytokine production. DOI: http://dx.doi. org/10.7554/eLife.10087. 001
Satisfiability Checking and Query Answering for Large Ontologies

In this paper we develop a sound, complete and terminating superposition calculus plus a query answering calculus for the BSH-Y2 fragment of the Bernays – Schönfinkel Horn class of first-order logic. BSH-Y2 can be used to represent expressive ontologies. In addition to checking consistency, our calculus supports query answering for queries with arbitrary quantifier alternations. Experiments on BSH-Y2 (fragments) of several large ontologies show that our approach advances the state of the art.
A study of the phase transition behavior of mixed ammonium sulfate – malonic acid aerosols

A study of the phase transition behavior of mixed ammonium sulfate – malonic acid aerosols C. F. Braban and J. P. D. Abbatt Centre for Atmospheric Sciences, Department of Chemistry, Lensfield Road, University of Cambridge, Cambridge, CB2 1EW, UK Department of Chemistry, University of Toronto, Toronto, ON, M5S 3H6, Canada Received: 22 March 2004 – Accepted: 3 May 2004 – Published: 2 June 2004 Correspondence to: J. P. D. Abbatt (jabbatt@chem.utoronto.ca)
Component-Wise Modeling of Articulated Objects

We introduce a novel framework for modeling articulated objects based on the aspects of their components. By decomposing the object into components, we divide the problem in smaller modeling tasks. After obtaining 3D models for each component aspect by employing a shape deformation paradigm, we merge them together, forming the object components. The final model is obtained by assembling the components using an optimization scheme which fits the respective 3D models to the corresponding apparent contours in a reference pose. The results suggest that our approach can produce realistic 3D models of articulated objects in reasonable time.
Continuous Authentication with Keystroke Dynamics

In this chapter we will discuss how keystroke dynamics can be used for true continuous authentication.We have collected keystroke dynamics data of 53 participants who used the computer freely and we have analysed the collected data. We will describe a system that decides on the genuineness of the user based on each and every single keystroke action of the current user and we will represent the results in a new manner. The continuous authentication system will lock out a user if the trust in genuineness of the current user is too low. Ideally such a system would never lock out a genuine user and detect an impostor user within as few keystroke actions as possible.
An EEPROM Programming Controller for Passive UHF RFID Transponders with Gated Clock Regulation Loop and Current Surge Control

This paper presents an EEPROM programming controller imbedded in a passive UHF RFID transponder. It generates a 14 V programming voltage for a 224-bit EEPROM memory array from a rectified voltage supply of 2-3 V. A gated clock regulation loop is proposed to keep the programming voltage constant over a wide range of received RF input power, in order to improve the write-erase endurance of the memory. A current surge control scheme is proposed to allow the EEPROM programming voltage ramping in steps, therefore, preventing the collapse of the rectified supply in the remotely powered transponder. Also presented is a nano-power switched bandgap reference to reduce die area through the reduction of Meg-ohm resistors needed for nano-power operation. Measurement results show that a 0.35 mum CMOS transponder IC provides a stable 14 V EEPROM programming voltage and consumes only 7 muW during write operation. The EEPROM programming controller occupies 0.092 mm2 die area.
Towards Accurate Monitoring of Extra-Functional Properties in Real-Time Embedded Systems

Management and preservation of Extra-Functional Properties (EFPs) is critical in real-time embedded systems to ensure their correct behavior. Deviation of these properties, such as timing and memory usage, from their acceptable and valid values can impair the functionality of the system. In this regard, monitoring is an important means to investigate the state of the system and identify such violations. The monitoring result can also be used to make adaptation and re-configuration decisions in the system as well. Most of the works related to monitoring EFPs are based on the assumption that monitoring results accurately represent the true state of the system at the monitoring request time point. In some systems this assumption can be safe and valid. However, if in a system the value of an EFP changes frequently, the result of monitoring may not accurately represent the state of the system at the time point when the monitoring request has been issued. The consequences of such inaccuracies can be critical in certain systems and applications. In this paper, we mainly introduce and discuss this practical problem and also provide a solution to improve the monitoring accuracy of EFPs.
Results on the PASCAL PROMO challenge

A solution to the PASCAL PROMO challenge “Simple causal effects in time series” is presented. The data is modeled as a sum of a constant-plus-sin term (autonomous part) and a term that is a linear function of a small number of inputs. The problem of identifying such a model from the data is nonconvex in the frequency and phase parameters of the sin and is combinatorial in the number of inputs. The proposed method is suboptimal and exploits several heuristics. First, the problem is split into two phases: 1) identification of the autonomous part and 2) identification of the input dependent part. Second, local optimization method is used to solve the problem in the first phase. Third, l1 regularization is used in order to find a sparse solution in th e second phase.