Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning algorithms for process analytical technology

Increased globalisation and competition are drivers for process analytical technologies (PAT) that enable seamless process control, greater flexibility and cost efficiency in the process industries. The paper will discuss process modelling and control for industrial applications with an emphasis on solutions enabling the real-time data analytics of sensor measurements that PAT demands. This research aims to introduce an integrated process control approach, embedding novel sensors for monitoring in real time the critical control parameters of key processes in the minerals, ceramics, non-ferrous metals, and chemical process industries. The paper presents a comparison of machine learning algorithms applied to sensor data collected for a polymerisation process. Several machine learning algorithms including Adaptive Neuro-Fuzzy Inference Systems, Neural Networks and Genetic Algorithms were implemented using MATLAB® Software and compared in terms of accuracy (MSE) and robustness in modelling process progression. The results obtained show that machine learning-based approaches produce significantly more accurate and robust process models compared to models developed manually while also being more adaptable to new data. The paper presents perspectives on the potential benefits of machine learning algorithms with a view to their future in the industrial process industry.
0 Ju l 2 00 8 ON DEFORMATION TYPES OF REAL ELLIPTIC SURFACES

We study real elliptic surfaces and trigonal curves (over a base of an arbitrary genus) and their equivariant deformations. We calculate the real TateShafarevich group and reduce the deformation classification to the combinatorics of a real version of Grothendieck’s dessins d’enfants. As a consequence, we obtain an explicit description of the deformation classes of M and (M −1)(i.e., maximal and submaximal in the sense of the Smith inequality) curves and surfaces.
On-line rigid object recognition and pose estimation based on inertial parameters

This paper proposes an object recognition and gripping pose estimation approach based on on-line estimation of the complete set of inertial parameters, i.e. the mass, the coordinates of the center of mass, and the elements of the inertia matrix, of an object gripped by or attached to a manipulator. A multi-sensor fusion approach combining 6D force/torque, 6D acceleration, 3D angular velocity, and joint angle data to estimate these parameters is presented. In order to facilitate practical implementation, approaches to handling force/torque sensor offsets and to compensating the forces/torques caused by the distal mounting plate of the force/torque sensor and the gripper are incorporated. Regarding the joint angle signals, preprocessing steps to derive the angular velocity, linear acceleration and angular acceleration vector w.r. t. the sensor frame are addressed. The estimation of the complete set of inertial parameters employing the recursive instrumental variables (RIV) method is discussed. The extraction of features that are invariant w.r. t. translation and rotation, i.e. the mass and the principal moments of inertia, as well as a recognition approach based on the Kullback-Leibler divergence are presented. Experimental results show very low errors in the estimates of the inertial parameters, good pose estimation accuracy, and the viability of the recognition approach.
Effects of Losartan and Vanillic Acid Co-Administration on Ischemia-Reperfusion-Induced Oxidative Stress in Isolated Rat Heart

Background: Experimental studies have demonstrated that angiotensin II (ANG-II)-induced oxidative stress contributes to the pathogenesis of I/R injury. Objectives: This study was aimed to investigate the protective effects of co-administration of losartan, as a selective ANG-II type 1 receptor (AT1R) blocker, and vanillic acid (VA), as an antioxidant, in I/R-induced oxidative stress in isolated rat heart. Materials and Methods: Adult male Wistar rats were randomly divided to sham, control, and five treatment groups (n = 10). Two doses of VA (5 and 10 mg/kg), one dose of losartan (20 mg/kg) alone, and one dose of losartan in combination with either doses of VA were administered orally for 10 days. The hearts were isolated and exposed to 30 minutes ischemia and 60 minutes reperfusion, using Langendorff apparatus. I/R-induced myocardial injury was assessed by estimating the release of lactate dehydrogenase (LDH), creatine phosphokinase (CPK) and myocardial creatine kinase (CK-MB) in coronary effluent at 5, 15, and 60 minutes of reperfusion. The oxidative stress in the hearts was assessed by estimating malondialdehyde (MDA). The effects of treatments on endogenous antioxidant enzymes were assessed by measuring superoxide dismutase (SOD), glutathione peroxidase (GPx) and catalase (CAT). Results: There was a more significant decrease in the levels of LDH, CPK, CK-MB, and MDA as well as increase in the levels of SOD, CAT and GPx in groups that had received combined treatment in comparison to VA or losartan alone. Conclusions: It may be concluded that combination of losartan with higher dose of VA decreases ischemic markers and lipid peroxidation and augments endogenous antioxidant and hence, protects myocardium against I/R-induced oxidative stress injuries.
Semantic Analysis and Helpfulness Prediction of Text for Online Product Reviews

Predicting the helpfulness of product reviews is a key component of many ecommerce tasks such as review ranking and recommendation. However, previous work mixed review helpfulness prediction with those outer layer tasks. Using nontext features, it leads to less transferable models. This paper solves the problem from a new angle by hypothesizing that helpfulness is an internal property of text. Purely using review text, we isolate review helpfulness prediction from its outer layer tasks, employ two interpretable semantic features, and use human scoring of helpfulness as ground truth. Experimental results show that the two semantic features can accurately predict helpfulness scores and greatly improve the performance compared with using features previously used. Cross-category test further shows the models trained with semantic features are easier to be generalized to reviews of different product categories. The models we built are also highly interpretable and align well with human annotations.
Bacterial Fimbriae and Their Peptides Activate Human Gingival Epithelial Cells through Toll-Like Receptor 2

ABSTRACT Gingival epithelial cells are a central component of the barrier between oral microflora and internal tissues. Host responses to periodontopathic bacteria and surface components containing fimbriae are thought to be important in the development and progression of periodontal diseases. To elucidate this mechanism, we established immortalized human gingival epithelial cells (HGEC) that were transfected with human papillomavirus. HGEC predominantly expressed Toll-like receptor (TLR) 2, but not TLR4 or CD14. They also induced interleukin-8 (IL-8) production when stimulated withPorphyromonas gingivalis fimbriae andStaphylococcus aureus peptidoglycan, but notEscherichia coli-type synthetic lipid A. Furthermore, an active synthetic peptide composed of residues 69 to 73 (ALTTE) of the fimbrial subunit protein, derived from P. gingivalis and similar to a common component of cell wall peptidoglycans in parasitic bacteria,N-acetylmuramyl-l-alanyl-d-isoglutamine (MDP), significantly induced IL-8 production and NF-κB activation in HGEC, and these cytokine-producing activities were augmented by a complex of soluble CD14 and lipopolysaccharide-binding protein (LBP). IL-8 production in HGEC stimulated with these bacterial components was clearly inhibited by mouse monoclonal antibody to human TLR2. These findings suggest that P. gingivalis fimbrial protein and its active peptide are capable of activating HGEC through TLR2.
Data Compression and Clustering: A Blind Approach to Classification

: Data Compression is today essential for a wide range of applications: for example Internet and the World Wide Web infrastructures benefits from compression. New general compression methods are always being developed, in particular those that allow indexing over compressed data or error resilience. Compression also inspires information theoretic tools for pattern discovery and classification, in particular it is possible to use data compression as a metric for clustering. This leads to a powerful clustering strategy that does not use any “semantic” information on the data to be classified but does a “blind” and effective classification that is based only on the compressibility of digital data and not on its “meaning”. Here we experiment with this strategy and show its effectiveness.
Intermingling speech groups

This paper investigates the outcomes of contact-induced language change in two neighboring speech groups (Pana and Northern Samo) in an African multilingual setting. It describes the historical and social conditions of the area as a prerequisite for the understanding of linguistic contact processes. The typological fingerprints of the languages then establish the background for understanding linguistic changes. It demonstrates that morpho-syntactic elements such as negation, copula and focus constructions, phrase-final plural, and definiteness marking are in part the result of contact in this complex setting. Finally, it proposes a possible scenario that accounts for the different contact-induced features and also raises the question of the significance of extra-linguistic parameters for a theory of language contact.
Characterization and compensation of DC offset on adaptive MIMO direct conversion transceivers

The effect of DC offset on multi-input multi-output (MIMO) direct transceivers with adaptive modulation (AM) is discussed in this paper. A variable-rate variable-power (VRVP) AM system with perfect channel state information (P-CSI) at both the transmitter and receiver in a MIMO scenario is considered. The DC offset is modeled as a zero mean complex Gaussian distributed random variable. By this modeling of the DC offset, the analytical expression for degraded bit error rate has been derived. To derive this analytical expression, some approximation has been made. The good agreement between the analytical and simulation results shows that proper approximation has been made and confirms the accuracy of the analytical expressions. Moreover, an approach to compensate the DC offset in these systems is introduced. The compensation technique is applied and the improvement has been examined.
Sentiment analysis and dataset collection: A comparitive study

The channels for expressing opinions seem to increase daily and hence, they are important sources of business insight. For product planning, marketing and customer service, it is necessary to capture and analyse these opinions. The social blogs are massive corpus for text and opinion mining. So, in this competitive and greedy world we find the need of analysis of the opinions about the products a vital task, to evaluate and improve the product quality, and as a result these opinions are relevant to companies. These opinions are easily accessed through the social blogs. This paper discusses various methods in performing sentiment analysis. We can see that presently we are provided facility to post the opinions in different languages and this paper tried to consider the processing of texts of different languages that are posted in blogs. Along with that the relevance of the quality of dataset used for the analysis are also the subject of discussion in this paper.
Measuring lesion growth from 3D medical images

Evaluating precisely the temporal variations of lesion volumes is very important for at least three types of practical applications: pharmaceutical trials, decision making for drug treatment or surgery and patient follow-up. The authors present a volumetric analysis technique, combining precise rigid registration of 3D medical images, nonrigid deformation computation and flowfield analysis. Their analysis technique has two outcomes: the detection of evolving lesions and the quantitative measurement of volume variations. The originality of their approach is that no precise segmentation of the lesion is needed but the approximative designation of a region of interest, which can be automatized. They distinguish between tissue transformation (image intensity changes without deformation) and expansion or contraction effects reflecting a change of mass within the tissue; a real lesion being generally the combination of both effects. The method is tested with synthesized 3D image sequences and applied, in a first attempt to quantify in vivo a mass effect, to the analysis of a patient with multiple sclerosis (MS).
High-Speed DPI Method Using Multi-Stage Packet Flow Analyses

Deep packet inspection (DPI) is becoming more im- portant as an effective way of providing application-aware ser-vices and improving bandwidth efficiency. In DPI, URL analysis is key because HTTP is used for most network applications. In large-scale networks, high-speed URL matching with a large-scale URL list must be capable of handling various applications and content. It must also support wildcard expressions in the URL list to achieve flexibility and compactness. We propose a high-speed matching method that uses the hierarchical structure of the URL to support wildcard-matching through multi-stage analysis. Early hash-matching stages reduce the number of executions of the following full-text-search stage by extending the levels in the URL hierarchy. Our evaluation results show that our method is 500 times faster than the conventional binary search method in certain situations.
Sexual abuse and associations with psychosocial aspects of health. A population-based study with Swedish adolescents

Background: Few population-based Nordic studies with adolescents investigate the associations between sexual abuse (SA) and psychosocial health. Aim: Associations between adolescents’ self-reported experiences of SA different severity and aspects of psychosocial health such as emotional and behavioral problems, sense of coherence and self-esteem were investigated. Methods: A school-based study with 1107 Swedish high school seniors was conducted. The students completed the Strengths and Difficulties Questionnaire (SDQ), the Sense of Coherence Scale (SOC), I think I am and a self-report survey that included questions about SA, socio-demographic variables and family variables. Results: SA was related to more emotional and behavioral problems, weaker SOC and lower self-esteem when compared with non-abuse. There was also a dose–response effect insofar as more severe abuse was related to poorer psychosocial health. When adjusted for socio-demographic and family-related variables, the associations between penetrating SA and most of the health variables weakened or disappeared. SOC was associated with penetrating SA even after adjustment. Conclusions: This population-based study suggests that SA should not be regarded as an isolated factor in relation to psychosocial health as the associations at a group level may be smaller than expected. Among a number of covariates, family variables such as parental bonding showed to be especially important. Sense of Coherence may be of special interest in further research and clinical treatment.
Correlations among First-degree Relatives for Responses on the Self-rating of the Effects of Alcohol Questionnaire in Teenagers*

co-principal investigator), which comprises nine centers at which data collection, analysis and/or storage take place. The nine sites, with principal investigator and co-investigator(s) listed respectively for each, ABSTRACT. Objective: The level of response (LR) to alcohol is an intermediate phenotype related to the alcoholism risk, with a heritabil-ity of at least 0.4 as estimated from alcohol challenge experiments. A measure of LR that can be used in adolescence at a time close to the first drinking experience, and that is less expensive than alcohol challenge experiments, is the Self-Rating of the Effects of Alcohol (SRE) Questionnaire. This questionnaire contains questions related to the number of drinks required for up to four different effects early in the drinking career (" first five " score). The familial characteristics of SRE responses have been estimated in adults; however, no study has evaluated familial and potential genetic components of the first five SRE score in adolescents. This article presents data regarding the familial nature of SRE-based scores among a sample of teenagers. Method: As part of the Collaborative Study on the Genetics of Alcoholism Phase II (follow-up) Protocol, SRE scores were available on 251 females and 236 males ages 13-19 years. These analyses compare the correlations among father-offspring, mother-offspring, sibling pairs and comparable unrelated individuals. Results: For the 487 subjects, correlations among first-degree relatives ranged from 0.14 to 0.22 and were all significant. Correlations among comparable unrelated pairs ranged from 0.02 to 0.06 and were nonsignificant. When males and females were evaluated separately , the pattern of results, with higher correlations among first-degree relatives than among unrelated individuals, was similar, although, perhaps reflecting fewer subjects, correlations were more variable. Conclusions: Although not providing a definitive evaluation of heritability, the results are consistent with a potential proportion of the variance related to genes for first five SRE scores of between 0.3 and 0.4. These results parallel previously published data in adults and are similar to heri-tability estimates for LR on alcohol challenges. The data support the potential use of the first five SRE score in adolescents as a measure of LR in genetic and environmental model-based studies in young populations for whom the evaluation of LR is taking place at a time close to the onset of drinking. A LOW LEVEL of response (LR) to alcohol is one of several independent, genetically influenced characteristics or phenotypes that have an impact on the risk for alcohol …
Robot contact task state estimation via position-based action grammars

Uncertainty is a major difficulty in endowing robots with autonomy. Robots often fail due to unexpected events. In robot contact tasks are often design to empirically look for force thresholds to define state transitions in a Markov chain or finite state machines. Such design is prone to failure in unstructured environments, when due to external disturbances or erroneous models, such thresholds are met, and lead to state transitions that are false-positives. The focus of this paper is to perform high-level state estimation of robot behaviors and task output for robot contact tasks. Our approach encodes raw low-level 3D cartesian trajectories and converts them into a high level (HL) action grammars. Cartesian trajectories can be segmented and encoded in a way that their dynamic properties, or “texture” are preserved. Once an action grammar is generated, a classifier is trained to detect current behaviors and ultimately the task output. The system executed HL state estimation for task output verification with an accuracy of 86%, and behavior monitoring with an average accuracy of: 72%. The significance of the work is the transformation of difficult-to-use raw low-level data to HL data that enables robust behavior and task monitoring. Monitoring is useful for failure correction or other deliberation in high-level planning, programming by demonstration, and human-robot interaction to name a few. The data, code, and supporting resources for this work can be found at: https://www.juanrojas. net/research/position_based_action_grammae.
Emotion induction through virtual avatars and its impact on reasoning: evidence from autonomous nervous system measurements and cognitive assessment

Many studies have shown the impact of emotion on cognition (Damasio 1995; Phelps 2004), however these influences remain ambiguous. The contradictions may be explained by a lack of experimental control but also by the existence of complex cross-influences between the dorsolateral prefrontal cortex, a major substratum of the executive functions (EFs) and the ventromedial prefrontal cortex, an area strongly connected to the limbic system (Simpson 2001a). This work aims at gaining a more precise view of the links between emotion and EFs thanks to an experimental protocol that uses Virtual Reality (avatars) for a controlled emotional conditioning, measurements of the autonomous nervous system (ANS) as evidence of the emotional variations and a neuropsychological test battery for the detection of EFs variations, especially reasoning. The battery’s major tasks consist in deductive reasoning and reasoning in dynamic situations. The experimental data show that positive conditioning leads to a performance decrease (in agreement with Phillips et al. (2002a)), together with physiological variations (cardiac and pupillary activity). Moreover negative conditioning leads to ineffective actions: more actions (Dynamic task), more quickly (Deductive task) with no performance variation. These results may have applications in neuropsychology, for the assessment and the rehabilitation of patients (Mateer et al. 2005) and in neuroergonomics in the field of complex working situations where emotions may cause accidents (e.g. potential source of air crashes, Dehais et al. 2003).
Investigating the boosting framework for face recognition

The boosting framework has shown good performance in face recognition. By combining a set of features with Adaboost, a similarity function is developed which determines if a pair of face images belongs to the same person or not. Recently, many features have been used in combination with Adaboost, achieving good results on the FERET database. In this paper we compare the results of several features on the same database and discuss our solutions on some of the open issues in this method. We compare the boosting framework with some standard algorithms and test the boosting algorithm under difficult circumstances, like illumination and registration noise.
AUDIO MUSIC MOOD CLASSIFICATION USING SUPPORT VECTOR MACHINE

The system submitted to the MIREX Audio Music Mood Classification task is described here. It uses a set of 133 descriptors and a Support Vector Machine classifier to predict the mood cluster. The features are spectral, temporal, tonal but also describe loudness and danceability. The features were selected previously according to experiments on our annotated databases. The SVM is optimized using a grid search algorithm.
Reducing the Stigma of Depression Among Asian Students

In North America, Asians reliably report higher levels of stigma toward people with depression than do Europeans. Possible methods of reducing this discrepancy have rarely been explored. Asian undergraduate students (n = 132) were presented with one of four antistigma videos with two actresses: one portraying a student with depression and the other a professor. The videos used the concept of social proof, presenting either positive or negative descriptive norms, to effect change in stigma, measured by social distance. It was hypothesized that the positive descriptive norms intervention would show significantly greater positive change in social distance compared with the negative descriptive norms intervention. All videos were effective in reducing preferred social distance toward people with depression relative to the control condition. The effectiveness of the positive descriptive norm video was mediated through descriptive norms and self-efficacy. The effectiveness of the negative descriptive norm video was mediated through injunctive norms and perceived value of support. The findings can help guide interventions that aim to encourage social engagement with people with depression among Asian student populations. Manipulating social norms and increasing self-efficacy may be especially effective.