Miracle-dz/UniFAR-FTUs huggingface dataset:
AVATAR Therapy for Refractory Auditory Hallucinations

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Gesture Features for Sentence Segmentation

One of the ways in which gesture supplements communication is by helping to identify the “meta-data” that comprises the organizational structure of the discourse. One such type of meta-data is sentence unit boundaries;the detection of sentence boundaries in informal, spontaneous speech is a difficult problem. In this abstract, we explore whether gestural cues can improve sentence boundary detection. We have hand-annotated a corpus of 26 short videos of spontaneous speech and gesture (see [1] for a more complete account of this research). We employ the movement phase and gesture phrase taxonomies as summarized by McNeill [2]. These gesture features correlate well with sentence boundaries in this corpus. Table 1 shows the probabilities of various gesture features, conditioned on the presence of sentence boundary events.
Contribution of the Cpx envelope stress system to metabolism and virulence regulation in Salmonella enterica serovar Typhimurium

The Cpx-envelope stress system regulates the expression of virulence factors in many Gram-negative pathogens. In Salmonella enterica serovar Typhimurium deletion of the sensor kinase CpxA but not of the response regulator CpxR results in the down regulation of the key regulator for invasion, HilA encoded by the Salmonella pathogenicity island 1 (SPI-1). Here, we provide evidence that cpxA deletion interferes with dephosphorylation of CpxR resulting in increased levels of active CpxR and consequently in misregulation of target genes. 14 potential operons were identified to be under direct control of CpxR. These include the virulence determinants ecotin, the omptin PgtE, and the SPI-2 regulator SsrB. The Tat-system and the PocR regulator that together promote anaerobic respiration of tetrathionate on 1,2-propanediol are also under direct CpxR control. Notably, 1,2-propanediol represses hilA expression. Thus, our work demonstrates for the first time the involvement of the Cpx system in a complex network mediating metabolism and virulence function.
Collaborative Filtering Recommendation Algorithm Based on Semantic Similarity Between Items

Collaborative filtering is one of the most popular technologies in the personal recommendation system. With user rating data becoming extremely sparse, traditional collaborative filtering recommendation algorithm calculates similarity between users using the intersection of different user rating items, and it does not consider the semantic relationship between different Items, thus recommendation quality is very poor. In order to avoid the problems above, a novel collaborative filtering algorithm based on semantic similarity between items is presented. Firstly, this method calculates similarity between items according to domain ontology, fills user rating matrix, and calculates users’ similarity with adjusted cosine measure. The experiment result shows that this method can effectively improve recommendation quality even with extreme sparse of user rating data.
The Optimal High Performance Computing Infrastructure for Solving High Complexity Problem

The high complexity of the problems today requires increasingly powerful hardware performance. Corresponding economic laws, the more reliable the performance of the hardware, it will be comparable to the higher price. Associated with the high-performance computing (HPC) infrastructures, there are three hardware architecture that can be used, i.e. Computer Cluster, Graphical Processing Unit (GPU), and Super Computer. The goal of this research is to determine the most optimal of HPC infrastructure to solve high complexity problem. For this reason, we chose Travelling Salesman Problem (TSP) as a case study and Genetic Algorithm as a method to solve TSP. Travelling Salesman Problem is belong often the case in real life and has a high computational complexity. While the Genetic Algorithm (GA) is belong a reliable algorithm to solve complex cases, but has the disadvantage that the time complexity level is very high. In some research related to HPC infrastructure comparison, the performance of multi-core CPU single node for data computation has not been done. Whereas the current development trend leads to the development of PCs with higher specifications like this. Based on the experiments results, we conclude that the use of GA is very effective to solve TSP. the use of multi-core single-node in parallel for solving high complexity problems as far as this is still better than the two other infrastructure but slightly below compare to multi-core single-node serially, while GPU deliver the worst performance compared to others infrastructure. The utilization of a super computer PC for data computation is still quite promising considering the ease of implementation, while the GPU utilization for the purposes of data computing is profitable if we only utilize GPU to support CPU for data computing.
Learning Gene Regulatory Network from Microarrays Based on Bayesian Network

The development of modern medical technology gives a big boost to analyze microarrays and construct gene regulatory network. To get a clean and straightforward gene regulatory network from enormous microarray expression data, we propose a learning algorithm based on Bayesian network. We use K-Medoids to obtain medoid genes and greedy search with BDe to construct Bayesian network structure related to the certain disease. Experimental result on REGED0 demonstrates that the gene regulatory network of lung cancer we get from microarrays contains representative genes and reveals core regulatory relationships with high effectiveness and efficiency.
2005-01 Multi-Unit Auctions to Allocate Water Scarcity Simulating bidding behaviour with agent based models

Multi-unit auctions are promising mechanisms for the reallocation of water. The main advantage of such auctions is to avoid the lumpy bid issue. However, there is great uncertainty about the best auction formats when multi-unit auctions are used. The theory can only supply the structural properties of equilibrium strategies and the multiplicity of equilibria makes comparisons across auction formats difficult. Empirical studies and experiments have improved our knowledge of multi-unit auctions but they remain scarce and most experiments are restricted to two bidders and two units. Moreover, they demonstrate that bidders have limited rationality and learn through experience. This paper constructs an agent-based model of bidders to compare the performance of alternative auction formats under circumstances where bidders submit continuous bid supply functions and learn over time to adjust their bids to improve their net incomes (reinforcement learning of Roth and Erev). We demonstrate that under the generalized Vickrey, simulated bids converge towards truthful bids as predicted by the theory and that bid shading is the rule for the uniform auction. Our study allows us to assess the potential gains from agent-based modelling approaches in the assessment of the dynamic performance of multi-unit procurement auctions. Some recommendations on the desirable format of water auctions are drawn.
Improved χ~2 statistics method for text feature selection

Feature selection is a hot topic in current search field,especially in the field of text categorization.In this paper,χ2 statistical method has two defects.One is reducing the weight of the low-frequency words.The other is increasing the weight of the characteristics in the designated class.The characteristics little appear in designated class but other classes.Through simulation and comparison experiment,the result is better than before.
Design, modeling and implementation of open programmable networks

Βασικοί στόχοι των τεχνολογιών δικτύων είναι η διαχείρισή τους και η δημιουργία νέων παρεχόμενων υπηρεσιών. Τα δίκτυα όμως είναι εν γένει ετερογενή και αυτό δημιουργεί πρόβλημα στην ευελιξία τους και στην ενσωμάτωση νέων υπηρεσιών. Οι τρέχουσες λύσεις αντιμετωπίζουν το πρόβλημα σποραδικά χωρίς κάποιο κοινό πλαίσιο. Στόχος της Διδακτορικής Διατριβής ήταν να προταθεί ένα κοινό πλαίσιο και μοντέλο για τον έλεγχου και τη διαχείριση πόρων ενός δικτύου παράλληλα με βοηθητικές και αυτόματες λειτουργίες ώστε να υποστηρίξουν τεχνικές υλοποίησης νέων υπηρεσιών σε ανοικτών δικτυακών συστημάτων. Στα πλαίσια της διδακτορικής διατριβής τέθηκαν οι απαιτήσεις του μοντέλου που θα χρειαζόταν και επιλέχθηκε το πλαίσιο ForCES από τα τρέχοντα πρότυπα πλαίσια. Η διδακτορική διατριβή συνεισέφερε στο πρότυπο ForCES με πολλαπλούς τρόπους. Εντόπισε κενά και εξέλιξε το τρέχον μοντέλο, το σύγκρινε με τα όμοιά του και επέκτεινε το πρότυπο στο επίπεδο διαχείρισης των πόρων. Επίσης προτάθηκαν και δημιουργήθηκαν βιβλιοθήκες χρησιμοποιώντας το μοντέλο που έχουν σχέση με την υψηλή διαθεσιμότητα δικτυακών οντοτήτων, για παραλληλισμό εργασιών και βοηθητικές εργασίες για εγκατάσταση και ενεργοποίηση νέων οντοτήτων. Χρησιμοποιώντας το πρότυπο αυτό, τέθηκαν τα δομικά υλικά του κοινού πλαισίου και περιγράφηκε η αρχιτεκτονική δομή των ανοιχτών δικτύων. Παράλληλα προτάθηκε η εννοιολογική και αρχιτεκτονική εξέλιξη της αρχιτεκτονικής προγραμματιζόμενων δικτύων και να συμπεριληφθούν το επίπεδο διαχείρισης και το επίπεδο λειτουργικότητας των συσκευών. Όσον αφορά την αρχιτεκτονική της εικονικοποίησης δικτυακών λειτουργιών, προτάθηκε η επέκταση της αρχιτεκτονικής με την ενσωμάτωση της διεπαφής μεταξύ του ελεγκτή και της εικονικοποιημένης λειτουργίας χρησιμοποιώντας το προτεινόμενο από την διατριβή πλαίσιο. Όσον αφορά βοηθητικές και αυτόματες λειτουργίες υλοποιήθηκε μία γλώσσα ειδικού σκοπού. Στη γλώσσα αυτή ο χρήστης περιγράφει το μοντέλο του και αυτό αυτόματα μετατρέπεται σε κομμάτια κώδικα τα οποία υλοποιούν το πρότυπο ForCES. Μετά τα κομμάτια κώδικα αυτά πρέπει να συμπληρωθούν μόνο με την λειτουργικότητα που απαιτείται. Τέλος δημιουργήθηκε μία πρωτότυπη υλοποίηση στο πλαίσιο των δικτύων κινητής τηλεφωνίας 4ης γενιάς, στον εξελιγμένο πυρήνα πακέτων. Στα δίκτυα αυτά αντιμετωπίστηκαν τα προβλήματα της δυσανάλογης αύξησης σηματοδοσίας και μεγάλης απόστασης τμημάτων της αρχιτεκτονικής χρησιμοποιώντας το προτεινόμενο πλαίσιο υλοποιώντας το τμήμα διαχείρισης του πρωτοκόλλου τούνελ GTP. Συμπερασματικά η χρήση ενός πλαισίου για ολική διαχείριση όλου του κύκλου ζωής των πόρων ενός δικτύου είναι εφικτή χρησιμοποιώντας το εξελιγμένο πρώτυπο ForCES.
PACIFY BASED VIDEO RETRIEVAL SYSTEM

Recently, Video is becoming a catholic medium for e-learning. As per the popularity of online video information over the World Wide Web (WWW) is mostly dependent on user-assigned tags or specification, which is the system by which we can access such videos. However, this system have limitations for retrieval and frequently we want access to the content (pacify) of the video itself is directly matched against a user’s query except manually assigned tags or specifications. In e-lecturing videos it contains visual and aural medium: slides of presentation and speech. in this system, we are going to retrieve the text from the videos automatically. To abstract visible information, we apply video content analysis to detect slides and optical character recognition to obtain their text. We abstract textual metadata by applying video Optical Character Recognition (OCR) technology on keyframes and Automatic Speech Recognition (ASR) on lecture audio. The ASR and OCR translate and discover slide text line types are accept for keywords abstraction, in which video and fragment-level keywords are abstracted for video searching on the basis of contents.
Right brain psychotherapy

One of the core reasons for the continued interest of the neuroscientific research community in laterality is its strong link to psychopathology. Reduced or reversed hemispheric asymmetries have been observed in several developmental and neuropsychiatric disorders such as schizophrenia (Hirnstein & Hugdahl, 2014), autism spectrum disorder (Markou, Ahtam, & Papadatou-Pastou, 2017), and dyslexia (Eglinton & Annett, 1994). Large numbers of scientific papers investigating laterality in these disorders have been published. For example, as of May 2019, the scientific search engine PubMed lists well over 1900 papers for the search term “schizophrenia laterality”. Given this ubiquity of studies investing the relation between laterality and psychopathology, it is somewhat surprising that comparatively few researchers have tried to systematically integrate laterality research with psychotherapy. Therefore, it is refreshing to see that Allan N. Schore, the author of “Right Brain Psychotherapy”, wrote a book that aims to do exactly that: outlining how our knowledge of the specific functions of the left and right hemisphere could help make psychotherapy more efficient. So what is the book about? In 1983, John Banmen from the University of British Colombia wrote in the International Journal for the Advancement of Counselling (pp. 99):
Title Visible light based occupancy inference using ensemblelearning

As a key component of building management and security, occupancy inference through smart sensing has attracted a lot of research attention for nearly two decades. Recently, a cutting edge technique visible light sensing (VLS) that utilizes the LED luminaires as light sensors has shown its promising application potentials in occupancy inference as it piggybacks on pervasive lighting infrastructure without extra equipment deployment. Although existing inference algorithms based on the VLS data set can achieve high accuracy, the performance degrades when the occupants are moving. This paper focuses on the occupancy inference issue and presents an ensemble learning algorithm to improve the inference accuracy. We use heterogeneous learning algorithms to generate diverse learners. Consequently, we adopt forward sequential pruning to enhance the ensemble that pursues inference error minimization. We conduct extensive experiments based on the field data. The experiment results show that the proposed algorithm is able to improve inference accuracy, especially for highly dynamic occupancy data set. INDEX TERMS Ensemble, occupancy inference, visible light sensing.
Camera Calibration for Stereo Vision Based on LS-SVM

Least Squares Support Vector Machines(LS-SVM) are used to learn the relationships between the image information and the 3D information,which doesn’t need to confirm internal and external parameters of the camera.In the case of binocular vision,the location relationship between the two cameras is implicated in the map relations.According to the LS-SVM and camera calibration characteristic,a new method of camera calibration for stereo vision based on the LS-SVM is proposed.The image pixel coordinate which gathers from the camera is made as input,the world coordinate is be used as output,and the given mapping relation of network realization is completed by the LS-SVM.The comparison with BP neural network shows that the calibration accuracy can be improved by using the LS-SVM.The real-time performance is good and response velocity is quick.
La arquitectura deposicional de los carbonatos del Jurásico inferior y medio relacionados con los materiales volcánicos del sureste de la Cordillera Ibérica

Esta Tesis Doctoral ha sido realizada bajo la direccion del Profesor Juan Jose GOMEZ FERNANDEZ y la tutela de la Profesora Yolanda SANCHEZ MOYA, del Departamento de Geodinamica, Estratigrafia y Paleontologia de la Universidad Complutense de Madrid. El trabajo de investigacion se ha centrado en la consecucion de dos objetivos fundamentales: 1) Datar mediante metodos bioestratigraficos el emplazamiento estratigrafico de cada uno de los niveles volcanicos interestratificados en las sucesio-nes sedimentarias del Jurasico Inferior y Medio (p.p.), localizados en la confluencia de las provincias de Teruel, Valencia y Castellon (sureste de la Cordillera Iberica), y 2) Determinar la arquitectura de-posicional de las unidades litoestratigraficas que contienen estos depositos volcanicos. Para ello, se realizo una cartografia geologica detallada y se llevaron a cabo estudios bioestratigraficos, sedimen-tologicos y cicloestratigraficos.La Tesis ha sido dividida en 4 Partes y 7 Capitulos, distribuidos de manera no equitativa entre cada una de las Partes. La PARTE I, titulada INTRODUCCION Y ANTECEDENTES, consta de dos capitu-los. En el Capitulo 1 (INTRODUCCION) se describen los objetivos planteados, los metodos de trabajo empleados y, posteriormente, la localizacion geografica, geologica y paleogeografica del area estu-diada. El Capitulo 2 se ha llamado REVISION CRITICA DE LOS ANTECEDENTES porque se pretendio que no fuese una mera enumeracion de trabajos previos, sino mas bien un analisis y critica motivados de su contenido.
WHICH FIRMS OUTPERFORM THE OTHERS UNDER UNCERTAINTY: REVISITING MILES AND SNOW TYPOLOGY

Ekonomik krizler, firmalar icin belirsiz bir ortam yaratan oldukca donusturucu olaylardir. 1930'larin Buyuk Buhranindan bu yana yasanan ve en buyuk finansal krizlerden biri olarak gorulen 2008 Kuresel Mali Krizi, 2008 ve 2009 yillarinda da Turkiye'deki kurumlari etkilemis, tum sektorlerden firmalar icin oldukca belirsiz bir ortam yaratmistir. Cok benzer kosullar ile karsi karsiya olmalarina ragmen, kuruluslar krize farkli tepkiler vermis ve farkli sonuclar elde etmislerdir. Bu calismanin amaci metal urunler ve makine endustrisindeki kuruluslarin stratejik davranislarini (Miles ve Snow’un strateji tipolojisine dayali olarak) incelemek ve kriz zamanindaki belirsizlik altinda hangi stratejinin daha iyi bir sirket performansi (ROA) yarattigini arastirmaktir.
An Automatic Video Annotation Method Based on Multiple Complementary Classifiers

A novel semi-supervised learning method based on multiple complementary classifiers is proposed according to the analysis of the existing co-training algorithms,which is applied to the automatic video semantic annotation.By constructing the classifiers with feature complementarity and model complementarity,the hidden information in unlabeled dataset is effectively exploited.Moreover,the local consistency and temporal relationship of video sequences is further applied to improve the final annotation accuracy,the improvement of annotation accuracy is about 7%.
A secure localization algorithm for WSNs based on iterative vote

Majority of localization algorithms calculate coordinates of unknown nodes based on coordinate information of beacon nodes,therefore,reliability of beacon nodes is crucial. Wireless sensor networks( WSNs) is vulnerable to be attacked in hostile environment,a secure localization algorithm is proposed. In this algorithm,nodes don't need to exchange coordinate information,it can choose reliable beacon nodes by iterative voting directly and the algorithm validity is verified by simulation.
Multiple Moving Objects Detection and Tracking under Traffic Scene

A simplified and fast algorithm for detecting and tracking multiple moving objects under real-time traffic scene is presented.This algorithm firstly uses morphological operation and reduces the frames′ resolution to get the connected regions of moving objects which are segmented out by adaptive background subtraction algorithm. ,then uses the improved nearest neighbor algorithm to match and track objects.Experimental results demonstrate that this method has better solved the contradictions between continuity,real-time and precision in tracking.
Forecasting chaotic time series based on improved genetic WNN

The chaotic time series forecast using Wavelet Neural Networks(WNN) was researched in this paper.An improved training method for WNN was presented.This method combines the Genetic Arithmetic(GA) and gradient descent BP method,and the BP method was embedded in the GA operation in order to resolve the GA's limitation in detail search capability.In the last step of this method the WNN trained by GA searches the best solution using BP method once again.The experiment on predicting the chaotic time series from Henon map validates the performance of the method in this paper;the experimental result also shows the method could assure the WNN convergence and have high forecasting precision.
Imprecisely Supervised Learning

Modern technologies have enabled us to collect large quantities of data. The proliferation of such data has facilitated knowledge discovery using machine learning techniques. However, it has also imposed great challenges for human annotators to label the massive data, which then offers proper supervision to learning algorithms. How we can improve learning accuracy with limited or imprecise supervision has recently become a much focused research subject. In this dissertation, we design effective algorithms based on modern machine learning theories to address the following two problems of: (1) learning from inconsistent labels collected from multiple annotators with varying expertise; and (2) knowledge transfer among related tasks to build more accurate predictive models. The proposed solutions will be evaluated not only on benchmark datasets but also in real-world scenarios from across disciplines. In the first direction, we develop bi-convex optimization algorithms to address annotation ambiguity from inconsistent labels. We extend the well-known support vector machine (SVM) algorithm and optimize SVM classifiers with respect to a weighted consensus of different labelers’ labels. The weights in the consensus are also automatically learned by our learning formulation. A variety of bi-convex programs are derived corresponding to different assumptions on the labeler competencies. Adding another layer of annotation ambiguity is that a labeler’s label may be associated with a set of data points rather than each individual one. We then further generalize the formulation to deal with the multiple points’ labels. Empirical results on benchmark datasets with synthetic labelers and real-life crowdsourced labels demonstrate the superior performance of our methods over the state of the art. Along the second direction, we develop new Multitask Learning (MTL) algorithms. MTL improves the generalization of the estimated models for multiple related learning tasks by capturing and exploiting the task relationships. We investigate a general framework of multiplicative multi-task feature learning which decomposes each task’s model parameters into a multiplication of two components. One component is used across all tasks and the other is task specific. Several previous methods have been proposed as special cases of our framework. We prove that this framework is mathematically equivalent to the widely used multi-task feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers. Two new learning formulations are proposed by varying the parameters in the proposed framework. We further study the method to learn task grouping with multiplicative feature sharing patterns in each group of tasks. We cluster tasks into one cluster if they select the same subset of features. We formulate an optimization problem to jointly optimize the models and grouping structure of the tasks. Empirical studies have revealed the advantages of our formulations by comparing with the state of the art. Imprecisely Supervised Learning