Miracle-dz/UniFAR-FTUs huggingface dataset:
Methods and Application of Archeological Cloud Platform for Grand Sites Based on Spatio-Temporal Big Data

Grand sites are important witnesses of human civilization. The archeology of grand sites has the characteristics of a long period, interdisciplinary study, irreversibility and uncertainties. Because of the lack of effective methods and valid tools, large amounts of archeological data cannot be properly processed in time, which creates many difficulties for the conservation and use of grand sites. This study provides a method of integrating spatio-temporal big data of grand sites, including classification and coding, spatial scales and a spatio-temporal framework, through which the integration of archeological data of multiple sites or different archeological excavations is realized. A system architecture was further proposed for an archeological information cloud platform for grand sites. By providing services such as data, visualization, standardizations, spatial analysis, and application software, the archeological information cloud platform of grand sites can display sites, ruins, and relics in 2D and 3D according to their correlation. It can also display the transformation of space and time around archeological cultures, and restored ruins in a 3D virtual environment. The platform provides increased support to interdisciplinary study and the dissemination of research results. Taking the Origin of Chinese Civilization Project as a case study, it shows that the method for data aggregation and fusion proposed in this study can efficiently integrate multi-source heterogeneous archeological spatio-temporal data of different sites or different periods. The archeological information cloud platform has great significance to the study of the origin of Chinese civilization, dissemination of Chinese civilization, and the public participation in archeology, which would promote the sustainable development of the conservation and use of grand sites.
A Novel Sequence Generation Approach to Diagnose Faults in Reconfigurable Scan Networks

With the complexity of nanoelectronic devices rapidly increasing, an efficient way to handle large number of embedded instruments became a necessity. The IEEE 1687 standard was introduced to provide flexibility in accessing and controlling such instrumentation through a reconfigurable scan chain. Nowadays, together with testing the system for defects that may affect the scan chains themselves, the diagnosis of such faults is also important. This article proposes a method for generating stimuli to precisely identify permanent high-level faults in a IEEE 1687 reconfigurable scan chain: the system is modeled as a finite state automaton where faults correspond to multiple incorrect transitions; then, a dynamic greedy algorithm is used to select a sequence of inputs able to distinguish between all possible faults. Experimental results on the widely-adopted ITC'02 and ITC'16 benchmark suites, as well as on synthetically generated circuits, clearly demonstrate the applicability and effectiveness of the proposed approach: generated sequences are two orders of magnitude shorter compared to previous methodologies, while the computational resources required remain acceptable even for larger benchmarks.
Valoración de la implantación de un Programa Integral de Cuidados Paliativos. Visión retrospectiva 1995-1999

Background: health authorities in Catalonia have designed a palliative care program that includes attention to terminally ill patients both at home (home care team) and in specific inpatients units located in long term stay centres as well in general hospitals. We study the % of dying patients of cancer that receive a specialist care by one of the different teams available in the southern area of the sanitary region of Lleida, with a population of 292,650 inhabitants. We study the progress of the covering rate offered by the different specific palliative care teams within 1995 and 1999. We also focus the study in the following related items: age, gender and main diagnosis. Methods: retrospective and descriptive study included into the category of ecological studies. We review clinical files of those patients who died because an oncological disease and who were looked after by one of the specific teams availables in the area. Results: on 1995, the different palliative care teams attended the 31.07% of the patients who died because of an oncological disease. Those figures increased up to 41.58% on 1999. The home care teams (PADES team) had the most important increase in their activity (8.36% on 1995 and the 17.73% on 1999). The mean of age of the patients was of 70 years on 1995 (0.5 =70.1/69.9), while on 1999 was of 71 years (0.5 =73.7/69.3). Regarding the different teams, on 1995 were the long term stay hospitals who had the higher mean of age among the patients attended (77.1 years); On 1999, the highest figures were seen among patients attended at home (72.5 years). Also, the prescription of strong opioids in the sanitary region of Lleida (mesurated in DDD) increases proportionally to the % of terminally ill patients attended by the different palliative care teams. Conclusions: we observe an increase in the % of patients seen by the different palliative care teams as well an increase in the total number of patients attended, mainly due to the palliative home care teams' activity. The patients' mean of age remains almost invariable over the period 1995-1999, as well the ratio male/female. Cancer of the lung, breast, gynecological, urological and digestive areas are the 2/3 of the main diagnosis seen. The increase found was not uniform in the different geographical areas studied. Data obtained are not uniform in the different geographical areas where the study was carried out, even if the accessibility to the different specialist palliative care teams were similar in all the cases.
BiLSTM-CRF Chinese Named Entity Recognition Model with Attention Mechanism

In order to make up for the weakness of insufficient considering dependency of the input char sequence in the deep learning method of Chinese named entity recognition task, this paper proposes a method, which integrate Bidirectional Long Short-Term Memory (BiLSTM), attention mechanism and add the information of word vector. Firstly, the proposed model obtains the char vector feature extracted from the text corpus, which is then input to the BiLSTM model; Secondly, the attention mechanism is used to calculate the relevance between the current input char and the other input char of the BiLSTM model; Finally, the global feature is obtained according to relevance, concatenating the word vector feature, which is introduced to the Conditional random field(CRF) layer to perform the mutual constraint between tags. Thus, the classified result can be obtained. Based on the corpus of the Chinese Peoples’ Daily Newspaper in 1998, our experiments show that the proposed method can improved the performance and efficiency of named entity recognition, compared to the existing deep-learning method that combines word vector and char vector.
Distributed Information and Control system reliability enhancement by fog-computing concept application

The paper focuses on the information and control system reliability issue. Authors of the current paper propose a new complex approach of information and control system reliability enhancement by application of the computing concept elements. The approach proposed consists of a complex of optimization problems to be solved. These problems are: estimation of computational complexity, which can be shifted to the edge of the network and fog-layer, distribution of computations among the data processing elements and distribution of computations among the sensors. The problems as well as some simulated results and discussion are formulated and presented within this paper.
Multi-scale design methods for Topology Optimization

In this thesis multi-scale design methods for topology optimizations are presented. The goal of these methods is to find manufacturable designs, with a close to optimal stiffness at a reduced computational cost compared to well-established topology optimization methods. First, the theory of homogenization-based topology optimization is discussed. The modeling of microscopic details is considered, as well as optimal microstructures that have extremal stiffness. This theory is well-developed and can be used to find an overall optimal material distribution at low computational cost. A downside of these optimal multi-scale designs is that they cannot be manufactured on a single length-scale. The main contribution of the research in this thesis is to develop and extend on new methods, such that these optimal designs can be interpreted on a single scale, while still being close to what is theoretically possible. Simple and close to optimal single-scale microstructures are presented that are optimized for multiple anisotropic loading conditions. A method to approximate optimal microstructures on a single-scale is proposed, which are close (e.g. 10-15%) to the theoretical bounds. When used as starting guess for topology optimization these proposed microstructures can be further improved, outperforming topology optimized designs using classical starting guesses both in performance and simplicity. Furthermore, a class of simple periodic truss lattice structures is presented that exhibit near-optimal performance in the high porosity limit, while still being well-connected. The performance difference between closed and open-walled microstructures is presented for anisotropic loading situations, where it is demonstrated that the maximum difference occurs when isotropic microstructures are considered. Furthermore, a method to interpret spatially varying microstructures on a singlescale is presented. Using this method high-resolution and near optimal designs can be achieved on a standard PC in less than 10 minutes. An extension of this method to enforce a minimum feature size and a method to locally adapt the microstructure spacing are shown. The promise and drawbacks of this multi-scale design method are discussed with emphasis on the full-scale performance. Furthermore, the overall solution procedure is shown as well as an extension of the method to obtain coated designs, i.e. a solid shell surrounding porous infill material. In a similar work, the link between Michell’s theory of least-weight trusses and optimal laminates is exploited to extract a discrete frame structure from a continuum design. Subsequent frame optimization results in convergence towards known optimal solutions at low computational cost.
Noise Sensitivity on the p -Biased Hypercube

The noise sensitivity of a Boolean function measures how susceptible the value of f on a typical input x to a slight perturbation of the bits of x: it is the probability f(x) and f(y) are different when x is a uniformly chosen n-bit Boolean string, and y is formed by flipping each bit of x with small probability ε. The noise sensitivity of a function is a key concept with applications to combinatorics, complexity theory, learning theory, percolation theory and more. In this paper, we investigate noise sensitivity on the p-biased hypercube, extending the theory for polynomially small p. Specifically, we give sufficient conditions for monotone functions with large groups of symmetries to be noise sensitive (which in some cases are also necessary). As an application, we show that the 2-SAT function is noise sensitive around its critical probability. En route, we study biased versions of the invariance principle for monotone functions and give p-biased versions of Bourgain's tail theorem and the Majority is Stablest theorem, showing that in this case the correct analog of ``small low degree influences'' is lack of correlation with constant width DNF formulas.
RECOMMENDER SYSTEM USING UNSUPERVISED MACHINE LEARNING FOR SATISFACTION SURVEYS

Satisfaction surveys are being used more and more by companies to improve their sales force. With the development of new technologies the piloting of these satisfaction surveys is digitized in a partial way. Piloting these surveys often involves the expertise of a human agent in order to make a judgment on the results obtained from satisfaction surveys. This is a tedious task for the decision-maker, as it faces a huge and heterogeneous amount of data. This problem may be mitigated by using a recommendation engine based on the unsupervised machine learning algorithm. This recommendation system (RS) will be oriented towards two axes: decision-making (DM) and machine learning (ML). In our approach, we use RS for consistency between the user and the recommended items. ML will allow us to include in our list of recommendations, unexpected items, items that are not derived from the algorithmic logic of the recommendation system and to make the system partially autonomous on decision-making (to less involving the recommendation engine). Our approach is divided into a) the recommendation process for decision-making, b) unsupervised ML and c) partial "empowerment" for decision-making.
Eye Blink Rate Predicts and Dissociates Effective Execution of Early and Late Stage Creative Idea Generation

In the present study, the correlations of eye blink rate (EBR) with the effective execution of early and late creative idea generation were explored. Participants engaged in a real-world idea generation task. Resting state EBR (before the task) and task-evoked EBR (during the task) were measured using eye-tracking. The results showed that resting state EBR negatively correlated with the amount of generated ideas during early stage, but not late stage idea generation. Task-evoked EBR did not correlate with the amount of generated ideas during early nor late stage idea generation. However, the change in EBR (from resting state to during early or late stage idea generation) positively correlated with the amount of ideas generated during early, but not during late stage idea generation. The contribution of this study is that it shows that EBR predicts and dissociates the effective execution of early and late stage creative idea generation.
DeepGaze III: Using Deep Learning to Probe Interactions Between Scene Content and Scanpath History in Fixation Selection

Many animals make eye movements to gather relevant visual information about the environment. How fixation locations are selected has been debated for decades in neuroscience and psychology. One hypothesis states that “priority” or “saliency” values are assigned locally to image locations, independent of saccade history, and are only later combined with saccade history and other constraints to select the next fixation location. A second hypothesis is that there are interactions between saccade history and image content that cannot be summarised by a single value. Here we discriminate between these possibilities in a data-driven manner. Using transfer learning from the VGG deep neural network, we train a model of scanpath prediction “DeepGaze III” on human free-viewing eye scanpath data. DeepGaze III can either be forced to use a single saliency map or can be allowed to learn complex interactions via multiple saliency maps. We find that using multiple saliency maps gives no advantage in scanpath prediction compared to a single saliency map. This suggesest that – at least for free-viewing – no complex interactions between scene content and scanpath history exist and a single saliency map may exist that does not depend on either current or previous gaze locations.
DOCUMENTATION, ANALYSIS AND REPRESENTATION OF MODERNIST
HERITAGE THROUGH BUILDING INFORMATION MODELING

Abstract. The paper is focused on research activities on documentation, analysis and representation of Modernist Heritage in Latin America. The research is part of a broad project developed by the Department of Architecture of the University of Ferrara following the main aim of documentation for knowledge, enhancement and preservation of cultural heritage. In addition to several on site projects developed in cooperation with local institutions, mainly in Brazil, and based on historical analysis, documentation, survey, modelling and diagnostic assessment, the research has then opened up to an in-depth study of Modernist architecture. The field of investigation has been extended to Latin America, through the analysis of the main architectures of the iconic figures of Modernism, in order to collect a database of models as support for different research avenues. The methodological process is based on the “survey of the project”, an analysis of documentation as the starting point for the representation in BIM environment from which to extract conventional representations and different levels of interpretation.
Multi-Image Blind Deconvolution Using Low-Rank Representation

Blind deconvolution is a restoration process of an image which is blurred by an unknown point spread function (PSF) (a.k.a. blur kernel). Some previous works [1], [2], [3], [4] have shown that multiple blurred captures of the same scene can improve the quality of blind deconvolution result. However, the previous multi-image blind deconvolution methods are prone to inconsistencies between observations such as moving objects, illumination change or mis-alignment. In this paper, we present a new multi-image blind deconvolution algorithm using low-rank representation which utilizes similar components of the multiple images for collaboration. Our framework alternatively solves a Schatten-0 norm low-rank approximation and a MAP-based L0 norm blind deconvolution for finding the true latent images and their corresponding PSFs and warping parameters. The experimental results show that our approach can recover high quality images in the presence of possible corruptions on both static and moving scenes and outperforms the state-of-the-art results.
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning

Recently, neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC). However, these approaches often offer little transparency w.r. t. their inner working mechanisms and lack interpretability. In this paper, to simulating the steps of analyzing aspect sentiment in a document by human beings, we propose a new Hierarchical Reinforcement Learning (HRL) approach to DASC. This approach incorporates clause selection and word selection strategies to tackle the data noise problem in the task of DASC. First, a high-level policy is proposed to select aspect-relevant clauses and discard noisy clauses. Then, a low-level policy is proposed to select sentiment-relevant words and discard noisy words inside the selected clauses. Finally, a sentiment rating predictor is designed to provide reward signals to guide both clause and word selection. Experimental results demonstrate the impressive effectiveness of the proposed approach to DASC over the state-of-the-art baselines.
A kernel-induced weighted object-cluster association-based ensemble method for educational data clustering

ABSTRACT Exploiting single models for a better ensemble one is popular for a classification or clustering task. For educational data clustering, our work proposes a kernel-induced weighted object-cluster association ensemble method, named kWOCA, to discover the inherent structure of the data and return the student clusters of higher quality. kWOCA is an advanced version that can automatically determine the number of desired clusters based on Bayesian Information Criterion. It also conducts consensus clustering in the feature space for non-linearly separated clusters where the discrimination between the objects is captured from the base clusterings. Besides, it encodes the differences between the clusters in each base clustering and those between the base clusterings in the ensemble when making a synthesis of the base clusterings. A kernel-induced weighted object-cluster association matrix is defined to store such rich information. Using this matrix, kWOCA outperforms the existing clustering ensemble methods. Experimental results on the real educational data sets and the benchmark Iris data set show the better effectiveness of kWOCA with higher Normalized Mutual Information values. As a result, groups of the most similar students based on their study performance can be discovered better. These resulting student clusters can be then further analyzed for academic affairs.
Towards Jumping Locomotion for Quadruped Robots on the Moon

Jumping locomotion has the potential to enable legged robots to overcome obstacles and travel efficiently on low-gravity celestial bodies. We present how the 22 kg quadruped robot SpaceBok exploits lunar gravity conditions to perform energy-efficient jumps. The robot achieves repetitive, vertical jumps of more than 0.9m meter and powerful single leaps of up to 1.3m. We present the implementation of a reaction wheel, which allows for control of the robots pitch orientation during the flight phase. We also demonstrate the implementation of a parallel elasticity in the legs providing the capability of temporarily storing and reusing energy during jumping. The jumping and attitude controller are subsequently presented. Finally, we analyze the energetics of the system and show that jumping with the integrated elasticity significantly reduces energy consumption compared to non-elastic jumps.
Information Theoretical Measurements From Induction Motors Under Several Load and Voltage Conditions for Bearing Faults Classification

Three-phase induction motors are widely employed in industrial production processes, due to their low costs of acquisition and maintenance as well as their adaptation in front of different coupled load conditions and robustness for use in harsh environments. Methods that detect an incipient fault when these motors are in operation, even before significant faults may occur, have received special attention from the industry. Thus, a proper diagnosis can lead to a reduction of process losses, service time out, and equipment replacement, also ensure consistent and reliable operation of industrial systems. Thus, this article presents a comprehensive study concerning the detection of bearing faults in induction motors using information theoretical measurements and intelligent tools. Various motor operation conditions are considered, such as variations in coupled load torque and two kinds of power supply: line connected or inverter fed. This article proposes the extraction of fault characteristics in time domain, based on mutual information measurements between two-phase current signals. The method is evaluated under various fault severity levels and the performance of three different pattern recognition techniques were compared: artificial neural networks, specifically multilayer perceptron, k-nearest neighbors, and support vector machines. The experimental results presented in this article validate the robustness and effectiveness of the proposed methodology.