Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning relations using semantic-based vector similarity

The amount of electronic medical documents is growing rapidly every day. While they carry much information, it becomes more and more difficult to manually process it. Our work represents small steps towards automatic knowledge extraction from medical documents using deep learning and similarity based methods. Our goal here is to identify in an unsupervised manner relations between known medical concepts employing a deep learning strategy with Word2Vec. The current solution requires concepts annotations, as it evaluates the similarities between concepts to identify the relationship between them. The experiments suggest that the strategy we considered (to include the POS as part of the information associated to concepts and relation) represents an important step towards a fully unsupervised learning strategy. Although the POS tags alone are not good enough predictors, the addition of other meta-information and sufficient (quantitative and qualitative) training data may enhance the relation identification process, allowing for a meta learning strategy.
Optimal design of an Indian carpet weaving loom structure

Present looms to weave the carpets in India are made of wood, which are susceptible to termite attacks and low service life. Besides, the high tension in the warps is generated manually through the pull of a rope by 2-3 persons. In order to avoid the above difficulties, an improved metallic loom was developed at IIT Delhi in 2001, which made weaving easy but the cost of loom is high. In this paper, optimisation of the metallic loom is carried out resulting in relatively lightweight and reduced cost. The design is verified using the Finite Element Analysis software, ANSIS.
Transient Analysis and Execution-Level Power Tracking Control of the Concentrating Solar Thermal Power Plant

Concentrating solar power (CSP) is a promising technology for exploiting solar energy. A major advantage of CSP plants lies in their capability of integrating with thermal energy storage; hence, they can have a similar operability to that of fossil-fired power plants, i.e., their power output can be adjusted as required. For this reason, the power output of such CSP plants is generally scheduled to maximize the operating revenue by participating in electric markets, which can result in frequent changes in the power reference signal and introduces challenges to real-time power tracking. To address this issue, this paper systematically studies the execution-level power tracking control strategy of an CSP plant, primarily aiming at coordinating the control of the sluggish steam generator (including the economizer, the boiler, and the superheater) and the fast steam turbine. The governing equations of the key energy conversion processes in the CSP plant are first presented and used as the simulation platform. Then, the transient behavior of the CSP plant is analyzed to gain an insight into the system dynamic characteristics and control difficulties. Then, based on the step-response data, the transfer functions of the CSP plant are identified, which form the prediction model of the model predictive controller. Finally, two control strategies are studied through simulation experiments: (1) the heuristic PI control with two operation modes, which can be conveniently implemented but cannot coordinate the control of the power tracking speed and the main steam parameters, and (2) advanced model predictive control (MPC), which overcomes the shortcoming of PI (Proportional-Integral) control and can significantly improve the control performance.
A Multi-step Approach for Scientific Data Exploration

In the modern age, the technological development allows the acquisition of more and more complex scientific data and today data analysis systems need advanced methodologies in order to mine and discover useful information. Aim of this work, therefore, is to propose an interactive multi-step approach for interactive data exploration. With data exploration we refer here, specifically, to clustering and visualization of complex scientific data. The data analysis process permits to explore and study raw datasets following, roughly, four steps: preprocessing, pre-clustering, agglomerative clustering, visual and interactive data exploration. One of the main advantages of the proposed approach is to divide the clustering phase in two steps: the first one is used to build an initial rough partition of the data in order to reduce the complexity of the problem. In the second step these clusters are presented to the user which searches interactively for structures in the data by means of a hierarchical agglomerative clustering approach. The proposed method has been developed in the Matlab computing environment and the implementation result is a user-friendly software tool, namely AstroNeural, which offers several clustering, visualization and data interaction techniques.
Toward bio-inspired network robustness - Step 1. Modularity

Biological systems have evolved themselves to withstand against perturbations so that a characteristic, called robustness, is the most commonly observed feature in all living organisms. To find out the secret of robustness in biological systems, many researchers have investigated the system level structure of biological organizations. One of the known structural features that enable biological systems to be robust is modularity. In this paper we study the correlation between modularity structure and robustness in IP networks. We carry out a simulation study to observe resistibility of different topologies, which have different level of modularity structure, against a perturbation created synthetically. The numerical results show that the quantified modularity seems to be more important measure to understand robustness of IP networks than any other common properties such as clustering coefficient, degree distribution, and average path length.
A layered algorithm image coding based on combination of wavelet and ridgelet

This paper proposes a new layered image coding algorithm combined the advantages of wavelet and ridgelet transformations. First of all, the origin image I smoothed by an improved anisotropic diffusion method, obtained an smooth image R1, then subtracted R1 from I and obtained an texture image R2. R1 encoded by the SPIHT algorithm based on DWT, and R2 encoded by a amending SPIHT based on ridgelet. The experimental results show that this paper algorithm is better than the SPIHT compression algorithm in the case of the same rate, especially for rich texture image.
A comparative study of discretization methods for naive-Bayes classifiers

Discretization is a popular approach to handling numeric attributes in machine learning. We argue that the requirements for eec- tive discretization dier between naive-Bayes learning and many other learning algorithms. We evaluate the eectiveness with naive-Bayes clas- sifiers of nine discretization methods, equal width discretization (EWD), equal frequency discretization (EFD), fuzzy discretization (FD), entropy minimization discretization (EMD), iterative discretization (ID), propor- tional k-interval discretization (PKID), lazy discretization (LD), non- disjoint discretization (NDD) and weighted proportional k-interval dis- cretization (WPKID). It is found that in general naive-Bayes classifiers trained on data preprocessed by LD, NDD or WPKID achieve lower classification error than those trained on data preprocessed by the other discretization methods. But LD can not scale to large data. This study leads to a new discretization method, weighted non-disjoint discretiza- tion (WNDD) that combines WPKID and NDD's advantages. Our ex- periments show that among all the rival discretization methods, WNDD best helps naive-Bayes classifiers reduce average classification error.
Brownian Motion and Harnack Inequality for Schrodinger Operators

1. Introduction In this paper we shall discuss solutions of the time independent Schrodinger equation Since V will not be required to go to zero at infinity, one can obtain results on solutions of Hu = Eu by changing V to V-E. Two kinds of results will interest us. The first are often called subsolution estimates since they hold for functions with Hu 5 0, u 2 0 : the constant D depending only on some local norms of V. In many cases (1.3) will hold with a constant D which is independent of x. The second kind are results of the type of the Harnack inequaliy. It states that if 52,Q' are bounded open sets with c 52', then there is a constant C, depending only on 52,C and norms of V , such that all solutions of (1.1) with u 2 0 on 9' satisfy (1-4) u (x) S C u (y) for all x, y E 52. We remark that if HI+ = 0, then H lI+l S 0 so that (1.3) holds with Both types of results were proven for a wide class of potentials V by Trudinger [36], following the approach of Stampacchia (341, which uses in part a set of ideas due to Moser [20]. Our interest in these results was stimulated by their use in work by M. and T.
Using Social Network Analysis to Examine the Intersection of Adolescent Friendships and Health Behavior

52 Introduction . 53 Method . 57 x Page Results . 63 Discussion . 66 5. SUMMARY . 71 REFERENCES . 77 APPENDICES . 85 Appendix A: APA Copyright for Chapter 2 . 86 Appendix B: Coauthor Permission for Tyson Barrett . 92 Appendix C: Coauthor Permission for Thomas Valente . 94 CURRICULUM VITAE . 96
Dentin hipersensitivity following non-surgical periodontal therapy with hand or ultrasonic instruments

Periodontal instrumentation aims to remove plaque and calculus from the root surface. Both manual and ultrasonic instruments have been used for such decontamination; however, establishing a healthy periodontium can result in adverse effects, such as dentin hypersensitivity. Objective: To evaluate the effects of hand or ultrasonic instrumentation on dentin hypersensitivity in patients undergoing non-surgical periodontal treatment. For this controlled clinical trial of a “split mouth” design, 14 patients were selected with homologous teeth in the incisor to premolar region and probing depth ≥ 5 mm on the buccal aspect of the teeth. One side (control) was instrumented with hand instruments and the other side (test) with ultrasonic instruments. Dentin hypersensitivity was assessed on baseline and during the 4 weeks after treatment, with a periodontal probe scratching the root surface and with an air jet. The patient’s response was detected by a visual analog scale of 10 cm. There was no statistical difference between the effectiveness and the occurrence of hypersensitivity between the proposed therapies. After the instrumentation the occurrence of hypersensitivity occurred at low levels, and disappeared completely at 4 weeks of evaluation. In the first week, there was a statistically significant increase in hypersensitivity in the control group after stimulation with air. The air jet stimulus, in comparison to scratching caused more discomfort during the hypersensitivity evaluation after the both treatments on the 1st, 2nd and 3rd weeks. Based on these conditions, this study demonstrated that there was no difference in dentin hypersensitivity following ultrasonic or hand instrumentation. UNITERMOS: Dentin sensitivity, Dental scaling, Ultrasonic. R Periodontia 2018; 28: 13-18.
The AT&T WATSON speech recognizer

This paper describes the AT&T WATSON real-time speech recognizer, the product of several decades of research at AT&T. The recognizer handles a wide range of vocabulary sizes and is based on continuous-density hidden Markov models for acoustic modeling and finite state networks for language modeling. The recognition network is optimized for efficient search. We identify the algorithms used for high-accuracy, real-time and low-latency recognition. We present results for small and large vocabulary tasks taken from the AT&T VoiceTone/sup /spl reg// service, showing word accuracy improvement of about 5% absolute and real-time processing speed-up by a factor between 2 and 3.
On diagnosing Alzheimer's disease: assessing abstract thinking and reasoning

A series of abstract thinking and reasoning tasks was administered to patients with Alzheimer's disease (AD) and a sample of nondemented older adults matched on age, education, and gender variables. The performance of the AD patients was inferior to control subjects on all verbal and nonverbal reasoning tests, including a newly developed test of analogical reasoning, the Goranson Analogy Test (GAT). Preliminary psychometric analyses of the GAT revealed very high internal consistency, good convergent and divergent validity, and adequate predictive validity. Further analyses revealed that reasoning with pictures was just as easy as reasoning with words for AD patients, indicating that modality of presentation has little effect on reasoning performance. Error analyses revealed no qualitative differences in performance between AD patients and nondemented controls. Taken together, the findings suggest that abstract thinking and reasoning abilities decline with the onset of Alzheimer's dementia. A neurocognitive model of analogical reasoning is proposed to account for the study findings.
Human skeleton matching for e-learning of dance using a probabilistic neural network

With the growing interest in the domain of human computer interaction (HCI) these days, budding research professionals are coming up with novel ideas of developing more versatile and flexible modes of communication between a man and a machine. Using the attributes of internet, the scientists have been able to create a web based social platform for learning any desired art by the subject himself/herself, and this particular procedure is termed as electronic learning or e-learning. In this paper, we propose a novel application of gesture dependent e-learning of dance. This e-learning procedure may provide help to many dance enthusiasts who cannot learn the art because of the scarcity of resources despite having great zeal. The paper mainly deals with recognition of different dance gestures of a trained user such that after detecting the discrepancies between the gestures shown and actually performed by a novice; the user can rectify his faults. The elementary knowledge of geometry has been employed to introduce the concept of planes in the feature extraction stage. Actually, five planes have been constructed to signify major body parts while keeping the synchronous parts in one unit. Then four distances and four angular features have been obtained to provide entire positional information of the different body joints. Finally, using a probabilistic neural network the dance gestures have been classified after training the said network with sufficient amount of data recorded from numerous subjects to maintain generality.
Iuriservice II: ontology development and architectural design

This paper shows the ontology development for the second version of the prototype Iuriservice II, an i-FAQ for judicial use containing a repository of professional judicial knowledge. We define the epistemological and ontological levels as separate issues. The domain and middle-out ontology will be connected to the SEKT top and upper level ontology PROTON. In the second part of the paper, we introduce a refinement of the architectural design, especially conceived to improve the user/system interaction by using searching, scoring and matching algorithms for a multidisciplinary ontological processing.
Efficient In-Network Processing for a Hardware-Heterogeneous IoT

As the number of small, battery-operated, wireless-enabled devices deployed in various applications of Internet of Things (IoT), Wireless Sensor Networks (WSN), and Cyber-physical Systems (CPS) is rapidly increasing, so is the number of data streams that must be processed. In cases where data do not need to be archived, centrally processed, or federated, in-network data processing is becoming more common. For this purpose, various platforms like DRAGON, Innet, and CJF were proposed. However, these platforms assume that all nodes in the network are the same, i.e. the network is homogeneous. As Moore's law still applies, nodes are becoming smaller, more powerful, and more energy efficient each year; which will continue for the foreseeable future. Therefore, we can expect that as sensor networks are extended and updated, hardware heterogeneity will soon be common in networks - the same trend as can be seen in cloud computing infrastructures. This heterogeneity introduces new challenges in terms of choosing an in-network data processing node, as not only its location, but also its capabilities, must be considered. This paper introduces a new methodology to tackle this challenge, comprising three new algorithms - Request, Traverse, and Mixed - for efficiently locating an in-network data processing node, while taking into account not only position within the network but also hardware capabilities. The proposed algorithms are evaluated against a naïve approach and achieve up to 90% reduction in network traffic during long-term data processing, while spending a similar amount time in the discovery phase.
Rubber bands, convex embeddings and connectivity

We give various characterizations of k-vertex connected graphs by geometric, algebraic, and ”physical” properties. As an example, a graph G is k-connected if and only if, specifying any k vertices of G, the vertices of G can be represented by points of R so that no k are on a hyperplane and each vertex is in the convex hull of its neighbors, except for the k specified vertices. The proof of this theorem appeals to physics. The embedding is found by letting the edges of the graph behave like ideal springs and letting its vertices settle in equilibrium. As an algorithmic application of our results we give probabilistic (Monte-Carlo and Las Vegas) algorithms for computing the connectivity of a graph. Our algorithms are faster than the best known (deterministic) connectivity algorithms for all k ≥ n, and for very dense graphs the Monte Carlo algorithm is faster by a linear factor.
Odin/OSIRIS observations of stratospheric NO 3 through sunrise and sunset

The nitrate radical (NO 3 ) has been detected in visible limb-scattered spectra measured by the Optical Spectrograph and InfraRed Imager System (OSIRIS) on-board the Odin satellite when observing at large solar zenith angles (91–97°). Apparent slant column densities of NO 3 at tangent heights between 10 and 45 km are derived via spectral fitting in the 610–680 nm window. Using observations from multiple scans spanning solar zenith angles of 91–97°, the rapid evolution of NO 3 through sunrise and sunset can be traced. Slant column densities are found to be generally consistent with those simulated using a radiative transfer model with coupled photochemistry. In addition, a strong dependence of NO 3 with temperature is observed. These results indicate that our current knowledge of NO 3 photochemistry is generally consistent with OSIRIS observations to within the limitations of the radiative transfer modeling. Furthermore, they reveal that OSIRIS possesses signal-to-noise sufficient to make useful measurements of scattered sunlight out to solar zenith angles of 91–97° and suggest the possibility of retrieving profile information for NO 3 and other species at large solar zenith angles.
Pneumatic Networks for Soft Robotics that Actuate Rapidly

Soft robots actuated by inflation of a pneumatic network (a “pneu‐net”) of small channels in elastomeric materials are appealing for producing sophisticated motions with simple controls. Although current designs of pneu‐nets achieve motion with large amplitudes, they do so relatively slowly (over seconds). This paper describes a new design for pneu‐nets that reduces the amount of gas needed for inflation of the pneu‐net, and thus increases its speed of actuation. A simple actuator can bend from a linear to a quasi‐circular shape in 50 ms when pressurized at ΔP = 345 kPa. At high rates of pressurization, the path along which the actuator bends depends on this rate. When inflated fully, the chambers of this new design experience only one‐tenth the change in volume of that required for the previous design. This small change in volume requires comparably low levels of strain in the material at maximum amplitudes of actuation, and commensurately low rates of fatigue and failure. This actuator can operate over a million cycles without significant degradation of performance. This design for soft robotic actuators combines high rates of actuation with high reliability of the actuator, and opens new areas of application for them.
Pattern-sensitive Time-series Anonymization and its Application to Energy-Consumption Data

Time series anonymization is an important problem. One prominent example of time series are energy consumption records, which might reveal details of the daily routine of a household. Existing privacy approaches for time series, e.g., from the field of trajectory anonymization, assume that every single value of a time series contains sensitive information and reduce the data quality very much. In contrast, we consider time series where it is combinations of tuples that represent personal information. We propose (n; l; k)-anonymity, geared to anonymization of time-series data with minimal information loss, assuming that an adversary may learn a few data points. We propose several heuristics to obtain (n; l; k)-anonymity, and we evaluate our approach both with synthetic and real data. Our experiments confirm that it is sufficient to modify time series only moderately in order to fulfill meaningful privacy requirements.
Application-based Characterization of the Traffic of a Campus-wide Wireless Network

With the increasing deployment of WLANs, network management and configuration of wireless Access Points (APs) has become one of the main concerns of network operators. While statistics and measurements regarding the overall usage of individual APs are readily available, the limited knowledge of the wireless traffic demand in terms of application types hinders efficient network provisioning. In this paper, we provide an extensive application characterization of a large scale campuswide wireless LAN going beyond the port-number limitation across three dimensions. Specifically, we examine the application cross-section at the network, client and AP level. We find that the most popular application types both in terms of the number of flows, bytes and clients are web and peer-to-peer. Further, we show that while the majority of the APs is dominated by these two applications, APs that belong to the same building types appear to have large differences in their application traffic mix. Finally, we observe that clients use the wireless network mostly for one specific application, but the traffic share of each application is significantly affected as users move within the wireless network.
Improving 3D Visualizations: Exploring Spatial Interaction with Mobile Devices

3D data visualizations, while offering a lot of potential, have also well-known issues regarding occlusion and readability. Immersive technologies might help overcoming these issues by addressing the perceptional problems and increasing the tangibility of the data. In this work, we explore the potential of spatial interaction with mobile devices. Building on the related work and our own experiences, we report on visualizations that are fixed in space or fixed on the device, as well as combining them with head-coupled perspective. A number of prototypes we developed, helped us to gain practical insights in the possibilities and limitations of these techniques.