Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Experience in Endoscopic Transoral Resection for Parapharyngeal Space Tumors

The purpose of this study was to investigate the feasibility, safety, and efficacy of the resection of parapharyngeal space (PPS) tumors via an endoscopic transoral approach. We reviewed 9 patients who were diagnosed with PPS tumors and who were treated with an endoscopic transoral approach. PPS tumors ranging from 2.5 to 6 cm were removed completely with no complications and excellent recovery (mean inpatient hospital stay: 6.89 days). Pathology was pleomorphic adenoma (n =7), schwannoma (n = 1) and malignant pleomorphic adenoma (n = 1). For the malignant lesion, the patient underwent postoperative radiotherapy (70 Gy). There was no radiographic evidence of recurrences, with mean follow-up of 11.22 months (range: 3 to 20). We conclude that resection of PPS tumors via an endoscopic transoral approach appears to be feasible, safe, and effective. Potential advantages of this approach include an excellent surgical view, rapid surgical access, less tissue injury, avoidance of external scar, fewer postoperative complications, and less morbidity.
A Robust Step Detection Algorithm and Walking Distance Estimation Based on Daily Wrist Activity Recognition Using a Smart Band

Human activity recognition and pedestrian dead reckoning are an interesting field because of their importance utilities in daily life healthcare. Currently, these fields are facing many challenges, one of which is the lack of a robust algorithm with high performance. This paper proposes a new method to implement a robust step detection and adaptive distance estimation algorithm based on the classification of five daily wrist activities during walking at various speeds using a smart band. The key idea is that the non-parametric adaptive distance estimator is performed after two activity classifiers and a robust step detector. In this study, two classifiers perform two phases of recognizing five wrist activities during walking. Then, a robust step detection algorithm, which is integrated with an adaptive threshold, peak and valley correction algorithm, is applied to the classified activities to detect the walking steps. In addition, the misclassification activities are fed back to the previous layer. Finally, three adaptive distance estimators, which are based on a non-parametric model of the average walking speed, calculate the length of each strike. The experimental results show that the average classification accuracy is about 99%, and the accuracy of the step detection is 98.7%. The error of the estimated distance is 2.2–4.2% depending on the type of wrist activities.
Language independent text summarization of western European languages using shape coding of text elements

The majority of text summarization techniques in literature depend, in one way or another, on language dependent pre-structured lexicons, databases, taggers and/or parsers. Such techniques require a prior knowledge of the language of the text being summarized. In this paper we propose an extractive text summarization tool, UnB Language Independent Text Summarizer (UnB-LITS), which is capable of performing text summarization in a language independent manner. The new model depends on intrinsic characteristics of the text being summarized rather than its language and thus eliminates the need for language dependent lexicons, databases, taggers or parsers. Within this tool, we develop an innovative way of coding the shapes of text elements (words, n-grams, sentences and paragraphs), in addition to proposing language independent algorithms that is capable of normalizing words and performing relative stemming or lemmatization. The proposed algorithms and Shape-Coding routine enable the UnB-LITS tool to extract intrinsic features of document elements and score them statistically to extract a representative extractive summary independent of the document language. In this paper we focused on single document summarization of western European languages. The tool was tested on hundreds of documents written in English, Portuguese, French and Spanish and showed better performance as compared with the results obtained in literature as well as from commercial summarizers.
Indoor Person Identification Using a Low-Power FMCW Radar

Contemporary surveillance systems mainly use video cameras as their primary sensor. However, video cameras possess fundamental deficiencies, such as the inability to handle low-light environments, poor weather conditions, and concealing clothing. In contrast, radar devices are able to sense in pitch-dark environments and to see through walls. In this paper, we investigate the use of micro-Doppler (MD) signatures retrieved from a low-power radar device to identify a set of persons based on their gait characteristics. To that end, we propose a robust feature learning approach based on deep convolutional neural networks. Given that we aim at providing a solution for a real-world problem, people are allowed to walk around freely in two different rooms. In this setting, the IDentification with Radar data data set is constructed and published, consisting of 150 min of annotated MD data equally spread over five targets. Through experiments, we investigate the effectiveness of both the Doppler and time dimension, showing that our approach achieves a classification error rate of 24.70% on the validation set and 21.54% on the test set for the five targets used. When experimenting with larger time windows, we are able to further lower the error rate.
People counting in smart buildings

Today almost everyone carries a mobile device. The authors test whether these devices may be used to ascertain the number of people in a building. The number of mobile devices present (number of persons) is determined using methods that capture network packets sent by mobile devices using the 802.11 protocol. The packets are captured using a Wi-Fi scanner, a hardware device that allows information to be gathered from 802.11/a/b/g/n/ac networks. The number of people in a building with an adequate count of Wi-Fi scanners may be determined with a definite probability which, aside from its statistical importance, might also be key in emergency situations in which it is imperative to determine how many people are inside a particular building.
Ontologies and Relational Databases Meta-Schema Design: a Three-Level Unified Lifecycle

To integrate ontologies and relational databases, in this paper, we propose to build a common meta-schema upon an overall unified lifecycle. Our key idea is to consider that the database is a model for the domain ontology, and conversely. To this end, the ontology-based knowledge and the business-entities are merged with the same conceptual abstractions principles, according to the three-level ANSI/SPARC architecture. Finally, the goal is to show that ontologies' features can be managed by relational databases. A case of study outlines the applicability of our approach.
An innovation indoor robot self-localization system with fast vision content matching in global map

In order to solve similar objects interference and kidnap problem for robot self-localization, an innovation indoor robot self-localization system with fast vision content matching in global map (FCG-SLAM) is presented, and localization time consuming of this method is very less. FCG-SLAM is composed by ceiling feature points picking out algorithm and image content matching algorithm. The ceiling feature points picking out algorithm could pick out the feature points on the ceiling for extracting the overlap regions between robot vision image and keyframes in the global map. This method could also identify and delete these keyframes, which are less similar to present robot vision, for saving time consuming. The core of image content matching algorithm is sub-block matching and rebuilding method, which could take advantage of overlap regions to find out the most similar keyframe from global map for present robot vision image. Before matching, images compress is used to decrease time consuming. The experiment site includes three rooms and three corridors. Total 61 keyframes are taken to build global map. FCG-SLAM could finish each matching time less than 1.5s, and robot vision matching accurate is larger than 95%, position RMSE is less than 0.5m, and kidnap problem can also been eliminated.
A Simple Four-Beam Reconfigurable Antenna Based on Monopole

A simple pattern reconfigurable antenna based on monopole is presented in this paper. The antenna consists of four folded monopoles and a windmill-shape ground plane. The central patch is fed by a coaxial probe to decide which monopole is excited. Four reconfigurable modes can be achieved by changing the states of four PIN diodes mounted on the feeding lines. And the maximum radiation directions of the proposed antenna are deflected from <inline-formula> <tex-math notation="LaTeX">$\varphi =30^{\circ }$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\varphi =120^{\circ }$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\varphi =210^{\circ }$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\varphi =300^{\circ }$ </tex-math></inline-formula> at azimuth plane in four operating modes. The proposed antenna is simulated, fabricated, and measured. The experimental results indicate that the antenna can achieve a measured 10-dB bandwidth of 25.7% (3.32–4.3 GHz), a 10-dB front-to-back ratio of 17.7% (3.6–4.3 GHz), and a stable average gain of 3.3 dBi within the operating band. These characteristics make the designed antenna fit for <inline-formula> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula>-band satellite communication system.
Atomic-scale study of stacking faults in Zr hydrides and implications on hydride formation

We performed atomic-scale ab initio calculations to investigate the stacking fault (SF) properties of the metastable ζ-Zr2H zirconium hydride. The effect of H near the SF was found to entail the existence of negative SF energies, showing that the ζ compound is probably unstable with respect to shearing in the basal plane. The effect of temperature on SFs was investigated by means of free energy calculations in the quasiharmonic approximation. This evidenced unexpectedly large temperature effects, confirming the main conclusions drawn at 0 K, in particular the ζ mechanical instability. The complex behaviour of H atoms during the shear process suggested ζ-hcp → Zr2H-fcc as a plausible shear path leading to an fcc compound with same composition as ζ. Finally, as shown by an analysis based on microelasticity, this Zr2H-fcc intermediate compound may be relevant for better interpreting the currently intricate issue of hydride habit planes in zirconium.
Design of wide input voltage range high step-up DC-DC converter based on secondary-side resonant tank full bridge LLC

Battery are widely utilized in uninterruptible power supply (UPS) to provide the power temporarily during power failure. In applications up to a few kilo watts, low voltage battery stack are typically used since it is not economically feasible to use high voltage battery stack in such application. To interface the battery to the dc-link of the UPS, power electronic converter are widely used. Due to the wide voltage variation at battery terminal that are mainly caused by battery state of charge and battery stack current, power converter for battery application are required to be capable to interface with wide battery voltage range. In this paper, a wide input range high step up converter was designed based on LLC converter. To significantly reduce the resonant capacitor current, secondary side resonant tank LLC converter was proposed. Finally, a 2 kW prototype was designed for 36 V∼ 72 V input voltage and 410 V output voltage, built, and from experimental test 93.59% peak efficiency was measured.
The effect of programme testimonials on registrations for an online cognitive behaviour therapy intervention: a randomised trial

Objective Suboptimal visit-to-registration rates limit the reach and potential impact of online health interventions. In this study, we examined whether testimonials presented during a new-user registration process could increase the uptake rates of MoodGYM, an open-access automated intervention designed to reduce the symptoms of depression. Methods We conducted a three-armed parallel randomised controlled trial on the MoodGYM website involving variations of the first page of the registration process. Spontaneous visitors saw either the current pre-registration content, the current content supplemented by a first-person past user testimonial or the current content accompanied by a testimonial from a third-party health professional. The primary outcome measure was the proportion of visitors seeing each version who completed registration. Results A total of 14,267 visits were recorded across the three conditions. The past user, health professional and control condition registration rates were 45.6%, 45.5% and 45.5% respectively, indicating that there was no effect of registration content on registration rate. (p > 0.05) Conclusions There may be limited use for testimonials in well-established interventions that have programme benefits clearly stated and presented in other forms. However, we cannot exclude the possibility that testimonial effects might be moderated by positioning or other elements within the registration flow or by visitor source. Further research is required to examine these factors and, more broadly, assess whether modifications to other aspects of the registration process can improve visit-to-registration rates.
Emotion stress detection using EEG signal and deep learning technologies

Brainwave reflects the change in electrical potential resulting from the conjunction between the thousands of brain neurons. A neuron can receive signals from other neurons and starts off cyclic discharge reaction when sufficient energy is accumulated. That is also the reason why people persistently emit brainwaves. According to experts from Laboratory of Brain Recognition and Behavior, Michigan University, long-term multitask operation results in the lack of efficiency and in filtering out irrelevant signals leads to the distraction of paying attention of the irrelevant message rather than work-related information. As a result, one would have problems in the transition from one job to another. However, for some people rely on their brain to deal with many things and it may lead to fatigue. Therefore, we did this experiment and tried to figure out the most efficient way to soothe the spiritual pressure and calm the mind down. We utilize deep learning as learning method to predict user's stress feeling through listening to the music. Through above research, by listening to music or create the atmosphere of a music background also with an artistic performance could provide not only psychological treatment effect but also improve the ability of the person to focus.
1 Non-destructive System Based on Electrical 2 Tomography and Machine Learning to Analyze 3 Moisture of Buildings 4

The article presents the results of research on a new method of spatial analysis of walls 11 and buildings moisture. Due to the fact that destructive methods are not suitable for historical 12 buildings of great architectural significance, a non-destructive method based on electrical 13 tomography has been adopted. A hybrid tomograph with special sensors was developed for the 14 measurements. This device enables the acquisition of data, which are then reconstructed by 15 appropriately developed methods enabling spatial analysis of wet buildings. Special electrodes that 16 ensure good contact with the surface of porous building materials such as bricks and cement were 17 introduced. During the research, a group of algorithms enabling supervised machine learning was 18 analyzed. They have been used in the process of converting input electrical values into conductance 19 depicted by the output image pixels. The conductance values of individual pixels of the output 20 vector made it possible to obtain images of the interior of building walls, both flat intersections (2D) 21 and spatial (3D) images. The presented group of algorithms has a high application value. The main 22 advantages of the new methods are: high accuracy of imaging, low costs, high processing speed, 23 ease of application to walls of various thickness and irregular surface. By comparing the results of 24 tomographic reconstructions, the most efficient algorithms were identified. 25
Managing Digital Platforms in User Organizations: The Interactions Between Digital Options and Digital Debt

As organizations increasingly use digital platforms to facilitate innovation, researchers are seeking to understand how platforms shape business practices. Although extant literature offers important insights into platform management from a platform-owner perspective, we know little about how organizations manage industry platforms provided by external parties to generate opportunities and overcome challenges in relation to their infrastructure and work processes. As part of larger ecosystems, these digital platforms offer organizations bundles of digital options that they can selectively invest in over time. At the same time, organizations’ previous investments in digital infrastructure and work processes produce a legacy of digital debt that conditions how they manage their digital platforms over time. Against this backdrop, we investigate how digital options and digital debt were implicated in a large Scandinavian media organization’s management of a news production platform over nearly 17 years. Drawi.
Link prediction via local structural information in complex networks

An approach for link prediction via the local structural information in complex networks is proposed in this paper. In this method, we reference to the thought of Strong Community Structural to achieve link prediction. The approach think that connections among a small community which consists of a pair of predicted nodes and their common neighbors are more important than connections within small community, so we call this approach Small Community (SC) index. Then, in order to promote the efficiency of forecasting, we simplify SC. In this paper, we also consider the different attributes of links which contain directions and weights. Therefore, we change three classical indices (Common Neighbors (CN) index, Resource Allocation (RA) index, Local Path (LP) index) and two simplified SC indices, at the same time, these indices are used in multifarious networks. We evaluate these indices by experiments in appropriate networks. The results show that the two simplified SC indices both achieve great success through comprehensive analysis forecast accuracies and actual computation time.
Target Reconstruction Based on 3-D Scattering Center Model for Robust SAR ATR

This paper proposes a robust synthetic aperture radar (SAR) automatic target recognition method based on the 3-D scattering center model. The 3-D scattering center model is established offline from the CAD model of the target using a forward method, which can efficiently predict the 2-D scattering centers as well as the scattering filed of the target at arbitrary poses. For the SAR images to be classified, the 2-D scattering centers are extracted based on the attributed scattering center model and matched with the predicted scattering center set using a neighbor matching algorithm. The selected model scattering centers are used to reconstruct an SAR image based on the 3-D scattering center model, which is compared with the test image to reach a robust similarity. The designed similarity measure comprehensively considers the image correlation between the test image and the model reconstructed image and the model redundancy as for describing the test image. As for target recognition, the model with the highest similarity is determined to the target type of the test SAR image when it is denied to be an outlier. Experiments are conducted on both the data simulated by an electromagnetic code and the data measured in the moving and stationary target acquisition recognition program under standard operating condition and various extended operating conditions to validate the effectiveness and robustness of the proposed method.
Circular sector antenna pair with harmonic rejection property for near-field wireless power transmission

In this paper, a circular sector antenna pair with harmonic rejection property for wireless power transmission (WPT) by near-field coupling is proposed. This antenna pair is adopted the coupled resonant method which can mutually compensate the impedance mismatching in the near field. When the transmitting antenna removes from the receiving antenna, the receiving antenna can also resonant independently and function as an antenna for Wi-Fi or energy harvesting. To improve the conversion efficiency, the circular sector antenna pair which can suppress re-radiation of the higher order harmonics to free space and reduce power loss due to the reradiation is adopted as the transmitting-receiving antenna. The conversion efficiency of the circular sector antenna pair is 80% when the input RF power is 19 dBm.
Feasibility Study of Switching Function Approaches in Sliding Mode Control for a Spacecraft's Attitude Control System

Sliding Mode Control (SMC) is well known as a robust control approach and is proven to be able to deal with nonlinear systems. To achieve this capability, the SMC controller input design is divided into two parts: a sliding surface design (continuous control) and a switching function design (discontinuous control). A spacecraft's attitude model is a multi-input and multi-output (MIMO) system and thus control design is difficult for some methodologies, however, in this case a SMC, is straightforward to construct. In this paper, for the continuous part, a reduction of order method (ROOM) is used to construct the sliding surface. For the discontinuous control, three different switching functions are designed and evaluated such as relays with constant gains, relays with state dependent gains and linear feedback with switched gains. The main contribution of this paper is to both analyse and investigate the limitations of these three switching functions at two different points (critical gains and proper gains) on a spacecraft's attitude model. The gains are selected using trial and error techniques as long as these gains meet the sufficiency conditions for the existence of a sliding mode. The discontinuous control is a high-speed switching function that produces chattering in the control input; however, solutions for chattering drawbacks are not discussed here. The best switching function is chosen based on the spacecraft's attitude transient performance requirements.
Information retrieval approach based on indexing text documents: Application to biomedical domain

The use of the controlled vocabulary for documents indexing makes it possible to index documents with other terms not present in the document. The proposed approach, based on Vector Space Model (VSM), allows matching partially document and MESH (Medical Subject Headings) terms. The stemming method applied for the preparation of terms allows favoring the partial matching. In this paper we propose an Information Retrieval Approach based on Indexing Text Documents which combine two methods statistical and semantic one. A measure of similarity is then considered to measure the correspondence between a document and term in MESH thesaurus. To filter the extracted concepts and keep the relevant ones, we exploited the MESH architecture for better indexing. The proposed approach is implemented to evaluate its effectiveness and the experimentation and the results analysis shows that the proposed method gives good results compared to standard measures.