Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of a 16-Week Multimodal Exercise Program on Individuals With Dementia: Study Protocol for a Multicenter Randomized Controlled Trial

Background The increasing prevalence of dementia in the next decades is accompanied by various societal and economic problems. Previous studies have suggested that physical activity positively affects motor and cognitive skills in individuals with dementia (IWD). However, there is insufficient evidence probably related to several methodological limitations. Moreover, to date adequate physical activity interventions specifically developed for IWD are lacking. Objective This study aims to investigate the effectiveness of a multimodal exercise program (MEP) on motor and cognitive skills in IWD in a high-quality multicenter trial. Methods A multicenter randomized controlled trial with baseline and postassessments will be performed. It is planned to enroll 405 participants with dementia of mild to moderate stage, aged 65 years and older. The intervention group will participate in a 16-week ritualized MEP especially developed for IWD. The effectiveness of the MEP on the primary outcomes balance, mobility, and gait will be examined using a comprehensive test battery. Secondary outcomes are strength and function of lower limbs, activities of daily living, and cognition (overall cognition, language, processing speed, learning and memory, and visual spatial cognition). Results Enrollment for the study started in May 2015. It is planned to complete postassessments by the beginning of 2017. Results are expected to be available in the first half of 2017. Conclusions This study will contribute to enhancing evidence for the effects of physical activity on motor and cognitive skills in IWD. Compared to previous studies, this study is characterized by a dementia-specific intervention based on scientific knowledge, a combination of motor and cognitive tasks in the intervention, and high standards regarding methodology. Findings are highly relevant to influence the multiple motor and cognitive impairments of IWD who are often participating in limited physical activity. Trial Registration German Clinical Trials Register DRKS00010538; https://drks-neu.uniklinik-freiburg.de/drks_web/navigate.do? navigationId=trial.HTML&TRIAL_ID=DRKS00010538 (Archived by WebCite at http://www.webcitation.org/6oVGMbbMD)
Distortion and Uncertainty Aware Loss for Panoramic Depth Completion

Standard MSE or MAE loss function is commonly used in limited field-of-vision depth completion, treating each pixel equally under a basic assumption that all pixels have same contribution during optimization. Recently, with the rapid rise of panoramic photography, panoramic depth completion (PDC) has raised increasing attention in 3D computer vision. However, the assumption is inapplicable to panoramic data due to its latitude-wise distortion and high uncertainty nearby textures and edges. To handle these challenges, we propose distortion and uncertainty aware loss (DUL) that consists of a distortion-aware loss and an uncertainty-aware loss. The distortion-aware loss is designed to tackle the panoramic distortion caused by equirectangular projection, whose coordinate transformation relation is used to adaptively calculate the weight of the latitude-wise distortion, distributing uneven importance instead of the equal treatment for each pixel. The uncertainty-aware loss is presented to handle the inaccuracy in non-smooth regions. Specifically, we characterize uncertainty into PDC solutions under Bayesian deep learning framework, where a novel consistent uncertainty estimation constraint is designed to learn the consistency between multiple uncertainty maps of a single panorama. This consistency constraint allows model to produce more precise uncertainty estimation that is robust to feature deformation. Extensive experiments show the superiority of our method over standard loss functions, reaching the state of the art.
MALÁ VODNOSŤ VYBRANÝCH VODNÝCH TOKOV SLOVENSKA

In this article the hydrologic measurements at selected Slovak streams writing 75 years observation period in term of low flow are evaluated. We entertain with deficit characteristics like low flow duration and deficit volumes for threshold discharge level Q90 (90% flow accidence). We calculate the appearance frequency, the distribution function of the maximum deficit volume and the maximum duration of low flow. We analysed also the most importance low flow events for the whole observation period. This procedure was applied for hydrologic year and for selected characteristics also for summer and winter seasons.
Optimization Method and Implementation of Fake Quantization from the Perspective of Hardware Performance

Deep learning networks can be accelerated by reducing the overall network volume using quantization or pruning techniques. The well-known quantization technique is Post Training Quantization (PTQ) and Quantization Aware Training (QAT). We applied an INT8 quantized network to design deep learning acceleration hardware and found that the performance of the deep learning network deteriorated due to errors occurring in the mult/shift based re-quantization step. This quantization error becomes a bigger problem in the training process rather than inference, and the FP32 arithmetic operator is applied to prevent the resulting accuracy drop. In this paper, we investigate whether the use of FP32 operators can outperform employing mult/shift operators under specific conditions. We accomplish this by analyzing the data flow based on output channel tiling and conducting a size analysis of the implemented hardware.
A Compact Mono-Cone Antenna for Time- and Frequency Combined Calibration of UWB Pulsed Sensors

In this paper, a compact Mono-Cone Antenna (MCA) for the calibration of ultra-wide band (UWB) pulsed sensors is developed. The working bandwidth and the time window are analyzed and optimized based on the full-wave electromagnetic model. As a result, the antenna height is the critical feature of the antenna and it is selected as 1700 mm and the characteristic impedance of the antenna is set as 75 $\Omega$ to make it compact. Aiming at the waveform distortion caused by the terminal reflection, the reflection model of the system radiation wave is established and the time window of calibration test is studied, and its general expression is proposed backed up with wave process analysis. Then, time- and frequency-domain performance of the proposed MCA is studied, and results indicate that the proposed MCA has an upper-cut-off frequency above 3.5 GHz and a high accuracy around 5% error compared with the theoretical values.
Efficient Wireless Charging System

The technology known as Wireless Power Transmission (WPT) is gaining popularity and finding use in a variety of industries. Without the need for interconnections, power is moved from a input device to an electrical load. When installing wiring is difficult or inconvenient, WPT can be used to power electrical devices. The mutual inductance principle is applied in this technology. Future applications will be in the automotive industry, particularly in electric vehicles. The primary objective of this research study is to design and develop wireless transmission-based charging system for electric vehicles by using a resonance coupling to transmit power. The charging system deals with a battery-powered electric load, converter, transmission coil, reception coil, and an AC source.
Based on Faster-RCNN for mobile phone packaging box defect detection

This paper proposes an efficient method for detecting defects in the outer packaging of mobile phones. The Faster RCNN algorithm is utilized as the primary detection method, which trains a deep neural network to automatically detect defects in mobile phone outer boxes. Compared to the traditional manual detection method, the deep learning-based approach exhibits stronger adaptive abilities and robustness. Our method employs the resnet-50 network with residual units as the backbone extraction network, and utilizes the cross-entropy loss function to train the classifier. This approach effectively addresses the diversity and complexity issues associated with the outer packaging box of mobile phones, resulting in improved detection performance and efficiency. This paper presents a comparative analysis and experimental verification of a proposed method for defect detection in the outer packaging box of mobile phones. The results demonstrate the feasibility and effectiveness of the proposed method, which accurately detects various defects and shows excellent performance in terms of detection speed, accuracy, and stability. The study highlights the superiority of the proposed method in the field of defect detection in the outer packaging box of mobile phones.
Assessing the Contribution of the Deinstitutionalization of the Mentally Ill to Growth in the

In this paper, we analyze U.S. census data covering the period from 1950 to 2000 to assess the degree to which the mentally ill who would have been in mental hospitals in years past have been trans‐institutionalized into prisons and jails. We document the fact that many of those who were institutionalized in the 1950s and 1960s, and were subsequently deinstitutionalized, did not experience large increases in incarceration. In particular, the deinstitutionalization of women (who comprised nearly half the mental hospital population) as well as the elderly (who were nearly 30 percent of the mental hospital inpatient population at its peak) did not lead to substantial increases in incarceration for these groups. Conversely, those most likely to be incarcerated as of the 2000 census experienced pronounced increases in overall institutionalization between 1950 and 2000 (with particularly large increases for black males). We then estimate the rate at which individuals who would have been institutionalized in years past have been trans‐institutionalized into prisons and jails. We construct a panel data set that varies by state, gender, race, and age and estimate the impact of changes in regional mental hospital inpatient rates on changes in regional incarceration rates controlling for a variety of fixed effects defined by the dimensions of the panel. We find no evidence of trans‐ institutionalization for any demographic groups for the period between 1950 and 1980. However, for the twenty‐year period from 1980 to 2000, we find significant trans‐institutionalization rates for all men and women, with a relatively large trans‐ institutionalization rate for men in comparison to women, and the largest trans‐ institutionalization rate observed for white men. Regarding the numerical contribution of deinstitutionalization to prison growth, our estimates suggest that between 4 and 7 percent of incarceration growth between 1980 and 2000 can be attributed to deinstitutionalization. While this is a relatively small contribution to prison growth overall, the results do suggest that a sizable portion of the mentally ill behind bars would not have been incarcerated in years past.
Interference Compatibility Analysis between 3.5GHz ATG System and Satellite Fixed Service

In order to address the issue of the general population not having access to high-speed internet services on airplanes, the ground-air broadband communication system (Air-to-Ground, ATG) has been widely promoted as a mature solution. This paper analyzes the interference of the ATG system to the satellite fixed service (Fixed Satellite Service, FSS) when the ATG system is operating on 3.5GHz. In the compatibility analysis, according to the deployment parameters of 5G base station and satellite fixed service given by ITU-R, the protection distance between the co-frequency coexistence and adjacent frequency deployment scenarios is analyzed respectively. The analysis's findings indicate that while the deployment of an ATG system and a co-frequency FSS system requires more than 18 km, the distance required for an ATG system and an adjacent frequency FSS system to live without interfering with one another is only about 1 km. Geographic isolation can be used to deploy the two systems.
Business Intelligence Implementation and its Impact on Decision-making

In this article Business Intelligence (BI) is defined as a process of analyzing and interpreting data to support business in decision-making. BI implementation requires several steps, including data collection, integration, analysis, and reporting. To ensure the success of a BI implementation, several key factors should be considered, such as data quality, data governance, user adoption, scalability, security, flexibility, integration, reporting, visualization and cost-benefit. A well-implemented BI solution ought to provide organizations with accurate and real-time data, allowing them to make better and more informed decisions, improving operational efficiency and increasing revenue.
Model for Prediction of Energy Consumption in Residential Buildings Based on Transfer Learning

As Climate change has become a major issue worldwide, the importance of building energy management systems (BEMS) is increasing. Because of obligation of public institutions to introduce BEMS and increase in BEMS installation due to the increase in smart buildings, It has become essential to understand and analyze about energy consumption to predict it in order to reduce carbon emissions and strengthen energy-saving policies. Through many previous studies, several analysis models have been proposed that can accurately predict energy consumption, and these models have shown excellent performance in each study. However, to obtain interesting results, a sufficient amount of training data is required. Obtaining a satisfactory amount of data sets generally requires a lot of time and effort, and sometimes it is impossible. This is an obstacle to applying deep learning models to many tasks. In this study, we propose a method for supplementing insufficient performance by transferring knowledge from similar types of data so that deep learning models can perform well even with sparse data sets.
Deep Neural Network Dataset Collection for Optimal Positioning of a Capacitive Compensated Schiffman Phase Shifter

This paper presents an accurate analysis and design of a Schiffman phase shifter with optimally positioned compensation capacitances for improved performances. The approach synthesized the microstrip coupler's even and odd mode characteristic impedance and its coupling effect to enhance the phase shift with minimal phase deviation. This was achieved by positioning artificial capacitances along the microstrip directional coupler. The new design method is valid for all substrate thicknesses and permittivity thereby overcoming the bandwidth limitation presented by the minimum spacing between the coupled lines in a conventional microstrip directional coupler. The simulation result demonstrated a maximum phase deviation of ± 3° and a maximum return loss of -0.613 dB. The source and load reflections were well below - 10 dB. The obtained dataset is highly accurate and can be used to train a deep neural network for improved phase error and return loss of a Schiffman phase shifter.
Metal surface defect detection based on improved YOLOv5

This paper proposes an improved YOLOv5 algorithm for metal surface defect detection to address the issues of slow detection speed and low accuracy in traditional YOLO algorithms. The proposed algorithm introduces a Convolutional Block Attention Module (CBAM) consisting of two sub-modules: Spatial Attention Module (SAM) and Channel Attention Module (CAM), which collect attention information in both spatial and channel dimensions and combine them to obtain more comprehensive and reliable attention information. Additionally, the paper introduces the SIOU loss function, which accurately measures the accuracy of object localization by considering the vector angle in boundary box regression. The proposed method achieves significant improvements in detection accuracy by minimizing the difference in vector angles, which allows for better adjustment of the position and orientation of the boundary boxes. Experimental results demonstrate that the improved algorithm increases detection accuracy by 5.7% compared to the traditional algorithm.
Internet of things Interoperability: A smart IoT gateway approach

The Internet of Things can offer substantial economic benefits by implementing different services and applications focused on various aspects of our lives, but it also faces many critical technical challenges. In particular, in an environment where countless heterogeneous IoT devices of different types and technical profiles operate, developing the ability to communicate with each other and make them easily accessible is one of the main challenges in IoT. For this reason, IoT interoperability emerges as an essential need for developing and adopting this disruptive technology. In this sense, a smart IoT gateway architecture is defined in this work to enable technical and syntactic interoperability of heterogeneous devices and integration with standard IoT platforms.
RCS Reduction of High-Gain Microstrip Antenna Based on Slot-loaded TM30 Mode

This paper Presents a high-gain, low-profile, low-radar cross section (RCS) microstrip antenna using high-order modes TM30. The proposed antenna consists of a rectangular patch, which operates in high-order mode TM30, three slots etched in y direction, and four identical slots etched in x direction. A three-element equivalent magnetic current array is constructed within a single cavity, including two from the radiating aperture of the patch and another one from the etched slots on the patch. Only one feeding and one single-dielectric layer are required in the proposed antenna, which exhibiting low profile and simple feeding. The gain of the single-layered antenna is 10.7 dB, which is 2-3 dB higher than conventional microstrip antenna operating in the fundamental TM10 mode. The RCS is reduced more than 5 dB by the slots etched in x direction. The size of the antenna is 0.81 λ0 ×0. 74λ0 ×0. 029λ0(λ0 is the free-space wavelength at the center frequency). The proposed antenna has the advantage of the high gain, low RCS, low profile, single feeding and low cost, with the great potential in conformal antennas and aerial vehicle.
Linkage Transformer: an Attention Based Neural Network for Multi-Cell Traffic Prediction

With the growing complexity of base station network, the research of traffic prediction has attracted increasingly attention in the world. Especially, artificial intelligence technology is applied to traffic prediction, which plays important role in communication system. In this article, we propose a Linkage Transformer model for multi-cell traffic prediction, which contains three main procedures. First, the traffic data of multi-cell with multi properties is sent to the encoder module for extracting data characteristics. Second, an attention module is designed in the middle part of the Linkage Transformer to learn spatial-temporal relation among cells’ traffic data. Finally, decoder module is utilized to predict traffic value of multi-cells. In the experiment, we use China Union traffic data of Haidian district to demonstrate the performance of Linkage Transformer in multi-cell traffic prediction task.
Big Data Collection and Building Material Price Estimation Based on Focused Web Crawler

This article presents a targeted crawler that collects web sites that are related to construction materials. The goal of this research is to gain accurate and timely information regarding the cost of building materials. In order to retrieve data, one must first describe the requirements for data on the cost of construction materials and then create a database structure. This study proposes a scheme to develop the system architecture of a targeted web crawler and then use it to gather a huge amount of data and conduct an analysis of the cost of construction materials. It shows that numerous applications are capable of being executed on the basis of the establishment of the database.
Why Address Translation Matter?: Analyzing Page Access Patterns in NAND Flash-Based SSDs

The use of NAND Flash-based SSD is on the rise in various domains such as Enterprise, Data centers, Personal computers, and Automotive. However, the address translation in the FTL has been a major performance bottleneck in the SSD systems due to frequent accesses to the L2P (Logical to Physical) mapping table. In this paper, we analyze the access patterns of logical and physical pages across popular storage workloads to find any correlations between them. The results from this paper will help system architects and researchers to design efficient address translation mechanisms for large-capacity SSDs.
Integrated information system for accessible tourism: study and design of a solution

Information and communication technologies (ICTs) are playing an increasingly crucial role in tourism. One of the areas that can benefit most from these technologies is clearly the area of accessible tourism. However, accessible tourism, despite being associated with a market with great potential, is still largely ignored, due to the existence of some barriers. Technology-based solutions can help to overcome some obstacles, increasing the potential of this market. Therefore, this paper aims to present a study in the area of accessible tourism, integrating the main requirements of this type of market, and, consequently, design a web-based information system. To design the solution, a methodology centered on the users was adopted, followed by the conceptualization, using the Unified Modeling Language (UML) notation. Given the importance of these types of solutions and the lack of them in the field of accessible tourism, this study is expected to contribute with a positive response to the needs raised in that market, presenting a proposal for a technological solution that can help in the promotion of accessible tourism products and services.
A 3V 12Bit 4MS/s Asynchronous SAR ADC with On-Chip 3-Step Background Calibration Using Split Structure

This paper presents an energy-efficient 12-bit successive approximation register analog-digital converter for 3-step calibration. This 3-step background calibration works only once during initial start-up to calibration capacitor mismatch, offset, and logic delay. Through this calibration, it was possible to have high linearity and a small offset error in a small area. After calibration, the SNDR improved from 55 dB to 69 dB at 4MS/s, and the offset error also improved by 2 mV from 20 mV. The Optimal effective number of bits is 11.2bit at the Nyquist-rate input, which is equivalent to a figure of merit of 167.23dB.
APT Attack Investigation via Fine-grained Sequence Construction and Learning

APT attack investigation aims to provide the security investigators a causal subgraph of the whole causal graph, so that they can easily analyze attacks. However, existing methods either output subgraphs that miss critical attack steps, or are too large and thus challenging to utilize. To address these limitations, we propose a new APT attack investigation approach based on fine-grained sequence construction and learning. Specifically, our approach is built upon the ATLAS framework, and constructs more attack sequences with a finer granularity. It then learns the attack behavior patterns from these constructed sequences. During inference, when presented with an attack symptom, our approach first predicts attack-related nodes in the causal graph and then constructs the causal subgraph based on these nodes. To evaluate our method, we conduct experiments using a simulated environment and four real attacks. The results demonstrate the effectiveness of the proposed approach compared to the state-of-the-art method ATLAS.