Miracle-dz/UniFAR-FTUs huggingface dataset:
Feasibility Study of an Image-based Supporting System for Sprint Training

: This study focuses on developing a novel system to improve the performance of short-distance races, where stride length, stride frequency, and maximum velocity are important factors. To estimate stride length and stride frequency, color-based image processing is adopted to extract the feet of a runner, based on cosine similarity in the RGB color space. The experimental results indicate that the stride length and stride frequency could be estimated with negligible errors. To estimate the running velocity; visual object detection, and pose estimation based on state-of-the-art deep learning schemes were applied: RetinaNet for visual object detection, and OpenPose for pose estimation. The experimental results using the real image dataset, indicated that the estimation error of the velocity by the proposed scheme was quite negligible.
A Review and Experimental Revisit of Alternative Catalysts for Selective Oxidation of Methanol to Formaldehyde

The selective oxidation of methanol to formaldehyde is a growing million-dollar industry, and has been commercial for close to a century. The Formox process, which is the largest production process today, utilizes an iron molybdate catalyst, which is highly selective, but has a short lifetime of 6 months due to volatilization of the active molybdenum oxide. Improvements of the process’s lifetime is, thus, desirable. This paper provides an overview of the efforts reported in the scientific literature to find alternative catalysts for the Formox process and critically assess these alternatives for their industrial potential. The catalysts can be grouped into three main categories: Mo containing, V containing, and those not containing Mo or V. Furthermore, selected interesting catalysts were synthesized, tested for their performance in the title reaction, and the results critically compared with previously published results. Lastly, an outlook on the progress for finding new catalytic materials is provided as well as suggestions for the future focus of Formox catalyst research.
eTAS: Enhanced Time-Aware Shaper for Supporting Nonisochronous Emergency Traffic in Time-Sensitive Networks

To guarantee stringent real-time requirements of time-critical traffic in industrial systems, the IEEE time-sensitive networking (TSN) task group has standardized time-aware shaping (TAS) in IEEE 802.1Qbv, which schedules precise and periodic transmission times using preassigned traffic information. However, nonperiodic/unexpected but time-critical traffic, such as emergency events or alarms, may occur in real industrial scenarios, and TAS does not provision for performance of traffic that are unknown a priori, nor the impact thereof on prescheduled traffic. Moreover, recalculating the schedule for every sporadic, nonisochronous event traffic is extremely difficult, complex, and costly. To address these challenges, we propose a novel enhancement to TAS, referred to as eTAS, which defines a new scheduling rule for immediate forwarding of emergency traffic to guarantee real-time performance, while dynamically extending the scheduled time windows to protect scheduled time-critical traffic from the interference of emergency traffic. We evaluate eTAS through extensive simulations on OMNeT++ under an advanced driver assistance system (ADAS) scenario for autonomous driving to show that eTAS effectively allows rapid transmission of event traffic with minimal impact on scheduled traffic, even for highly congested networks.
Development of AHMES (Automatical Higher Mathematics Examination System) Using Rational Unified Process

This study was aimed at developing an automatic exam system as an intelligent and high-quality higher math examination solution for Department of Computer Engineering at Pai Chai University (PCU) based on S/W Engineering. As a research and development (R&D) project, the study used the rational unified process (RUP) method for software development. RUP describes how to effectively use commercial and reliable methods to develop and deploy software system. It is a heavyweight process; therefore, it is particularly suitable for large teams to develop large projects. This paper describes the RUP process of this R&D project what we named it as Automatic Higher Mathematically Exam System (AHMES). AHMES provides a new way to automatically generate exam questions. In the study, our team and the requesting department collected the requirements and selected methods and tools. Then, our team designed and implemented the framework of the system, completed the development of some main functions, tested and summarized the system, and planned the future work.
Association of Serum Periostin Level with Classical Bone Turnover Markers and Bone Mineral Density in Shanghai Chinese Postmenopausal Women with Osteoporosis

Background It has been reported that serum periostin levels are significantly higher in postmenopausal patients with osteoporotic fractures. Nonetheless, the levels of serum periostin in postmenopausal women with different bone mass remain unclear. Purpose The objective of the study was to identify the levels of serum periostin in Chinese postmenopausal women with different bone mass, and the correlations between the periostin levels and the classical bone turnover markers (BTMs), and bone mineral densities (BMDs) at different sites. Patients and Methods This study enrolled 331 Chinese postmenopausal women in Shanghai; their clinical features were collected; their levels of serum periostin and traditional BTMs were measured by ELISA or the fully automated immunoassay analyzer; their BMDs at different sites were measured by dual-energy X-ray absorptiometry (DXA). Results According to the T-value of bone mineral density (BMD), these postmenopausal women were divided into normal group (n=84), osteopenia group (n=126) and osteoporosis group (n=121). There was no significant difference in the serum periostin levels among the above three groups of subjects. In addition, Spearman correlation analysis also revealed that no correlation was observed between the value of serum periostin and those of traditional BTMs, and BMDs at different sites, respectively. The values of traditional BTMs were negatively correlated with those of BMDs at all measured sites. Furthermore, the receiver-operating characteristic (ROC) curves analysis indicated that among the periostin and traditional BTMs mentioned above, the best predictors for postmenopausal osteoporosis in Shanghai Chinese postmenopausal women were osteocalcin (OC) and procollagen type 1 N-terminal propeptide (P1NP) [the areas under the ROC curve (AUC)=0.746 and 0.761, respectively]. Conclusion Serum periostin may not be used as a marker of systemic bone metabolism in Shanghai Chinese postmenopausal women without prior fracture. In addition, serum P1NP and OC levels may be the predictors of osteoporosis occurrence in Chinese postmenopausal women.
The Relationship Between Predictability and Earnings Volatility: The Case of Companies Listed on the Casablanca Stock Exchange

The study of the predictability of accounting results is extremely important for investors as well as for the various stakeholders of listed companies. The objective of this work is to study the relationship between the predictability of earnings and their volatility. In principle, the volatility of earnings is supposed to reduce its predictability. However, the existing literature, based on short-term horizon, fails to provide convincing results (Sloan, 1996; Lev and Thiagarajan 1993; Abarnell & Bushee, 1997). Through this work, we first seek to verify the meaning and intensity of the relationship between predictability and volatility, while applying a standpoint of a long-term study. Then, we check the load of each component of the volatility - accounting component or economic component - on the predictability of earnings. Our results confirm our prediction, ie the presence of a negative relationship between volatility and the predictability of earnings. Nevertheless, this capacity decreases with the presence of the extreme values of earnings
Social networking: the business case - [IT internet]

The tangible fruit of Web 2.0's rather nebulous branches, online social networking-epitomised by Web sites Facebook, Linkedln, MySpace and Twitter-is becoming increasingly pervasive in the enterprise workplace; this is a development that's causing repercussive reactions in the minds of many chief information officers (CIOs). Social networks do have the potential to become business enablers if you get to know them well enough. In this paper, the author explains how.
Cooperative Communications: Hardware, Channel and PHY

Facilitating Cooperation for Wireless Systems Cooperative Communications: Hardware, Channel & PHY focuses on issues pertaining to the PHY layer of wireless communication networks, offering a rigorous taxonomy of this dispersed field, along with a range of application scenarios for cooperative and distributed schemes, demonstrating how these techniques can be employed. The authors discuss hardware, complexity and power consumption issues, which are vital for understanding what can be realized at the PHY layer, showing how wireless channel models differ from more traditional models, and highlighting the reliance of PHY algorithm performance on the underlying channel models. Numerous transparent and regenerative relaying protocols are described in detail for a variety of transparent and regenerative cooperative schemes. Key Features: Introduces background, concepts, applications, milestones and thorough taxonomy Identifies the potential in this emerging technology applied to e.g. LTE/WiMAX, WSN Discusses latest wireless channel models for transparent and regenerative protocols Addresses the fundamentals as well as latest emerging PHY protocols Introduces transparent distributed STBC, STTC, multiplexing and beamforming protocols Quantifies regenerative distributed space-time, channel and network coding protocols Explores system optimization, such as distributed power allocation and relay selection Introduces and compares analog and digital hardware architectures Quantifies complexity, memory and power consumption of 3G UMTS & 4G LTE/WiMAX relay Highlights future research challenges within the cooperative communications field This book is an invaluable guide for professionals and researchers in communications fields. It will also be of interest to graduates of communications and electronic engineering courses. It forms part of an entire series dedicated to cooperative wireless systems.
Analysis of Deep Learning Action Recognition for Basketball Shot Type Identification

: Recent technologies have been developed to track basketball shooting and provide detailed data on shooting accuracy for players. Further segmenting this shot accuracy data based on shot types allows a more detailed analysis of player performance. Currently this segmentation must be performed manually. In this paper, we apply a state-of-the-art action recognition model to the problem of automated shot type classiﬁcation from videos. The paper presents experiments performed to optimize shot type recognition, a unique taxonomy for the labeling of shot types, and discusses key results on the task of categorizing three different shot types. Additionally, we outline key challenges we uncovered applying current deep learning techniques to the task of shot type classiﬁcation. The NOAH system enables the capture of basketball shooting data by recording every shot taken on a court along with its shooter and critical statistics. We utilized videos of 50,000 practice shots from various players captured through NOAH system to perform the task of classifying shot type. These three second video clips contain the shooting action along with movements immediately preceding and following the shot. On the problem of shot type classiﬁcation, the Temporal Relational Network achieved an accuracy of 96.8% on 1500 novel shots.
Making the Link: Teacher Professional Development on the Internet

From the Publisher: Looking for professional development opportunities that accommodate the messy realities of your teaching life? With Making the Link you need look no farther than your computer. Ron Owston will help you discover what a powerful tool the Internet can be for developing a continuous, collegial professional development experience - one that ideally suits your particular experience, needs, and learning style. Preservice and inservice teachers, school administrators, and professional development coordinators, regardless of their level of Internet experience, will all benefit from the suggestions and strategies Owston provides.
Accurate Landmark Localization for Medical Images Using Perturbations

Recently, various studies have been proposed to learn the rich representations of images during deep learning. In particular, the perturbation method is a simple way to learn rich representations that has shown significant success. In this study, we present effective perturbation approaches for medical landmark localization. To this end, we report an extensive experiment that uses the perturbation methods of erasing, smoothing, binarization, and edge detection. The hand X-ray dataset and the ISBI 2015 Cephalometric dataset are used to evaluate the perturbation effect. The experimental results show that the perturbation method forces the network to extract richer representations of an image, leading to performance increases. Moreover, in comparison with the existing methods that lack any complex algorithmic change of network, our methods with specific perturbation methods achieve superior performance.
Road Scene Recognition of Forklift AGV Equipment Based on Deep Learning

The application of scene recognition in intelligent robots to forklift AGV equipment is of great significance in order to improve the automation and intelligence level of distribution centers. At present, using the camera to collect image information to obtain environmental information can break through the limitation of traditional guideway and positioning equipment, and is beneficial to the path planning and system expansion in the later stage of warehouse construction. Taking the forklift AGV equipment in the distribution center as the research object, this paper explores the scene recognition and path planning of forklift AGV equipment based on a deep convolution neural network. On the basis of the characteristics of the warehouse environment, a semantic segmentation network applied to the scene recognition of the warehouse environment is established, and a scene recognition method suitable for the warehouse environment is proposed, so that the equipment can use the deep learning method to learn the environment features and achieve accurate recognition in the large-scale environment, without adding environmental landmarks, which provides an effective convolution neural network model for the scene recognition of forklift AGV equipment in the warehouse environment. The activation function layer of the model is studied by using the activation function with better gradient performance. The results show that the performance of the H-Swish activation function is better than that of the ReLU function in recognition accuracy and computational complexity, and it can save costs as a calculation form of the mobile terminal.
Structural Health Monitoring of Three Bridges in Connecticut

This report discusses the implementation of long-term structural health monitoring systems on three bridges in the State of Connecticut. The project began with the development of a generic set of guidelines for using readily available monitoring equipment to serve as a basis for designing long-term systems. These were then used to design individual monitoring systems that were tailored to each bridge, using sensors for strain, temperature, tilt and vibration. Monitoring has been conducted on a continuous basis, with excitation provided by normal traffic loading. The goal has been to use long-term monitoring to learn how bridges behave over multi-year periods and to use data obtained from the monitoring systems to establish the basis for long-term structural health monitoring.
Addressing separation of concerns in software engineering environments

Software engineering tools and environments have play an essential role to improve the quality and productivity of the software processes and products. However, most tools and environments have been closed isolated, presenting specific architectures and structures and, consequently, making difficult their integration. Reference models have been proposed for SEEs (Software Engineering Environments); however, they have not contributed to develop and integrate tools and environments in practice. In spite of the effort of many researchers in this direction, there are not mechanisms that contribute and facilitate the cooperative and incremental development of evolutive SEEs yet. In order to address this problem, we propose a new architectural organization to SEEs and establish a reference architecture, named RefASSET (Reference Architecture for Software Engineering Tools). RefASSET encompasses a new separation of concerns based on ISO/IEC 12207 and introduces the use of frameworks and aspect-oriented development to build and evolve these environments. RefASSET has firstly been applied in software testing domain and a testing tool and related tools are under development for that domain.
Acute Care Use by Breast Cancer Patients on Adjuvant Chemotherapy in Alberta: Demonstrating the Importance of Measurement to Improving Quality

Breast cancer patients receiving adjuvant chemotherapy are at increased risk of acute care use. The incidence of emergency department (ED) visits and hospitalizations (H) have been characterized in other provinces but never in Alberta. We conducted a retrospective population-based cohort study using administrative data of women with stage I-III breast cancer receiving adjuvant chemotherapy. Rates of ED and H use in the 180 days following chemotherapy initiation were determined, and logistic regression was performed to identify risk factors. We found that 47% of women receiving adjuvant chemotherapy experienced ED or H, which compared favourably to other provinces. However, Alberta had the highest rate of febrile neutropenia-related ED visits, and among the highest chemotherapy-related ED visits. The incidence of acute care use increased over time, and there were significant institutional differences despite operating under a single provincial healthcare system. Our study demonstrates the need for systematic measurement and the importance of quality improvement programs to address this gap.
Fault-Tolerant and Data-Intensive Resource Scheduling and Management for Scientific Applications in Cloud Computing

Cloud computing is a fully fledged, matured and flexible computing paradigm that provides services to scientific and business applications in a subscription-based environment. Scientific applications such as Montage and CyberShake are organized scientific workflows with data and compute-intensive tasks and also have some special characteristics. These characteristics include the tasks of scientific workflows that are executed in terms of integration, disintegration, pipeline, and parallelism, and thus require special attention to task management and data-oriented resource scheduling and management. The tasks executed during pipeline are considered as bottleneck executions, the failure of which result in the wholly futile execution, which requires a fault-tolerant-aware execution. The tasks executed during parallelism require similar instances of cloud resources, and thus, cluster-based execution may upgrade the system performance in terms of make-span and execution cost. Therefore, this research work presents a cluster-based, fault-tolerant and data-intensive (CFD) scheduling for scientific applications in cloud environments. The CFD strategy addresses the data intensiveness of tasks of scientific workflows with cluster-based, fault-tolerant mechanisms. The Montage scientific workflow is considered as a simulation and the results of the CFD strategy were compared with three well-known heuristic scheduling policies: (a) MCT, (b) Max-min, and (c) Min-min. The simulation results showed that the CFD strategy reduced the make-span by 14.28%, 20.37%, and 11.77%, respectively, as compared with the existing three policies. Similarly, the CFD reduces the execution cost by 1.27%, 5.3%, and 2.21%, respectively, as compared with the existing three policies. In case of the CFD strategy, the SLA is not violated with regard to time and cost constraints, whereas it is violated by the existing policies numerous times.
EFEK KONSUMSI KEFIR TERHADAP DISLIPIDEMIA TERKAIT SINDROM METABOLIK: SEBUAH TINJAUAN PUSTAKA

Latar belakang: Peningkatan insidensi sindrom metabolik sering disertai dengan dislipidemia. Kefir merupakan minuman fermentasi yang telah banyak diteliti manfaat kesehatannya. Tujuan: Tujuan dari tinjauan ini adalah untuk membahas pengrauh minuman kefir terhadap dislipidemia Metode: Pencarian literatur dilakuakn dengan menggunakan kata kunci “kefir”, “dislipidemia”, dan “sindrom metabolik” dengan mesin pencari PubMed dan Google Scholar. Hasil: Dislipidemia dapat dipengaruhi dari obesitas dan resistensi insulin. Kefir memiliki efek anti-obesitas dan antiinflamasi dengan memodulasi perbaikan metabolisme lipid. Kesimpulan: Konsumsi kefir memiliki efek antidislipidemia pada sindrom metabolik
Prospect Theory with Bounded Temporal Horizon for Modeling Prosumer Behavior in the Smart Grid

We study prosumer decision-making in the smart grid in which a prosumer must decide whether to make a sale of solar energy units generated at her home every day or hold (store) the energy units in anticipation of a future sale at a better price. Specifically, we enhance a Prospect Theory (PT)-based behavioral model by taking into account bounded temporal horizons (a time window specified in terms of the number of days) that prosumers implicitly impose on their decision-making in arriving at “hold” or “sell” decisions of energy units. The new behavioral model for prosumers assumes that in addition to the framing and probability weighting effects imposed by classical PT, humans make decisions that will affect their lives within a bounded temporal horizon regardless of how far into the future their units may be sold. Modeling the utility of the prosumer with parameters such as the offered price on a day, the available energy units on a day, and the probabilities of the forecast prices, we fit the PT-based proposed behavioral model with bounded temporal horizons to prosumer data collected over 10 weeks from 57 homeowners who generated surplus units of solar power and had the opportunity to sell those units to the local utility at the price set that day by the utility or hold the units for sale in the future. For most participants, a model with a bounded temporal horizon in the range of 1–6 days provided a much better fit to their responses than was found for the traditional EUT-based model, thus validating the need to model PT effects (framing and probability weighting) and bounded temporal horizons imposed in prosumer decision-making.