Miracle-dz/UniFAR-FTUs huggingface dataset:
New Proposals to Improve a MAC Layer Protocol in Wireless Sensor Networks

The evolution of Wireless Sensor Networks has led to the development of protocols that must comply with their new restrictions while being efficient in terms of energy consumption and time. We focus on a collision resolution protocol, the so-called Two Cell Sorted (2CS-WSN). We propose three different ways to improve its performance by minimising the collision resolution time or the energy consumption. After evaluating these proposals and carrying out the comparison with the original protocol, we recommend an improvement to the protocol which reduces the elapsed time by early 8% and the number of retries and conflicts more than 40%.
Anticipated Reductions in Tax Rates and Earning Management of Listed Companies: Evidence from China

The effects of tax rate changes on corporations’ earning management are not fully understood. As a transitional economy, China has listed companies with different ownership and special regulatory rules. We explore under the expectation of tax reduction whether there are differences in the way and degree of earnings management implemented by different types of companies. Our study shows that firms under the anticipation of tax reduction make use of earning management, including delaying sales, taking unusual income-decreasing discretionary accruals, and so on, which leads to lower income in higher tax rate period. We find that private enterprises have more attention to reduce earning in the higher tax rate period than state-owned enterprises (SOEs), indicating that the ownership has effect on the extent of earning management. Moreover, under the specific regulations in Chinese stock market, we find that for those listed companies with negative net profit in the previous year, the priority is how to reverse losses rather than tax saving.
Instant Video Revisiting for Reflection: Extending the Learning of Children & Teachers

This article discusses how instant video revisiting (IVR) promotes reflective thinking for both teachers and children. IVR was used as a daily classroom experience with both the children and the teachers throughout one semester in two preschool classrooms with children 2.5 to 5 years old. The teachers used a digital video camera to generate data to help them understand the behavior of the children and revisit the children's actions immediately, with the children using the video clips to extend their learning. Two classroom examples illustrate how IVR supports the children's learning and the teacher's reflection of this learning. The first example describes how IVR helped the children reflect on their actions and solve their own conflicts. The second example describes the use of IVR to scaffold the children's idea of the middle of a story, thereby strengthening their own thought processes in relation to a story construction.
Construction of Deep Convolutional Neural Networks For Medical Image Classification

Implementing image processing tools demands its components produce better results in critical applications like medical image classification. TensorFlow is one open source with a machine learning framework for high performance and operates in heterogeneous environments. It heralds broad attention at a fine tuning of parameters for obtaining the final models, to obtain better performance. The main aim of this article is to prove the appropriate steps for the classification techniques for diagnosing the diseases with better accuracy. The proposed convolutional network is comprised of three convolutional layers, preceded by average pooling with a size equal to the size of the final feature maps. The final layer in this network has two outputs, corresponding to the number of classes considered to be either normal or abnormal. To train and evaluate such networks like the Deep Convolutional Neural Network (DCNN), a dataset of 2000 x-ray images of lungs was used and a comparative analysis between the proposed DCNN against previous methods is also made.
Indigenous Songs as Instructional Aids in Teaching Mother Tongue to Pupils of Bontoc Central School: Its Educational Implications

Indigenous Songs play an important role in the development of young children learning the mother language. This paper begins by looking at why indigenous songs can be considered valuable pedagogical tools in teaching the mother tongue this K12 curriculum of the (DepEd) Department of Education. In particular, it will discuss how indigenous songs are used in communication arts that can help young learners (particularly those who are not from Bontoc) of Bontoc Central School in Bontoc, Mountain Province improve their communication skills. The researcher will also discuss how the selected indigenous songs can reflect culture and increase the young learner’s overall enjoyment of learning a mother language and second language on songs translated into English. The researcher will then attempt to show, through useful examples, how the selected indigenous songs (both in mother tongue and English translation) can be used as language tasks. Finally, the paper aims to explore how indigenous songs for children can be adapted to suit a particular theme or part of the curriculum a teacher in the primary level might wish to teach in the mother tongue teaching.
Effects of hyperoxia on retinal autophagy in a mouse model of retinopathy of prematurity

Purpose To evaluate the autophagy activation of retina in an oxygen-induced retinopathy of prematurity (ROP) mouse model. Methods C57BL/6 mice were exposed to 75 ± 0.5% oxygen from P7 to P12, after which they were brought into room air and raised to P17. Mice underwent fluorescein angiography of the retinal vasculature on P17. The thickness and ultrastructural of retina was observed on P17. The expression of autophagy in the retina tissue was assessed by Quantitative real-time polymerase chain reaction and Western blot analysis. Results The neovascularization was significantly higher in the oxygen-induced mice. The retinal thickness of the oxygen-exposed group was higher than that of the control group on P17. The autophagosomes in retina of ROP mouse were observed. During the hyperoxia stage (from P8 to P13), the mRNA levels of Beclin1 (mammalian Atg6), Uncoordinated-51 like kinase 1 (ULK1), and Autophagy 5 (Atg5) were increased. At the relative hypoxia stage (raised in room air from P14 to P17), the expression of Beclin1, ULK1, and Atg5 mRNA levels was downregulated in retinas of ROP mice compare to the controls. Over time, the protein expression of Beclin-1, ULK1, and Bcl-2-associated X protein (Bax) was upregulated, but the protein expression of B-cell lymphoma 2 (Bcl-2) was downregulated. Conclusions Hyperoxia and relative hypoxia not only cause retinal neovascularization, but also change the autophagy markers in the retina of newborn mice.
Building Blocks of Multi Cloud Computing Systems

Multi Cloud Computing Systems access simultaneously multiple clouds, interacts with each cloud service providers, process data and collection of results are required. Many organizations shifting towards multi cloud environment to improve performance, faster data access rate and to reduce cost. Multi cloud computing systems includes the geographically distributed clouds assimilation in a large open system to manage all the issues regarding collaboration of the Clouds entirely detached from vendors and providers. So, there is a need of automatic load coordination. The proposed architecture supports automatic load scaling with the vital elements of architecture like cloud exchange, cloud broker and cloud coordinator. Keywords—Multi Cloud Computing System, Cloud Exchange, Cloud Broker, Cloud Coordinator.
Congestion-Free Rerouting of Multiple Flows in Timed SDNs

Software-Defined Networks (SDNs) introduce great flexibilities in how packet routes can be defined and changed over time, and enable a more fine-grained and adaptive traffic engineering. The recently introduced support for more accurate synchronization in SDNs further improves the degree of control an operator can have over the packets’ forwarding paths, and also allows to avoid disruptions and inconsistencies during network updates, i.e., during the rerouting of flows. However, how to optimally exploit such technology algorithmically — to efficiently schedule the update of multiple flows in such timed SDNs — while accounting for possible interference and congestion, is not well-understood today. We, in this paper, initiate the study of the fundamental problem of how to reroute the updates of multiple network flows in a synchronized SDN in a congestion-free manner. We rigorously prove that the problem is NP-hard for flows of unit size and network links with unit delay. We also show that a greedy approach to update the network can delay the update significantly. Our main contribution is the first solution to this problem: Chronicle. Our approach is based on time-extended network construction and the resource dependency graph, which is implemented by Openflow 1.5 using the scheduled bundles feature. The evaluation results show that Chronicle can reduce the makespan by 63% and reduce the number of changed rules by 50% compared to state-of-the-art.
Automatic Underground Cylindrical Multi-Levels Car Parking Secured Management System

As the all technologies that relevant to the human are developing rapidly, it is found that the number of new models of cars are also increased with high technology. Due to the increasing growth of cars and investment in all aspect of life, the parking areas are definitely start to decrease to yield a significant problem. This work deals with the relevant problem as smart design to take part in finding better solution. The paper is divided into hardware and software and the integration of them to monitor and control the process automatically. The crucial feature that appended to the design is the security aspect to enable the user to use the system with high reliability. The design exploited PC instead of microcontroller to facilitate the human – machine interface. The design also equipped with printer to provide the user with security code when calling back or retrieving the car. The overall work shows the security, reliability, and high revenue. It is applicable in many different field such as hotels, shopping malls, and etc.
The Long-term Outcomes of Hepatic Resection Between Single Tumor and Multiple Tumors of HCC Meeting UCSF Criteria

Object: Searching the hepatectomy of hepatocellular carcinoma (HCC) within UCSF is limited. The study aims to compare the long-term surgical outcomes between single and multiple tumors to find an effective and safe treatment for the patients within UCSF criteria.Methods: We have enrolled 1006 patients meeting UCSF criteria, including 744 patients with single tumor and 262 patients with multiple tumors undergoing hepatic resection (HR). We divided the patients into two groups: single tumor group and multiple tumor group. By comparing the long-term outcomes between these two groups and analyzing the risk factors by the log-rank test and cox proportional hazards model in two groups.Results: The 1-, 3-, and 5-year OS rates in the single tumor group were significantly higher than the multiple tumors group (96.2%, 65.3% and 40.1% versus 93.1%, 47.6% and 22.5%, respectively, p<0.001). The 1-, 3- and 5-year RFS rates were 91.4%, 46.2%, and 24.6% in the single group and 90.8%, 31.1% and 13.0%, respectively (p<0.001). And the anatomic resection and MVI were the independent risk factors for single tumor, but it didn’t work for multiple tumors. The independent risk factors of multiple tumors are the diameter more than 2cm of the maximum tumor and the tumors located in different segments.Conclusion: The surgical resection will obtain a better outcomes for the HCC with single tumor meeting UCSF criteria than the multiple tumors. And the anatomic resection should be applied for the patients with single tumor, if possible.
Building and Visualization of Sleek 3D Surfaces without Misplaced Extremes

The paper is devoted to the visualization of functional dependencies expressed as ( ) by building sleek 3D surfaces based on discrete sets of points. The criteria of sleek surface quality are formulated taking into account the needs of scientific visualization and visual analytics. The most important criterion is the absence of misplaced extremes and oscillations on the result surface, because such artifacts can deliver false information about the process being represented by the visualized data. The methods of building smooth surfaces in the most popular scientific visualization software are examined against the formulated criteria and it is discovered that the misplaced extremes are an issue in modern visualization tools. To tackle this problem the new approach of building sleek surfaces is proposed. This approach is based on the previously developed algorithm of building smooth 2D curves that was generalized to the three-dimensional case. The developed surface building algorithm consists of the following main steps. Assuming to have the input data as a regular grid of 3D points, which correspond to the table-defined function ( ), we first propose to build the set of smooth 2D curves along X and Z axes. Afterwards, we propose to build bicubically blended Coons patches for all quads bounded by each 4 neighbor points from the original data set. Then we discretize each Coons patch by emitting new points to reach needed surface resolution. Next, we triangulate the created set of points and calculate vertex normals using smoothing groups algorithm. Last, we smooth the field of normals using Gaussian blur function. The proposed algorithm meets the formulated criteria and ensures high visual quality of result surfaces. It was integrated into multiplatform charting library NChart3D and scientific visualization system SciVi, where it proved its correctness and stability by solving real-world scientific visualization tasks.
Effects of Four Local Species of Beans Consumed in South-eastern Nigeria on Selected Biochemical Indices of Wistar Albino Rats

The chemical composition and the effects of four local species of beans on the liver and kidney function of Wistar albino rats were examined. The four beans species are Cajanus Cajan, Vigna unguiculata subsp. sesquipedalis, Phaseolus vulgaris L ‘Red kidney’, and Phaseolus vulgaris 'Black turtle’. The results obtained for the proximate composition analysis revealed that the protein content of the bean species was higher in P. vulgaris L ‘Red kidney’ (18.54±0.01a %), and P. vulgaris 'Black turtle’ (18.36±0.01a %) with no significant difference at P< 0.05. The phytochemical composition analysis revealed that the beans contain various phytochemicals including some anti-nutritional factors. The mineral composition analysis shows that V. u. subsp. Sesquipedalis had a higher level of minerals (4.80±4.43a mg/100g) followed by C. cajan (3.24±2.64b mg/100g). The experimental design comprises of 30 male Wistar albino rats distributed into 5 groups of 6 rats each. Each group except the control received 50g of normal rat feed + 100g of beans body weight. The effects of the beans species on the biochemical parameters suggest no adverse effects when compared to the control. Despite that, these beans contain high nutritional components that are of nutritional interest as as well as some anti-nutritional factors.
Design and analysis of a truncated elliptical-shaped chipless RFID tag

This article presents a novel polarization-insensitive chipless radio frequency identification tag having an encoding capacity of 11 bits. The proposed resonator design comprises discontinuous arc slots forming truncated elliptically shape offering 1:1 slot to bit correspondence with suppressed unwanted harmonic resonances. Electromagnetic performance analysis of the proposed tag design is done over an ungrounded Rogers RT duroid® 5880 laminate. The overall tag design covers a footprint of 15 × 15 × 0.508 mm offering convincingly appreciable bit density of 4.88 bits/cm . The realized tags are analyzed for real-world electromagnetic performance resulting in an agreement between measured and computed results. The proposed work finds its applications in the food and beverage industry.
The application of the CLOPE algorithm for clustering a set of the network packets

For the modern software that uses network communication protocols the problem of the ensuring reliability is acute. To solve such an important problem the stress testing is used. This type of the testing involves the generation of a large number of the test data, including sets of network packets. Reducing the stored data after the testing process is the main task. This task can be solved by clustering the set of received packets. To solve this problem it is proposed to use the clustering algorithm for categorical data of CLOPE. This algorithm allows to cluster datasets without the information about the source clusters. It has the low computational complexity and ease of implementation. The article describes the preparation and results of sets of the network packets processing experiments. The article shows that the CLOPE algorithm can be effectively used for the clustering network packets received during stress testing. The results of the research extend the toolkit for the SW stress testing process.
Blended computation of machine learning with the recurrent neural network for intra-day stock market movement prediction using a multi-level classifier

Intra-day stock market forecasting is one of the challenging tasks as its nature is difficult to predict due to the non-volatility of the stock market. Therefore, the building block concepts of machine learning techniques with the recurrent neural network have been used. These techniques are very supportive for developing the long short-term memory (LSTM) network. This network is mainly used for sequential learning while performing classification. This paper presents research on applied computing to assess the efficacy of a multi-level classifier over single classifier. The multi-level classifier is hybrid of machine learning techniques (logistic regression classifier, decision tree, support vector machine) and recurrent neural network (LSTM) aiming to maximize the predictive ability, increases the interpretability, minimizes the complexity, reduced the training time, and provides the generalized platform with an optimistic forecasting model. The developed model has the potential to act as an intelligent system with the ability to adapt the market behavior present in the dataset which analyzes the best performing feature subset based on model accuracy. The comparative results clearly reveal that the blended computation of multi-level classifiers outperforms the existing works and it also leads to more precise model development with the maximum predictive ability, approximately 10–12% increment in accuracy.
Voice of Charity: Prospecting the Donation Recurrence & Donor Retention in Crowdfunding

Online donation-based crowdfunding has brought new life to charity by soliciting small monetary contributions from crowd donors to help others in trouble or with dreams. However, a crucial issue for crowdfunding platforms as well as traditional charities is the problem of high donor attrition, i.e., many donors donate only once or very few times within a rather short lifecycle and then leave. Thus, it is an urgent task to analyze the factors of and then further predict the donors behaviors. Especially, we focus on two types of behavioral events, e.g., donation recurrence (whether one donor will make donations at some time slices in the future) and donor retention (whether she will remain on the crowdfunding platform until a future time). However, this problem has not been well explored due to many domain and technical challenges, such as the heterogeneous influence, the relevance of the two types of events, and the censoring phenomenon of retention records. In this paper, we present a focused study on donation recurrence and donor retention with the help of large-scale behavioral data collected from crowdfunding. Specifically, we propose a Joint Deep Survival model, i.e., JDS, which can integrate heterogeneous features, e.g., donor motives, projects recently donated to, social contacts, to jointly model the donation recurrence and donor retention since these two types of behavioral events are highly relevant. In addition, we model the censoring phenomenon and dependence relations of different behaviors from the survival analysis view by designing multiple innovative constraints and incorporating them into the objective functions. Finally, we conduct extensive analysis and validation experiments with large-scale data collected from Kiva.org. The experimental results clearly demonstrate the effectiveness of our proposed models for analyzing and predicting the donation recurrence and donor retention in crowdfunding.
A Low-Latency Content Dissemination Scheme for mmWave Vehicular Networks

In future vehicular networks, to satisfy the ever-increasing capacity requirements, ultrahigh-speed directional millimeter-wave (mmWave) communications will be used as vehicle-to-vehicle (V2V) links to disseminate large-volume contents. However, the conventional IP-based routing protocol is inefficient for content disseminations in high-mobility and dynamic vehicular environments. Furthermore, the vehicle association also has a significant effect on the content dissemination performance. The content segment diversity, defined as the difference of desired content segments between content requesters and content repliers, is a key consideration during vehicle associations. The relative velocity between vehicles, which highly influences the link stability, should also been taken into account. The beam management, such as the beamwidth control, determines the link quality and, therefore, the final content dissemination rate. Based on the above observations, to improve the content dissemination performance, we propose an information-centric network (ICN)-based mmWave vehicular framework together with a decentralized vehicle association algorithm to realize low-latency content disseminations. In the framework, by using the ICN protocol, contents are cached and retrieved at the edge of the network, thereby reducing the content retrieval latency. To enhance the content dissemination rate, the vehicle association algorithm, which jointly considers the content segment diversity, the relative velocity between vehicles, and the transceiver’s beamwidths, is operated in every vehicle. Considering the blindness of directional mmWave links, a common control channel operating at low frequency bands with omni-directional coverage is used to share the information related to vehicle associations. The simulation results demonstrate that the proposed algorithm can improve the content dissemination efficiency and reduce the content retrieval latency.
Context-sensitive Natural Language Generation for robot-assisted second language tutoring

This paper describes the L2TOR intelligent tutoring system (ITS), focusing primarily on its output generation module. The L2TOR ITS is developed for the purpose of investigating the efficacy of robot-assisted second language tutoring in early childhood. We explain the process of generating contextually-relevant utterances, such as task-specific feedback messages, and discuss challenges regarding multimodality and multilingualism for situated natural language generation from a robot tutoring perspective.