Miracle-dz/UniFAR-FTUs huggingface dataset:
Bone marrow osteoprogenitors are depleted whereas osteoblasts are expanded independent of the osteogenic vasculature in response to zoledronic acid

Zoledronic acid (ZOL) is an antiresorptive drug used to prevent bone loss in a variety of conditions, acting mainly through suppression of osteoclast activity. There is growing evidence that ZOL can also affect cells of the mesenchymal lineage in bone. We present novel data revealing significant changes in the abundance of perivascular mesenchymal stromal cells (MSCs)/osteoprogenitors and osteoblasts following the injection of ZOL, in vivo. In young mice with high bone turnover and an abundance of perivascular osteoprogenitors, ZOL significantly (P < 0.0001) increased new bone formation. This was accompanied by a decline in osterix‐positive osteoprogenitors and a corresponding increase in osteoblasts. However, these effects were not observed in mature mice with low bone turnover. Interestingly, the ZOL‐induced changes in cells of the mesenchymal lineage occurred independently of effects on the osteogenic vasculature. Thus, we demonstrate that a single, clinically relevant dose of ZOL can induce new bone formation in microenvironments enriched for perivascular MSC/osteoprogenitors and high osteogenic potential. This arises from the differentiation of perivascular osterix‐positive MSC/osteoprogenitors into osteoblasts at sites that are innately osteogenic. Collectively, our data demonstrate that ZOL affects multiple cell types in bone and has differential effects depending on the level of bone turnover. —Hughes, R. , Chen, X. , Hunter, K. D. , Hobbs, J. K. , Holen, I. , Brown, N. J. Bone marrow osteoprogenitors are depleted whereas osteoblasts are expanded independent of the osteogenic vasculature in response to zoledronic acid. FASEB J. 33, 12768–12779 (2019). www.fasebj. org
A Contemporary View on Online and Web Tools for Students with Sensory & Learning Disabilities

In the last 30 years, the use of information and communication technologies, the evolution of hardware and software for special needs people, as well as the spreading of the World Wide Web, is assisting people with disabilities in overcoming obstacles, accessing information, learning and participating in activities, which otherwise were not able to carry out. Special needs education is the process adjusting the education of learners with disabilities according to their unique requirements. Evolvement of technology has made computers and mobile devices capable of complementing usual teaching processes for students with special needs. In this report we present a summary of some representative online applications for assisting disabled people in the learning process and their daily lives.
Virtual Network Functions Scheduling Under Delay-Weighted Pricing

We consider a cost minimization problem for High Volume Servers (HVS) equipped with Virtual Machines (VMs) to serve Virtual Network Functions (VNF) demands for resources. Given a scheduling period, a central scheduler decides which VM to use for each VNF demand. Each VM can be activated or disabled with different costs. Each VNF has a delay-weighted pricing function to indicate its completion time tolerance. We prove the NP-completeness of the problem and develop an algorithm based on Lagrangian relaxation and subgradient optimization to deal with this computational complexity. Finally, our numerical results demonstrate our algorithm’s effectiveness compared to two benchmarks.
Availability Aware VNF Deployment in Datacenter Through Shared Redundancy and Multi-Tenancy

By means of network function virtualization (NFV), dedicated proprietary network devices can be implemented as software and instantiated flexibly on common-off-the-shelf servers, in the form of virtual network functions (VNF). NFV can bring great cost reduction as well as operation flexibility. However, it also brings new problems, one of which is how to meet the availability of network services in the VNF deployment process, because of the error prone nature of software. The availability aware VNF deployment problem has attracted attention by academics, and reserving redundancy has been treated as the de facto technology. Compared with traditional backup schemes for physical machines, resource orchestration in NFV is more flexible and the characteristics of software should be considered to improve resource utilization efficiency. Based on the above considerations, in this paper we further study the availability aware VNF deployment problem in datacenter networks. To improve the resource utilization efficiency, the sharing mechanism of redundancy and multi-tenancy technology are taken into account. Then we formulate the problem mathematically and propose a joint deployment and backup scheme (JDBS). Finally, we conduct a numerical simulation in detail and compare it with four contrasting schemes in the existing literature. The simulation results show that JDBS is obviously superior to the contrasting schemes and can save about 40% resources at most.
Stepwise Regression to Predict Market Performance in the Janet Yellen Era

This paper adds to research on the effect of past interest rates on current market performance; specifically, this papers attempts to determine if a model can be developed to predict market performance based on historical interest rates. This research utilizes data from two different time periods to construct two different models, and each model is tested to determine its predictive ability during the time period when Janet Yellen was Chair of the Board of Governors of the Federal Reserve System. This research finds that models with strong predictive ability can be developed within time periods, but due to issues of non-stationarity and overfitting, those models become much weaker when applied to a different time period. Future research is necessary to create more successful models, and that future research needs to involve rigorous analysis of specific time periods to avoid problems with non-stationarity, and that research needs to utilize a number of data analysis techniques to avoid problems with overfitting.
How to Infer Prior Knowledge in Water Distribution Data-Driven Models?

In this paper, it is proposed to apply the strategy of system identification to modelling relationships in Water Dynamical Networks (WDN). System identification is issued from control theory where a model is computed from acquired data of a given system. Nevertheless, unlike in machine-learning, the model produced is con- strained to belong to a predefined model class which embedding the available a priori knowledge of studied system. When this a priori knowledge is directly derived from mechanistic laws, the strategy is referred to as gray-box approaches. This paper exposes the pitfalls to avoid in gray-box models, with a detailed example on a tank level estimation. Moreover, when applying data-driven strategies to WDN, black-box modelling strategies are often used (water demand estimation), even for well-known relationships (e.g tanks level). In this setup, how to infer a priori knowledge? This is usually left to some automated optimisation based on fitting score only. This paper, while recognising the power of black-box approaches, emphasises that this optimisation is linked to the quality of the data at hand while forgetting the physical intuition. It shows that taking this knowledge in account, even in black-box models, leads to better and more plausible prediction capabilities.
Lecturers’ And Students’ Code Switching In EFL Classroom

This research aims to find out the use of code switching in English as Foreign Language (EFL) classroom between lecturer and students. The purposes of this research are to find out the types of code switching used by 3rd semester lecturers and students in English department Bung hatta University. The design of this research is descriptive qualitative. The populations were 22 students and 3 lecturers. The data collected through recording their activity in class from October 2nd to November 2nd, 2018. The findings of research showed that the inter-sentential switching seemed to be the most frequent type of switching that occurred in the recording (58.82%); the second type is intra-sentential switching (29.41%); and the last is tag switching (11.76%). The code switching occurence among lecturer and students is about 23,87%, it means that rarely. Meanwhile, the students used code switching due to limitation of vocabulary. To sum up, code switching can be helpful for lecturer and students to discuss about lesson especially in grammar class. Keyword: Code Switching, EFL classroom
Reinforcement Learning for Optimal Control of Network Epidemic Processes

Our society is increasingly interconnected, making it easy for cascades/epidemic (diseases, disinformation etc). Current epidemic control eﬀorts are based on approximate network epidemic models, which often ignore the unique complexity and rich information embedded in the complex interconnections of real-world networks/populations.Deep reinforcement learning (RL) is a powerful tool at learning policies for these nonlinear, complex processes in high-dimension. To control an epidemic outbreak on a Susceptible-Infected-Susceptible network epidemic model, we design a RL framework with a custom reward structure using the node2vec embedding technique. Results indicate deep RL is able to determine and converge on an optimal intervention policy in a relatively short time.
State of the Art on the Quality of Big Data: A Systematic Literature Review and Classification Framework

One of the most significant problems of Big Data is to extract knowledge through the huge amount of data. The usefulness of the extracted information depends strongly on data quality. In addition to the importance, data quality has recently been taken into consideration by the big data community and there is not any comprehensive review conducted in this area. Therefore, the purpose of this study is to review and present the state of the art on the quality of big data research through a hierarchical framework. The dimensions of the proposed framework cover various aspects in the quality assessment of Big Data including 1) the processing types of big data, i.e. stream, batch, and hybrid, 2) the main task, and 3) the method used to conduct the task. We compare and critically review all of the studies reported during the last ten years through our proposed framework to identify which of the available data quality assessment methods have been successfully adopted by the big data community. Finally, we provide a critical discussion on the limitations of existing methods and offer suggestions on potential valuable research directions that can be taken in future research in this domain.
Hybrid decode-amplify and forward protocol of FD EH relaying network: outage probability analysis

Nowadays, many research papers focus on the WPCN problem and how to improve its efficiency. In this research, we propose and investigate Hybrid Decode-Amplify and Forward Protocol (HDAF) of the Full-Duplex (FD) Energy Harvesting (EH) Relaying Network with the Time Switching (TS) protocol. In the beginning stage, we present the HDAF mode, which can be work like a Decode-and-Amplify (DF) or Amplify-and-Forward (AF) modes based on the best of its performance in the FD EH relaying network. Furthermore, the closed-form expression of the outage probability (OT) is analyzed and derived in connection with the primary system parameters. Besides, the comparison of the system performance in the AF, DF, and HDAF is proposed and investigated. Finally, all the results are convinced by the Monte Carlo simulation for all cases.
Possibilidades de uso de Blockchain em fluxos informacionais de cadeias produtivas

The aim is to identify the possibilities of using Blockchain as a model to support the infrastructure of data and information transactions in a supply chain, listing its potential applications for this informational flow. For that, a theoretical revision was performed in scientific articles retrieved in the SCOPUS Elsevier and IEEE Xplore knowledge bases. The analysis of the theoretical corpus was carried out by the application of the Content Analysis method, using the categorical analysis type. The results of the research present six categories to represent the possibilities of using Blockchain in the studied context: (i) Traceability of the data; (ii) Data integrity; (iii) Data audit and transparency; (iv) Reduction of information losses; (v) Interoperability with Internet of Things devices and other systems, and; (vi) Standardization of digital representations.
NFC Research Framework: A Literature Review And Future Research Directions

Near Field Communication (NFC) is one of the emerging and promising technological developments, provides means to short range contactless communication for mobile phones and other devices alike. NFC has become an attractive research area for many academics due to its exploding growth and its promising applications and related services. An understanding the current status of NFC research area is necessary to maintain the advancement of knowledge in NFC research and to identify the gap between theory and practice. In this paper, we present a literature review on NFC. To facilitate the analysis of the literature, we propose a research framework and organize the NFC literature into four major categories; theory and development, applications and services, infrastructure, ecosystem. This rigorous and holistic literature review with the objective of bringing to the state-of-art in NFC design science research provides advancement of knowledge in NFC research and further research directions.
Data Preprocessing for Agricultural IoT Based on RBF Neural Network

The collected data obtained in the agricultural environment is not simply linear and stable, complex nonlinear functional relationships can be found. While RBF neural networks have the ability to approximate arbitrary nonlinear mappings and can implement the functions of nonlinear predictors with superior performance for data forecasting. Considering the strong time correlation of agricultural data, this paper proposes a time series model based on RBF neural network for preprocessing raw data. A four layer of agricultural IoT system is designed, a data processing layer is inserted into the traditional three-layer IoT system. Then the abnormal value is identified and eliminated by the “t testing criteria” test on the data preprocessing. The experimental results indicate that, comparing with auto regressive moving average model, the proposed model can achieve more competitive prediction ability.
Graph-based language grounding

In recent years, phrase (or more generally language) grounding has emerged as a fundamental task in computer vision. Phrase grounding is a generalization of more traditional computer vision tasks with the goal of localizing a natural language phrase spatially in a given image. Most recent work use state-of-the-art deep learning techniques to achieve good performance on this task. However, they do not capture complex dependencies among proposal regions and phrases that are crucial for the superior performance on the task. In this work we try to overcome this limitation through a model that makes no assumptions regarding the underlying dependencies in both of the modalities. We present an end-to-end framework for grounding of the phrases in images that uses graphs to formulate more complex, non-sequential dependencies among proposal image regions and phrases. We capture intra-modal dependencies using a separate graph neural network for each modality (visual and lingual), and then use conditional message-passing in another graph neural network to fuse their outputs and capture cross-modal relationships. This final representation is used to make the grounding decisions. The framework supports many-to-many matching and is able to ground single phrase to multiple image regions and vice versa. We validate our design choices through a series of ablation studies and demonstrate state-of-the-art performance on the Flickr30k Entities dataset and the ReferIt Game dataset.
Determinación de los Índices CPO-D e IHOS en estudiantes de la Universidad Veracruzana, México

Introduction: Knowledge of the dental health of the population constitutes a fundamental task of public health, since oral diseases- such as cavities and periodontal disease- are the health conditions with highest incidence and prevalence around the world. The objective of this study was to determine the index of decayed, missing or filled teeth (DMFT) and the simplified oral health index (OHI-S) of first year university students in the state of Veracruz, province of Mexico. Material y method: A descriptive, cross-sectional, observational study was carried out, applying a comprehensive health exam which consisted of two parts: a questionnaire and a clinical exam. Results: The DMFT index was 13.1. The prevalence of cavities was 80.9, missing teeth was 37.9 y filled teeth was 47.0. The OHI-S was the following: 81.5%
Systematic Review of Internet of Things Security

The Internet of Things has become a new paradigm of current communications technology that requires a deeper overview to map its application domains, advantages, and disadvantages. There have been a number of in-depth research efforts to study various aspects of IoT. However, to the best of our knowledge, there is no literature that have discussed specifically and deeply about the security and privacy aspects of IoT. To that end, this paper aims at providing a more comprehensive and systematic review of IoT security based on the survey result of the most recent literature over the past three years (2015 to 2017). We have classified IoT security research based on the research objectives, application domains, vulner-abilities/threats, countermeasures, platforms, proto-cols, and performance measurements. We have also provided some security challenges for further research.
Fitness-Based Linkage Learning in the Real-Valued Gene-Pool Optimal Mixing Evolutionary Algorithm

The recently introduced real-valued gene-pool optimal mixing evolutionary algorthm (RV-GOMEA) has been shown to be among the state of the art for solving gray-box optimization problems where partial evaluations can be leveraged. A core strength is its ability to effectively exploit the linkage structure of a problem, which often is unknown <italic>a priori</italic> and has to be learned online. Previously published work on RV-GOMEA, however, demonstrated excellent scalability when the linkage structure is prespecified appropriately. A mutual information-based metric to learn linkage structure online, as commonly adopted in EDA’s and the original discrete version of the gene-pool optimal mixing evolutionary algorithm, did not lead to similarly excellent results, especially in a black-box setting. In this article, the strengths of RV-GOMEA are combined with a new fitness-based linkage learning approach that is inspired by differential grouping that reduces its computational overhead by an order of magnitude for problems with fewer interactions. The resulting new version of RV-GOMEA achieves scalability similar to when a predefined linkage model is used, outperforming also, for the first time, the EDA AMaLGaM upon which it is partially based in a black-box setting where partial evaluations cannot be leveraged. <xref ref-type="fn" rid="fn1"><sup>1</sup></xref><fn id="fn1"><label><sup>1</sup></label><p>This article is extended from the MSc thesis of Chantal Olieman, available at <uri>https://repository.tudelft. nl/</uri> <xref ref-type="bibr" rid="ref24">[24]</xref>. </p></fn>
Ranked Features Selection with MSBRG Algorithm and Rules Classifiers for Cervical Cancer

In this paper, an automatic three-phase cervical cancer diagnosis system is employed which includes feature extraction, feature selection followed by classiﬁcation. Firstly, the modified seed-based region growing (MSBRG) algorithm is implemented for automatic segmentation and feature extraction using 500 cervical cancer cells. Processes to obtain the threshold values and the initial seed location are carried out automatically using moving k-mean (MKM) algorithm and invariant moment techniques. Secondly, eight attribute evaluators are applied for selecting and ranking the features, which are Correlation-based Feature Selection, Classifier Attribute Evaluator, Correlation Attribute Evaluator, Gain Ratio, Info Gain, OneR, ReliefF, and Symmetrical Uncertainty. Finally, the classification is compared based on five classifiers: Decision Table, JRip, OneR, PART, and ZeroR. The performance of the classifiers is evaluated using 3 test options: the training percentage splits (50% to 98%), the full training data and the cross validation (2-fold to 10-fold). The experimental results prove the capability of the MSBRG algorithm as an automatic feature extraction method. Furthermore, this paper proves the ability of the ranked feature selection methods to select important features of a cervical cell, and favors the Decision Table as the best classifier for cervical cancer classification.
Finding the Stars in the Fireworks: Deep Understanding of Motion Sensor Fingerprint

With the proliferation of mobile devices and various sensors (e.g., GPS, magnetometer, accelerometers, gyroscopes) equipped, richer services, e.g. location based services, are provided to users. A series of methods have been proposed to protect the users’ privacy, especially the trajectory privacy. Hardware fingerprinting has been demonstrated to be a surprising and effective source for identifying/authenticating devices. In this work, we show that a few data samples collected from the motion sensors are enough to uniquely identify the source mobile device, i.e., the raw motion sensor data serves as a fingerprint of the mobile device. Specifically, we first analytically understand the fingerprinting capacity using features extracted from hardware data. To capture the essential device feature automatically, we design a multi-LSTM neural network to fingerprint mobile device sensor in real-life uses, instead of using handcrafted features by existing work. Using data collected over 6 months, for arbitrary user movements, our fingerprinting model achieves 93% F-score given one second data, while the state-of-the-art work achieves 79% F-score. Given ten seconds randomly sampled data, our model can achieve 98.8% accuracy. We also propose a novel generative model to modify the original sensor data and yield anonymized data with little fingerprint information while retain good data utility.
A 36-Gb/s Adaptive Baud-Rate CDR With CTLE and 1-Tap DFE in 28-nm CMOS

This letter presents the design details of a 36-Gb/s adaptive baud-rate clock and data recovery circuit (CDR) with continuous-time linear equalizers and 1-tap decision feedback equalizer (DFE) fabricated in 28-nm CMOS. The proposed on-chip adaptation engine is tailored to a baud-rate CDR, where the front-end comparators are shared between the DFE and the PD to save power. The adaptive scheme is demonstrated on a baud-rate CDR testchip that has sampler-based front-end equalization which is more suitable for low-power, short reach, applications instead of a power intensive ADC/DSP-based receiver approach that is usually meant for long-reach (LR) links. This testchip was validated with 34–36 Gb/s random input data (PRBS31 & 7) with channel loss of 15.05–18.25 dB at Nyquist. The total power consumption is measured to be 106.3 mW, equivalent to an FOM of 3.04 pJ/bit at 35 Gb/s.