Miracle-dz/UniFAR-FTUs huggingface dataset:
Building Scalable Cyber-Physical-Social Networking Infrastructure Using IoT and Low Power Sensors

Wireless sensors are an important component to develop the Internet of Things (IoT) Sensing infrastructure. There are enormous numbers of sensors connected with each other to form a network (well known as wireless sensor networks) to complete the IoT Infrastructure. These deployed wireless sensors are with limited energy and processing capabilities. The IoT infrastructure becomes a key factor to building cyber-physical-social networking infrastructure, where all these sensing devices transmit data toward the cloud data center. Data routing toward cloud data center using such low power sensor is still a challenging task. In order to prolong the lifetime of the IoT sensing infrastructure and building scalable cyber infrastructure, there is the requirement of sensing optimization and energy efficient data routing. Toward addressing these issues of IoT sensing, this paper proposes a novel rendezvous data routing protocol for low-power sensors. The proposed method divides the sensing area into a number of clusters to lessen the energy consumption with data accumulation and aggregation. As a result, there will be less amount of data stream to the network. Another major reason to select cluster-based data routing is to reduce the control overhead. Finally, the simulation of the proposed method is done in the Castalia simulator to observe the performance. It has been concluded that the proposed method is energy efficient and it prolongs the networks lifetime for scalable IoT infrastructure.
Study of control strategies for shunt active power filter for harmonics suppression

Excessive use of nonlinear and time varying devices results in harmonic currents in the secondary distribution system. The suppression of harmonics is a dominant issue and one of the practical ways to compensate harmonics is shunt active power filter (SAPF). The core part of the SAPF is control techniques used for reference current generation. This paper presents a comprehensive study of three control strategies namely instantaneous reactive power (p — q) theory, synchronous reference frame (SRF) theory and instantaneous active and reactive current (id — iq) component method for SAPF in a three phase three wire distribution system. These three control methods aims to compensate harmonics, reactive power and load unbalance under sinusoidal balanced supply voltage conditions. Simulation results present a relative investigation of three control techniques based on current THD and load unbalance.
Mental health service acceptability for the armed forces veteran community.

Background Despite developments in mental health services for armed forces veterans and family members, barriers to access associated with poor levels of acceptability regarding service provision remain. Adapting a Step 2 mental health service based on low-intensity cognitive behavioural therapy (CBT) interventions to represent a familiar context and meet the needs of the armed forces veteran community may serve to enhance acceptability and reduce help-seeking barriers. Aims To examine acceptability of a Step 2 low-intensity CBT mental health service adapted for armed forces veterans and family members provided by a UK Armed Forces charity. Methods Qualitative study using individual semi-structured interviews with armed forces veterans and family members of those injured or becoming unwell while serving in the British Armed Forces. Data analysis was undertaken using thematic alongside disconfirming case analysis. Results Adapting a Step 2 mental health service for armed forces veterans and family members enhanced acceptability and promoted help-seeking. Wider delivery characteristics associated with Step 2 mental health services within the Improving Access to Psychological Therapies (IAPT) programme also contributed to service acceptability. However, limitations of Step 2 mental health service provision were also identified. Conclusion A Step 2 mental health service adapted for armed forces veterans and family members enhances acceptability and may potentially overcome help-seeking barriers. However, concerns remain regarding ways to accommodate the treatment of post-traumatic stress disorder and provide support for family members.
Boosted Training of Convolutional Neural Networks for Multi-Class Segmentation

Training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of performing semantic segmentation on large volumetric data sets, such as CT scans. Our contribution is threefold: 1) We propose a boosted sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in a more informative loss. This results in a significant training speed up and improves learning performance for image segmentation. 2) We propose a novel algorithm for boosting the SGD learning rate schedule by adaptively increasing and lowering the learning rate, avoiding the need for extensive hyperparameter tuning. 3) We show that our method is able to attain new state-of-the-art results on the VISCERAL Anatomy benchmark.
Toward Rendering-Latency Reduction for Composable Web Services via Priority-Based Object Caching

Web services serve as the cornerstone of the Internet for rendering webpages. The initial rendering latency of webpages, which depends on a subset of critical objects required by the webpage, is a key metric for web services. In this work, we propose to identify this set of critical objects systematically with the goal of caching them at a higher priority to reduce the initial rendering time. We first conduct a measurement study on a mainstream content delivery network provider, the results of which suggest that not all currently cached objects are critical and that only a small portion of the critical objects are cached. Thus, we model the critical-object aware caching scheme as a constrained optimization problem. Using the stochastic optimization framework, we decompose the problem into a set of one-shot optimization problems, which are proved to be NP-hard. We then develop two greedy algorithms with different computational complexity but the same performance bound. Finally, we integrate the resulting approximation algorithms into an online algorithm. Through trace-based simulations, we verify that our proposed algorithm can reduce service latency and network traffic by ensuring a higher cache hit ratio.
A pseudo-receiver domain in Atg32 is required for mitophagy

ABSTRACT Mitochondria are targeted for degradation by mitophagy, a selective form of autophagy. In Saccharomyces cerevisiae, mitophagy is dependent on the autophagy receptor, Atg32, an outer mitochondrial membrane protein. Once activated, Atg32 recruits the autophagy machinery to mitochondria, facilitating mitochondrial capture in phagophores, the precursors to autophagosomes. However, the mechanism of Atg32 activation remains poorly understood. To investigate this crucial step in mitophagy regulation, we examined the structure of Atg32. We have identified a structured domain in Atg32 that is essential for the initiation of mitophagy, as it is required for the proteolysis of the C-terminal domain of Atg32 and the subsequent recruitment of Atg11. The solution structure of this domain was determined by NMR spectroscopy, revealing that Atg32 contains a previously undescribed pseudo-receiver (PsR) domain. Our data suggests that the PsR domain of Atg32 regulates Atg32 activation and the initiation of mitophagy. Abbreviations:AIM: Atg8-interacting motif; GFP: green fluorescent protein; LIR: LC3-interacting region; NMR: nuclear magnetic resonance; NOESY: nuclear Overhauser effect spectroscopy; PDB: protein data bank; PsR: pseudo-receiver; RMSD: root-mean-square deviation
WOKV: A write-optimized key-value store

In big data era, LevelDB is widely deployed in enterprise server for massive storage. However, with the growing size of stored data, LevelDB will inevidablely frequently call the background compaction thread to compact data between level, resulting in write amplifications and therefore degrading performance. In this paper, we propose WOKV, a Write-Optimized Key-Value store, to mitigate the LevelDB compact write amplification and improve system performance. WOKV adopts a Least-Rewrite Compaction Strategy to mitigate the write amplification induced by compaction operations, and uses Multi-Thread and Multi-Buffer to improve system performance. Evaluation results show that WOKV outperforms LevelDB significantly.
A Bayesian approach to investigate life course hypotheses involving continuous exposures

Abstract Background Different hypotheses have been proposed in life course epidemiology on how a time-varying exposure can affect health or disease later in life. Researchers are often interested in investigating the probability of these hypotheses based on observed life course data. However, current techniques based on model/variable selection do not provide a direct estimate of this probability. We propose an alternative technique for a continuous exposure, using a Bayesian approach that has specific advantages, to investigate which life course hypotheses are supported by the observed data. Methods We demonstrate the technique, the relevant life course exposure model (RLM), using simulations. We also analyse data from a case-control study on risk factors of oral cancer, with repeated measurements of betel quid chewing across life. We investigate the relative importance of chewing one quid of betel per day, at three life periods: ≤20 years, 21–40 years and above 40 years of age, on the risk of developing oral cancer. Results RLM was able to correctly identify the life course hypothesis under which the data were simulated. Results from the case-control study showed that there was 74.3% probability that betel quid exposure earlier in life, compared with later, results in higher odds of developing oral cancer later in life. Conclusions RLM is a useful option to identify the life course hypothesis supported by the observed data prior to the estimation of a causal effect.
The Self-Fulfilling Prophecy 1 Perceptual Biases 2 Expectancy Effects : Empirical Support 2 Chapter II : The Power of Expectancy Effects 5 Target Characteristics 5 Situational and Motivational Factors 7 Expectancy Valence 8 Accumulation of Expectancy Effects

Social reality can be created through self-fulfilling prophecies and perceptual biases. A selffulfilling prophecy is a false belief that leads to its own fulfillment. A perceptual bias occurs when an individual believes that her or his inaccurate expectation about another has been confirmed to a greater extent than it has in reality. Although research findings bearing on these processes are robust, these effects are typically small. Nonetheless, this does not mean that self-fulfilling prophecy and perceptual bias effects are always small. There are conditions under which such effects have the potential to be powerful. The current experiment tested this possibility by addressing two main goals: 1) to examine if expectancy effects accumulate across perceivers and 2) to examine whether unfavorable or favorable expectancies produce more powerful cumulative expectancy effects. Sessions were run with groups of two perceivers and one target (N=145 groups). Whereas some perceivers were induced with either an unfavorable (i.e., hostile) or a favorable (i.e., friendly) expectancy, other perceivers were not induced with any expectancy. All perceivers then interacted with a target in a reaction time task, during which they used a noise weapon on an alternating basis. After the reaction time task, participants’ impressions of each other were measured. The average noise level that targets administered to perceivers was the dependent variable used to test for self-fulfilling prophecy effects. Perceivers’ impressions of targets was the dependent variable used to test for perceptual bias effects. Results failed to support the occurrence of a self-fulfilling prophecy. However, perceptual bias effects were observed – perceivers induced with an unfavorable expectancy judged targets’ as significantly more hostile than perceivers who were not induced with an unfavorable expectancy. However, further analyses indicated that perceptual bias effects did not accumulate across perceivers. Possible implications and some limitations of the experiment are discussed.
Impacts of Finger Numbers on ON-State Characteristics in Multifinger SiC BJTs With Low Base Spreading Resistance

Impacts of finger numbers on ON-state characteristics in multifinger 10-kV-class SiC bipolar junction transistors (BJTs), whose base spreading resistance is sufficiently reduced by using aluminum ion implantation, were investigated by performing TCAD simulations. Common-emitter current–voltage characteristics of the BJTs with different base current densities and carrier lifetimes in the collector layer were analyzed. The simulation results exhibited that the BJTs with fewer finger numbers could achieve superior characteristics owing to an expansion of a conductivity-modulated region and to a higher current density per finger. In addition, we showed that BJTs with a punchthrough structure have a potential to achieve superior characteristics suitable for power device applications under a certain condition, where strong conductivity modulation occurs. The presented results indicate that the appropriate finger numbers should be designed for a better performance of the multifinger SiC BJTs.
Smart proxy: Generic cloud middleware framework for improving database performance

In the last decades, relational databases were the most obvious choice for business applications. Times have changed and we should learn to use new models. Nowadays, we are living in the connected world, with tons of data related to each other. Moreover, in the emerging trend of using containers and enormous popularity of the cloud systems we can finally increase the performance of our solutions while decreasing costs. In this paper, authors have presented a new approach to improve overall performance and usability of databases. Results of the analysis are based on repeatable and verifiable observation of REST-based interactions with four products: Neo4j, MongoDB, Azure Cosmos DB and Microsoft SQL Server.
Safe Active Feature Selection for Sparse Learning

We present safe active incremental feature selection~(SAIF) to scale up the computation of LASSO solutions. SAIF does not require a solution from a heavier penalty parameter as in sequential screening or updating the full model for each iteration as in dynamic screening. Different from these existing screening methods, SAIF starts from a small number of features and incrementally recruits active features and updates the significantly reduced model. Hence, it is much more computationally efficient and scalable with the number of features. More critically, SAIF has the safe guarantee as it has the convergence guarantee to the optimal solution to the original full LASSO problem. Such an incremental procedure and theoretical convergence guarantee can be extended to fused LASSO problems. Compared with state-of-the-art screening methods as well as working set and homotopy methods, which may not always guarantee the optimal solution, SAIF can achieve superior or comparable efficiency and high scalability with the safe guarantee when facing extremely high dimensional data sets. Experiments with both synthetic and real-world data sets show that SAIF can be up to 50 times faster than dynamic screening, and hundreds of times faster than computing LASSO or fused LASSO solutions without screening.
A study of energy consumption in battery/super capacitor hybrid system based on optimized driving strategy

These days, hybrid electric vehicles have become main stream of car sale in the world. Several automobile companies have plan to change all kinds of vehicles of them as hybrid electric vehicle. Hybrid system can combine the strengths of both systems if the systems are well controlled. Typical hybrid electric vehicle has only one electric storage system, battery. But battery is based on electrochemical and life cycle can change depends on situation like DoD(Depth of Discharge), peak current. Although super-capacitor has high cost and low energy density, the strength of super-capacitor is ultimately high efficiency and extremely robustness from physical characteristic. So if super-capacitor helps the battery when peak current loads or frequent charge-discharge, the electric storage system accomplishes better efficiency and longer life cycle in spite of low energy density of super-capacitor. In this study, we use optimal control strategy 2 times to find potential value of supercharger in series hybrid vehicle. The study shows very high potential using only lower than 5 percent of the battery capacity. The loss of electric storage system decreases almost 40%. We can study more about price of super-capacitor and benefit of efficient operating electricity as further research.
Dense residual network for X-ray images super-resolution

In view of the information loss problem caused by image compression, data packet loss and other reasons during X-ray nondestructive testing, we proposed a novel dense residual network to further improve the quality of steel cord conveyor belt X-ray images reconstruction. Compared with previous works, the special connections provide multiple short paths for the forward propagation to improve reconstruction accuracy and for the backward propagation of gradient flow to accelerate the convergence speed. Extensive experiments confirm the effectiveness of our dense residual connections. Both subjective visual effect and objective quantitative evaluation are better than bicubic interpolation and super-resolution convolutional neural network (SRCNN). Especially for execution time, our method only takes 0.142s and it is fast enough for real-time application. Moreover, our method can be extended to natural images reconstruction.
Electricity load forecasting using fuzzy logic: Short term load forecasting factoring weather parameter

Electrical energy demand of an area depends on many factors like population, regional development, weather, electricity price, industrialization etc. Weather plays an important role in estimating the total demand met to a greater degree of accuracy. Accuracy in the forecast helps the system operator to optimize the generation and to keep suitable reserves in view of any contingency. The control area load has only a part of it that is susceptible to the weather, mostly the load for household purposes, while others are independent of the weather. Short term load forecast will not assume the contribution of agricultural load which is season dependent and thus is assumed to be constant in the span of few days. This paper aims at developing a model using fuzzy logic to have the weather and temperature to forecast the future load on a short term basis.
A method for CIR fault diagnosis based on improved tri-training in big data environment

In recent years, China's railway industry has seen a rapid development, people's travel and transportation are increasingly being carried out by railway. How to ensure the safe operation of railway is a very important subject. Cab Integrated Radio communication equipment (CIR) is an important equipment for communication between train and train or between train and ground, the failure of the CIR equipment directly affects the safety of railway operation. This paper focus on the large amount of data generated by CIR devices in real time. Using big data technology to collect, preprocess and store them. Then, a new Tri-training algorithm is reformed according to the actual requirement of CIR fault diagnosis; Finally, a method for CIR fault diagnosis based on improved tri-training algorithm is proposed. Experiment shows that the proposed method can effectively use unlabeled CIR data to improve the accuracy of the model and can ensure the real-time and accuracy of CIR fault diagnosis. It has a high application value.
Identification of Potential Leverage Points for Self-Management Support Intervention in Thais with Type 2 Diabetes.

Background Although social-ecological models and multi-level interventions have been recommended for implementing the holistic self-management support for patients with type 2 diabetes, they are complex, costly, and need long-term effort. A more realistic approach would be to identify and implement a single or limited leverage point(s) that is most effective and feasible to create change. Objective To assess the independent relationships of the social-environmental supports with self-management behaviors in Thai patients with type 2 diabetes. Material and Method A cross-sectional survey was conducted among 1,000 type 2 diabetic patients from 64 healthcare facilities throughout Bangkok. A set of structured questionnaires were used to collect data related to social-environmental supports, and self-management behaviors. The predictor-outcome relationships were presented by beta (β) coefficients (95% confidence limits). Results Personal support was significantly associated with the overall self-management, dietary, physical activity, and medication taking behaviors. Neighborhood support was significantly associated with the overall self-management, physical activity, and medication taking behaviors. Personal support was found to interact negatively with neighborhood support on the overall self-management and medication taking behaviors. Conclusion Personal and neighborhood supports are two potential leverage points for self-management support interventions for Thai patients with type 2 diabetes. Patients with low and high personal supports may need different strategies for neighborhood support.
Efficient Data Integrity Verification Using CRC Based on HDFS in Cloud Storage

Data integrity verification is becoming a major challenge in cloud storage which can’t be ignored. This paper proposes an optimized variant of CRC (Checker Redundancy Cyclic) verification algorithm based on HDFS to improve the efficiency of data integrity verification in cloud storage through the research of CRC checksum algorithm and data integrity verification mechanism of HDFS. A new method is formulated to establish the deformational optimization and to accelerate the algorithm by researching characteristics of generating and checking the algorithm. Moreover, this method optimizes the code to improve the computational efficiency according to data integrity verification mechanism of HDFS. A data integrity verification system based on Hadoop is designed to verify proposed method. Experimental results demonstrate that proposed HDFS based CRC algorithm was able to improve the calculation efficiency and the utilization of system resource on the whole and outperformed well compared to existing models in terms of accuracy and time.