AuTraScale: An Automated and Transfer Learning Solution for Streaming System Auto-Scaling

This paper presents a wide-band half-rate frequency locked loop (FLL) circuit in 180 nm CMOS process. The bidirectional FLL includes a coarse frequency detector (CFD) and a fine frequency detector (FFD) to automatically detect a change in input data rate, acquires the new frequency without the need for an external reference clock or any programming. A modified CFD is proposed to reduce the frequency acquisition time. The simulation shows the FLL achieves wide lock range of 400Mb/s-to-2.6Gb/s and frequency acquisition time of $1.73 \mu \mathrm{s}$.

Transfer learning, which aims to reuse knowledge in different domains, has achieved great success in many scenarios via minimizing domain discrepancy and enhancing feature discriminability. However, there are seldom practical determination methods for measuring the transferability among domains. In this paper, we bring forward a novel meta-transfer feature method (MetaTrans) for this problem. MetaTrans is used to train a model to predict performance improvement ratio from historical transfer learning experiences, and can consider both the Transferability between tasks and the Discriminability emphasized on targets. We apply this method to both shallow and deep transfer learning algorithms, providing a detail explanation for the success of specific transfer learning algorithms. From experimental studies, we find that different transfer learning algorithms have varying dominant factor deciding their success, so we propose a multi-task learning framework which can learn both common and specific experience from historical transfer learning results. The empirical investigations reveal that the knowledge obtained from historical experience can facilitate future transfer learning tasks.

In the field of supervised machine learning, a transfer learning environment is defined as the training data having different distribution characteristics than the testing data. This is due to the lack of available labeled data for the domain of interest, which prompts an alternate domain to be used as the training data. Because there is insufficient labeled data from the domain of interest, validation techniques cannot be reliably used for the algorithm selection process in a transfer learning environment. A transfer learning algorithm is typically comprised of a domain adaptation step followed by a learning step. The learning step is usually implemented using a traditional machine learning algorithm. In this paper, we examine and analyze the impact that the traditional machine learning algorithm (the learning step) has on the overall performance of a transfer learning algorithm. Using the transfer learning test framework, we test five state-of-the-art transfer learning algorithms coupled with seven different traditional learning algorithms for a total of 35 unique transfer learning algorithms. For our experiment, no labeled data from the domain of interest is available for the training process. Since validation techniques cannot be reliably used for the algorithm selection process in a transfer learning environment, it is important for machine learning researchers and practitioners to understand the impact of a traditional machine learner on the overall performance of a transfer learning algorithm.

Internet based applications are mostly based on a reliable transport service. TCP (transmission control protocol) is one of the protocols responsible for applications performance. Mobile users access to Internet services through 2.5 and 3 Generation networks, which have a significant latency due to error mechanisms introduced at the link layer, as well as transmission delays in the radio access networks. High latency produces interaction with timers of upper protocols. Data rate is other limiting characteristic on these networks. Moreover, data rate changes during a TCP connection due to mobility. TCP can adapt its rate when slow data rate changes thanks to its self-clocking propriety. In case of a sudden change, TCP performance is degraded due to spurious retransmission, and the activation of its congestion control mechanisms. This paper will show the effects and optimization of some TCP parameters over GPRS (general packet radio service) and 802.11b real networks.

Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back‐dropout transfer learning (NB‐TL), which utilizes images that have been misclassified and further performs back‐dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB‐TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate.

In this work, we design and evaluate a computational learning model that enables a human-robot team to co-develop joint strategies for performing novel tasks that require coordination. The joint strategies are learned through "perturbation training," a human team-training strategy that requires team members to practice variations of a given task to help their team generalize to new variants of that task. We formally define the problem of human-robot perturbation training and develop and evaluate the first end-to-end framework for such training, which incorporates a multi-agent transfer learning algorithm, human-robot co-learning framework and communication protocol. Our transfer learning algorithm, Adaptive Perturbation Training (AdaPT), is a hybrid of transfer and reinforcement learning techniques that learns quickly and robustly for new task variants. We empirically validate the benefits of AdaPT through comparison to other hybrid reinforcement and transfer learning techniques aimed at transferring knowledge from multiple source tasks to a single target task. We also demonstrate that AdaPT's rapid learning supports live interaction between a person and a robot, during which the human-robot team trains to achieve a high level of performance for new task variants. We augment AdaPT with a co-learning framework and a computational bi-directional communication protocol so that the robot can co-train with a person during live interaction. Results from large-scale human subject experiments (n=48) indicate that AdaPT enables an agent to learn in a manner compatible with a human's own learning process, and that a robot undergoing perturbation training with a human results in a high level of team performance. Finally, we demonstrate that human-robot training using AdaPT in a simulation environment produces effective performance for a team incorporating an embodied robot partner.

Kiwifruit has a high decay rate, in part because quality changes during storage cannot be easily monitored in real time. In order to better monitor the shelf life of kiwifruit and understand the quality changing process during storage, internal quality indexes such as hardness, respiratory intensity and TSS(Total Soluble Solid) were considered into the prediction models. The prediction models were constructed based on BPNN (Back Propagation Neural Network), Random Forest (RF) and XGBoost (eXtreme Gradient Boosting) respectively. And transfer learning algorithm was used to construct the quality prediction models with BPNN, RF, and XGBoost algorithms as the base learner. In the experiments, sample data were augmented by adding Gaussian noise, which effectively prevented the model from over-fitting. The experimental results showed that the prediction accuracy of each index based on transfer learning was better than that of individual BPNN, RF and XGBoost. Moreover, the average prediction accuracy of the models based on transfer learning was 96.2%, and that of respiratory intensity was as high as 99.4%. Therefore transfer learning can be used to effectively analyze and predict changes of kiwifruit quality indexes during storage.

Nowadays, vehicles are not only merely used as a transportation medium, but also as a highly mobile Internet of Things (IoT) node, collecting data from all types of sources. The delivery of aggregated vehicle sensor data into the cloud for further analysis is very fragile, as vehicles move fast in their environment and channel conditions vary heavily. Next to mastering possible packet loss, communication protocols need to quickly adapt to the frequent data rate changes. Random Linear Network Coding (RLNC) has been proven as an efficient and robust mechanism for reliable data transfer especially on lossy channels. In this paper, the authors extend the Scalable Network Coding (ScalaNC) framework with the novel congestion control Bottleneck, Bandwidth and Round-Trip Time (BBR) in order to quickly adapt to the frequent changes in data rate and allow effective transfer of high amounts of data from vehicles and into the cloud. ScalaNC is validated in a Hardware-in-the-Loop field test consisting of a Long Term Evolution (LTE) base station and channel emulator.

Near-infrared spectroscopy mainly reflects the frequency-doubled and total-frequency absorption information of hydrogen-containing groups (O‒H, C‒H, N‒H, S‒H) in organic molecules for near-infrared lights with different wavelengths, so it is applicable to testing of most raw materials and products in the field of petrochemicals. However, the modeling process needs to collect a large number of laboratory analysis data. There are many oil sources in China, and oil properties change frequently. Modeling of each raw material is not only unfeasible but also will affect its engineering application efficiency. In order to achieve rapid modeling of near-infrared spectroscopy and based on historical data of different crude oils under different detection conditions, this paper discusses about the feasibility of the application of transfer learning algorithm and makes it possible that transfer learning can assist in rapid modeling using certain historical data under similar distributions under a small quantity of new data. In consideration of the requirement of transfer learning for certain similarity of different datasets, a transfer learning method based on local similarity feature selection is proposed. The simulation verification of spectral data of 13 crude oils measured by three different probe detection methods is performed. The effectiveness and application scope of the transfer modeling method under different similarity conditions are analyzed.

Multiple Kernel Based Transfer Learning for the Few-Shot Recognition Task in Smart Home Scene

The usage of Artificial intelligence in medical arena has proved to be a game changer in the detection and diagnosis of several medical conditions. In the current digital era, children with stressful medical issues are suffering from Deep Obsessive-Compulsive Disorder (DOCD). This kind of mental stress occurs in children because of the continuous usage of gadgets such as mobile phone, playing games using play stations, watching videos on tablets, etc. In most of the possibilities, single children are the ones affected with several obsessions such as stubborn activities, fighting for selfish priorities and so on. In medical terms, these kinds of complex behavioral changes are identified as DOCD. Genetic behaviors sometimes in a few group of children are also noticed as a modality difference. As symptoms are psychiatric impairment, such a child remains isolated, abnormal silence, being obsessive and repeating irrelevant words, high stress or anxiety. All medical challenges could be treated as healthcare research metrics and the gradual increase in DOCD disorder among children of this generation can be considered too. Early detection of DOCD is essential as it can help in early diagnosis but techniques to do so is unavailable currently. Deep learning-an artificial intelligence method can be utilized to detect DOCD, diagnose and treat it and bring about a positive character in children. Behavior changes in children can be classified and detected using transfer learning algorithms. In COVID-19 pandemic situation, 3% of DOCD has increased to 10-15% as a disorder. This information is retrieved from children by monitoring negative activities, unusual behavior such as nail biting, removing spectacles and placing them in the wrong place, watching tablets, mobile phones and television for more hours. Using Convolutional Neural Networks (CNN), input such as MRI (Magnetic resonance Imaging) is used for experimenting the variations in behavior with the high dimension that are analyzed from the image dataset. Using Transfer Learning with Inception V3-, CNN generalization of misophonia level can be statistically analyzed to avoid overfitting problems. By employing AI techniques, the aggression level can be predicted using data augmentation method with better accuracy and a low error rate than the existing systems. In the research it is observed that using the model employing Inception-V3 transfer learning CNN a better prediction of aggression levels can be achieved in comparison to the existing CNN model used.

Deep learning methods have huge success in task specific feature representation. Transfer learning algorithms are very much effective when large training data is scarce. It has been significantly used for diagnosis of diseases in medical imaging. This article presents a systematic literature review (SLR) by conducting a comparison of a variety of transfer learning approaches with healthcare experts in diagnosing diseases from medical imaging. This study has been compiled by reviewing research studies published in renowned venues between 2014 and 2019. Moreover, the data for the diagnosis performed by health care experts has also been acquired to perform a detailed comparative analysis for a wide range of diseases. The analysis has been performed on the basis of diseases, transfer learning approaches, type of medical imaging used. The comparative analysis is based on performance indices reported in studies which include diagnostic accuracy, true-positive (TP), false-positive (FP), true-negative (TN), false-negative (FN) sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). A total of5,188articles were identified out of which 63 studies were included. Among them 21 research studies contain sufficient data to construct the evaluation tables that enable process of test accuracy of transfer learning having sensitivity ranged from 71% to 100% (mean 85.25%) and specificity ranged from 64% to 100% (mean 81.92%). Furthermore, health experts having sensitivity ranged from 33% to 100% (mean 85.27%) and specificity ranged from 82% to 100% (mean 91.63%).This SLR found that diagnostic accuracy of transfer learning is approximately equivalent to the diagnosis of health experts. The results also revealed that convolutional neural networks (CNN) have been extensively used for disease diagnosis from medical imaging. Finally, inappropriate exposure of diseases in transfer learning studies restricts reliable elucidation of the outcomes of diagnostic accuracy.

The phase 1 upgrade of the ATLAS [1] pixel detector will be done by inserting a fourth pixel layer together with a new beampipe into the recent three layer detector. This new detector, the Insertable B-Layer (IBL) should be integrated into the recent pixel system with as few changes in services as possible, but deliver some advantages over the recent system. One of those advantages will be a new data transmission link from the detector modules to the off-detector electronics, requiring a re-design of the electro-optical converters on the off-detector side. First ideas of how to implement those, together with some ideas to reduce cost by increasing the systems throughput are discussed.

The agricultural sector has been, and still is, the most important economic sector in many countries. Due to advances in technology, the amount and variety of available data have been increasing over the years. However, compared to other economic sectors, there is not always enough quality data for one particular domain (crops, plantations, plots) to obtain acceptable forecasting results with machine learning algorithms. In this context, transfer learning can help extract knowledge from different but related domains with enough data to transfer it to a target domain with scarce data. This process can overcome forecasting accuracy compared to training models uniquely with data from the target domain. In this work, a novel instance weighting-based transfer learning algorithm is proposed and applied to the phenology forecasting problem. A new metric named DIAFAN is proposed to weight samples from different source domains according to their relationship with the target domain, promoting the diversity of the information and avoiding inconsistent samples. Additionally, a set of validation schemes is specifically designed to ensure fair comparisons in terms of data volume with other benchmark transfer learning algorithms. The proposed algorithm, DIAFAN-TL, is tested with a proposed dataset of 16 plots of olive groves from different places, including information fusion from satellite images, meteorological stations and human field sampling of crop phenology. DIAFAN-TL achieves a remarkable improvement with respect to 15 other well-known transfer learning algorithms and three nontransfer learning scenarios. Finally, several performance analyses according to the different phenological states, prediction horizons and source domains are also performed.

In order to solve the high bit error rate (BER) caused by rate oscillation in traditional Data Rate Change (DRC) algorithm, an improved DRC algorithm based on Adaptive Frame Length (AFL) was proposed. Firstly, the frame length and transmission rate of the initial transmission were determined by the parameters of the current channel and the information of previous empirical values. Then, if two frames with the same length were successively sent in the transmission process, the frame length would be accordingly increased. If the retransmission failed twice in a row, the frame length would be halved in the next transmission. Finally, the frame error rate was calculated based on the current frame length. The data rate would be updated If the value was less than the present threshold. Compared with RapidM DRC, the average bit error rate of the link decreased by 4.8 percentage points, and the link availability increased by 10 percentage points. The experimental results show that the proposed algorithm can eliminate the rate oscillation and improve the communication capability of the HF communication system.