Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy Efficient Cloud Control and Pricing in Geographically Distributed Data Centers

The fast pace of progress in the domain of applications provided over the Internet has created a need for computational resources delivered as an on-demand utility, without having to manually manage computer hardware. Cloud computing has emerged as a very popular paradigm where resources such as virtual machines are provided as a scalable, pay-as-you-go service, catering to applications in a multitude of fields. On the other hand, the rapid cloud computing growth has turned the energy consumption of data centers hosting the cloud’s hardware infrastructure into a global environmental problem and a major cost factor. It is estimated that data centers constitute 1.5% of global electricity usage. At the same time, to serve increasing user requirements, modern cloud providers are operating multiple geographically distributed data centers. Distributed data center infrastructure changes the rules of cloud control, as energy costs depend on current regional electricity prices and temperatures that we call geotemporal inputs. Furthermore, pricing policies at which cloud providers can offer computational resources depend on the quality of service (QoS). With such pricing schemes and the increasing energy costs in data centres, balancing energy savings with performance and revenue losses is a challenging problem for cloud providers. Existing cloud control methods are suitable only for a single data center or do not consider all the available cloud control actions that can reduce energy costs in geographically distributed data centers. In this thesis, we propose a pervasive cloud control approach consisting of multiple methods for dynamic resource reallocation and hardware configuration adapted to volatile geotemporal inputs. The proposed methods consider the QoS impact of cloud control actions and the data quality limits of time series forecasting methods. We offer a cloud controller design that supports future extensions when new decision support components need to be added. We also propose novel pricing schemes which account for the computational resource availability and costs that arise from our cloud control approach to enable both flexible, energy-aware and high performance cloud computing. We evaluate our cloud control methods empirically and in a number of simulations using historical traces of electricity prices, temperatures, workloads and other data. We estimate the potential energy cost savings by comparing our methods to state-of-the-art baseline methods. We explore a variety of input parameters to provide a range of guidelines for practical application of our methods in cloud systems. Our results show that significant energy cost savings are possible without harming the QoS or service revenue in geographically distributed cloud computing.
Distributed Large Neural Network with Centralized Equivalence

In this article, we develop a distributed algorithm for learning a large neural network that is deep and wide. We consider a scenario where the training dataset is not available in a single processing node, but distributed among several nodes. We show that a recently proposed large neural network architecture called progressive learning network (PLN) can be trained in a distributed setup with centralized equivalence. That means we would get the same result if the data be available in a single node. Using a distributed convex optimization method called alternating-direction-method-of-multipliers (ADMM), we perform training of PLN in the distributed setup.
Multi Task Learning with Positive and Unlabeled Data and its Application to Mental State Prediction

In real-world machine learning applications, we are often faced with a situation where only a small number of training samples is available due to high sampling costs. For instance, prediction of mental states such as drowsiness from physiological information is a typical example. To cope with this problem, classifier training methods only from positive and unlabeled data and multi-task learning methods for improving the classification performance by solving multiple related tasks simultaneously have been actively investigated recently. In this paper, we combine these methods and propose a multitask learning method that can handle positive-unlabeled tasks and positive-negative tasks in a unified manner. Through experiments on drivers' drowsiness prediction, we demonstrate the effectiveness of the proposed method.
Neural Machine Translation for English to Hindi

Language translation is one task in which machine is definitely lagging behind the cognitive powers of human beings. Statistical Machine Translation is one of the conventional ways of solving the problem of machine translation. This method requires huge data sets and performs well on similar grammar structured language pairs. In recent years, Neural Machine Translation (NMT) has emerged as an alternate way of addressing the same issue. In this paper, we explore different configurations for setting up a Neural Machine Translation System for Indian language Hindi. We have experimented with eight different architecture combinations of NMT for English to Hindi and compared our results with conventional machine translation techniques. We have also observed in this work that NMT requires very less amount of data size for training and thus exhibits satisfactory translation for few thousands of training sentences as well.
Optimal Power and Bit Allocation for Graph Signal Interpolation

We study centralized interpolation of bandlimited graph signals at a fusion center (FC), when sampled data are transmitted over rate-constrained links. In such a scenario, the performance of the reconstruction task is inevitably affected by several sources of errors such as observation noise and quantization due to source encoding. In this paper, we propose two strategies for optimally selecting transmission powers, quantization bits, and the sampling set, with the aim of interpolating a graph signal with guaranteed performance. Numerical results validate the proposed approach for interpolation of bandlimited graph signals under communication constraints.
Event Data Collection for Recent Personal Questions

In human-human conversation, people frequently ask questions about a person with whom to talk. Since such questions also asked in human-agent conversations, previous research developed a Person DataBase (PDB), which consists of questionanswer pairs evoked by a pre-defined persona to answer user’s questions. PDB contains static information including name, favorites, and experiences. Therefore, PDB cannot answer questions about events that occurred after it was built. It means that this approach does not focus on answering questions about more recent things (recent personal questions), e.g., Have you seen any movies lately? In contrast, since recent questions are frequently asked in a casual conversation, conversational agents are required to answer recent questions for maintaining a conversation. In this paper, we collect event data that consist of a large number of experiences and behaviors in daily lives, which enables to answer recent questions. We analyze them and show that our data is effective for answering recent questions.
A Spectral-Efficient Self-Homodyne-Detected Microwave Photonic Link With an Extended Fiber-Reach

In this letter, a simple and spectral-efficient self-homodyne-detected microwave photonic link is proposed and experimentally demonstrated. At the transmitter, a phase-modulated optical signal and an intensity-modulated optical local oscillator signal are polarization-multiplexed and delivered in a single fiber over long distance. At the receiver, the two signals are coherently detected by a self-homodyne interferometer and then demodulated through simple digital signal processing. The proposed scheme is experimentally validated by 25-km fiber transmission of the 50-Msymbol/s 16-QAM microwave vector signals at 2 and 2.5 GHz. The transmission performance compared with the conventional homodyne detection scheme in terms of error vector magnitude and bit error rate is evaluated. The proposed scheme can be a potential solution to wireless access networks for both high spectral efficiency and low-cost implementation.
Fully Convolutional Neural Networks for Road Detection with Multiple Cues Integration

Road detection from images is a key task in autonomous driving. The recent advent of deep learning (and in particular, CNN or convolutional neural networks) has greatly improved the performance of road detection algorithms. In this paper, we show how to fuse multiple different cues under the same convolutional network framework. Specifically, we adopt a pre-trained Resnet-lOl to extract feature maps from RGB images; we then connect it with three extra deconvolution layers. These deconvolution layers is trained conditioning on appropriate image cues, and in our case they are a height image (i.e. elevation map obtained by e.g. Lidar scanner), image gradient, and position map. We also design two skip layers to speed up the convergence. Experiments on KITTI benchmark show competitive performance of our new networks.
A Random Matrix and Concentration Inequalities Framework for Neural Networks Analysis

This article provides a theoretical analysis of the asymptotic performance of a regression or classification task performed by a simple random neural network. This result is obtained by leveraging a new framework at the crossroads between random matrix theory and the concentration of measure theory. This approach is of utmost interest for neural network analysis at large in that it naturally dismisses the difficulty induced by the non-linear activation functions, so long that these are Lipschitz functions. As an application, we provide formulas for the limiting law of the random neural network output and compare them conclusively to those obtained practically on handwritten digits databases.
Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection Using Phonocardiogarm Signals

In this paper, we present completely automated cardiac anomaly detection for remote screening of cardio-vascular abnormality using Phonocardiogram (PCG) or heart sound signal. Even though PCG contains significant and vital cardiac health information and cardiac abnormality signature, the presence of substantial noise does not guarantee highly effective analysis of cardiac condition. Our proposed method intelligently identifies and eliminates noisy PCG signal and consequently detects pathological abnormality condition. We further present a unified model of hybrid feature selection method. Our feature selection model is diversity optimized and cost-sensitive over conditional likelihood of the training and validation examples that maximizes classification model performance. We employ multi-stage hybrid feature selection process involving first level filter method and second level wrapper method. We achieve 85% detection accuracy by using publicly available MIT-Physionet challenge 2016 datasets consisting of more than 3000 annotated PCG signals.
CTC Loss Function with a Unit-Level Ambiguity Penalty

This paper presents a modified loss function for training connectionist temporal classification (CTC)-based acoustic models. CTC-based acoustic models have been studied as alternatives to conventional hidden Markov models (HMMs), but have often shown worse performance than conventional deep neural network (DNN)-HMM hybrid models. In this paper, we attempt to identify the primary factor preventing CTC-based models from achieving their full potential, and hypothesize this constraint lies in the ambiguity in the identification boundaries among unit-level labels (phonemes or characters). In accordance with this hypothesis, we propose a modified CTC loss function using an ambiguity penalty. This penalty is defined by the conditional entropy and works to increase the separation metrics among unit-level labels. We evaluate the proposed method on the WSJ and CHiME4 tasks, and demonstrate that our modification improves the word error rate compared with that of the conventional CTC-based model when the training dataset is small.
Deep Neural Network Based Discriminative Training for I-Vector/PLDA Speaker Verification

In the studies of i-vector based speaker verification, the discriminative training of probabilistic linear discriminative analysis (PLDA) model has been proven to be an effective way to improve performance. This paper focuses on using a deep neural network (DNN) to strengthen the original discriminatively trained classifiers by its strong capability of nonlinear modeling representation. We first propose a deep neural network based dimensionality reduction model to replace the linear discriminant analysis (LDA) process, and then a discriminative training algorithm is also proposed to jointly optimize the network and PLDA scoring function under single discriminative criterion. Our experiments show that performance improvements are achieved in the male trials of short2-short3 core data set of NIST SRE08.
Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data

We consider a problem of learning a binary classifier only from positive data and unlabeled data (PU learning) and estimating the class-prior in unlabeled data under the case-control scenario. Most of the recent methods of PU learning require an estimate of the class-prior probability in unlabeled data, and it is estimated in advance with another method. However, such a two-step approach which first estimates the class prior and then trains a classifier may not be the optimal approach since the estimation error of the class-prior is not taken into account when a classifier is trained. In this paper, we propose a novel unified approach to estimating the class-prior and training a classifier alternately. Our proposed method is simple to implement and computationally efficient. Through experiments, we demonstrate the practical usefulness of the proposed method.
Video Stabilization and Text-Based Sentence Identification for Video-Based Answering Systems

Recently, teaching using video clips is a simple and direct method instead of the text-based approaches. On the E-learning platform, students ask a question, and teachers answer the question using video clips especially the derivation of mathematical equations. Teachers write their answers down on an A4-size paper, use an APP to record the whole process, and upload the video clips to the platform. However, reading the sentences or equations from videos is very uncomfortable due to the paper movements when teacher wrote the sentences. In addition, teacher's hand frequently occludes the answers during the recording process. In the study, a video stabilizer with hand removal is proposed for comfortably reading. There are two key components: Video stabilization and sentence identification. Video stabilization estimates the smooth camera paths using SURF -based corner detection, hand part removal, image registration, and frame calibration. In sentence identification, sentences are segmented and extracted using contour detection and the convex hull generation algorithm. After the calibration process, users won't fell dizzy in reading sentences from calibrated and un-shaky videos. In addition, time stamps are marked at the beginning and the end of each sentence in the processed videos. When users click a specified sentence, a stabilized clip with good quality will be played.
Improving Wait Time for Patients in a Pediatric Echocardiography Laboratory - a Quality Improvement Project

Background: Prolonged wait times for echocardiograms in the outpatient pediatric cardiology clinic led to patient and provider dissatisfaction at our institution. The aims of this project were to measure our baseline performance with regard to echocardiogram wait time (EWT), to implement a formal quality improvement (QI) program to improve EWT, and to measure the impact of QI on EWT. Methods: A QI team was formed comprising of cardiologists (A.P., T.S.), sonographer (A.W.), and QI mentor (J.M.). EWT was defined as time in minutes from initiation of the order in the electronic medical record to start of the echocardiogram. Goal EWT was set as ⩽ 20 minutes for 90% patients. Flowcharts were created after process observation to identify sources of potential delay contributing to EWT. QI methodology such as driver diagrams were utilized to identify interventions, which were then implemented and studied as Plan-Do-Study-Act cycles. Results: Sequential interventions included early start time, huddles involving clinic and echo laboratory staff, patient tracking system, and repurposing of a clinic room for echo. EWT was tracked for 840 patients. Mean EWT was 22.5 ± 17.5 minutes at baseline and decreased to 15.3 ± 7.8 minutes postintervention (P < 0.001). Postintervention, 81% of the patients waited < 20 minutes for their echo, and 98% patients waited < 30 minutes, compared with baseline numbers of 62% and 76%, respectively (P < 0.001). Conclusions: We were able to utilize QI methodology to derive interventions and track changes, resulting in quantifiable improvement in EWT in a busy pediatric echo laboratory.
Learning Manipulation Graphs from Demonstrations Using Multimodal Sensory Signals

Complex contact manipulation tasks can be decomposed into sequences of motor primitives. Individual primitives often end with a distinct contact state, such as inserting a screwdriver tip into a screw head or loosening it through twisting. To achieve robust execution, the robot should be able to verify that the primitive's goal has been reached as well as disambiguate it from erroneous contact states. In this paper, we introduce and evaluate a framework to autonomously construct manipulation graphs from manipulation demonstrations. Our manipulation graphs include sequences of motor primitives for performing a manipulation task as well as corresponding contact state information. The sensory models for the contact states allow the robot to verify the goal of each motor primitive as well as detect erroneous contact changes. The proposed framework was experimentally evaluated on grasping, unscrewing, and insertion tasks on a Barrett arm and hand equipped with two BioTacs. The results of our experiments indicate that the learned manipulation graphs achieve more robust manipulation executions by confirming sensory goals as well as discovering and detecting novel failure modes.
Performance Modelling of a Cloud based Health Monitoring System Using Dynamic Control System

Emergence of Mobile Technology and Cloud computing has brought rapid development in the globe especially in the health sector. This involves the use of cloud based monitoring system where wearable equipment, Mobile and Cloud Technologies are integrated. While this has produced positive change to the health sector, this system faces other critical performance challenges. These include cost, energy and resource performance. Literature reveals that researchers are working on that of energy while much is yet to be discussed on effective cost and resource performance of cloud based monitoring system. This paper addresses the issue of cost and resource performance. This is done by modelling a cloud based monitoring system using a dynamic control mechanism. This mechanism works by making certain numbers of medical experts (Resources) say n available and allows the system to dynamically switch to m other medical experts when the number of patients on the queue reaches a threshold say k. We use the queuing theory as the mathematical proof of concept and discrete event simulator to demonstrate a real life scenario. A comparative analysis of this model is done with the static system based on cost. This paper addresses two issues: the first is to determine which of the two models provide optimal solution in terms of resource management and the second is the profitability concept. That is, the model that produces optimal solution. The results reveal a better cost effective and a competitive Cost-Benefit Ratio (CBR) of Dynamic Control System over the static model.
Trends in the diffusion of misinformation on social media

In recent years, there has been widespread concern that misinformation on social media is damaging societies and democratic institutions. In response, social media platforms have announced actions to limit the spread of false content. We measure trends in the diffusion of content from 569 fake news websites and 9540 fake news stories on Facebook and Twitter between January 2015 and July 2018. User interactions with false content rose steadily on both Facebook and Twitter through the end of 2016. Since then, however, interactions with false content have fallen sharply on Facebook while continuing to rise on Twitter, with the ratio of Facebook engagements to Twitter shares decreasing by 60%. In comparison, interactions with other news, business, or culture sites have followed similar trends on both platforms. Our results suggest that the relative magnitude of the misinformation problem on Facebook has declined since its peak.
Estimation of Time-Varying Room Impulse Responses of Multiple Sound Sources from Observed Mixture and Isolated Source Signals

This paper proposes a method for online estimation of time-varying room impulse responses (RIR) between multiple isolated sound sources and a far-field mixture. The algorithm is formulated as adaptive convolutive filtering in short-time Fourier transform (STFT) domain. We use the recursive least squares (RLS) algorithm for estimating the filter parameters due to its fast convergence rate, which is required for modeling rapidly changing RIRs of moving sound sources. The proposed method allows separation of reverberated sources from the far-field mixture given that their close-field signals are available. The evaluation is based on measuring unmixing performance (removal of reverberated source) using objective separation criteria calculated between the ground truth recording of the preserved sources and the unmixing result obtained with the proposed algorithm. We compare online and offline formulations for the RIR estimation and also provide evaluation with blind source separation algorithm only operating on the mixture signal.
An Effective Model for Edge-Side Collaborative Storage in Data-Intensive Edge Computing

Edge Computing is a new computing paradigm that performs data processing at the edge of the network (i.e., edge servers) to lower data processing latency. Existing research works have paid lots of attention to how to offload computation tasks from terminals to edge servers, but most of them ignored how to store tasks' necessary data like pretrained models or databases on edge servers. Recently, the data-intensive tasks like deep learning and augmented reality are becoming common, which need large data storages and powerful computation resources. This leads to a cumbersome challenge, since many lightweight edge servers have limited resources. If an edge server does not have a task's necessary data, it needs to offload the task to cloud data centers or download the necessary data from the cloud. Both cases could increase the data processing latency. To address this problem, this paper proposes an edge-side collaborative storage framework (ECS). In ECS, the edge servers collaboratively store and process data-intensive tasks' necessary data. Particularly, if an edge server does not have the necessary data, it will forward the task to the nearest servers that contain the data. An effective iterative data placement algorithm is also proposed to improve ECS's performance. The experimental results show that ECS is 2 x better than the traditional non-shared storage framework in terms of the cache hit rate.