Miracle-dz/UniFAR-FTUs huggingface dataset:
Heuristic Methods for Fine-Grain Exploitation of FDSOI

Fully depleted silicon on insulator (FDSOI) is attractive for its low cost and low power; the mixed-Vt and body-bias levers that it affords expand the performance-power solution space. However, in FDSOI, different-Vt (i.e., low Vt and regular Vt) devices must be isolated from each other, which makes realization of fine-grained mixed-Vt/body-biasing in layout extremely challenging. In this article, we study heuristic methods aimed at exploitation of fine-grained mixed-Vt in FDSOI implementation. We propose a novel speed domain partitioning (SDP) problem formulation that comprehends the spatial contiguity restrictions arising from flip-well structure of low Vt regions in popular 28-nm commercial FDSOI offerings. We explore a wide space of implementation flows that include an integer linear programming (ILP)-based approach, and a heuristic (sensitivity-based) optimization. Our experimental studies have been performed across multiple commercial enablements. We observe that outcomes are library- and design-dependent. For implementations using generic library options, up to 20% speed improvement with 54% low Vt region is seen for one out of four testcases studied. For implementations using “rich” library options, up to 7% speed improvement with 26% low Vt region is achieved. We provide a discussion that summarizes root-cause, intrinsic difficulties of fine-grained exploitation of mixed-Vt. Finally, we suggest a “decision tree” to help assess a design’s amenability to fine-grained mixed-Vt implementation, and to help guide design flow selection for better design QoR.
Sentiment classification with deep neural networks

Yi Zhou: Sentiment classification with deep neural networks Master of Science thesis Tampere University Master’s Degree Programme in Software Development May 2019 Sentiment classification is an important task in Natural Language Processing (NLP) area. Deep neural networks become the mainstream method to perform the text sentiment classification nowadays. In this thesis two datasets are used. The first dataset is a hotel review dataset (TripAdvisor dataset) that collects the hotel reviews from the TripAdvisor website using Python Scrapy framework. The pre-processing steps are then applied to clean the dataset. A record in the TripAdvisor dataset consists of the text review and corresponding sentiment score. There are 5 sentimental labels: very negative, negative, neutral, positive, and very positive. The second dataset is the Stanford Sentiment Treebank (SST) dataset. It is a public and common dataset for sentiment classification. Text Convolutional Neural Network (Text-CNN), Very Deep Convolutional Neural Network (VDCNN), and Bidirectional Long Short Term Memory neural network (BiLSTM) were chosen as different methods for the evaluation in the experiments. The Text-CNN was the first work to apply convolutional neural network architecture for the text classification. The VD-CNN applied deep convolutional layers, with up to 29 layers, to perform the text classification. The BiLSTM exploited the bidirectional recurrent neural network with long short term memory cell mechanism. On the other hand, word embedding techniques are also considered as an important factor in sentiment classification. Thus, in this thesis, GloVe and FastText techniques were used to investigate the effect of word embedding initialization on the dataset. GloVe is a unsupervised word embedding learning algorithm. FastText uses shallow neural network to generate word vectors and it has fast convergence speed for training and high speed for inference. The experiment was implemented using PyTorch framework. It shows that the BiLSTM with GloVe as the word vector initialization achieved the highest accuracy 73.73% while the VD-CNN with FastText had the lowest accuracy 71.95% on the TripAdvisor dataset. The BiLSTM model achieved 0.68 F1-score while the VD-CNN model obtained 0.67 F1-score on the TripAdvisor dataset. On the SST dataset, BiLSTM with GloVe again achieved the highest accuracy 36.35% and 0.35 F1-score. The VD-CNN model with GloVe had the worst evaluation result in terms of accuracy and F1-score. The Text-CNN model performed better than the VD-CNN model even thought the VD-CNN model has more layers in most cases. By analyzing the misclassified reviews in the TripAdvisor dataset from the three deep neural networks, it is shown that the hotel reviews with more contradictory sentimental words were more prone to misclassification than other hotel reviews.
A Study of x-Vector Based Speaker Recognition on Short Utterances

The aim of this work is to gain insights into how the deep neural network (DNN) models should be trained for short utterance evaluation conditions in an x-vector based speaker verification system. The study suggests that the speaker embedding can be extracted with reduced dimensions for short utterance evaluation conditions. When the speaker embedding is extracted from deeper layer which has lower dimension, the x-vector system achieves 14% relative improvement over baseline approach on EER on NIST2010 5sec-5sec truncated conditions. We surmise that since short utterances have less phonetic information speaker discriminative x-vectors can be extracted from a deeper layer of the DNN which captures less phonetic information. Another interesting finding is that the x-vector system achieves 5% relative improvement on NIST2010 5sec-5sec evaluation condition when the back-end PLDA is trained using short utterance development data. The results confirms the intuitive expectation that duration of development utterances and the duration of evaluation utterances should be matched. Finally, for the duration mismatch condition, we propose a variance normalization approach for PLDA training that provides a 4% relative improvement on EER over baseline approach.
Large-scale resource scheduling method using improved genetic algorithm combined with secondary coding in cloud computing environment

This paper proposes a large-scale resource scheduling method based on improved genetic algorithm combined with secondary coding. This method is used to solve the problem that traditional genetic algorithm can not meet the resource scheduling problem in large-scale cloud computing environment under multi-user. In the selective replication phase, a dual fitness function based on minimum task completion time and matching degree is used to screen the populations by double criteria. Next, the cross-mutation probability of the algorithm is adaptively optimized, and its adaptive ability is further improved, which ensures that the algorithm converges to the optimal solution as soon as possible. Finally, the improved genetic algorithm (IGA) is analyzed on the CloudSim platform. It shows that the improved genetic algorithm can be well applied to large-scale resource scheduling, and the result is better than the comparison algorithm.
Investigation of the Functional Stability of Neural Network Algorithm for Solving the Ordinary Differential Equations

The paper analyzes the neural network approach for solving the Cauchy problem of ordinary differential equations of the first order, based on the representation of the function as a superposition of elementary functions. The use of neural network approach allows obtaining the desired solution in the form of a functional dependence, which satisfies the required conditions of smoothness. On the basis of a two-layer perceptron, a model of neural network solution of the problem and a numerical algorithm implementing the search for a solution are constructed. The software-algorithmic solution of the Cauchy problem is obtained. To determine the stability of the neural network approach, a series of experiments were conducted to find a solution to a particular Cauchy problem of ordinary differential equation of the first order with an analytical solution. The study shows that the considered neural network algorithm has no functional stability. This may be due to the problems of weights minimization, scalability in network training and other factors. Keywords—ordinary differential equations, artificial neural network, optimization methods, functional stability
Surface Blemishes of Aluminum Material Image Recognition Based on Transfer Learning

Image datasets in the field of industrial manufacturing usually have the problem of uneven distribution of samples. To solve this problem, this paper utilizes transfer learning to recognize surface blemishes of Aluminum material image. In order to get rid of the disadvantage of VGGNet too many parameters, this paper combines VGGNet with Network-in-Network to generate a new model. The results of experiments is shown that it has achieved significant improvement by using transfer learning. Additionally, the new model also achieves a better performance and the number of parameters of the new model is much smaller than that of the original VGGNet.
Indoor formaldehyde monitoring system based on fog computing

With the enhancement of people’s awareness of environmental protection, environmental monitoring technology has gradually entered into all aspects of people’s life and work. However, due to the high demand of real-time monitoring for data transmission delay, the traditional cloud computing architecture cannot meet the demand, and the fog computing architecture emerges at the historic moment. In this paper, the traditional cloud computing architecture is improved by using the fog computing architecture combined with the function as a service mode of the serverless architecture, and an environment monitoring architecture based on fog computing is proposed. Taking indoor formaldehyde monitoring as an example, the delay of the system is significantly improved, and the resource utilization efficiency is higher and the energy consumption is lower. It solves the deficiency of traditional cloud computing architecture and better meets the demand of real-time environmental monitoring.
System Identification Guidance for Multirotor Aircraft: Dynamic Scaling and Test Techniques

State-space system identification was performed to extract flight dynamic models for hovering flight of a 55 cm, 1.56 kg hexacopter unmanned aerial vehicle. Different input excitation techniques were tested to determine which maneuvers provided high-quality system identification results for small-scale multirotor vehicles. These input excitation techniques included automated frequency sweeps, varying in amplitude, and multisine sweeps. Coherence, Cramer–Rao bounds, and insensitivities were used as metrics for comparing the system identification results. A parametric variation of frequency sweep amplitudes were performed in all axes (roll, yaw, pitch, and heave) to provide guidance on frequency sweep amplitude for small-scale multirotor unmanned aerial systems. The dynamics of the 55 cm hexacopter were used to estimate the dynamics of a larger 127-cm hexacopter via Froude scaling based on hub-to-hub distance as the characteristic length. The scaled results were compared to an actual system identification model of a 127-cm hexacopter.
A learning method to optimize depth accuracy and frame rate for Time of Flight camera

Time of Flight cameras attracts increasing attention due to their ability to robustly predict depth values of the scene. The principle of actively emitting modulated signals and receiving sampled signals causes that the artifacts generated by motion blur seriously affect quality of imaging and the power consumption of the light source. The problem of shorter exposure time is that the raw measurements have a lower signal-to-noise ratio. Reducing the four frames of raw measurements for depth reconstruction to two frames can make it difficult to effectively suppress noise from the background infrared signal and capacitive components. However, shorter exposure time and reducing four frames of raw data to two frames will greatly reduce the motion blur of the time-of-flight camera with a higher frame rate and lower power consumption. We propose a method based on deep learning and make it possible to reliably recover the depth information even under extremely low signal-to-noise ratio and the noise of electronic components for alleviating the motion blur problem. The experiment results fully demonstrate the reliability of our solution. Our algorithms and data will be published in the future.
Analysis Using Various Approaches for Residual Life Estimation of Power Transformers

Transformers are the most important links between generation, transmission and distribution of a power system. Their failure can cause huge revenue loss to utilities as there is non-availability of power for long hours. Several factors like high load, overheating and short circuits influence the aging of the insulating system present in transformer. Many diagnostic tests are carried out and remedial actions are taken for ensuring non-interrupted, trouble free power supply to customers. End of useful life of a power transformer is determined by the remaining life of paper insulation in the transformer. The aging in the paper insulation is determined by mechanical strength or tensile strength of the solid insulation and is measured in terms of its Degree of Polymerization (DP). The traditional method for determining DP is called the direct method and it requires actual paper samples therefore making it costly and intrusive. Recently an indirect method is used, which estimates the DP value from the furan compounds dissolved in the oil and this is a non-intrusive process. This paper presents analysis of various indirect methods used in remaining life estimation and also comparative studies have been made among the mathematical equations developed by various researchers correlating 2furaldehyde and DP with respect to ageing process.
Exploring the dominant features of social media for depression detection

Recently, social media have been used by researchers to detect depressive symptoms in individuals using linguistic data from users’ posts. In this study, we propose a framework to identify social information as a significant predictor of depression. Using the proposed framework, we develop an application called the Socially Mediated Patient Portal (SMPP), which detects depression-related markers in Facebook users by applying a data-driven approach with machine learning classification techniques. We examined a data set of 4350 users who were evaluated for depression using the Center for Epidemiological Studies Depression (CES-D) scale. From this analysis, we identified a set of features that can distinguish between individuals with and without depression. Finally, we identified the dominant features that adequately assess individuals with and without depression on social media. The model trained on these features will be helpful to physicians in diagnosing mental diseases and psychiatrists in analysing patient behaviour.
Survey of deployment locations and underlying hardware architectures for contemporary deep neural networks

This article overviews the emerging use of deep neural networks in data analytics and explores which type of underlying hardware and architectural approach is best used in various deployment locations when implementing deep neural networks. The locations which are discussed are in the cloud, fog, and dew computing (dew computing is performed by end devices). Covered architectural approaches include multicore processors (central processing unit), manycore processors (graphics processing unit), field programmable gate arrays, and application-specific integrated circuits. The proposed classification in this article divides the existing solutions into 12 different categories, organized in two dimensions. The proposed classification allows a comparison of existing architectures, which are predominantly cloud-based, and anticipated future architectures, which are expected to be hybrid cloud-fog-dew architectures for applications in Internet of Things and Wireless Sensor Networks. Researchers interested in studying trade-offs among data processing bandwidth, data processing latency, and processing power consumption would benefit from the classification made in this article.
Attack on a Scheme for Obfuscating and Outsourcing SAT Computations to the Cloud

The emergence of cloud computing gave users the capability to offload computations that cannot be executed locally to cloud servers with large computational power. One such computationally demanding problem is solving large satisfiability (SAT) instances. Although many problems from AI, circuit verification, etc. can be converted to SAT, outsourcing SAT instances may leak considerable information that can put a user’s security at risk. Hence the privacy of outsourcing computations to the cloud is a major issue. In this work we look at the techniques of Qin et al. (Qin and Jia., 2014; Qin and Du., 2018) which have been used to obfuscate SAT formulas before they are released to the cloud. We came up with a realistic attack against their technique that demonstrates how a malicious cloud provider can obtain significant information about the underlying SAT instance. Our work shows that ad hoc schemes cannot offer the required security guarantees for outsourcing SAT computations, hence more formal frameworks should be used instead.
Preliminary study of big data transfer over computer network

The transfer of Big Data over computer network is important and unavoidable operation in the past, now and in any feasible future. There are a number of methods to transfer the data over computer global network (Internet) with a range of tools. In this paper the transfer of one piece of Big Data from one point in the Internet to another point in Internet in general over long range distance: many thousands kilometers. Several free of charge systems to transfer the Big Data are analyzed here. The most important architecture features are emphasized and suggested idea to add SDN Openflow protocol technique for fine tuning the data transfer over several parallel data links.
Estimation of Driver Drowsiness Using Blood Perfusion Analysis of Facial Thermal Images in a Driving Simulator

Background and Objective: Driver drowsiness is a major cause of fatal driving accidents worldwide, which can be prevented by an early detection. Driver drowsiness results in reduction of blood flow and change in facial thermal patterns. In this paper, a drowsiness detection system was designed to estimate the level of drowsiness by analyzing facial skin temperature patterns. Materials and Methods: 12 subjects drove on a highway in a driving simulator, while their facial temperature image was captured by a thermal camera. The subjects’ drowsiness levels were independently estimated by the observer rating of drowsiness (ORD) method. Facial blood vessels were located by a four-step algorithm and tracked in each frame. The mean value of the pixel intensities of the facial blood regions formed a sequence of data. Variations of facial skin temperature signals were investigated for three main blood vessels. Results: Facial skin temperature decreased at three levels of sleepiness from wakefulness to extreme drowsiness. Temperature near the supratrochlear, angular, and facial arteries decreased by 0.54 oC, 0.33 oC, and 0.32 oC from wakefulness to extreme drowsiness, respectively. All subjects except one experienced skin temperature decrease from wakefulness to extreme drowsiness. Conclusion: It was shown that the drivers’ facial blood vessels temperature decreased as the level of drowsiness increased. Driver drowsiness can be detected by monitoring the change in the facial skin temperature signal during driving. Facial thermal imaging is a promising non-intrusive method to detect driver drowsiness. © 2018 Tehran University of Medical Sciences. All rights reserved.
Computer Network Virus Propagation Model Based on Biology Principle

Since existing SIR models do not accurately capture the properties of node state transition in real computer networks,this paper proposes a new SIRS computer viruses propagation model based on the principle of biological epidemic viruses.By analyzing the dynamic of this model using the theory of differential equations,it finds that the basic renewal parameter,R0,is the key factor which determines whether the computer viruses can be completely controlled.Numerical simulation results show that the model provides a good theoretical basis for the control and prediction of the propagation performance of the computer viruses.
DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis Version 3.0 Reference Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogatebased optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the Dakota software, providing input overviews, option descriptions, and example specifications.
SenSALDO: a Swedish Sentiment Lexicon for the SWE-CLARIN Toolbox

The field of sentiment analysis or opinion mining consists in automatically classifying text according to the positive or negative sentiment expressed in it, and has become very popular in the last decade. However, most data and software resources are built for English and a few other languages. In this paper we compare and test different corpus-based and lexicon-based methods for creating a sentiment lexicon. We then manually curate the results of the best performing method. The result, SenSALDO, is a comprehensive sentiment lexicon for Swedish containing 7,618 word senses as well as a full-form version of this lexicon containing 65,953 items (text word forms). SenSALDO is freely available as a research tool in the SWE-CLARIN toolbox under an open-source CC-BY license.