Miracle-dz/UniFAR-FTUs huggingface dataset:
BEHAVIORAL MACROMODELING OF VOLTAGE FEEDBACK AMPLIFIERS

This paper presents a new voltage feedback amplifier macromodel at behavioral level. It accurately models basic differential-mode and common-mode electrical characteristics, the input offset voltage and current, the differential-mode and common-mode amplification, the input voltage noise, the output voltage swing, short-circuit current and the input and output impedances. For creating the model, simplification and build-up techniques known from modeling operational amplifiers have been adapted. Model parameters are extracted for the OP177F from Analog Devices as an example. To confirm the validity of the proposed macromodel, simulation results are compared with the manufacturer’s data and with the behavior of the SPICE-based macromodel OP177F/AD from the standard library. The proposed behavioral macromodel leads to a low analysis time, with higher accuracy and a better convergence in comparison with other op amps methods of modeling.
Measuring Student Engagement Level Using Facial Information

In this paper, we propose a novel framework that measures the engagement level of students either in a class environment or in an e-learning environment. The proposed framework captures the user’s video and tracks their faces’ through the video’s frames. Different features are extracted from the user’s face e.g., facial fiducial points, head pose, eye gaze, learned features, etc. These features are then used to detect the Facial Action Coding System (FACS), which decomposes facial expressions in terms of the fundamental actions of individual muscles or groups of muscles (i.e., action units). The decoded action units (AU’s) are then used to measures the student’s willingness to participate in the learning process (i.e., behavioral engagement) and his/her emotional attitude towards learning (i.e., emotional engagement). This framework will allow the lecturer to receive a real-time feedback from facial features, gaze, and other body kinesics. The framework is robust and can be utilized in numerous applications including but not limited to the monitoring the progress of students with various degrees of learning disabilities, and the analysis of nerve palsy and its effects on facial expression and social interactions.
A Low-Power, High-Performance Speech Recognition Accelerator

Automatic Speech Recognition (ASR) is becoming increasingly ubiquitous, especially in the mobile segment. Fast and accurate ASR comes at high energy cost, not being affordable for the tiny power-budgeted mobile devices. Hardware acceleration reduces energy-consumption of ASR systems, while delivering high-performance. In this paper, we present an accelerator for large-vocabulary, speaker-independent, continuous speech-recognition. It focuses on the Viterbi search algorithm representing the main bottleneck in an ASR system. The proposed design consists of innovative techniques to improve the memory subsystem, since memory is the main bottleneck for performance and power in these accelerators’ design. It includes a prefetching scheme tailored to the needs of ASR systems that hides main memory latency for a large fraction of the memory accesses, negligibly impacting area. Additionally, we introduce a novel bandwidth-saving technique that removes off-chip memory accesses by 20 percent. Finally, we present a power saving technique that significantly reduces the leakage power of the accelerators scratchpad memories, providing between 8.5 and 29.2 percent reduction in entire power dissipation. Overall, the proposed design outperforms implementations running on the CPU by orders of magnitude, and achieves speedups between 1.7x and 5.9x for different speech decoders over a highly optimized CUDA implementation running on Geforce-GTX-980 GPU, while reducing the energy by 123-454x.
Adverse Drug Event Detection Using a Weakly Supervised Convolutional Neural Network and Recurrent Neural Network Model

Social media and health-related forums, including the expression of customer reviews, have recently provided data sources for adverse drug reaction (ADR) identification research. However, in the existing methods, the neglect of noise data and the need for manually labeled data reduce the accuracy of the prediction results and greatly increase manual labor. We propose a novel architecture named the weakly supervised mechanism (WSM) convolutional neural network (CNN) long-short-term memory (WSM-CNN-LSTM), which combines the strength of CNN and bi-directional long short-term memory (Bi-LSTM). The WSM applies the weakly labeled data to pre-train the parameters of the model and then uses the labeled data to fine-tune the initialized network parameters. The CNN employs a convolutional layer to study the characteristics of the drug reviews and active features at different scales, and then the feed-forward and feed-back neural networks of the Bi-LSTM utilize these salient features to output the regression results. The experimental results effectively demonstrate that our model marginally outperforms the comparison models in ADR identification and that a small quantity of labeled samples results in an optimal performance, which decreases the influence of noise and reduces the manual data-labeling requirements.
Toward the optimization resource discovery service in grid systems: a survey

As Grid systems distribute geographically, they become more heterogeneous and dynamic in terms of resources. Discovery service is an important issue in grid systems. Grid efficiently is as regards resource discovery in scalable and dynamic environments. In this paper, we review the peer-to-peer (P2P) based decentralized discovery methods: Unstructured and Super-Peer. We describe major development of these approaches and provide discussion concerning efficiently and scalability features.
Constellation design and performance analysis of the parallel spatial modulation

Spatial modulation (SM) is a relatively recent multiple‐input multiple‐output (MIMO) system in which information is carried by the index of the antenna used for transmission as well as by the conventional signal symbols. Several systems that build upon SM have since been proposed including the generalized SM (GSM), a variant of GSM with multiple active antennas (MA‐SM), quadrature SM (QSM), and parallel SM (PSM), among others. The PSM system can increase the spectral efficiency by splitting the antenna set into groups and applying SM independently in each group using the same signal symbol. In this paper, we first derive the upper bound on the error probability of the PSM. The search of the optimal constellation set is then formulated as a multi‐objective optimization problem, where the obtained constellation minimizes the asymptotic error probability. We conclude that as the number of antenna groups increases, the proposed constellation converges to the conventional phase‐shift keying at relatively low number of transmit antennas. The simulation results show that the proposed constellation outperforms conventional constellations by as much as 5 dB, for high‐modulation orders. Since the multi‐objective optimization is independent of the channel matrix, it can be easily done off‐line. This implies that these gains come at no complexity or delay cost.
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree

We propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence. Although a large majority of works typically focus on leveraging the expressive power of neural networks in handling this task, we explore the possibility of integrating dependency trees with neural networks for representation learning. To this end, we present a convolution over a dependency tree (CDT) model which exploits a Bi-directional Long Short Term Memory (Bi-LSTM) to learn representations for features of a sentence, and further enhance the embeddings with a graph convolutional network (GCN) which operates directly on the dependency tree of the sentence. Our approach propagates both contextual and dependency information from opinion words to aspect words, offering discriminative properties for supervision. Experimental results ranks our approach as the new state-of-the-art in aspect-based sentiment classification.
Throughput Maximization for Cognitive Radio With Wirelessly Powered Primary Users

This article deals with resource allocation to maximize the secondary user (SU) throughput in cognitive radio where primary users (PUs) are wirelessly powered by SU signals during the first part of a time-slot, and PUs and SUs share the spectrum during the second part of the time-slot. An optimization problem is formulated to maximize SU throughput under a constraint of minimum PU throughput (due to quality-of-service). As the optimization problem is NP-hard, we decompose the problem into two subproblems by introducing an auxiliary variable, i.e., the consumed energy at PU, and the subproblems are decoupled by minimizing the PU consumed energy. A three-step resource allocation scheme is then proposed to solve the optimization problem with low complexity. Numerical simulations are provided to demonstrate the superiority of the proposed scheme in terms of both performance and computational complexity.
Coverage of Information Extraction from Sentences and Paragraphs

Scalar implicatures are language features that imply the negation of stronger statements, e.g., “She was married twice” typically implicates that she was not married thrice. In this paper we discuss the importance of scalar implicatures in the context of textual information extraction. We investigate how textual features can be used to predict whether a given text segment mentions all objects standing in a certain relationship with a certain subject. Preliminary results on Wikipedia indicate that this prediction is feasible, and yields informative assessments.
Forecasting Gathering Events through Trajectory Destination Prediction: A Dynamic Hybrid Model

Identifying urban gathering events is an important problem due to challenges it brings to urban management. In our prior work, we proposed a hybrid model (H-VIGO-GIS) to predict future gathering events through trajectory destination prediction. Our approach consisted of two models: historical and recent and continuously predicted future gathering events. However, H-VIGO-GIS has limitations. (1) The recent model does not capture the newly-emerged abnormal patterns effectively, since it uses all recent trajectories, including normal ones. (2) The recent model is sparse due to limited number of trajectories it learns, i.e., it cannot produce predictions in many cases, forcing us to rely only on the historical model. (3) The accuracy of both recent and historical models varies by space and time. Therefore, combining them the same way at all times and places undermines the overall accuracy of the hybrid model. Addressing these issues, in this paper we propose a Dynamic Hybrid model called (DH-VIGO-TKDE) that addresses the above-mentioned issues. We perform comprehensive evaluations using two large real-world datasets and an event simulator. The experiments show the proposed model significantly improves the prediction accuracy and timeliness of forecasting gathering events, resulting in average precision of 0.91 and recall of 0.67 as opposed to 0.74 and 0.50 of H-VIGO-GIS.
Research on Image Generation and Style Transfer Algorithm Based on Deep Learning

Aiming at the current process of artistic creation and animation creation, there are a lot of repeated manual operations in the process of conversion from sketch to the stylized image. This paper presented a solution based on a deep learning framework to realize image generation and style transfer. The method first used the conditional generation to resist the network, optimizes the loss function of the training mapping relationship, and generated the actual image from the input sketch. Then, by defining and optimizing the perceptual loss function of the style transfer model, the style features are extracted from the image, thereby forming the actual The conversion between images and stylized art images. Experiments show that this method can greatly reduce the work of coloring and converting with different artistic effects, and achieve the purpose of transforming simple stick figures into actual object images.
Memorize-Generalize: An online algorithm for learning higher-order sequential structure with cloned Hidden Markov Models

Sequence learning is a vital cognitive function and has been observed in numerous brain areas. Discovering the algorithms underlying sequence learning has been a major endeavour in both neuroscience and machine learning. In earlier work we showed that by constraining the sparsity of the emission matrix of a Hidden Markov Model (HMM) in a biologically-plausible manner we are able to efficiently learn higher-order temporal dependencies and recognize contexts in noisy signals. The central basis of our model, referred to as the Cloned HMM (CHMM), is the observation that cortical neurons sharing the same receptive field properties can learn to represent unique incidences of bottom-up information within different temporal contexts. CHMMs can efficiently learn higher-order temporal dependencies, recognize long-range contexts and, unlike recurrent neural networks, are able to natively handle uncertainty. In this paper we introduce a biologically plausible CHMM learning algorithm, memorize-generalize, that can rapidly memorize sequences as they are encountered, and gradually generalize as more data is accumulated. We demonstrate that CHMMs trained with the memorize-generalize algorithm can model long-range structure in bird songs with only a slight degradation in performance compared to expectation-maximization, while still outperforming other representations.
Mangan: Assisting Colorization Of Manga Characters Concept Art Using Conditional GAN

Colorization is a challenging task that has recently been tackled by deep learning. Line art colorization is particularly difficult because there is no grayscale value to indicate the color intensities as there is in black-and-white photograph images. When designing a character, concept artists often need to try different color schemes, however, colorization is a timeconsuming task. In this article, we propose a semi-automatic framework for colorizing manga concept arts by letting concept artists try different color schemes and obtain colorized results in fashion time. Our approach uses Conditional Generative Adversarial Networks (cGAN) and outperforms current hint-based line-art colorization techniques by providing natural-looking arts with only minor coloring mistakes.
Coupling Global and Local Context for Unsupervised Aspect Extraction

Aspect words, indicating opinion targets, are essential in expressing and understanding human opinions. To identify aspects, most previous efforts focus on using sequence tagging models trained on human-annotated data. This work studies unsupervised aspect extraction and explores how words appear in global context (on sentence level) and local context (conveyed by neighboring words). We propose a novel neural model, capable of coupling global and local representation to discover aspect words. Experimental results on two benchmarks, laptop and restaurant reviews, show that our model significantly outperforms the state-of-the-art models from previous studies evaluated with varying metrics. Analysis on model output show our ability to learn meaningful and coherent aspect representations. We further investigate how words distribute in global and local context, and find that aspect and non-aspect words do exhibit different context, interpreting our superiority in unsupervised aspect extraction.
Ranking and Sampling in Open-Domain Question Answering

Open-domain question answering (OpenQA) aims to answer questions based on a number of unlabeled paragraphs. Existing approaches always follow the distantly supervised setup where some of the paragraphs are wrong-labeled (noisy), and mainly utilize the paragraph-question relevance to denoise. However, the paragraph-paragraph relevance, which may aggregate the evidence among relevant paragraphs, can also be utilized to discover more useful paragraphs. Moreover, current approaches mainly focus on the positive paragraphs which are known to contain the answer during training. This will affect the generalization ability of the model and make it be disturbed by the similar but irrelevant (distracting) paragraphs during testing. In this paper, we first introduce a ranking model leveraging the paragraph-question and the paragraph-paragraph relevance to compute a confidence score for each paragraph. Furthermore, based on the scores, we design a modified weighted sampling strategy for training to mitigate the influence of the noisy and distracting paragraphs. Experiments on three public datasets (Quasar-T, SearchQA and TriviaQA) show that our model advances the state of the art.
Experimenting with Power Divergences for Language Modeling

Neural language models are usually trained using Maximum-Likelihood Estimation (MLE). The corresponding objective function for MLE is derived from the Kullback-Leibler (KL) divergence between the empirical probability distribution representing the data and the parametric probability distribution output by the model. However, the word frequency discrepancies in natural language make performance extremely uneven: while the perplexity is usually very low for frequent words, it is especially difficult to predict rare words. In this paper, we experiment with several families (alpha, beta and gamma) of power divergences, generalized from the KL divergence, for learning language models with an objective different than standard MLE. Intuitively, these divergences should affect the way the probability mass is spread during learning, notably by prioritizing performances on high or low-frequency words. In addition, we implement and experiment with various sampling-based objectives, where the computation of the output layer is only done on a small subset of the vocabulary. They are derived as power generalizations of a softmax approximated via Importance Sampling, and Noise Contrastive Estimation, for accelerated learning. Our experiments on the Penn Treebank and Wikitext-2 show that these power divergences can indeed be used to prioritize learning on the frequent or rare words, and lead to general performance improvements in the case of sampling-based learning.
TCP / IP

Les réseaux d'ordinateurs ont pris peu à peu une importance considérable dans la vie de tous les jours. La plupart des informations peuvent prendre un format électronique, ce qui permet de les échanger facilement avec d’autres utilisateurs. Afin de pouvoir échanger ces informations, il est nécessaire de connecter les ordinateurs entre eux. Une fois reliés, il forment un réseau ou Lan (Local Area Network).
Predicting Software Faults Based on K-Nearest Neighbors Classification

Software defect prediction is one of the most important task during the development of software systems in order to save developers’ time and effort. Discovering defects in an early stage of software development will allow programmers and developers to take action and resolve these faulty parts in software before its launch. In this paper, the KNearest Neighbor (KNN) machine learning algorithm is used to predict faulty software projects. Experimental studies are conducted on five public datasets with different similarly measures. Results showed that KNN can be used to predict software faults with accuracy rate that can achieve up to 87.2%.
Efficient Automation of an HEV Heterogeneous Fleet Using a Two-Stage Methodology

An influential factor in enhancing the attendance services, mainly in commercial and emergency sectors, is the vehicular technology used to transport people, goods, or equipment. Although hybrid electric vehicles (HEVs) represent a sustainable transport alternative, the existing technical limitations such as battery and fuel capacities, and autonomy, among others, highlight the provision of an efficient automation tool. The tool can serve to enhance the operational performance of the HEV by selecting the proper driving mode (on fuel or electricity), and the navigation strategies to the delivery and charging points in urban areas. This paper proposes a two-stage methodology that allows the HEVs operators to automate the operational performance of a heterogeneous HEV fleet on a city map. Each stage is handled by its corresponding optimization model. In the first stage, the total navigation time and the battery lifetime of the fleet during the operation are optimized. In this stage, constraints related to charge-sustaining/charge-depleting modes, state of charge (SoC) of the HEVs battery, and deliveries schedules are taken into account. To this end, operating strategies related to the performance of different types of existing HEV technologies are anonymously considered. In the second stage, the best operating strategy among all the operating strategies is selected while considering the capacity of HEVs to deliver a given quantity of goods. Moreover, uncertainties during the HEV navigation are simulated considering the change in traffic density of the urban roads as a function of the levels of service (LOS). Results show that the proposed methodology establishes an efficient operational scheme for a HEVs fleet, ensuring a significant reduction of energy usage as well as mitigating the CO2 emissions.
Outlier-Robust Moving Object and Background Decomposition via Structured ${\ell }_p$-Regularized Low-Rank Representation

Low-rank and sparse decomposition is a powerful tool in background extraction and moving object detection problems since the static background usually lies in a low dimensional subspace while the rapidly changing object maintains a sparse property. Conventional techniques for low-rank and sparse decomposition can handle noise-free or Gaussian noise scenarios. However, their performance significantly degrades in challenging scenarios contaminated by impulsive noise or outliers. In this paper, a new model with a separable noise component, which we call unstructured <inline-formula><tex-math notation="LaTeX">${\boldsymbol {{\ell }_p}}$</tex-math></inline-formula>-regularized low-rank representation (ULLR), is proposed. Instead of incorporating the Frobenius norm constraint on the noise component, the proposed ULLR incorporates flexible <inline-formula><tex-math notation="LaTeX">${\boldsymbol {{\ell }_p}}$</tex-math></inline-formula>-regularized schemes with <inline-formula><tex-math notation="LaTeX">$0 < p \leq 2$</tex-math></inline-formula> to handle varied scenarios. For the moving object detection problem, we further incorporate the local group structure and homogeneous group structure to encode the underlying object. Then, a new model called the structured <inline-formula><tex-math notation="LaTeX">${\boldsymbol {{\ell }_p}}$</tex-math></inline-formula>-regularized low-rank representation (SLLR) is proposed to enhance the detected foreground while suppressing the noise and dynamic background. Especially, when <inline-formula><tex-math notation="LaTeX">$0 < p \leq 1$</tex-math></inline-formula>, the SLLR achieves robustness against outliers and hence handles challenging scenarios well. The resultant optimization problem is solved using the framework of alternating direction method of multipliers (ADMM). Each ADMM subproblem can be efficiently solved. More importantly, the solutions for the noise-based subproblem are rigorously derived with respect to different <inline-formula><tex-math notation="LaTeX">${\boldsymbol {{\ell }_p}}$</tex-math></inline-formula>-regularized schemes. Simulation results demonstrate the efficiency, accuracy, and robustness to outliers of the proposed algorithms.