Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of two Algorithms for Routing Questions and Answers, Applied to Group of Students Software Engineering

Community Question Answering (CQA) have become a popular web services where users respond and can ask questions. In this search we present the comparison of two algorithms to verify which of the two is better suited to the problem of routing questions. We began by taking the characteristics of the questions, then presented the Perceptron Back propagation learning and the Self Organizing Map (SOM) network to calculate the set of people most apt to answer the question, the adaptation of both algorithms was compared, concluding that the SOM network adapts better, being 14.81% more accurate than Perceptron in this type of problems.
Design of a High-Speed Time-Interleaved Sub-Ranging SAR ADC With Optimal Code Transfer Technique

This paper discusses design methodologies for high-speed SAR ADCs. A comparison of various architectures and the study of benefits and limits identify the best solution for high-speed and medium resolution. It is an interleaving architecture with the channel implemented by a fast coarse SAR quantizer and 2-way time-interleaved (TI) fine SAR ADCs. We propose a float-then-write code transfer technique for optimizing the transfer sequence and reducing the reference interference. Furthermore, we also study and compare the output impedance of the reference generation as well as the reference interference for the optimized code transfer scheme and the conventional bit-by-bit in both single-channel and TI scenarios. In addition, the mismatches in TI channels and two sub-ADCs are discussed. A 10-bit test vehicle fabricated in 65-nm CMOS confirms experimentally the proposed methods operating with 1.2-V supply at 700 MS/s. The circuit occupies an active area of 0.084 mm2 and achieves a signal-to-noise and distortion ratio at a Nyquist of 53.3 dB, with a power consumption of 9.5 mW. The Walden figure-of-merit is 36 fJ/conversion-step.
DAPP: Accelerating Training of DNN

Deep Neural Networks (DNNs) are one of the leading classification algorithms and have achieved big milestones such as GoogleNet and AlphaGo. Training of neural network is a time-consuming affair and is proportional to the depth of network and number of computations carried out in each layer. If the network is deep, the time taken is considerably high as it works sequentially on batches of dataset using Sequential Back-propagation (BP) Algorithm. This paper presents an acceleration technique DAPP Accelerating Training of DNN using Ping-Pong approach to reduce the training time using distributed local memory. DAPP exploits model parallelism without compromising the accuracy of the model. The proposed pipeline design concurrently works on consecutive set of batches to compute gradients at various layers, while eliminating the need for global memory. Simulation of DAPP is done on MNIST and CIFAR-10 datasets using System-C. Additionally, this technique has been adapted for multi-core architectures. This generic methodology has been implemented for CNN, Vanilla RNN and LSTM networks. This design reduces training time by 40% for a 3 layer CNN, 92% for a 10 layer CNN, 38% for Vanilla RNN, and 40% for LSTM while maintaining the accuracy in all above networks.
Automatic Relationship Verification in Online Medical Knowledge Base: a Large Scale Study in SemMedDB

Automatically generated public medical knowledge bases (KBs), such as SemMedDB, are commonly used in various medical informatic tasks because of their comprehensive coverage. However, due to the imperfectness of the automatic algorithms for generating those KBs, they often contain noisy statements about medical concepts and relationships. For example, the extraction precision of SemRep, the tool used for constructing SemMedDB, is reported be 74.5%. Previous work focused on improving the algorithms for more accurate extraction. In this paper, however, we propose a supervised learning method to automatically verify the medical relationships. Through a study conducted on SemMedDB, we develop a method for generating a large set of training data with a relative small human labor annotation cost. We further propose nine features to characterize each medical relationship instance. After testing on several classifiers, our proposed methods can achieve the best F1 score and Accuracy at 80%, which demonstrates the effectiveness of our approach. In summary, our study demonstrates that noisy relationships in large scale medical KBs can be identified and removed without much human involvement.
Improving the Signal-to-Noise Ratio of Superresolution Imaging Based on Single-Pixel Camera

Based on the theories of single-pixel camera and compressed sensing image reconstruction, the sparse basis, the projection method of measurement matrix, and the signal reconstruction algorithm are optimized. First, for the sparse representation of image, the restraint matrix is designed combining the characteristics of wavelet sparse coefficients to enhance the sparse representation ability of the discrete wavelet base, which improves the quality of image reconstruction and imaging by single-pixel camera. Then, in the single-pixel camera system, the projection method is improved according to the characteristics of the digital mirror device micromirror, and a new bilateral projection method based on the block diagonal measurement matrix is proposed to realize the superresolution imaging of the outdoor scene, reducing the number of measurement and improving the imaging quality. Finally, for the algorithm of image reconstruction, combining the characteristics of convex optimization and nonconvex optimization algorithms, a new algorithm from convex optimization approximately to nonconvex optimization algorithm is proposed, and compared with the traditional image reconstruction algorithm, such as greedy pursuit algorithm, minimum norm algorithm, and image interpolation, the peak signal-to-noise ratio and imaging quality of reconstructed images are effectively improved. The feasibility of the proposed methods is demonstrated by simulation experiments and imaging experiments of single-pixel camera.
Multi-View Face Detection and Landmark Localization Based on MTCNN

As the basic tasks of face application technology, face detection and facial landmark detection are two important research directions in the fields of computer vision. In this paper, we employ the multi-task cascaded convolutional networks (MTCNN)to realize the multi-view face detection and landmark localization in complex environments. Firstly, a MTCNN-based frontal face detector is trained for frontal face detection and landmark localization. The detector can achieve high accuracy on FDDB benchmark for face detection and AFLW benchmark for facial landmark detection. Secondly, we construct a non-frontal face dataset including 10026 images and train a non-frontal face detection model to solve the detection problem of missing large-angle faces and improve the detection accuracy of non-frontal face. Finally, the frontal face detector and the non-frontal face detector are combined for multi-task and multi-view face detection. The experimental results have shown the effectiveness of the proposed method.
Detection of Fake Twitter Accounts with Machine Learning Algorithms

Today, social networks have been part of many people’s lives. Many activities such as communication, promotion, advertisement, news, agenda creation have started to be done through social networks. Some malicious accounts on Twitter are used for purposes such as misinformation and agenda creation. This is one of the basic problems in social networks. Therefore, detection of malicious account is significant. In this study, machine learning-based methods were used to detect fake accounts that could mislead people. For this purpose, the dataset generated was pre-processed and fake accounts were determined by machine learning algorithms. Decision trees, logistic regression and support vector machines algorithms are used for the detection of fake accounts. Classification performances of these methods are compared and the logistic regression proved to be more successful than the others.
Design and Experiment Evaluation of a Magneto-Rheological Damper for the Legged Robot

This paper proposes a Magneto-Rheological (MR)-based damper that can avoid high impulsive forces caused by the legged robot while walking and jumping. Traditional high stiffness system can ensure precise positioning of actuators, and accomplish repetitive tasks efficiently. Nevertheless, it has great hardness and is easy to cause impact, which seriously affects the stability of robot while walking and jumping. The previous approach to solve this problem adopt variable impedance actuators. They can absorb impact shock and store energy but typically have a very low intrinsic damping for efficient energy storage and retrieval. This paper introduces a novel MR-based damper that can change intrinsic damping by adjusting the current in the coil. The models of this damper are established and several experiments are carried out to test its performance. The results show that it can change the internal Magneto-Rheological Fluid (MRF) to absorb impact effectively.
High-Accuracy, Low-Power Switched-Capacitor Differential-Capacitance-to-Time Converter

A high-accuracy switched-capacitor (SC) capacitance-to-time (C/T) converter for differential capacitive sensors is presented. In the proposed circuit the propagation delays and the offset voltage of the comparator, the finite gain of the op-amp, and the channel charge injection and clock feedthrough due to analog switches have negligible effect on the accuracy of the circuit. Therefore, the design constraints of op-amp and comparator can be relaxed even for high conversion accuracy. Performances of the proposed circuit are simulated using 0.18 micrometers CMOS process parameters. Simulated results have demonstrated that the gain error is 0.0035 % and the maximum nonlinear error is about 0.015 % of the full scale and indicate that 0.1% resolution is achievable even when low-gain amplifier is used. The power consumption of the proposed circuits was 41.3 microwatts for ± 0.9 V supply voltages.
Discriminative Clustering of High-Dimensional Data Using Generative Modeling

We approach unsupervised clustering from a generative perspective. We hybridize Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) in a novel way to obtain a vigorous clustering model that can effectively be applied to challenging high-dimensional datasets. The powerful inference of the VAE is used along with a categorical discriminator that aims to obtain a cluster assignment of the data, by maximizing the mutual information between the observations and their predicted class distribution. The discriminator is regularized with examples produced by an adversarial generator, whose task is to trick the discriminator into accepting them as real data. We demonstrate that using a shared latent representation greatly helps with discriminative power of our model and leads to a powerful unsupervised clustering model. The method can be applied to raw data in a high-dimensional space. Training can be performed end-to-end from randomly-initialized weights by alternating stochastic gradient descent on the parameters of the model. Experiments on two datasets including the challenging MNIST dataset show that the proposed method performs better than the existing models. Additionally, our method yields an efficient generative model.
An ontology-based approach to improve data querying and organization of Alzheimer’s Disease data

The recent advances in biotechnology and IT have led to an ever-increasing availability of public biomedical data distributed in large databases. Analyzing this huge volume of data is a challenging task because of its complexity, high heterogeneity and its multiple and numerous correlated factors. In the framework of neurodegenerative diseases, the last years have witnessed the creation of specialized databases such as the international projects ADNI (Alzheimer’s Disease Neuroimaging Initiative). The main problems to fully exploit this database are related to the querying, integration, and analysis of data themselves. Here, we aim to develop a detailed ontology for clinical multidimensional datasets from ADNI repository in order to simplify the data access and to obtain new diagnostic knowledge about Alzheimer’s Disease.
A New Training Structure for Multiple-to-Multiple Traffic Flow Prediction

In this paper, we consider the problem of multiple-to-multiple traffic flow prediction. In order to better distinguish the differences in different traffic flow, due to the influence of the dispersion between classes in the linear classifier and convolution, we define a new parameter WTT (Weight of Traffic Flow to Temperature Flow) for the classification of different kinds of traffic flow. We also propose a method for training of different classifications of traffic flow data-SCT (Structure of classification training) to train prediction model. Through the experiment, we give empirical parameter of the traffic flow variance corresponding to the best classifier model. Experiment results show that compared with K-means and other traditional clustering methods, WTT can better classify different kinds of traffic flow. Compared with the traditional one-to-one or multiple-to-one prediction models, SCT with WTT can reduce computational complexity well and get a good result for the problem of multiple-to-multiple traffic flow prediction.
Intelligent Walk Authentication: Implicit Authentication When You Walk with Smartphone

The popularity of smartphone makes mobile phone security even more important. We proposed Intelligent Walking Authentication (IWA), a lightweight, non-disturbing and automatically update smartphone authentication scheme. By collecting phone acceleration sensor data and calculate statistical features while walking, gait feature vector is generated in real time and compared with gait feature vector template to complete the authentication. The authentication has low computational complexity and not require large amounts of tagged data as previous work. A success rate of 93.63% is achieved on a 30person walking data set. A 91.00% authentication success rate is achieved on a data set of 11 people real-life simulated scenarios. The experimental results show that IWA can effectively resist the attack behavior in the authentication process and perform well in different locations while only increasing the power by 78.22mW.
Graph-Based Suggestion For Text Summarization

One of the methods of text summarization within the context of Natural Language Processing (NLP) works is to summarize the text by selecting sentences from the original text. There are different approaches to summarize sentence selection. In this study, texts that do not have a certain structure have been preprocessed and transfer of the proposed diagram in a structured format in the form of an expression. Different feature extraction methods could be applied on the charts. Our method uses conceptually the diagrams obtained in the representation of the text. This study aims to suggest a method of summarization of texts with a linear weighting of the importance of sentences. Moreover, the method presented does not require the use of deep linguistic knowledge and this work can be adapted to different languages.
One Self-Adaptive Memory Scheduling Algorithm for the Shuffle Process in Spark Platform

The Shuffle module is one of the core modules in Spark platform, its performance directly influences the performance and throughput of the whole Spark platform. The existing memory scheduling algorithm for the Shuffle process only equitably allocates tasks according to the number of tasks without considering the different memory requirements of different tasks, which causes memory utilization to drop and low running efficiency when data is skewed. To solve this problem, one self-adaptive memory scheduling algorithm for the Shuffle process (SAMSAS) is proposed in this paper, which does not need to set the priority of task processing in advance. Instead, it can adjust memory allocation self-adaptively through constantly monitoring and learning the actual memory requirements of task execution. The experimental results show that SAMSAS algorithm can improve the utilization rate of the entire memory pool and the running efficiency of each Task, and specially it can effectively improve the running efficiency of Spark platform when processing skew data.
Evaluation of Muscle Fatigue Based on CRP and RQA for Upper Limb Exoskeleton

The movement of lifting heavy objects with upper limb exoskeleton is a dynamic process of multi-muscle coordination. Muscle fatigue occurs after repetitive lifting heavy objects with exoskeleton robot for a long time. Due to the dynamic characteristics of the moving process, the traditional linear analysis methods can not fully characterize the muscle fatigue problem of upper limb. In this paper, a novel approach base on ross recurrence plot (CRP)and recurrence quantification analysis (RQA)is proposed to analyze the progression of muscle fatigue of upper limb. Firstly, distance threshold and date length of the recurrence plot is chosen according to the RP dynamical structures of the electromyography (EMG)signals. Then the phase space reconstruction techniques and recurrence plot theory are utilized to analyze the nonstationarity characteristics of the multichannel and single-channel surface EMG signals during the fatigue process. Finally, the RQA indices of the overall and local muscles are calculated. The experiments show that the CRP can qualitatively reflect the change of muscle fatigue state. Meanwhile, the RQA indices of multi-channel and single-channel EMG signals can quantitatively reveal the changes of muscle fatigue from the overall and local perspectives. This method provides a theoretical basis for evaluating muscle fatigue during repeated moving in the way of nonlinear dynamics.
Predicting Links in Complex Bipartite Networks Based on Strengthening Projections

Link prediction in complex networks is a topic of high interest for many scientists that studied different areas. Many real-world complex networks have naturally bipartite structured. In this work, we proposed a link prediction method for the complex bipartite network. Recently, while link prediction in complex bipartite networks has been extensively studied, there isn’t still sufficiently suitable methods for this networks. The previous works usually have some limitations. They first map the bipartite network into the unimodal projection networks. Then, they perform the link prediction using only the properties of the projection networks. However, these projection networks involve many node pairs with weak relationships when used in networks constructed from real-world data. These weak relationships lead to low probability predictions and high computation time for the algorithm. In this paper, to overcome these limitations, we first map the bipartite network into strengthening projection network model that consists of nodes that have only strong relationships. Based on this projection network, we presented the link prediction method for the complex bipartite network. We conducted extensive experiments on complex bipartite networks. The results of the experiments demonstrate that our proposed method can effectively solve the link prediction problem in complex bipartite networks.
Restricted Boltzmann Machines for Recommender Systems with Implicit Feedback

Implicit feedback such as video watch time is commonly seen in many internet products. Though recommender systems with explicit feedback have been abundantly researched, there are not many methods proposed for building recommender systems with implicit feedback. Restricted Boltzmann Machine (RBM), which uses a two-layer graphical model to describe response variables through hidden units, is a promising approach. An RBM model for collaborative filtering was proposed in the literature, but it only deals with categorized ratings, which is explicit feedback. We propose a novel extension of the RBM method for collaborative filtering with implicit feedback. The model parameters can be learned efficiently through the contrastive divergence algorithm. Compared to other methods, the proposed RBM method can directly predict preferences for a new user given feedback on a few items. Also, it protects privacy by keeping user data locally and only sends updated parameters to a central server. The results on real data with several million records show it works superiorly compared to other prevalent methods.
An Identity-Based Fair Contract Signing Protocol Constructed by the Confirmation Signature

The contract signing protocol has been a mature technique developed in previous literature and adopts the fair exchange of signatures to achieve the purpose. However, two critical issues, practicality and efficiency, become bottlenecks in the studies of many previous proposed protocols. Since through using the bilinear pairings over elliptic curves, the identity-based cryptographic primitives can be constructed more efficiently and practically in the key generation stage, this paper aims to present an identity-based contract signing protocol from bilinear pairings. All parties, including the two signers and the trusted third party (TTP), have their own extended IDs to be used as the public keys and thus no certificate is needed so that the key management can be simplified. A new building block called the “identity-based confirmation signature” (IDCS), which makes the construction of contract signing protocol more flexible, will be introduced and its implementation details will also be stated. The fairness is achieved in the proposed protocol by adopting an offline TTP who participates in the signature exchange procedure only when a dispute occurs between the two signing parties. In that case, TTP converts IDCS into a universally verifiable signature for unlimited verification by everyone to resist the cheating of dishonest participants.
Towards Simplification of Analytical Workflows With Semantics at Siemens (Extended Abstract)

Analytical workflows are heavily used in large and data intensive companies. An important application of such workflows in Siemens is equipment analytics when equipment KPIs and reports are computed by aggregating equipment’s operational, master, and analytical data. In Siemens this data satisfies big data dimensions and this dependence poses significant challenges in authoring, reuse, and maintenance of analytical workflows by engineers and data scientists. In this work we propose to address these problems by relying on semantic technologies: we use ontologies to give a high level representation of equipment’s operational and master data and offer a high level language to express KPIs over ontologies. We implemented our approach, integrated it with KNIME, and evaluated at Siemens. This is a preliminary work and we are excited about its further extensions.
Adaptive output - feedback DSC of nonstrict-feedback switched stochastic nonlinear with time delays

This note is devoted to the output dynamic surface control (DSC) design for a class of nonlinear switched stochastic time-varying delay systems. Based on a switched state observer and an appropriate stochastic Lyapunov-Krasovskii functional as well as the adaptive neural network (NN) technique, a novel adaptive NN control scheme is developed. On the basis of Lyapunov theory and the average dwell time approach, the analysis is conducted on semiglobal uniformly ultimately boundedness (SGUUB) and tracking error convergence the resulting closed-loop system. Finally, simulation results illustrate the effectiveness and potential of the approach developed in the note.