Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature-Induced Partial Multi-label Learning

Current efforts on multi-label learning generally assume that the given labels of training instances are noise-free. However, obtaining noise-free labels is quite difficult and often impractical, and the presence of noisy labels may compromise the performance of multi-label learning. Partial multi-label learning (PML) addresses the scenario in which each instance is annotated with a set of candidate labels, of which only a subset corresponds to the ground-truth. The PML problem is more challenging than partial-label learning, since the latter assumes that only one label is valid and may ignore the correlation among candidate labels. To tackle the PML challenge, we introduce a feature induced PML approach called fPML, which simultaneously estimates noisy labels and trains multi-label classifiers. In particular, fPML simultaneously factorizes the observed instance-label association matrix and the instance-feature matrix into low-rank matrices to achieve coherent low-rank matrices from the label and the feature spaces, and a low-rank label correlation matrix as well. The low-rank approximation of the instance-label association matrix is leveraged to estimate the association confidence. To predict the labels of unlabeled instances, fPML learns a matrix that maps the instances to labels based on the estimated association confidence. An empirical study on public multi-label datasets with injected noisy labels, and on archived proteomic datasets, shows that fPML can more accurately identify noisy labels than related solutions, and consequently can achieve better performance on predicting labels of instances than competitive methods.
Exploiting Spatio-Temporal Correlations with Multiple 3D Convolutional Neural Networks for Citywide Vehicle Flow Prediction

Predicting vehicle flows is of great importance to traffic management and public safety in smart cities, and very challenging as it is affected by many complex factors, such as spatio-temporal dependencies with external factors (e.g., holidays, events and weather). Recently, deep learning has shown remarkable performance on traditional challenging tasks, such as image classification, due to its powerful feature learning capabilities. Some works have utilized LSTMs to connect the high-level layers of 2D convolutional neural networks (CNNs) to learn the spatio-temporal features, and have shown better performance as compared to many classical methods in traffic prediction. However, these works only build temporal connections on the high-level features at the top layer while leaving the spatio-temporal correlations in the low-level layers not fully exploited. In this paper, we propose to apply 3D CNNs to learn the spatio-temporal correlation features jointly from lowlevel to high-level layers for traffic data. We also design an end-to-end structure, named as MST3D, especially for vehicle flow prediction. MST3D can learn spatial and multiple temporal dependencies jointly by multiple 3D CNNs, combine the learned features with external factors and assign different weights to different branches dynamically. To the best of our knowledge, it is the first framework that utilizes 3D CNNs for traffic prediction. Experiments on two vehicle flow datasets Beijing and New York City have demonstrated that the proposed framework, MST3D, outperforms the state-of-the-art methods.
Heterogeneous Hyper-Network Embedding

Heterogeneous hyper-networks is used to represent multi-modal and composite interactions between data points. In such networks, several different types of nodes form a hyperedge. Heterogeneous hyper-network embedding learns a distributed node representation under such complex interactions while preserving the network structure. However, this is a challenging task due to the multiple modalities and composite interactions. In this study, a deep approach is proposed to embed heterogeneous attributed hyper-networks with complicated and non-linear node relationships. In particular, a fully-connected and graph convolutional layers are designed to project different types of nodes into a common low-dimensional space, a tuple-wise similarity function is proposed to preserve the network structure, and a ranking based loss function is used to improve the similarity scores of hyperedges in the embedding space. The proposed approach is evaluated on synthetic and real world datasets and a better performance is obtained compared with baselines.
Suitable LMS Content and Format for Training

The current paper explored Learning Management Systems (LMS) preferred content and format for training in Saudi Arabian universities. In this study, 222 faculty members from six selected universities responded to the questionnaires. The findings revealed that despite 50% of the respondents made use of LMS in their various universities, only 15, 6% preferred using LMS actively for contents/activities concerning teaching, learning and assessments. It was indicated that LMS be used for distributing only course content specifications, broadcasting announcements, and preparing teaching handouts rather than conducting on-line examinations. Furthermore, training on LMS must be organised based on individual courses, be delivered by familiar expects having university’s teaching programs and practices at heart and be offered in 2-hour sessions over several days or weeks. There was moderately strong association between computer proficiency and preference for tailored LMS delivery $\varphi$ = 0.384, p = 0.000. The authors argue that in conducting future LMS training courses, the proposed content and format could be built based on LMS usages and trainees’ preferences. In broad view, addressing the shortage of faculty members’ technical skills could lead to increased LMS adoption in Saudi universities.
Colloidal Synthesis of Metal Nanoparticles for Surface Enhanced Spectroscopy and Electrocatalytic Applications

Noble metal nanoparticles have attracted intensive research interests in many fields such as sensing, energy, biology and medicine attributed to their attractive optical and electrochemical properties. The size, shape and chemical composition of metal nanoparticles play a key role in governing their optical and electrochemical properties. To date, various types of metal nanoparticles have been produced, mostly via colloidal synthetic pathway. This review focuses on presenting pioneer works and recent progresses in the synthesis of colloidal metal nanoparticles with precise control of morphology, size and chemical composition. Properties-related applications of metal nanoparticles derived from their localized surface plasmon resonance and electrocatalytic activities are also emphasized.
Lossless Data Compression Algorithm to Save Energy in Wireless Sensor Network

In the Internet of Things system, including wireless sensor networks, energy savings are a matter of prime concern today. While electronic devices as well as software have been developed ceaselessly, the technology of battery development does not keep pace. Beside that each sensor node usually uses batteries for power, these sensors often cannot change or recharge in the usual way. In all the operations of a sensor node, wireless communication is the most energy consuming operations. So, in order to extend the life of the battery in the sensor node, we need to limit the transmission of information over the wireless network, however, we must also ensure that the sensor node transmits enough information about the host. We can do this by data compression. We have to choose an efficient and simple data compression algorithm because energy, memory and computational resources of sensor node are very limited. In this article, we adjust a lossless data compression algorithm to suit the wireless sensor network and meet the requirements set forth above. Accompanying that, with the best knowledge we have, we also offer a comparison of the efficiency of this algorithm with two well-known compression algorithms, namely bzip2 and gzip.
Opinion and Sentiment Polarity Detection Using Supervised Machine Learning

This paper focuses on Opinion Mining (OM) and Sentiment Analysis (SA) for Arabic language. As there is a lack and size limitedness at lexicon level, we aim to build a new lexical resource following different methods, manually by extracting sentimental words from a selected dataset and semiautomatically by translating an English lexicon into Arabic. We also created a lemmatized version from an existing resource. These resources were subsequently used in the development of a polarity classifier. We begin this article by explaining the construction steps of these resources. Then, we present the supervised approach we developed to determine the polarity of the new data. The results of the tests carried out show the relevance of our choices.
Discovery of Rare Key Phrases

The frequency of phrases is important to discover key phrases. A high occurrence gives more contexts to assess them while many meaningful rare phrases are ignored. In this paper, we address the problem by exploring the semantic structure of rare phrases. An unsupervised method is presented to discover key phrases that occurs even only one time in a corpus. Comparing with related methods, the experiment shows an effective result. Moreover, a relation extraction task is conducted to evaluate the utilization of rare phrases for supporting NLP applications.
Iterative Channel Equalization for ADO-OFDM in Short-Haul Fiber-Optical Links

Asymmetrically clipped DC biased optical OFDM (ADO-OFDM) is theoretically more optically power efficient than DC-biased OFDM (DCO-OFDM) and spectral efficiency of asymmetrically clipped optical OFDM (ACO-OFDM). In this paper, we investigate the effect of power ratio between the ACO-OFDM and DCO-OFDM to optimize the performance of ADO-OFDM. Then we propose iterative channel equalization with nonlinear compensation to further improve the system performance. Finally, we experimentally demonstrate ADO-OFDM transmission over 20-km single mode fiber at 1.5 dB and 2 dB performance improvement for 4-QAM and 16-QAM formats.
Video Summarization using Hierarchical Shot Boundary Detection Approach

Video summarization and retrieval plays a vital role in video management systems involving modules such as shot boundary detection, key frame extraction and feature representation. Among these modules, shot boundary detection (SBD) is the important primary step, over which the entire retrieval performance relies on. In this paper, a new hierarchical shot boundary detection mechanism is proposed to alleviate the redundant key frames in video summarization. Initially, the abrupt transitions in the videos are detected using Weber Local Descriptor (WLD) and primary key frames are extracted. After primary key frame extraction, Fourier Transform (FT) which is complementary to WLD is used to prune the redundant key frames on the highly challenging videos with varying lighting effects, object and camera motion. The proposed hierarchical SBD algorithm is evaluated on three datasets viz. TRECVID, OPEN VIDEO. The validation of the proposed algorithm on these dataset yields promising results in terms of precision, recall, F-score, and Compression rate.
Identifying Microgrid Disturbances Using Independent Component Analysis

This paper focuses on the identification of islanding and power quality (PQ) disturbances in microgrids using signal processing techniques such as wavelet transform (WT) and independent component analysis (ICA). WT is known to be a suitable approach for detecting various disturbances such as PQ events, islanding, transients and harmonics due to better multiresolution analysis. However, it is observed that WT is susceptible to increased noise levels or transients in the voltage signal extracted at the point of common coupling (PCC). Therefore, ICA is proposed to find the significant components of the voltage signal which will help in identifying the disturbances more accurately. A comparative analysis, both qualitative and quantitative, is presented in this paper to demonstrate the superior detection capabilities of the proposed ICA approach when compared with the WT approach under noisy scenarios. The proposed technique is validated in real time using OPAL-RT's OP5600 to demonstrate its feasibility for practical applications.
Event Recognition in Unconstrained Video using Multi-Scale Deep Spatial Features

Event recognition in an unconstrained video is a challenging problem due to its complex nature in the field of computer vision. In this paper, we have designed a new technique using deep learning framework to recognize an event during video classification. This technique is enough capable for modeling multiscale spatial information to correctly classify events either in short or long videos. Three Convolutional Neural Networks (CNN) followed by Long Short Term Memory (LSTM) architectures are used to extract multi-scale spatial features from each video. The main contribution of this work is the design and development of deep learning framework that can model unconstrained videos based on several important aspects. The performance of the proposed system is tested on popular and challenging benchmark database namely, Columbia Consumer Videos (CCV). The extensive experiment shows that the proposed deep learning framework is able to recognize an event in the video quite successfully compared to many state-of-the art methods.
Nanoscale Scanning Probe Thermometry

We present further development and application examples of a thermometry approach capable of producing real-space temperature maps of operating nanoscale devices with sub-10nm resolution. The technique relies on scanning thermal microscopy with a resistive element coupled to a cantilevered tip. The resistive element acts both as integrated heater and as sensing element. A modulated temperature field is generated in the sample by exciting the device under investigation with an electrical AC bias. A map of the sample's temperature response due to different physical effects, such as Joule heating and thermoelectric effects, can be inferred from the demodulated sensor signal. As application examples, we demonstrate the investigation of thermoelectric heating/cooling at crystal phase heterostructures in InAs nanowires and Joule self-heating of individual multi-walled carbon nanotubes.
Unsupervised Filterbank Learning for Speech-based Access System for Agricultural Commodity

This paper presents an automatic speech recognition (ASR) system developed as a part of a speech-based access system for an agricultural commodity in the Gujarati language. Speech database was collected from the farmers in the villages of Gujarat state (India) with various dialectal variations and real noisy acoustic environments. We have used the recently proposed Convolutional Restricted Boltzmann Machine (ConvRBM) to learn the filterbank as a front-end. Self-taught learning framework is applied to train Conv RBM using extra Gujarati speech database other than an agricultural commodity. Stochastic data sweeping technique is used to enhance the training speed of ConvRBM. Experiments using time delay deep neural networks (TDNNs) show that ConvRBM features give relative improvements of 5.5% in WER compared to the Mel filterbank features. The system-level combination of both features further improves the performance (3.55 % absolute reduction in WER).
Highly sensitive bend measurements using a miniature fiber collimator-based Fabry-Perot Interferometer

A fiber optical based on a miniature fiber collimator Fabry-Perot Interferometer (FPI) is demonstrated for bend measurements. The device consists of a quarter-pitch graded index fiber (GIF) spliced with a section of silica tube, which is interposed between single mode fibers. The divergence angle of the included miniature fiber collimator was 0.058 rad. In comparison, the divergence angle without the collimator was 0.130 rad. The sensitivity of a miniature fiber collimator FPI is found to enhance to −3.353 dB/degree over the applied angle range.
IRCI-Free CP-OFDM SAR Signal Processing

Recent literature shows that with sufficient cyclic prefix, orthogonal frequency-division multiplexing signals can convert a synthetic aperture radar (SAR) image with interrange cell interference (IRCI) to an IRCI-free SAR image. In this letter, the imaging algorithm is carefully investigated and it is shown that the range reconstruction accuracy can be further improved. By reformulating the system model in a linear model form, the minimum variance unbiased estimator of the range profile is derived, which can attain the Cramer–Rao lower bound, making our algorithm more effective than current algorithms under noisy conditions. Simulation results validate the advantages of the proposed algorithm, especially under low signal-to-noise ratio conditions.
SDN-Based Solutions to Improve IOT: Survey

The vertical integration of equipment in the current networks doesn't automatically adapt to different high-level policies, the network operator is forced to configure manually all distributed equipment to add each new feature in the network. We talk about the Internet of Things when the number of devices connected to the Internet exceeds the number of people connected, the IOT adds new challenges to the closed and traditional nature of today's networks like: network management which become difficult to maintain, the intense use of network resources, mobility and massive energy consumption. the SDN makes it possible to remedy these constraints with the centralized management of all the equipment, and the exploitation of the global view of the network.
Development of Multi-Axis High-Precision Stage Using Multistep Wireless Power Transfer

The most critical problem in high-precision stages for semiconductor and LCD manufacturing is cable disturbance, which can be caused by power cables and communication lines. In our previous study, we designed a novel single-axis high-precision stage using wireless power transfer technology, which avoids cable disturbance. Here, we further develop this concept and propose a multi-axis wireless high-precision. Experimental results demonstrate the effectiveness of the multi-axis wireless high-precision stage.
On the Unification of k-Harmonic Means and Fuzzy c-Means Clustering Problems under Kernelization

This paper presents a common algorithm for the kernel k-harmonic means (KKHM) and the kernel fuzzy c-means (KFCM) clustering problems. We incorporate kernel functions in a generalized fuzzy c- means cost function, forming the cost function of a kernelized general fuzzy c-means (KGFCM) problem, and design an algorithm to locally minimize this cost function. The KGFCM cost function has two parameters: the exponent p of the Euclidean distance, and the fuzzy weighting exponent m. By setting proper values for p and m in our algorithm, one can execute the KKHM or the KFCM algorithm. Using the algorithm for KKHM, we compare its clustering performance with the popular kernel k-means and KFCM algorithms. Experiments performed on real-world and synthetic datasets show the superior clustering capabilities of KKHM. We also show that KKHM retains the advantages of the original KHM algorithm, resulting in better clustering performance when high number of clusters are present.
Simultaneous Wireless Information and GaN-Based Power Transfer Exploiting a Dual Frequency Band

A simultaneous wireless information and power transfer system employing separated frequency bands for energy and data, dubbed dual-band SWIPT, is investigated. Elementary circuit elements are optimized numerically. An experimental testbed based on a gallium nitride (GaN) full-bridge converter demonstrates data rates in excess of 450 kbps employing on-off keying in conjunction with a simple diode detector for data recovery.
The Effect of Cellular Network-Based Communication Delays in an EV Aggregator’s Domain on Frequency Regulation Service

Provision of frequency regulation service with a large number of electric vehicles (EVs) through EV aggregators requires to exchange regulation related messages through communication infrastructures in EV aggregators’ domain. Communication delays in the aggregators’ domain may significantly affect regulation performance. We investigate the effect of communication delays in an EV aggregator’s domain on the regulation performance. We present an underlying communication network for the EV aggregator controlling a group of EVs at a parking lot. The underlying communication network consists of two separate subnetworks: 1) a power line communication network between a pair of EV and EV supply equipment (EVSE) and 2) an LTE network between one EV aggregator and a group of EVSEs. In order to evaluate the delay effect on the regulation performance, we propose a co-simulation platform which is composed of an LTE network model and a load frequency control model. For the LTE network, we analyze the potential sources of delays and identify the repeated service request and release procedures as a critical delay source. We implement the identified repeated procedure by using network simulator-3 and evaluate the delay performance of frequency regulation service. We evaluate the effect of the communication delay in the EV aggregator’s domain on regulation performance.
A specication of a Dialogue Game for Group Model Building

Collaborative modeling is an important component of Information Systems development. However it poses challenges regarding the involvement of novice modelers for various reasons such as syntactical complexity of modeling languages. Attempts have been made to make the modeling process more accessible and easier to manage. One particular direction is the use of games. When modeling is viewed as an interaction producing models these interactions could be supported by a game. In this research a dialogue game (a type of game used in education to teach critical reasoning) is used to support Group Model Building, a methodology for collaborative modeling in the organizational science domain. The dialogue gaming tool InterLoc is used to host a facilitated Group Model Building session. One full session of this game has been played and showed that this type of modeling game is certainly playable. However the used software has serious limitations in the modeling dimension. Some suggestions for improvement are given. Further research should be conducted to develop a more integrated modeling dialogue game.
Mathematical methods of signal processing

The aim of this project is to present in a systematic way the more relevant mathematical methods of signal processing, and to explore how they are applied to speech and image precessing. After explaining the more common parts of a standard course in signal processing, we put special emphasis in two new tools that have played a significant role in signal processing in the past few years: pattern theory and wavelet theory. Finally, we use all these techniques to implement an algorithm that detects the wallpaper group of a plane mosaic taking an image of it as input and an algorithm that returns the phoneme sequence of a speech signal. The material in this memory can be grouped in two parts. The first part, consisting of the first six chapters, deals with the theoretical foundation of signal processing. It also includes materials related to plane symmetry groups. The second part, consisting of the last two chapters, is focussed on the applications.