Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Attention Enhanced Recurrent Neural Networks for Sentence Classification

In this paper we propose self-attention enhanced Recurrent Neural Networks for the task of sentence classification. The proposed framework is based on Vanilla Recurrent Neural Network and Bi-directional Recurrent Neural Network architecture. These architectures have been implemented over two different recurrent cells namely Long Short-Term Memory and Gated Recurrent Unit. We have used the multi-head self-attention mechanism to improve the feature selection and thus preserve dependency over longer lengths in the recurrent neural network architectures. Further, to ensure better context development, we have used Mikolov’s pre-trained word2vec word vectors in both the static and non-static mode. To check the efficacy of our proposed framework, we have made a comparison of our models with the state-of-the-art methods of Yoon Kim on seven benchmark datasets. The proposed framework achieves a state-of-the-art result on four of the seven datasets and a performance gain over the baseline model on five of the seven datasets. Furthermore, to check the effectivity of self-attention on the task of sentence classification, we compare our self-attention based framework with Bahdanau’s attention based implementation from our previous work.
EPE-based Huge-Capacity Reversible Data Hiding in Encrypted Images

Reversible data hiding in encrypted images (RDHEI) consists of embedding data in the encrypted domain. In current state-of-the-art methods, most of them use least significant bit (LSB) substitution or prediction, but fail to embed a significant amount of information. Recently, a new class of RDHEI method, based on most significant bit (MSB) substitution, has emerged. By exploiting the natural correlation between pixels in the clear domain, it is possible to have a payload close to 1 bpp with a very high image quality, without adding overhead. In particular, in the approach based on embedded prediction errors (EPE-based approach) [6], the authors propose to embed the prediction error location information in the encrypted MSB-plane. In this paper, we present a huge-capacity RDHEI (HCRDHEI) method. In fact, we are interested in improving the proposed EPE-based RDHEI approach by using recursively other bit-planes, from MSB to LSB as long as it is possible. Indeed, depending on the image content, bit-planes can easily be predicted, and so most of them can be substituted by bits of a secret message. According to the obtained results, the payload can be much higher than 1 bpp (median equal to 1.749 bpp, on average 1.836 bpp, and 5.408 bpp in the best case), while preserving perfect reversibility.
Generative Neural Networks for Anomaly Detection in Crowded Scenes

Security surveillance is critical to social harmony and people’s peaceful life. It has a great impact on strengthening social stability and life safeguarding. Detecting anomaly timely, effectively and efficiently in video surveillance remains challenging. This paper proposes a new approach, called <inline-formula> <tex-math notation="LaTeX">$S^{2}$ </tex-math></inline-formula>-VAE, for anomaly detection from video data. The <inline-formula> <tex-math notation="LaTeX">$S^{2}$ </tex-math></inline-formula>-VAE consists of two proposed neural networks: a Stacked Fully Connected Variational AutoEncoder (<inline-formula> <tex-math notation="LaTeX">$S_{F}$ </tex-math></inline-formula>-VAE) and a Skip Convolutional VAE (<inline-formula> <tex-math notation="LaTeX">$S_{C}$ </tex-math></inline-formula>-VAE). The <inline-formula> <tex-math notation="LaTeX">$S_{F}$ </tex-math></inline-formula>-VAE is a shallow generative network to obtain a model like Gaussian mixture to fit the distribution of the actual data. The <inline-formula> <tex-math notation="LaTeX">$S_{C}$ </tex-math></inline-formula>-VAE, as a key component of <inline-formula> <tex-math notation="LaTeX">$S^{2}$ </tex-math></inline-formula>-VAE, is a deep generative network to take advantages of CNN, VAE and skip connections. Both <inline-formula> <tex-math notation="LaTeX">$S_{F}$ </tex-math></inline-formula>-VAE and <inline-formula> <tex-math notation="LaTeX">$S_{C}$ </tex-math></inline-formula>-VAE are efficient and effective generative networks and they can achieve better performance for detecting both local abnormal events and global abnormal events. The proposed <inline-formula> <tex-math notation="LaTeX">$S^{2}$ </tex-math></inline-formula>-VAE is evaluated using four public datasets. The experimental results show that the <inline-formula> <tex-math notation="LaTeX">$S^{2}$ </tex-math></inline-formula>-VAE outperforms the state-of-the-art algorithms. The code is available publicly at <uri>https://github.com/tianwangbuaa/</uri>.
Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-Based Sentiment Analysis

Sentiment analysis can categorize an overall opinion from a sentence or a document. However, there are sentences with more than one opinion in a single sentence statement. This problem is solved by aspect-based sentiment analysis. We conduct experiments on this problem using Indonesian dataset with 2-step process: aspect detection and sentiment classification. On aspect detection, we compare two deep neural network models with different input vector and topology: word embedding vector which is processed using gated recurrent unit (GRU), and bag-of-words vector which is processed using fully-connected layer. On sentiment classification, we also compare two approaches of deep neural network. The first approach uses word embedding, sentiment lexicon and POS tags as the input vector, with bi-GRU based as the topology. The second one uses aspect matrix to rescale the word embedding vector as the input vector and convolutional neural network (CNN)based as the topology. Our work is compared to a baseline framework which uses different model for each aspect. The dataset has approximately 9800 reviews collected from various categories on popular online marketplaces in Indonesia. Our models generalize well over all aspects and achieve state-of-the-art performance on 4 out of 7 aspects compared to the baseline framework.
Improving the Intrusion Detection System for NSL-KDD Dataset based on PCA-Fuzzy Clustering-KNN

Nowadays, information security is extremely critical issues for every organization to protect information from the useless data on the manipulation of network traffic or intrusion. Intrusion detection system has one of the important roles to prevent data or information from malicious behaviors because its capable of detecting attacks in several available environments. Thereafter, many researches concentrate on developing new algorithms to treat the Dataset by different way. In this work, we suggest a new proposed PCA-fuzzy Clustering-KNN method that means ensemble of Analysis of Principal Component and Fuzzy Clustering with K-Nearest Neighbor feature selection technics. However, we perform two main class classifications to construct our suggested model. Then, to check the robustness of model we used as well-known Dataset NSL-KDD used for analysis of anomaly. This Dataset is based on benchmark data used for intrusion detection, KDDCup 1999. Therefore, we analyse NSL-KDD Dataset using PCA-fuzzy Clustering-KNN analytic and try to define the performance of incident using machine learning algorithms, the algorithm learns what type of attacks are found in which classes in order to improve the classification accuracy and reduce high false alarm rate and detects the maximum of detection rate from Dataset as shown by the numerical results.
Plasmonic superradiance in metallo-dielectric nanohybrids

Placer des nanostructures plasmoniques a proximite d’emetteurs quantiques est une approche prometteuse pour concevoir des nanolasers plasmoniques ou realiser l’intrication de bits quantiques a longue distance. Des etudes theoriques recentes suggerent que le champ plasmonique peut induire un couplage efficace entre emetteurs et mener a la formationd’etats collectifs superradiants. Dans ce travail de these, nous avons developpe un modele theorique afin d’analyser les effets collectifs pour un ensemble de dipoles couples a un nanoresonateur electromagnetique. Nous avons etudie experimentalement la superradiance plasmonique d’emetteurs organiques greffes a une distance controlee d’une nanosphere metallique,a temperature ambiante. Nous avons mesure le taux de relaxation de ces structures hybrides, en ensemble et a l’echelle de l’objet unique. Nous observons que le taux de relaxation augmente i) avec le nombre d’emetteurs et ii) lorsque la distance entre les emetteurs et le coeur metallique diminue, une preuve directe et claire de la superradiance plasmonique.Cette tendance a ete observee pour deux types de structure hybride, differentes par la taille du coeur metallique et par le type de molecule utilisee comme emetteur. L’observation de la superradiance plasmonique a temperature ambiante ouvre des questions sur la robustesse d’un etat superradiant contre des mecanismes de decoherence. Cette robustesse presente un interet majeur pour des applications potentielles de systemes quantiques a temperature ambiante.
Multi-Label Topic Classification of Hadith of Bukhari (Indonesian Language Translation)Using Information Gain and Backpropagation Neural Network

Hadith is the second source of law and guidance for Muslims after the Qur'an; it is a collection of traditions containing sayings of the Prophet Muhammad, with accounts of his daily practice (the Sunnah). Experts of hadith have narrated numerous ahadith. This research builds a system that can classify hadith saheeh (sound)compiled by Bukhari that was translated to the Indonesian language. This area of study is important, so that Muslims could easily recognize the suggestions and restrictions contained in a hadith. The Backpropagation Neural Network algorithm was chosen because it can perform classification with a huge number of varied features. This method is combined with information gain as feature selection in order to select influential features for each class label of multi-label data and single-label data, which has never been done before. The results show that 88.42% of multi-label hadith data can be correctly classified. Meanwhile, 65.275% of the single-label hadith data can be classified correctly using the information gain feature selection.
Identification of Specific Nuclear Genetic Loci and Genes That Interact With the Mitochondrial Genome and Contribute to Fecundity in Caenorhabditis elegans

Previous studies have found that fecundity is a multigenic trait regulated, in part, by mitochondrial-nuclear (mit-n) genetic interactions. However, the identification of specific nuclear genetic loci or genes interacting with the mitochondrial genome and contributing to the quantitative trait fecundity is an unsolved issue. Here, a panel of recombinant inbred advanced intercrossed lines (RIAILs), established from a cross between the N2 and CB4856 strains of C. elegans, were used to characterize the underlying genetic basis of mit-n genetic interactions related to fecundity. Sixty-seven single nucleotide polymorphisms (SNPs) were identified by association mapping to be linked with fecundity among 115 SNPs linked to mitotype. This indicated significant epistatic effects between nuclear and mitochondria genetics on fecundity. In addition, two specific nuclear genetic loci interacting with the mitochondrial genome and contributing to fecundity were identified. A significant reduction in fecundity was observed in the RIAILs that carried CB4856 mitochondria and a N2 genotype at locus 1 or a CB4856 genotype at locus 2 relative to the wild-type strains. Then, a hybrid strain (CNC10) was established, which was bred as homoplasmic for the CB4856 mtDNA genome and N2 genotype at locus 1 in the CB4856 nuclear background. The mean fecundity of CNC10 was half the fecundity of the control strain. Several functional characteristics of the mitochondria in CNC10 were also influenced by mit-n interactions. Overall, experimental evidence was presented that specific nuclear genetic loci or genes have interactions with the mitochondrial genome and are associated with fecundity. In total, 18 genes were identified using integrative approaches to have interactions with the mitochondrial genome and to contribute to fecundity.
Performance Evaluation of Low-Power Wide Area based on LoRa Technology for Smart Metering

The concept of smart cities is one of innovative concept in our societies today. The paradigm of Internet-of-Thing consists the basis of the realization of the ICT platform(s) for the future cities. Two major challenges are faced by such realization. On one hand, smart cities cover several domains, such smart gird, smart transportation, smart building/homes, etc. On the other hand, the communications layer to support all the traffic generated by smart applications must have a large coverage, guarantee a given quality of service, cope with strongly heterogeneous traffic, low-cost, low power consumption, etc. Recently, different wireless/mobile technologies are proposed for the connectivity of IoT sensors/actuators in the smart city and to fulfil the cited requirements. This new wave of technologies is referred as “Low-Power Wide Area” (LPWA) wireless networks, which is represented by solutions like LoRa, SigFox, Narrowband IoT Long Term Evolution (NB-IoT LTE), etc. In this work, we make a performance evaluation of the LoRa system in connecting applications of smart distribution grid, as a part of smart cities; with main focus on smart metering.
Combining rTMS With Intensive Language-Action Therapy in Chronic Aphasia: A Randomized Controlled Trial

Neuromodulation technologies, such as transcranial magnetic stimulation (TMS), are promising tools for neurorehabilitation, aphasia therapy included, but not yet in common clinical use. Combined with behavioral techniques, in particular treatment-efficient Intensive Language-Action Therapy (ILAT, previously CIAT or CILT), TMS could substantially amplify the beneficial effect of such behavioral therapy alone (Thiel et al., 2013; Martin et al., 2014; Mendoza et al., 2016; Kapoor, 2017). In this randomized study of 17 subjects with post-stroke aphasia in the chronic stage, we studied the combined effect of ILAT and 1-Hz placebo-controlled navigated repetitive TMS (rTMS) to the right-hemispheric inferior frontal cortex—that is, to the anterior part of the non-dominant hemisphere's homolog Broca's area (pars triangularis). Patients were randomized to groups A and B. Patients in group A received a 2-week period of rTMS during naming training where they named pictures displayed on the screen once every 10 s, followed by 2 weeks of rTMS and naming combined with ILAT. Patients in group B received the same behavioral therapy but TMS was replaced by sham stimulation. The primary outcome measures for changes in language performance were the Western Aphasia Battery's aphasia quotient AQ; the secondary outcome measures were the Boston naming test (BNT) and the Action naming test (Action BNT, ANT). All subjects completed the study. At baseline, no statistically significant group differences were discovered for age, post-stroke time or diagnosis. ILAT was associated with significant improvement across groups, as documented by both primary and secondary outcome measures. No significant effect of rTMS could be documented. Our results agree with previous results proving ILAT's ability to improve language in patients with chronic aphasia. In contrast with earlier claims, however, a beneficial effect of rTMS in chronic post-stroke aphasia rehabilitation was not detected in this study. Clinical Trial Registration: www.ClinicalTrials. gov, identifier: NCT03629665
Sentiment analysis via Doc2Vec and Convolutional Neural Network hybrids

In this study, we proposed two ensembled Convolutional Neural Network architectures viz. (CNNcuPSONN) and CNN-PNN, where cuPSONN is a CUDA enabled particle swarm optimization optimized neural network and PNN is the probabilistic neural network. We compared their performance with that of the standalone PNN. The techniques are invoked after distributed Doc2Vec was employed on movie review dataset of the size of 992 MB, to get the paragraph embeddings of the dataset. Apache Spark framework was used to get the embeddings. Here, we considered CNN as a feature extractor from the embeddings, while cuPSONN and PNN perform classification. The third proposed model is PNN preceded by t-statistic based feature selection (t-statistic-PNN). Among the three classifiers employed, CNN-PNN yielded an area under ROC curve of 96.63% under the 10fold cross validation framework. The proposed CNN-PNN turned out to be statistically significant with respect to the CNN-cuPSONN and t-statistic-PNN, while it is statistically the same as the distributed multi-layer perceptron (DMLP), which is considered as baseline classifier to compare the performance of our proposed algorithms.
Efficient Fixed-Width Adder-Tree Design

Conventionally, fixed-width adder-tree (AT) design is obtained from the full-width AT design by employing direct or post-truncation. In <italic>direct-truncation</italic>, one lower order bit of each adder output of full-width AT is post-truncated, and in the case of <italic>post-truncation</italic>, {<inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>} lower order-bits of final-stage adder output are truncated, where <inline-formula> <tex-math notation="LaTeX">${p}={{\lceil }{\log }_{{2}}{N}{\rceil }}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> is the input-vector size. Both these methods does not provide an efficient design. In this brief, a novel scheme is presented to obtain fixed-width AT design using truncated input. A bias estimation formula based on probabilistic approach is presented to compensate for the truncation error. The proposed fixed-width AT design for input-vector sizes 8 and 16 offers (37%, 23%, 22%) and (51%, 30%, 27%) area-delay product saving for word-length sizes (8, 12, 16), respectively, and calculates the output almost with the same accuracy as the post-truncated fixed-width AT, which has the highest accuracy among the existing fixed-width AT. Further, we observed that Walsh–Hadamard transform based on the proposed fixed-width AT design reconstruct higher-texture images with higher peak signal-to-noise ratio (PSNR) and moderate-texture images with almost the same PSNR compared to those obtained using the existing AT designs. Besides, the proposed design creates an additional advantage to optimize other blocks appear at the upstream of the AT in a complex design.
An 88-fJ/40-MHz [0.4 V]–0.61-pJ/1-GHz [0.9 V] Dual-Mode Logic 8  $\times$  8 bit Multiplier Accumulator With a Self-Adjustment Mechanism in 28-nm FD-SOI

The unique ability of dual-mode logic (DML) to self-adapt to computational needs by providing high speed and/or low energy consumption is demonstrated for the first time by silicon measurements in 28-nm fully depleted silicon on insulator. At the gate level, the DML design offers the possibility to operate either in the static mode to save energy or in the dynamic mode to increase speed, albeit with higher delay or energy consumption, respectively. In this paper, these two operational modes of the DML gates are optimally managed by a self-adjustment mechanism to increase speed or reduce the energy of gates/blocks in the design at run time. As a test case, a two-stage pipelined multiply–accumulate (MAC) circuit was selected to assess the advantages of DML in terms of speed, energy, and area as compared to a conventional CMOS design. We show that the self-adjusted DML MAC achieves both a performance boost of up to 92% and 16% less energy consumption than the equivalent standard CMOS implementation. The energy saved can be even greater (up to 35%) when the low-power (fully static) mode is enabled. In addition, the DML MAC occupies 25% less area.
Artificial Intelligence for Vehicle-to-Everything: A Survey

Recently, the advancement in communications, intelligent transportation systems, and computational systems has opened up new opportunities for intelligent traffic safety, comfort, and efficiency solutions. Artificial intelligence (AI) has been widely used to optimize traditional data-driven approaches in different areas of the scientific research. Vehicle-to-everything (V2X) system together with AI can acquire the information from diverse sources, can expand the driver’s perception, and can predict to avoid potential accidents, thus enhancing the comfort, safety, and efficiency of the driving. This paper presents a comprehensive survey of the research works that have utilized AI to address various research challenges in V2X systems. We have summarized the contribution of these research works and categorized them according to the application domains. Finally, we present open problems and research challenges that need to be addressed for realizing the full potential of AI to advance V2X systems.
Investigation of Adoption Behaviour for Social Commerce in the Kindom of Saudi Arabia

Social media fundamentally has changed the consumer’s decision process. There are many research studies from different context for understanding the importance of social media, regrettably, not until recently, its business impact and opportunities were not seriously addressed. This is what led the Information System (IS) research community to come up with a new discipline known as Social Commerce (s-commerce). Hence an alternative means businesses through online social interactions emerge. This has made the social networking platform drives consumers to participate in social commerce. It is necessary to identify the significant factors that affect its acceptance. Thus, this research aimed to empirically examine the factors that affect s-commerce acceptance in Saudi Arabia. The quantitative research methodology was chosen to test the formulated hypothesis by subjective assessment of attitudes, opinions, and behavior of the people of Saudi Arabia.
Necessary and Sufficient Conditions of Exponential Stability for Delayed Linear Discrete-Time Systems

This paper is concerned with the problem of stability analysis for delayed linear discrete-time systems (DLDTSs). In order to obtain the necessary and sufficient criteria of exponential stability for DLDTSs, we first address the boundary value problem (BVP) of matrix difference equations, which is a generalization of the Lyapunov matrix equation (LME) for delay-free linear discrete-time systems. Also, the existence, uniqueness, and properties of solution to the BVP are investigated. In addition, for DLDTSs, we introduce a new concept—Lyapunov matrix that can be viewed as a generalization of the unique positive definite solution of delay-free LME. It should be noted that the Lyapunov matrix can be represented by the solution of the BVP. Then, the Lyapunov condition—a necessary and sufficient condition that guarantees the existence and uniqueness of Lyapunov matrix is introduced and characterized. Furthermore, by constructing Lyapunov matrix-based complete Lyapunov–Krasovskii functional, some necessary and sufficient conditions under which the considered DLDTSs are exponentially stable are proposed. Finally, two numerical examples are presented to show the advantages of the proven theoretic results.
CMOS First-Order All-Pass Filter With 2-Hz Pole Frequency

A CMOS fully integrated all-pass filter with an extremely low pole frequency of 2 Hz is introduced in this paper. It has 0.08-dB passband ripple and 0.029-mm<sup>2</sup>Si area. It has 0.38-mW power consumption in strong inversion with ±0.6-V power supplies. In subthreshold, it has 0.64-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> quiescent power and operates with ±200-mV dc supplies. Miller multiplication is used to obtain a large equivalent capacitor without excessive Si area. By varying the gain of the Miller amplifier, the pole frequency can be varied from 2 to 48 Hz. Experimental and simulation results of a test chip prototype in 130-nm CMOS technology validate the proposed circuit.