Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling the Incongruity Between Sentence Snippets for Sarcasm Detection

Sarcasm is a form of irony used to mock or convey con-tempt, which occurs when there is an incongruity between the literal meaning of an utterance and its intended meaning. Many studies identify sarcasm by capturing the incongruity in-between the words. However, consider the following example, “I love waking up at 4 am on Saturday”, there is no apparent incongruity in-between the words. Intuitively, the word “love” and the snippet “waking up at 4 am on Saturday” form a strong contrast. Thus, capturing the in-congruity among the sentence snippets is more reasonable since a sentence snippet usually contains more semantic information than a single word. Additionally, not all snippets are equally important when human beings identify sarcasm. Thus, inspired by the above observations, we propose the Self-Attention of Weighted Snippets (SAWS) model for sarcasm detection, which overcomes the problem that the previous models are inefﬁcient in determining the sar-casm caused by snippet incongruity. The experiment results show that our model achieves state-of-the-art performance on four benchmark datasets, including two short text Twitter datasets and two long text Internet Argument Corpus (IAC) datasets.
Impingement Cooling Using a Virtual Orifice Synthetic Jet

Electronic devices continue to become smaller, more sophisticated and faster performing, needing to dissipate the heat generated in smaller areas. Researchers have turned to unconventional methods to enhance the local heat transfer rate in small, confined spaces, such as synthetic jets. These jets use oscillating flow, which have a zero net mass flux, to produce jet flow through a given orifice. These devices have a significant momentum flux, creating vortex rings that increase mixing and the heat transfer rate. Previous experiments have shown that a contracting orifice diameter can produce a higher momentum flux, enhancing the heat transfer rate by up to 6 times that of natural convection. This used a mechanical aperture, which presented problems with miniaturization as well as fatigue strength. To avoid this issue, a virtual orifice is developed, which siphons fluid via a bypass slot and redirects it radially inward to the primary flow as it exits the orifice. The virtual aperture reduces the complexity of the design and the volume needed. Computational fluid dynamics simulations were used to optimize the geometry for the design, selecting dimensions for optimal exit speeds and impingement plate cooling. Simulations demonstrated that the virtual orifice synthetic jet produced a 5% increase in convective heat transfer rate compared to an ordinary synthetic jet with the same characteristic dimensions. The design was fabricated and used to cool a square heater mounted on an impingement plate. The heater temperatures were measured for distances ranging from 2 to 8 jet diameters, showing comparable performance to the simulations. The virtual orifice design did not prove as promising as originally hoped, only providing a 3% increase in heat transfer, and should be looked at for other applications such as flow control or propulsion.
Discrimination of the Diastolic Murmurs in Coronary Heart Disease and in Valvular Disease

Previous studies have shown that the acoustic characteristics of coronary heart disease (CHD) are the diastolic turbulent murmurs. But there is little research on how to distinguish the differences between diastolic turbulent murmurs and diastolic valvular murmurs. In order to improve the diagnosis accuracy of CHD based on the analysis of diastolic murmurs, empirical wavelet transform was applied to distinguish the difference of diastolic murmurs between in CHD and in valvular disease. Firstly, the spectrum of diastolic heart sounds was divided into three modes and the segmentation boundaries were set as [150], 200] Hz. Next, the diastolic modal spectrums were obtained. It is found that the essential difference of diastolic murmurs between in CHD and in mitral stenosis is the third modal spectrum. Finally, the characteristic parameter P3 was defined and used to distinguish the diastolic murmurs in CHD and in valvular disease. The performance of the proposed method is tested and the results show when the proposed method was used to identify the diastolic murmurs of CHD, Se is 94.7%, Pp is 93.4% and Oa is 88.7%; When the proposed method identifying the diastolic murmurs of valvular disease, Se is 93.3%, Pp is 94.6% and Oa is 88.6%.
Improving Supervised Superpixel-Based Codebook Representations by Local Convolutional Features

Deep learning techniques are the current state-of-the-art in image classiﬁcation techniques, but they have the drawback of requiring a large number of labelled training images. In this context, Visual Dictionaries (Codebooks or Bag of Visual Words - BoVW) are a very interesting alternative to Deep Learning models whenever we have a low number of labelled training images. However, the meth-ods usually extract interest points from each image independently, which then leads to unrelated interest points among the classes and they build one visual dictionary in an unsupervised fashion. In this work, we present: 1) the use of class-speciﬁc superpixel segmentation to deﬁne interest points that are common for each class, 2) the use of Convolutional Neural Networks (CNNs) with random weights to cope with the absence of labeled data and extract more representative local features, and 3) the construction of specialized visual dictionaries for each class. We conduct experiments to show that our method can outperform a CNN trained from a small set of labelled images and can be equivalent to a CNN with pre-trained features. Also, we show that our method is better than other traditional BoVW approaches.
The LSTM-Based Advantage Actor-Critic Learning for Resource Management in Network Slicing With User Mobility

Network slicing aims to efficiently provision diversified services with distinct requirements over the same physical infrastructure. Therein, in order to efficiently allocate resources across slices, demand-aware inter-slice resource management is of significant importance. In this letter, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We primarily leverage advantage actor-critic (A2C), one typical deep reinforcement learning (DRL) algorithm, to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. However, given that the user mobility toughens the difficulty to perceive the environment, we further incorporate the long short-term memory (LSTM) into A2C, and put forward an LSTM-A2C algorithm to track the user mobility and improve the system utility. We verify the performance of the proposed LSTM-A2C through extensive simulations.
Energy Detection Based Spectrum Sensing Strategy for CRN

Cognitive Radio (CR) is an emerging technology which enables a secondary user (SU) to utilize the unoccupied spectrum holes owned by a primary user (PU) through spectrum sensing in order to maximize the utilization of finite spectrum resources. Although CRNs maximize the channel bandwidth resources without any impact upon well-established regulation of spectrum allocation but on the other hand energy consumption can be a challenge as efficient performance and energy consumption coexists. Spectrum sensing is very important part of spectrum management in CRNs as it detects the free bands to shift over the transmission of SU to it when PU comes back to its licensed band. In this research we proposed a novel spectrum sensing strategy that performs sensing in an energy efficient manner to select the most suitable target hole. We have compared our proposed strategy with existing spectrum sensing strategies in CRNs in a theoretical manner. Results show that proposed spectrum sensing strategy has better performance with comparison of recent technologies.
A Distributed Architecture for DDoS Prediction and Bot Detection

Distributed Denial-of-Service (DDoS) attacks disrupt servers, services and the network, overloading the target resources and denying normal traffic. In order to defend from this attack, mitigation actions usually overprovision and sinkhole malicious traffic. Sooner the attack is detected, better is the mitigation. Hence, we advocate for using a prediction technique aiming to anticipate actions against the possible attack, before it effectively starts. Then, this article contributes to advance the state-of-the-art presenting a distributed architecture that identifies early signals of a possible DDoS attack and detects bots composing a botnet. The architecture identifies the malicious actors (bots) participating in the attack. The bot detection technique is triggered by the prediction of DDoS supported by early signals. Prediction identifies signals of attack on the network before it reaches advanced stages. Based on the metastability theory, it provides unsupervised statistical learning and identifies the imminence of DDoS attacks. The botnet detection is challenging because of the high dimension of data involved and because of resource constraints (memory and processing) in network devices. Network devices are clustered based on features extracted from the traffic and based on the causality between devices. Detection is performed per cluster. Performance evaluations took as input the CTU-13 Czech Republic University, CAIDA and Botnet 2014 datasets, efficiently detecting the bots in the dataset with an accuracy of 99.9%.
DIEN Network: Detailed Information Extracting Network for Detecting Continuous Circular Capsulorhexis Boundaries of Cataracts

Robotic surgery is an arising area to satisfy the tremendous demand of modern clinical application, and it is becoming more and more acceptable by the normal. In this paper, we are dedicated to finding a more modern solution for continuous circular capsulorhexis of cataract surgery via deep learning method. We take inspiration from former works and propose a detailed information extracting network structure that is suitable applied in the area of clinical, where more side-output layers are used in the convolutional modules, rather than single side-output of specific layer. Moreover, to balance the positive samples and the negative samples and make the network model more stable convergence, we introduce focal loss as the loss function of the model. Instead of exploiting network structure deeper and deeper, the boundary of continuous circular capsulorhexis extracted by above approaches can satisfy the demand of the surgery robots need during a cataract surgery. We evaluate the results on the dataset provided by clinical ophthalmologists, and achieves F =. 808.
Suppression of Ambipolar current in Tunnel Field-Effect Transistor using Field-Plate

This paper proposes a tunnel field-effect transistor (TFET) with an additional field-plate over the drain region in the device. Using 2-D device simulations, it is shown that, by introducing a field-plate with an appropriate work function, placed on the top of the drain region, the ambipolar current of the device can be effectively suppressed. Furthermore, it is demonstrated that when a field-plate is placed at an appropriate height on the drain region, the gate-to-drain capacitance of the device does not increase significantly. Using simulations, it is shown that, compared to the gate-on-drain overlap technique, the proposed technique results in a substantial decrease in the gate-to-drain capacitance. Consequently, the fan-out of four (FO4) inverter delay is 22% smaller in the circuit realized using the proposed device compared to the circuit realized using TFETs having gate-on-drain overlap.
Accurate Detection of Sinkhole Attacks in IoT Networks Using Local Agents

In this paper we explore the feasibility of employing local security agents to detect sinkhole attacks in IoT multihop networks. Sinkhole attacks, which divert traffic towards the compromised node(s), prevent information from reaching their intended destination. Sinkhole attacks are unique in their operation and are often difficult to be recognized locally. We examine three types of local agents and employ thresholds, binary logistic regression (BLR) and support vector machines (SVM) as anomaly detectors. The local security agents’ deployment and evaluation is done both in a simulated environment and in a real network of resource-constrained nodes. We have concluded that threshold-based detection is not suitable for deployment in local agents. During the evaluation phase, the BLR and SVM detection modules for the Sinkhole attack are found to be able to detect the presence of the Sinkhole attack, with exceptionally high accuracy.
On Learning Spectral Masking for Single Channel Speech Enhancement Using Feedforward and Recurrent Neural Networks

Human speech in real-world environments is typically degraded by the background noise. They have a negative impact on perceptual speech quality and intelligibility which causes performance degradation in various speech-related technological applications, such as hearing aids and automatic speech recognition systems. It also degrades the original phase of the clean speech and introduces perceptual disturbance which leads to the negative impacts on the quality of speech. Therefore, speech enhancement must vigilantly be dealt with in everyday listening environments. In this article, speech enhancement is performed using supervised learning of spectral masking. Deep neural networks (DNN) and recurrent neural networks (RNN) are trained to learn the spectral masking from the magnitude spectrograms of the degraded speech. An iterative procedure is adopted as a post-processing step to deal with the noisy phase. Additionally, an intelligibility improvement filter is also used to incorporate the critical band importance function weights where higher weights contribute more towards intelligibility. Systematic experiments demonstrated that the proposed approaches greatly attenuated the background noise. Also, they led to large improvements of the perceived speech quality and intelligibility, as well as automatic speech recognition. In experiments, TIMIT database is used. The STOI is improved by 17.6% over the noisy speech. Also, SDR and PESQ are improved by 5.22dB and 19% over the noisy speech utterances. These comparisons showed that the proposed speech enhancement approaches outperformed the related speech enhancement methods.
B-SPARQL: A typed language for Querying the Big Knowledge

We introduce B-SPARQL as an extension of the classical SPARQL to deal with the problem of querying big knowledge. B-SPARQL extends SPARQL mainly in two directions. The first one is to query big knowledge in a global way such as `how much of the knowledge items of the knowledge base are confidential? ’ The second one is to query big knowledge in its translated digital form instead of in its original symbolic form. The former is necessary when evaluating and maintaining an immense knowledge base. The latter is useful when it is very inefficient or even impossible to deal with an immense knowledge base. In accordance, B-SPARQL not only deals with basic knowledge elements such as subjects, predicates and objects of RDF triples. It also deals with triples and knowledge bases and knowledge areas (parts of knowledge bases), including SPARQL endpoints, immediately by considering them as basic knowledge elements as well.
Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Short-term load forecasting (STLF) for industrial customers has been an essential task to reduce the cost of energy transaction and promote the stable operation of smart grid throughout the development of the modern power system. Traditional STLF methods commonly focus on establishing the non-linear relationship between loads and features, but ignore the temporal relationship between them. In this paper, an STLF method based on ensemble hidden Markov model (e-HMM) is proposed to track and learn the dynamic characteristics of industrial customer’s consumption patterns in correlated multivariate time series, thereby improving the prediction accuracy. Specifically, a novel similarity measurement strategy of log-likelihood space is designed to calculate the log-likelihood value of the multivariate time series in sliding time windows, which can effectively help the hidden Markov model (HMM) to capture the dynamic temporal characteristics from multiple historical sequences in similar patterns, so that the prediction accuracy is greatly improved. In order to improve the generalization ability and stability of a single HMM, we further adopt the framework of Bagging ensemble learning algorithm to reduce the prediction errors of a single model. The experimental study is implemented on a real dataset from a company in Hunan Province, China. We test the model in different forecasting periods. The results of multiple experiments and comparison with several state-of-the-art models show that the proposed approach has higher prediction accuracy.
Implications of the Multi-Modality of User Perceived Page Load Time

Web browsing is one of the most popular applications for both desktop and mobile users. A lot of effort has been devoted to speedup the Web, as well as in designing metrics that can accurately tell whether a webpage loaded fast or not. An often implicit assumption made by industrial and academic research communities is that a single metric is sufficient to assess whether a webpage loaded fast. In this paper we collect and make publicly available a unique dataset which contains webpage features (e.g., number and type of embedded objects) along with both objective and subjective Web quality metrics. This dataset was collected by crawling over 100 websites—representative of the top 1 M websites in the Web—while crowdsourcing 6,000 user opinions on user perceived page load time (uPLT). We show that the uPLT distribution is often multi-modal and that, in practice, no more than three modes are present. The main conclusion drawn from our analysis is that, for complex webpages, each of the different objective QoE metrics proposed in the literature (such as AFT, TTI, PLT, etc.) is suited to approximate one of the different uPLT modes. Index Terms—Web Performance, Quality Of Experience, Measurements.
Studies on Genetic Diversities of Dai-ju Stock and Min-yue Stock Pseudosciaena crocea(Richardson)

Genetic diversities of reared Pseudosciaena crocea(Richardson) collected from Zhoushan(Dai-ju) stock of Zhejiang province,and Lianjiang(Min-yue) stock of Fujian province were analyzed by the ways of morphological measurement and random amplified polymorphic DNA(RAPD). The results were as follows:(1) The differences between two reared stocks didn't get to the sub-species level,but there were significantly differences in five indexes(p0.05): TL/BL(Total length/Body length),BL/BH(body length/body high),ML/BL(mouth length/body length),ED/HL(eye diameter/head length),and relative fatness,furthermore,the differences in the five indexes above except BL/BH(body length/body high) got to extremely significantly difference level(p0.01),so the two tested reared stocks might belong to different geographical populations. (2) The genetic similarity indexes of Lianjiang(Min-yue) stock tested were 0.923~0.987,average of 0.962,and 0.0381 of the genetic diversity;while those of the Zhoushan(Dai-ju) stock were 0.874~0.982,0.949,and 0.061 respectively.The genetic distance of two cultural stocks was 0.005 1.Therefore,the genetic diversities of both stocks were low,and the genetic distance between them was close.
On the UC and UC* properties and the existence of best proximity points in metric spaces

We investigate the connections between UC and UC* properties for ordered pairs of subsets (A,B) in metric spaces, which are involved in the study of existence and uniqueness of best proximity points. We show that the $UC^{*}$ property is included into the UC property. We introduce some new notions: bounded UC (BUC) property and uniformly convex set about a function. We prove that these new notions are generalizations of the $UC$ property and that both of them are sufficient for to ensure existence and uniqueness of best proximity points. We show that these two new notions are different from a uniform convexity and even from a strict convexity. If we consider the underlying space to be a Banach space we find a sufficient condition which ensures that from the UC property it follows the uniform convexity of the underlying Banach space. We illustrate the new notions with examples. We present an example of a cyclic contraction T in a space, which is not even strictly convex and the ordered pair (A,B) has not the UC property, but has the $BUC$ property and thus there is a unique best proximity point of T in A.
DNA and Plaintext Dependent Chaotic Visual Selective Image Encryption

Visual selective image encryption can both improve the efficiency of the image encryption algorithm and reduce the frequency and severity of attacks against data. In this article, a new form of encryption is proposed based on keys derived from Deoxyribonucleic Acid (DNA) and plaintext image. The proposed scheme results in chaotic visual selective encryption of image data. In order to make and ensure that this new scheme is robust and secure against various kinds of attacks, the initial conditions of the chaotic maps utilized are generated from a random DNA sequence as well as plaintext image via an SHA-512 hash function. To increase the key space, three different single dimension chaotic maps are used. In the proposed scheme, these maps introduce diffusion in a plain image by selecting a block that have greater correlation and then it is bitwise XORed with the random matrix. The other two chaotic maps break the correlation among adjacent pixels via confusion (row and column shuffling). Once the ciphertext image has been divided into the respective units of Most Significant Bits (MSBs) and Least Significant Bit (LSBs), the host image is passed through lifting wavelet transformation, which replaces the low-frequency blocks of the host image (i.e., HL and HH) with the aforementioned MSBs and LSBs of ciphertext. This produces a final visual selective encrypted image and all security measures proves the robustness of the proposed scheme.
A Lightweight Cyber-Security Defense Framework for Smart Homes

Inevitably, the explosion of the Internet of Things (IoT) has alerted the modern human life. Apart from the benefits that this new technology offers to the users of the IoT devices, there are also dangers related to Cyber Security. Traditional methodologies that support and strengthen the Cyber Security can not be applied to the low consumption IoT devices. Hence, many attack detection methodologies and systems that respect the constraints of the IoT have been presented recently. In this paper, an anomaly detection mechanism is proposed that focuses on the threat detection, by combining statistical and machine learning methodologies to detect abnormalities in time-series of the network traffic. Moreover, this new framework is a lightweight Cyber Security solution, since its computational logic is included in the edge layer. The results of the experiments that were conducted in a test bed, indicated the high performance of the methodology in terms of Accuracy, Precision, Recall and F-measure.