Reviewerly/exHarmony huggingface dataset:
Dual-Channel Microblog Emotion Analysis Based on RoBERTa-WWM and Multi-Head Attention

Microblog text has always been the focus and difficulty of text emotion analysis due to its small size, strong timeliness and randomness. Traditional deep learning emotion analysis methods lack the focus on emotional knowledge such as emotional words, expressions, and degree adverbs in sentences. To this end, this paper proposes a dual-channel microblog emotion analysis method based on RoBERTa-WWM and multi-head attention. This paper use the extended emotional resource library to construct the emotional knowledge set of each sentence, and use the RoBERTa-WWM pre-training model suitable for Chinese text to obtain the feature representation of the sentence and the emotional knowledge set respectively. Then, the sentence feature representation and the emotional knowledge representation are inputted into a TextCNN-BiGRU network and a Multi-Head Attention network to obtain deeper semantics feature and attention feature of emotional knowledge. Finally, the semantic features and the emotional knowledge attention feature are combined to train the Microblog emotion analysis model. The experimental results on the NLPCC2014 data set show that it has achieved better results than the comparative experimental model. Experiments show that this method can effectively perform emotion analysis on Microblog texts.
Everyone is a Winner: Interpreting MLPerf Inference Benchmark Results

MLPerf Inference benchmark suite version 1.0 was recently released. It is a third release and the version number along with minor changes from the previous version indicate a maturity of the suite. With 33 benchmarks and almost 2,000 results it provides a wealth of data on Artificial Intelligence (AI) performance, but the large amount makes it difficult for users to fully comprehend the results. At the same time, it gives opportunity for most of the submitters to claim a leadership in certain aspects of AI Inference. To clarify the situation, here we present analysis of the benchmark suite from both users and submitters perspective. We also describe ways of improving the suite going forward.
Performance Evaluation of the Classic McEliece Key Encapsulation Algorithm

In late 2016, the US NIST announced a post-quantum cryptography open competition to select public-key cryptoalgorithms (digital signature, asymmetric encryption, and key encapsulation) suitable for use when quantum computing is widely available. Based on the results of the 2nd round of the competition, several solutions were selected that could potentially be standardized as post-quantum cryptographic algorithms. In this article, we are looking at one of the bids based on codes. The Classic McEliece key encapsulation algorithm is a variant of the well-known (over 40 years) public key cryptosystem in codes. We investigate the various characteristics of the Classic McEliece, in particular, we provide estimates of its performance using various computing platforms and testing technologies.
Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

Facial expression for different emotional feelings makes it interesting for researchers to develop recognition techniques.Facial expression is the outcome of emotions they feel, behavioral acts, and the physiological condition of one's mind.In the world of computer visions and algorithms, precise facial recognition is tough.In predicting the expression of a face, machine learning/artificial intelligence plays a significant role.The deep learning techniques are widely used in more challenging real-world problems which are highly encouraged in facial emotional analysis.In this article, we use three phases for facial expression recognition techniques.The principal component analysis-based dimensionality reduction techniques are used with Eigen face value for edge detection.Then the feature extraction is performed using swarm intelligence-based grey wolf with particle swarm optimization techniques.The neural network is highly used in deep learning techniques for classification.Here we use a deep belief network (DBN) for classifying the recognized image.The proposed method's results are assessed using the most comprehensive facial expression datasets, including RAF-DB, AffecteNet, and Cohn-Kanade (CK+).This developed approach improves existing methods with the maximum accuracy of 94.82%, 95.34%, 98.82%, and 97.82% on the test RAF-DB, AFfectNet, CK+, and FED-RO datasets respectively.
Human Action Recognition in Dark Videos

Image processing and action recognition in images are one of the most researched topics in Deep learning. Combining these two concepts for action recognition in lowlight footage is useful in a variety of applications, including night surveillance and self-driving at night. Due to the low photon count and SNR, video in low light is difficult. Short exposures videos are prone to noise, while long exposures can result in blur and are often impractical. To get a better understanding of the presented Action Recognition in Dark(ARID) dataset, which has low light videos divided into it’s action, making it an image classification problem. We examined it in depth and demonstrated it’s utility using simulated dark images. On this dataset, we also benchmarked the performance of existing action recognition models and investigated possible strategies for improving their performance. We introduce a novel pipeline for low-light images using RenNets and statistical image processing methods to identify the human’s actions in it to support the development of learning-based pipelines for human actions recognition in dark videos. We present promising findings from the latest dataset improving the top-1 accuracy by 3.8%. We also examined performance-related causes, and identify areas for potential research.
A Study on CP-ABE Based Data Sharing System that Provides Signature-based Verifiable Outsourcing

Recently, with the development of the cloud environment, users can store their data or share it with other users. However, various security threats can occur in data sharing systems in the cloud environment. To solve this, data sharing systems and access control methods using the CP-ABE method are being studied, but the following problems may occur. First, in an outsourcing server that supports computation, it is not possible to prove that the computed result is a properly computed result when performing the partial decryption process of the ciphertext. Therefore, the user needs to verify the message obtained by performing the decryption process, and verify that the data is uploaded by the data owner through verification. As another problem, because the data owner encrypts data with attribute-based encryption, the number of attributes included in the access structure increases. This increases the size of the ciphertext, which can waste space in cloud storage. Therefore, a ciphertext of a constant size must be output regardless of the number of attributes when generating the ciphertext. In this paper, we proposes a CP-ABE based data sharing system that provides signature-based verifiable outsourcing. It aims at a system that allows multiple users to share data safely and efficiently in a cloud environment by satisfying verifiable outsourcing and constant-sized ciphertext output among various security requirements required by CP-ABE.
Mapping Health Trajectories on Self Organizing Maps using COVID-19 Patient’s Blood Tests

Since COVID-19 appeared in December 2019, scientists are researching new ways to improve the management of the disease. Considering machine learning approaches have proven to be very useful tools to discover hidden patterns in data, we propose in this paper to apply a Self Organizing Map (SOM) to characterize the health-status evolution of COVID-19 patients. The SOM is a neural network whose neurons can be represented as cells in a bi-dimensional grid preserving the mapping from the original space to the map units. We consider real-world data of hospitalized COVID-19 patients in a Spanish hospital during the first wave of the pandemic. Patients are represented by six blood tests (leukocytes and D-dimer, among others) in a daily basis. Besides, each patient is associated with one of two different health-status: favorable evolution (discharged home) and unfavorable evolution (exitus or admission to the intensive care unit). We show the potential of our approach by detailing the mapping of the health trajectory associated with different particular cases and drawing their trajectory on the bi-dimensional map of the SOM.
Intent Focused Semantic Parsing and Zero-Shot Learning for Out-of-Domain Detection in Spoken Language Understanding

In Spoken Language Understanding (SLU), the ability to detect out-of-domain (OOD) input dialog plays a very important role (e.g., voice assistance and chatbot systems). However, most of the existing OOD detection methods rely heavily on manually labeled OOD. Manual labeling of the OOD data for a dynamically changing and evolving area is time-consuming and not immediately possible. It limits the feasibility of these models in practical applications. So, to solve this problem, we are considering the scenario of having no OOD labeled data (i.e., zero-shot learning). To achieve this goal, we have used the intent focused semantic parsing, extracted with the help of Transformer-based techniques (e.g., BERT [26]). The two main components of the intent-focused semantic parsing are - (a) the sentence-level intents and (b) token-level intent classes, which show the relation of slot tokens with intent classes. Finally, we combine both information and use a One Class Neural Network (OC-NN) based zero-shot classifier. Our devised system has shown better results compared to the state-of-the-art on four publicly available datasets.
Experimental Evaluation of Stereoscopic Wavelet-based Optical Flow Velocimetry (swOFV)

An implementation of stereoscopic wavelet-based optical flow velocimetry (swOFV) using a general mapping approach for stereoscopic reconstruction is presented that is suitable for experiments. The methodology is evaluated quantitatively using synthetic tracer particle images generated from a direct numerical simulation (DNS) of homogeneous isotropic turbulence (HIT) and experimental data from a laminar axisymmetric vortex ring. Statistical comparisons between velocity components and gradient quantities are made between swOFV and stereoscopic PIV (sPIV) results. Overall, swOFV shows higher accuracy (approximately 35\% less error near the optimal off-axis angle) and increased spatial resolution as evident through field quantity visualization. The increases in spatial resolution also are observed in the energy and dissipation spectra, as swOFV is capable of resolving the finest dissipative scales. Experimental particle images from a laminar axisymmetric vortex ring were were evaluated and showed a strong topological agreement between swOFV and sPIV as expected for laminar flows. However, the swOFV results exhibited much less noise in the velocity field estimates, which was especially evident in the reconstruction of the out-of-plane velocity ($w$ )component and the out-of-plane vorticity, $\omega_z$. Overall, the updated and generalized implementation of swOFV, which can be implemented experimentally, has been shown to provide higher accuracy and spatial resolution capabilities when compared to sPIV.
Stance Detection with Knowledge Enhanced BERT

Microblog stance detection aims to determine an author’s stance (for or against) towards a specific topic or claim in a post. It has become a key component in applications like truth finding, intention mining and rumor detection. Recently, researchers have becoming more and more interested in using neural models to detect user’s stance. Most of the work directly models the word sequence in the text and learns its text representation. However, few researches have explored the integration of external knowledge into stance detection to enrich the learned text representation. In this paper, a knowledge-enhanced BERT model for Microblog stance detection is proposed. In this model, the triples in knowledge graphs are used as domain knowledge injected into the sentences. We conduct experiments and test the proposed method on a public Chinese Microblog stance detection dataset. Experimental results show that our model significantly outperforms the competitive baseline methods. Furthermore, the incorporation of knowledge graph gives more than 11.3% improvement in F1 score compared with state-of-the-art method.
Peculiarities of face detection and recognition

Paper describes the analysis of methods for detecting and tracking faces, as well as recognition algorithms in the development of systems for recognizing faces in images and videos. The difficulties arising in solving problems of detection and recognition are considered.
Text information aggregation with centrality attention

A lot of natural language processing problems need to encode the text sequence as a fix-length vector, which usually involves an aggregation process of combining the representations of all the words, such as pooling or self-attention. However, these widely used aggregation approaches do not take higher-order relationships among the words into consideration. Hence we propose a new way of obtaining aggregation weights, called eigen-centrality self-attention. More specifically, we build a fully-connected graph for all the words in a sentence, then compute the eigen-centrality as the attention score of each word. The explicit modeling of relationships as a graph is able to capture some higher-order dependency among words, which helps us achieve better results in 5 text classification tasks and one SNLI task than baseline models such as pooling, self-attention, and dynamic routing. Besides, in order to compute the dominant eigenvector of the graph, we adopt a power method algorithm to get the eigen-centrality measure. Moreover, we also derive an iterative approach to get the gradient for the power method process to reduce both memory consumption and computation requirement.
A Hybrid Filter for Denoising of MRI Brain Images using Fast Independent Component Analysis

Denoising of MRI images is very essential for the effective diagnosis of various brain diseases. In this paper, a new hybrid ROFTV-Fast ICA algorithm is proposed to enhance the MRI images corrupted by Gaussian noise. The original MRI image is subjected to Gaussian noise. The corrupted brain image is denoised by the combination of both Rudin-Osher- Fatemi (ROF) Total variation filter and Fast-Independent Component Analysis (ICA) algorithms. The total variation in the noisy brain images is minimized by using ROFTV filter. Again, the recovered image is denoised further by Fast ICA algorithm, by separating the noise and noiseless components in the image. The performance of this hybrid ROFTV -Fast ICA filter is evaluated by means of Peak Signal to Noise Ratio (PSNR). The proposed method is also compared with Adaptive Median Filter (AMF), Progressive Switching Median Filter (PSMF) and Bilateral filter (BF). The result shows that the proposed hybrid algorithm outperforms rest of the filters and smoothens the MRI images very well also preserving the edges and corners.
Detection of records of weak local earthquakes using neural networks

Manual processing of large volumes of continuous observations produced by local seismic networks takes a lot of time, therefore, to solve this problem, automatic algorithms for detecting seismic events are used. Deterministic methods for solving the problem of detection, which do an excellent job of detecting intensive earthquakes, face critical problems when detecting weak seismic events (earthquakes). They are based on principles based on the calculation of energy, which causes multiple errors in detection: weak seismic events may not be detected, and high-amplitude noise may be mistakenly detected as an event. In our work, we propose a detection method capable of surpassing deterministic methods in detecting events on seismograms, successfully detecting a similar or more events with fewer false detections.
A Random Opposition-Based Sparrow Search Algorithm for Path Planning Problem

In this paper, an improved intelligence algorithm is proposed for path planning problem. The algorithm is based on Sparrow Search Algorithm and is combined with Random Opposition-based Learning and linear decreasing strategy, named ROSSA. The mobile robot path planning problem can be mathematically transformed into an optimization problem, which can be solved by intelligent optimization algorithms. With this consideration, an SSA-based optimization algorithm is proposed. Random opposition-based learning increases the diversity of the population and enhances the exploration ability of the algorithm; the linear decreasing strategy balances the ability of the algorithm to explore globally and exploit locally by adjusting the algorithm parameters. Meanwhile, the Bezier curve satisfies the requirement of path smoothness for the robot path planning problem. The superiority of the proposed algorithm is verified by conducting experiments with three standard algorithms for 11 benchmark test functions, and some comparison experiments on the path planning problem with PSO and SSA to confirm that the proposed algorithm can find a safe and optimal path in the mobile robot path planning problem.
A comprehensive survey on container resource allocation approaches in cloud computing: State-of-the-art and research challenges

The allocation of resources in the cloud environment is efficient and vital, as it directly impacts versatility and operational expenses. Containers, like virtualization technology, are gaining popularity due to their low overhead when compared to traditional virtual machines and portability. The resource allocation methodologies in the containerized cloud are intended to dynamically or statically allocate the available pool of resources such as CPU, memory, disk, and so on to users. Despite the enormous popularity of containers in cloud computing, no systematic survey of container scheduling techniques exists. In this survey, an outline of the present works on resource allocation in the containerized cloud correlative is discussed. In this work, 64 research papers are reviewed for a better understanding of resource allocation, management, and scheduling. Further, to add extra worth to this research work, the performance of the collected papers is investigated in terms of various performance measures. Along with this, the weakness of the existing resource allocation algorithms is provided, which makes the researchers to investigate with novel algorithms or techniques.
Modulation Recognition Based on Deep Co-Training

While deep learning has significantly improved signal modulation recognition performance, these algorithms need a large number of labeled samples for training. But in real-world communication conditions, a large number of unlabeled signal samples is often more easily accessible. To address this problem, we propose a semi-supervised approach based on Deep Co-Training that maximizes the utilization of unlabeled data. We first augment the signal samples and initialize two different CLDNN network by pre-training. Then, we construct multiple views using the gradient attack algorithm and measure the consistency of the outputs with Jensen-Shannon Divergence. The simulation findings indicate that the strategy outperforms supervised learning under limited sample conditions, improving recognition accuracy by 5.75% to 11.01%.
A Security Analysis of the Authentication Mechanism of Password Managers

A password manager is a tool that generates, stores and manages user's passwords for local and online services. The security of the popular password managers relies on their authentication mechanism. This paper gives a brief security analysis of the authentication mechanism of three popular cross-platform password managers. We will analyze their authentication mechanism through a unified security analysis model. Based on this mode and experiments on GPU, we show that the security can be reduced to the master password and iteration number.
AI and Security: A Game Perspective

In this short paper, we survey some work at the intersection of Artificial Intelligence (AI) and security that are based on game theoretic considerations, and particularly focus on the author's (our) contribution in these areas. One half of this paper focuses on applications of game theoretic and learning reasoning for addressing security applications such as in public safety and wildlife conservation. In the second half, we present recent work that attacks the learning components of these works, leading to sub-optimal defense allocation. We finally end by pointing to issues and potential research problems that can arise due to data quality in the real world.
Performance Evaluation for a Post-Quantum Public-Key Cryptosystem

Quantum Computing threatens security of today’s systems. Confidence in today’s security technologies largely relies on Public Key Cryptography (PKC), which depends on computational difficulty of mathematical problems that cannot be solved efficiently using any technology available today. This will, however, change once a sufficiently capable quantum computer will become available. Similarly, security of symmetric crypto algorithms will also be substantially weakened. Current progress in research proves that Quantum Computing is no longer science fiction. Hence, research and development of post-quantum cryptographic algorithms that can withstand attacks in Quantum Computing era are of paramount importance. This paper complements existing research in this domain with a performance analysis of a post-quantum cryptosystem capable of encrypting and decrypting messages either bit-wise or string-wise. Specifically, we describe a workflow for implementing the scheme, design a reproducible hardware performance evaluation testbed for an IoT and an online shopping scenario, define performance metrics, and perform performance evaluation case studies. Our performance analysis shows that bit-wise encryption and decryption and the corresponding key generation fits resource-constrained IoT microcontrollers as well as on average laptops. The encryption and decryption of a bit each take less than 30 ms and the key generation less than 300 ms.
Visual Cryptography Using Binary Amplitude-Only Holograms

Visual cryptography (VC) is developed to be a promising approach to encoding secret information using pixel expansion rules. The useful information can be directly rendered based on human vision without the usage of decryption algorithms. However, many VC schemes cannot withstand occlusion attacks. In this paper, a new VC scheme is proposed using binary amplitude-only holograms (AOHs) generated by a modified Gerchberg-Saxton algorithm (MGSA). During the encryption, a secret image is divided into a group of unrecognizable and mutually-unrelated shares, and then the generated shares are further converted to binary AOHs using the MGSA. During image extraction, binary AOHs are logically superimposed to form a stacked hologram, and then the secret image can be extracted from the stacked hologram. Different from conventional VC schemes, the proposed VC scheme converts a secret image into binary AOHs. Due to the redundancy of the generated binary AOHs, the proposed method is numerically and experimentally verified to be feasible and effective, and possesses high robustness against occlusion attacks.
Convolutional Neural Networks

A convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, widely used for analyzing and processing images. Multilayer perceptrons, which we discussed in the previous chapter, usually require fully connected networks, where each neuron in one layer is connected to all neurons in the next layer. Unfortunately, this type of connections inescapably increases the number of weights.