Reviewerly/exHarmony huggingface dataset:
Analysis of text classification methods with large volume of tweets using deep learning

In day-to-day life, a large amount of data is generated by social media. Analysing the data and providing opinions about data also increased in the modern era. Sentiment Analysis plays a vibrant role in developing opinion mining systems. Much research work has been carried out in finding the important features and provides better text classification. Compared to traditional feature extraction methods, deep learning-based text feature extraction methods provide accurate text classification. This paper focused on analysing text classification using radial basis function (RBF), multilayer perceptron, and support vector machine (SVM) and improved classification accuracy. In our proposed work, an analysis has been done with the model containing the best features extracted using recurrent neural networks (RNN), long short term memory (LSTM), and autoencoder methods. 50,000 Tweets are collected from Twitter for extracting the best features related to social and political information. The improved text classification accuracy is obtained using various deep learning approaches compared to extracting the relevant features.
OSI Pharm., LLC v. Apotex Inc. United States Court of Appeals of the Federal Circuit, 2019 939 F.3d 1375

Biotechnology Law ReportVol. 38, No. 6 As presented by Chris HolmanOSI Pharm., LLC v. Apotex Inc. United States Court of Appeals of the Federal Circuit, 2019 939 F.3d 1375Edited for Biotechnology Law Report by CHRISTOPHER M. HOLMAN*Published Online:9 Dec 2019https://doi.org/10.1089/blr.2019.29151.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 38Issue 6Dec 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:OSI Pharm., LLC v. Apotex Inc. United States Court of Appeals of the Federal Circuit, 2019 939 F.3d 1375.Biotechnology Law Report.Dec 2019.427-433.http://doi.org/10.1089/blr.2019.29151.cmhPublished in Volume: 38 Issue 6: December 9, 2019PDF download
Echo State Network Performance Analysis Using Non-random Topologies

Echo State Network (ESN) has been widely studied and applied to many problems due to the simplicity of its training phase. This is because since in this network only the output weights are trained, avoiding to deal with the gradient’s vanishing problem presents in most of the recurrent neural networks. However, this technique has been criticized recently because of the echo property limitation and its random topology that may cause chaotic activity in the reservoir layer. In this paper, we present an application of the classic ESN model modifying the reservoir topology to a non-random approaches: clustered and complex networks, as an alternative solution to the chaotic activity problem. Further, the modified and classical models are compared considering two study cases: Rössler and Lorenz systems. Numerical experiments show that the proposed model has a better performance than the classical model.
Cascaded parallel crowd counting network with multi-resolution collaborative representation

Accurately estimating the size and density distribution of a crowd from images is of great importance to public safety and crowd management during the COVID-19 pandemic, but it is very challenging as it is affected by many complex factors, including perspective distortion and background noise information. In this paper, we propose a novel multi-resolution collaborative representation framework called the cascaded parallel network (CP-Net), consisting of three parallel scale-specific branches connected in a cascading mode. In the framework, the three cascaded multi-resolution branches efficiently capture multi-scale features through their specific receptive fields. Additionally, multi-level feature fusion and information filtering are performed continuously on each branch to resist noise interference and perspective distortion. Moreover, we design an information exchange module across independent branches to refine the features extracted by each specific branch and deal with perspective distortion by using complementary information of multiple resolutions. To further improve the robustness of the network to scale variance and generate high-quality density maps, we construct a multi-receptive field fusion module to aggregate multi-scale features more comprehensively. The performance of our proposed CP-Net is verified on the challenging counting datasets (UCF_CC_50, UCF-QNRF, Shanghai Tech A&B, and WorldExpo'10), and the experimental results demonstrate the superiority of the proposed method.
Visualizing multiqubit correlations using the Wigner function

Abstract Quantum engineering now allows to design and construct multi-qubit states in a range of physical systems. These states are typically quite complex in nature, with disparate, but relevant properties that include both single and multi-qubit coherences and even entanglement. All these properties can be assessed by reconstructing the density matrix of those states—but the large parameter space can mean physical insight of the nature of those states and their coherence can be hard to achieve. Here, we explore how the Wigner function of a multipartite system and its visualization provides rich information on the nature of the state, not only at illustrative level but also at the quantitative level. We test our tools in a photonic architecture making use of the multiple degrees of freedom of two photons. Graphic abstract
H-KPP: Hypervisor-Assisted Kernel Patch Protection

We present H-KPP, hypervisor-based protection for kernel code and data structures. H-KPP prevents the execution of unauthorized code in kernel mode. In addition, H-KPP protects certain object fields from malicious modifications. H-KPP can protect modern kernels equipped with BPF facilities and loadable kernel modules. H-KPP does not require modifying or recompiling the kernel. Unlike many other systems, H-KPP is based on a thin hypervisor and includes a novel SLAT switching mechanism, which allows H-KPP to achieve very low (≈6%) performance overhead compared to baseline Linux.
Validation of Trace Element Analysis of Geological Materials by Single-Pulse Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS)

Laser ablation–inductively coupled plasma – mass spectrometry (LA–ICP–MS) is a powerful tool for determining the elemental and isotopic compositions of various samples. The disadvantage of LA–ICP–MS is that it consumes more sample material than other types of in situ analytical methods. To extend the application of this method to trace element analysis, we conducted single-pulse LA–ICP–MS analysis of the Reference Materials of the National Institute of Standards and Technology (SRM 610, 612, and 614) and the United States Geological Survey (BCR-2G, BHVO-2G, and BIR-1G). The accuracy of the determined concentrations and the precision of the single-pulse LA–ICP–MS analysis were verified by comparison with recommended concentrations for these standards and those obtained by conventional analysis with the same system. Time-resolved profiles of the analyzed elements in the glass samples show that the background-corrected intensity reaches a peak immediately after the start of the laser pulse and then gradually decreases. During each analysis, the isotope ratios are stable until the background-corrected intensity of the elements reaches 5% of the peak values. The relative standard deviation of the single-pulse LA–ICP–MS analyses is significantly higher than for conventional LA–ICP–MS analysis. However, most of the determined concentrations obtained from the reference materials except for NIST SRM614, NIST SRM612, BCR-2G, and BHVO-2G mostly agreed with the recommended values within 20%. In addition, the geochemical features of basaltic reference glasses were accurately reproduced by the single-pulse LA–ICP–MS analyses. In conclusion, the single-pulse LA–ICP–MS method provides usable results for the determination of multiple trace elements in geological materials.
Using computerised comparative judgement to assess translation

Abstract Translation assessment represents a productive line of research in Translation Studies. An array of methods has been trialled to assess translation quality, ranging from intuitive assessment to error analysis and from rubric scoring to item-based assessment. In this article, we introduce a lesser-known approach to translation assessment called comparative judgement . Rooted in psychophysical analysis, comparative judgement grounds itself on the assumption that humans tend to be more accurate in making relative judgements than in making absolute judgements. We conducted an experiment, as both a methodological exploration and a feasibility investigation, in which novice and experienced judges were recruited to assess English-Chinese translation, using a computerised comparative judgement platform. The collected data were analysed to shed light on the validity and reliability of assessment results and the judges’ perceptions. Our analysis shows that (1) overall, comparative judgement produced valid measures and facilitated judgement reliability, although such results seemed to be affected by translation directionality and judges’ experience, and (2) the judges were generally confident about their decisions, despite some emergent factors undermining the validity of their decision making. Finally, we discuss the use of comparative judgement as a possible method in translation assessment and its implications for future practice and research.
Analysis of the Effect of Learners’ Preference for Online Classes on Online Course Satisfaction －Focusing on the Case of a General English Course at K University

This study aims to investigate learners’ perception of online learning for a general English course in Covid 19 situation, explore how online class preference affects degree of satisfaction of general English online class and examine whether the influence varies depending on the controlling variables such as age, instructor, English level, and English preference. The subjects of this study were 870 undergraduate students from K University who took the same compulsive general English course, and the survey was conducted through the Google Forms questionnaire. This study processed an independent sample-t test, response sample-t test, and Pearson correlation analysis to discover the effect of bichronous online learning(blending of both asynchronous and synchronous online learning) preference on English class satisfaction, and hierarchical multiple regression to observe the effect of online class preference. The results showed that students' preference to online learning was lower than that of online learning, but in the case of their satisfaction of general English online course, satisfaction with the synchronous online class was higher than that of the online class. In addition, There was a high positive correlation between synchronous online learning preference and general English class satisfaction. It was also found that the hierarchy of factors influencing English class satisfaction was in the order of synchronous online learning preference, English preference, online class preference, and English proficiency level.
CUMTGAN: An instance-level controllable U-Net GAN for facial makeup transfer

Instance-level makeup transfer refers to transferring a specific makeup style to a particular face without cosmetic, while keeping its facial structure. To solve the challenges such as face structure loss and uncontrollable shade of makeup transfer in the existing makeup transfer methods, in this paper, we propose an instance-level face makeup transfer algorithm with generative adversarial network (CUMTGAN, Controllable U-Net Makeup Transfer GAN). The network uses the results of face semantic segmentation to put on or remove makeup on specific areas such as eyes, lips, while constraining the parts such as image background. It uses the characteristics of the skip connections of the U-Net structure to achieve hierarchical feature fusion. A Superimposed Module is also proposed to provide a controllable shade of makeup for the generated images, making the control of the makeup more arbitrary. Especially, we propose a new quantitative evaluation method to evaluate facial structure and makeup style respectively. To our best knowledge, it is the first time to evaluate generated samples from two aspects. The experimental results on the MT dataset show that the proposed method can generate natural and visual makeup images and makeup removal images, which is superior to the baseline methods in facial structure and makeup style.
Editorial paper for pattern recognition letters VSI on multi-view representation learning and multi-modal information representation

Multi-view data has become fairly important since large amounts of information are constantly generated from different sources. So far, most multi-view research on unsupervised learning has focused on clustering algorithms suitable for analyzing vector data. Instead, our study presents two batch SOM algorithms for multi-view dissimilarity data, namely a batch SOM with each cluster representative as a vector of a subset of objects (vector of set-medoids) and another batch SOM for multi-view dissimilarity data with each cluster representative as a matrix whose elements are the weights of its objects regarding the dissimilarity matrices (weighted medoids). These algorithms are suitable for clustering and visualizing multi-view dissimilarity data. In addition, they provide the relevance weights on the importance of each dissimilarity matrix (view) concerning each cluster related to the neurons in the map. Furthermore, the weight can be computed both locally, for each cluster, or globally, for the whole partition. Experiments with 12 datasets showed the importance of multi-view algorithms with respect to single-view algorithms in some contexts. Among the multi-view algorithms, the results showed the need to consider the relevance weights of the dissimilarity matrices. Moreover, an application study on the FOREST TYPE dataset found that some of the proposed methods provide better-quality maps.
Manifold Neural Network with Non-Gradient Optimization

Deep neural network (DNN) generally takes thousands of iterations to optimize via gradient descent and thus has a slow convergence. In addition, softmax, as a decision layer, may ignore the distribution information of the data during classification. Aiming to tackle the referred problems, we propose a novel manifold neural network based on non-gradient optimization, i.e., the closed-form solutions. Considering that the activation function is generally invertible, we reconstruct the network via forward ridge regression and low rank backward approximation, which achieve rapid convergence. Moreover, by unifying the flexible Stiefel manifold and adaptive support vector machine, we devise the novel decision layer which efficiently fits the manifold structure of the data and label information. Consequently, a jointly non-gradient optimization method is designed to generate the network with closed-form results. Furthermore, an acceleration strategy is utilize to reduce the time complexity for handling high dimensional datasets. Eventually, extensive experiments validate the superior performance of the model.
Sarcastic Meaning of the Slightly Smiling Face Emoji from Chinese Twitter Users: When A Smiling Face Does Not Show Friendliness

In most cultures, the Slightly Smiling Face (smiley) icon indicates friendliness and niceness. However, this SSF symbol in emoji may also indicate a negative meaning of sarcasm and irony to some Chinese social media users. This research analyses the sentiment reflected in the use of the SSF emoji as used by Chinese users on Twitter and applies quantitative methods to investigate the linguistic and social constraints of the SSF emoji's negative variable from 2016 to 2020. Results show that positive or negative emotional expression of SSF emoji is highly dependent on the content of the sentence and its context. Therefore, the SSF emoji has no semantic value as a word for expressing emotions but acts as an emotive anaphora or a modal particle. Simplified Chinese users from mainland China use the SSF emoji with a negative sense more than Traditional Chinese users from Taiwan. These differences may reflect the users’ media preference and cultural identification through the use of emoji, a global language for the digital age. Most Chinese users use a single SSF emoji, which can convey either emotion, at the end of the sentence, but when the SSF emoji is used in a repetitive manner, it is more likely to indicate a sarcastic emotion. Both variables, (single use / repetition) and Chinese types (Simplified / Traditional), significantly correlate with the use of the negative variant of the SSF emoji (p &lt; 0.05). The change in the meaning of the SSF emoji from the expression of positive to negative sentiment demonstrates that emojis may change through time in ways similar to other forms of language.
Stitching method based on dual quaternion for cylindrical mirrors

In this study, a stitching method based on dual quaternion is proposed. The application of a dual quaternion in sub-aperture stitching interferometry is analyzed in detail, and a calculation method for sub-aperture stitching based on a dual quaternion is deduced. The experimental results demonstrate the accuracy of the stitching method proposed in this study (residuals of overlapping area approximately 0.22 nm RMS). Finally, the residual differences of 0.79 nm RMS between the figure errors are acquired with a stitching by parts algorithm based on the dual quaternion and long trace profiler (FSP at HEPS). The high-accuracy and high-efficiency stitching method proposed in this study will expand its application in the metrology and manufacture of long cylindrical mirrors.
A Linear Algebraic Framework for Quantum Internet Dynamic Scheduling

Future quantum internet aims to enable quantum communication between arbitrary pairs of distant nodes through the sharing of end-to-end entanglement, a universal resource for many quantum applications. As in classical networks, quantum networks also have to resolve problems related to routing and satisfaction of service at a sufficient rate. We deal here with the problem of scheduling when multiple commodities must be served through a quantum network based on first generation quantum repeaters, or quantum switches. To this end, we introduce a novel discrete-time algebraic model for arbitrary network topology, including transmission and memory losses, and adapted to dynamic scheduling decisions.Our algebraic model allows the scheduler to use the storage of temporary intermediate links to optimize the performance, depending on the information availability, ranging from full global information for a centralized scheduler to partial local information for a distributed one. As an illustrative example, we compare a simple greedy scheduling policy with several Max-Weight inspired scheduling policies and illustrate the resulting achievable rate regions for two competing pairs of clients through a network.
True Random Number Generator (TRNG) Using Sensors for Low Cost IoT Applications

The requirement of encryption is everywhere whether data is being transmitted or being stored in some devices. Random numbers can be used in data encryption. Every IoT device which is connected to a network needs secure and low cost techniques for encryption. In modern day the introduction of cryptocurrency wallet and other wallet services need more secure and random key for encryption which cannot be generated through algorithm-based Pseudo Random Number Generator (PRNG). The advanced video games require true random numbers. The connected portable IoT devices also transfer sensitive data. So, a low cost and simple circuit of True Random Number Generator (TRNG) effectuate the requirements. A non-deterministic signal or a noise signal is a source of true randomness. Detection of non-deterministic signal using sensors is a simple approach which is used in this paper to generate True Random Numbers. The TRNG proposed in this paper have simple hardware setup which includes an Arduino Uno board, LDR sensor and sound sensor. A simple modular equation and EXOR logic is used as processing algorithm. The random number generated through this setup passes all the tests of randomness given in the NIST-STS test.
Applying Cooperative Search Strategies to Improve Butterfly Optimization Algorithm

Considering that Butterfly Optimization Algorithm (BOA) has low precision, slow convergence speed and is easy to fall into local optimal problems, this paper tries to apply cooperative search strategies for improvement of BOA. Firstly, the Levy flight search strategy is utilized to expand both the global search ability and the local search ability for BOA. Secondly, the differential variation search strategy is introduced in the local search phase, which improves local search ability and helps to jump out of the local optimum. Finally, a dynamic adaptive search strategy is proposed to balance both the global search ability and local search ability, and accelerate the convergence speed of BOA. Simulation results show that, compared with other algorithms, the improved BOA algorithm based on cooperative search strategies accelerates the convergence speed and increases the search accuracy.
A Low-Overhead and High-Security Cryptographic Circuit Design Utilizing the TIGFET-Based Three-Phase Single-Rail Pulse Register against Side-Channel Attacks

Side-channel attack (SCA) reveals confidential information by statistically analyzing physical manifestations, which is the serious threat to cryptographic circuits. Various SCA circuit-level countermeasures have been proposed as fundamental solutions to reduce the side-channel vulnerabilities of cryptographic implementations; however, such approaches introduce non-negligible power and area overheads. Among all of the circuit components, flip-flops are the main source of information leakage. This article proposes a three-phase single-rail pulse register (TSPR) based on the three-independent-gate field effect transistor (TIGFET) to achieve all desired properties with improved metrics of area and security. TIGFET-based TSPR consumes a constant power (MCV is 0.25%), has a low delay (12 ps), and employs only 10 TIGFET devices, which is applicable for the low-overhead and high-security cryptographic circuit design compared to the existing flip-flops. In addition, a set of TIGFET-based combinational basic gates are designed to reduce the area occupation and power consumption as much as possible. As a proof of concept, a simplified advanced encryption algorithm (AES), SM4 block cipher algorithm (SM4), and light-weight cryptographic algorithm (PRESENT) are built with the TIGFET-based library. SCA is implemented on the cryptographic implementations to prove its SCA resilience, and the SCA results show that the correct key of cryptographic circuits with TIGFET-based TSPRs is not guessed within 2,000 power traces.