Reviewerly/exHarmony huggingface dataset:
Beyond Legitimacy, Also With Identity: Your Smart Earphones Know Who You Are Quietly

User authentication and identification on smart devices has great significance in keeping data privacy and recommending personalized services. With the rising popularity of smart earphones recently, they open up a new world for users to enjoy music individually, but also bring about privacy concerns at the same time. Existing few research works propose positive sensing systems that emit and receive inaudible acoustic signals to authenticate users. However, they share shortcomings of intrusiveness to users, high power consumption, and purely focusing on authentication. Instead, in this paper, we propose a passive sensing system called <inline-formula><tex-math notation="LaTeX">${{\sf EarID}}$</tex-math></inline-formula> with low-cost customized earphones which attains user authentication and identification at once. It makes use of a embedded microphone to sense body sounds spread out through ear canals and extract ‘fingerprints’ as a novel biometric feature. With self-designed earphones, we design a deep learning-based real-time data processing pipeline and cope with external interference. Extensive experiments under different real-world settings show that <inline-formula><tex-math notation="LaTeX">${{\sf EarID}}$</tex-math></inline-formula> can achieve a rather low false acceptance rate of <inline-formula><tex-math notation="LaTeX">$3.4\%$</tex-math></inline-formula> for user authentication and a high <inline-formula><tex-math notation="LaTeX">$F1$</tex-math></inline-formula> score of <inline-formula><tex-math notation="LaTeX">$95.5\%$</tex-math></inline-formula> for legitimate user identification.
Assessment of nestin biomarker in ladies with breast malignant growth

Chest cancer is the subsequent closed successive mostly tumors around ladies, thereafter many pores and skin disease. It is close commonly decided of women utilized 55 in congruity with 64. The point concerning the ebb and flow instruction is as per consider nestin biomarker, moreover estimated the affinity among this biomarker or a few boundaries include: bothered determination, youth, stage, standard yet physical make-up substance record between young ladies with mamma most tumors infection. Strategy: The life span existing examination covered 55 patients over AL-Najaf life span women analyzed along bosom disease. The examples have been soundness pulled adjacent to the victims at the intention in a minute illusion in conformity encircling AL-forat Al-Awsat strive for in the air AL-Najaf megalopolis suitable to battle arrest then treat quit the range of the length delayed from 1/12/2018 to 2/3/2019. The practical dispose in fastidious underline was in advance exhausted concerning of thirty-five (35) be included as ask pardon public manifest were gathered obsolete research center desist neurotic dissects central AL-Najaf AL-Ashraf of exceptional occasions. The really a for ages c in depth over vivifying rank had been coordinated touch more influenced individual ages. (5 ml) helter-skelter venous cherished had forgotten hither of bowels to the fullest extent diseases victims then solid group at 9-12 A.M past gamble cubital venipuncture the persistence of a unneeded prong then needles of plastic authoritative income: The impacts showed a on every side and wide grow (P<0.05) in nestin remainder of amenable along cherished greatest tumors infirmity into looking at along government gathering. The outcomes over this discrimination firmness naked a generous make outdo (P<durability 0,05) around nestin life-span median lobular carcinoma inside assessing along with ductal cancer. The (50- 59 y) uncover a sharp considerably (P<0.05) inside nestin stage contrasted and (40-49 y) and (60-69 y). A largish appreciative raise inside the levels contain nestin (P<0.05) in tribune 4 (Ιⅴ) or in consequence whereof 3 between squad with breast most tumors sickness, to the fullest extent a finally The true criticism preordained life span no considerable contrasts around the levels over nestin level inside women along with bosom malignant growth problem (P>0.05) consenting after their BMI.
A Multi-objective Optimal Trajectory Planning for Autonomous Vehicles Using Dragonfly Algorithm

Trajectory planning is considered as a major challenge in autonomous driving, which faces significant issues like safety and efficiency. This chapter proposes a novel swarm intelligence meta-heuristic optimization algorithm, called dragonfly algorithm, for the lane-change behaviour in the navigation of autonomous vehicles. A multi-objective lane-changing trajectory planning method has been proposed to optimize the trajectory and avoid collision, which mimics the dynamic and static swarm behaviours of the natural dragonflies. Whenever the autonomous vehicle senses an obstacle, automatically the lane-change manoeuvre should take place. The feasibility and the effectiveness of the algorithm are verified by simulation results using the lane-change data from the benchmark NGSIM dataset. Simulation results show that the proposed algorithm for trajectory planning gives an optimal path for lane-change scenario considering both static and dynamic obstacles.
Data Distribution and Competition Law Issues

This study summarizes the issues in the promotion of data distribution, how huge digital platformers conduct in the market, and the actual regulations and initiatives for them. In particular, it briefly examines the actual status of litigation under US antitrust law and the issues. The study discusses the following five points: First, it highlights the delineation of the relevant market, which is also the first entry point in competition analysis. Second, it undertakes the existence of monopoly power, which requires the analysis of dominant firms. Third, it analyzes the conduct of forming, maintaining, and strengthening market power. Fourth, it analyzes the consumer damages. Fifth, it discusses whether antitrust actions are needed to solve the problems and remedies.
A Segment-Based Layout Aware Model for Information Extraction on Document Images

Information extraction (IE) on document images has attracted considerable attention recently due to its great potentials for intelligent document analysis, where visual layout information is vital. However, most existing works mainly consider visual layout information at the token level, which unfortunately ignore long contexts and require time-consuming annotation. In this paper, we propose to model document visual layout information at the segment level. First, we obtain segment representation by integrating the segment-level layout information and text embedding. Since only segment-level layout annotation is needed, our model enjoys a low cost in comparison with the full annotation as needed at the token level. Then, word vectors are also extracted from each text segment to get the fine-grained representation. Finally, both segment and word vectors are fused for obtaining prediction results. Extensive experiments on the benchmark datasets are conducted to demonstrate the effectiveness of our novel method.
IBFM: An Instance-weight Balanced Factorization Machine for Sparse Prediction

The prediction accuracy rate of user response prediction tasks has significant practical implications. A slight improvement in this metrics can lead to a significant gain of commercial value for real-world applications. Factorization machine (FM) is a popular prediction model that has been used widely to predict user response, such as click, purchase, and browse. However, since FM learns a generalized representation for each feature but ignores the importance of a given feature in different instances, its prediction accuracy is compromised. In order to address this problem, a recent research proposes an input-aware factorization machine (IFM) that refines the representation by weighting the importance of a given feature in the different instances. Unfortunately, IFM overemphasizes the effect of instances, while ignoring the generalized representation for each feature, which limits the prediction accuracy. In this paper, we propose an instance-weight balanced factorization machine called IBFM, which considers the importance of instances and retains the fundamental effectiveness of generalized representation by properly balancing the instance weight for the representation of each feature. Comprehensive experimental results show that the IBFM model obtains a higher prediction accuracy rate over FM, IFM, and three other state-of-the-art models.
Piecewise Clustering Prediction Model of Distributed Photovoltaic Power Based on Principal Component Analysis

Photovoltaic power generation is an important field of renewable energy development. However, when photovoltaic power generation is connected to the grid, the random fluctuation and intermittence of solar photovoltaic power generation caused by various environmental factors should be fully considered. Reliable prediction of photovoltaic power is a solution. Based on the analysis of two data analysis theories, which are cluster analysis and principal component analysis, this paper proposes a piecewise clustering method to improve the accuracy of RBF photovoltaic power prediction model. Firstly, the historical operational data after cleaning process are systematically clustered, and the corresponding target similar sample sets are obtained by two clustering methods. The RBF neural network algorithm is used to verify the prediction of the output power, which can effectively improve the stability and accuracy of the prediction results and simplify the number of input samples. Secondly, principal component analysis is carried out based on historical data, and the extracted principal component is taken as the input of RBF neural network photovoltaic power prediction model. It is proved by a case study that the proposed method can effectively improve the accuracy of prediction results and reduce the dimension of input samples, thus simplifying the network structure and shortening the learning time of neural networks. The effectiveness and reliability of the piecewise clustering method are verified by the case study.
Unsupervised domain adaptation using shallow and deep representations

The inputs to a computer vision system often contain novel variations owing to the diverse operating conditions in the external world. Machine learning models fail to generalize to such variations, and this raises reliability concerns especially in safety-critical applications such as medical imaging and autonomous driving. In this chapter, we present an approach called unsupervised domain adaptation that focuses on improving the predictive ability of computer vision models to such out-of-distribution inputs. First, we present classical manifold-based approaches in which the source and the target feature distributions are modeled to lie in a low-dimensional manifold, which then helps us to adapt to data shift. Then, we present some dictionary and deep-learning based approaches for domain adaptation. The three approaches are validated on some benchmark computer vision datasets.
Classification Model Based Automatic Static Evaluation Method for C Programs

To improve the accuracy of automatic evaluation for C programs, an automatic static evaluation method based on classification model is presented. Firstly, the program to be scored is preprocessed with standardization rules to eliminate the diversities of C expressions and statements, and then is transformed into a program statement dependency graph (PSDG) to accurately represent the control and data dependencies of the program. Subsequently, the PSDG is split into the list of composed syntax trees according to its compound statements. Finally, a hybrid neural network classification model is employed to classify the program to obtain the final score. The experimental results show that compared with the existing static scoring method of C program, the method proposed in this paper can significantly improve the accuracy of program scoring.
How to pretrain an efficient cross-disciplinary language model: The ScilitBERT use case

Transformer based models are widely used in various text processing tasks, such as classification, named entity recognition. The representation of scientific texts is a complicated task, and the utilization of general English BERT models for this task is suboptimal. We observe the lack of models for multidisciplinary academic texts representation, and on a broader scale, a lack of specialized models pretrained on specific domains, for which general English BERT models are suboptimal. This paper introduces ScilitBERT, a BERT model pretrained on an inclusive cross-disciplinary academic corpus. ScilitBERT is half as deep as RoBERTa, and has a much lower pretraining computation cost. ScilitBERT obtains at least 96&#x0025; of RoBERTa&#x0027;s accuracy on two academic domain downstream tasks. The presented cross-disciplinary academic model has been publicly released<sup>1</sup><sup>1</sup>https://github.com/JeanBaptiste-dlb/ScilitBERT. The results obtained show that for domains that use a technolect and have a sizeable amount of raw text data; the pretraining of dedicated models should be considered and favored.
Research on Monte Carlo Tree Search Method of Adaptive Resource Scheduling for Multi-agent Game

In order to give full play to the advantages of different search algorithms based on different game environments, different search algorithms can be combined and "promote strengths and circumvent weaknesses", so that agents can make favorable decisions in the random sequence of legal actions. Based on the Pommerman's game characteristics, this paper proposes the adaptive resource scheduling Monte Carlo tree search algorithm. The optimization algorithm switches the search algorithm according to the level of danger around the agents. If the danger factor is large, the algorithm with higher switching accuracy is proposed. Otherwise, the algorithm with the shorter search time is switched. The design of the adaptive resource scheduling Monte Carlo tree search algorithm is proved to be effective in improving the performance of the game by comparing the search algorithms before and after the optimization.
An Improved YOLOv4 Target Detection Algorithm Combined with DCA

To further perfect the accuracy of the You Only Look Once (YOLO) v4 target detection algorithm, in this research an improved YOLOv4 target detection algorithm combined with a novel attention mechanism Dilated Coordinate Attention (DCA) is proposed. DCA is changed on the basis of Coordinate Attention (CA) attention mechanism in three ways. First, two parallel dilated convolutions with different dilation rates are added. Second, the secondary division of features in the horizontal and vertical directions is abandoned. Third, the information after global average pooling is processed directly. Finally, DCA is embedded into the neck of YOLOv4. The performance of the improved YOLOv4 algorithm is verified on the PASCAL VOC2007+2012 (VOC) dataset, and the average absolute error is introduced during training to optimize the loss function. The experimental results show that compared with other attention mechanisms, DCA achieves the best performance. The mean Average Precision(mAP)@0.5 of the improved YOLOv4 algorithm combined with DCA is 4.27% higher than that of the original YOLOv4. In addition, the precision of the YOLOv3 algorithm integrated with DCA has also been significantly improved to further prove the effectiveness of DCA.
Efficient Classification of Darknet Access Activity with Partial Traffic

Abstract In this paper we propose a novel approach to classify darknet-access traffic with only partial traffic data, which significantly reduces resource consumption and is as accuracy as prior work. Besides, in order to keep up with the users’ real access activity, we simulate new and old user by simply whether delete the cached consensus document before each access and apply our approach. The experiment results confirm that there does exist a window of cell sequence contributes greatly to distinguish darknet-access traffic. With the window size 75 and the start point 67, we can achieve 95.97% accuracy for new user access scenario. Similarly, with the window size 85 and the start point 44, we achieve 94.43% accuracy for old user access scenario.
Blockchains, DLTs and the Future of Payments

This chapter examines how distributed ledger technology (DLT) could transform payments and the benefits that could ensue. This is then followed by an examination of how DLT can facilitate machine-to-machine interactions and the transformative impacts of this.
A First Look at Zoombombing

We conducted the first data-driven study of calls for zoombombing attacks. We analyzed 223 calls for zoombombing posted on Twitter and 4chan. We found that most calls for zoombombing come from insiders who have legitimate access to the meetings, calling into question widely adopted mitigation techniques to secure online meetings.
System Design and Effect Evaluation of English Online Teaching in Higher Vocational Colleges Under the Influence of the New Crown Epidemic

Affected by the new crown pneumonia epidemic, primary and secondary schools across the country have conducted online teaching. Online class teaching guarantees students' study hours during the epidemic and helps them to better complete their learning tasks, thereby effectively ensuring the suspension of classes during the epidemic. However, in the actual work of online teaching, there will also be large and small problems. These problems will affect the quality of students' learning. They are also the content that teachers need to pay attention to, and relevant teaching design and teaching should be carried out according to the actual situation of online teaching. Arrangement of the plan. This article will analyze and discuss the problems of online teaching and the classroom design plan of online teaching.
Performance analysis of genetic algorithm for grammatical inference

The performance of the genetic algorithm for grammatical inference is presented in this chapter. The chapter offers implementation details of algorithms that were explained in Chapter 4. Extensive computer simulations are performed to validate the performance. The Taguchi method is incorporated for parameter tuning, which makes the algorithms more robust, statistically sound, and quickly convergent. Algorithms are effectively applied to context-free languages as well as regular languages of varying complexity. The computational results are collected and analyzed. The results show that both of the proposed algorithms are able to find an optimal or close to optimal grammar with the best fitness and can also determine results in less time. Boolean operators introduced diversity in the population, which helps explore the search space adequately. The performance of the algorithms is tested against other global optimization algorithms and against algorithms implemented for grammatical inference. Statistical tests are performed to determine the performance significance of the algorithms.
Cryptocurrencies on a bank’s balance sheet

More and more established companies are getting involved in the cryptomarket and/or considering cryptocurrencies as an alternative to cash. In addition, recently, El Salvador became the first country to declare Bitcoin as a legal tender. Against this background, this paper examines how holdings of cryptocurrencies on a bank’s balance sheet would be treated under EU banking law. After a brief consideration of the permissibility of investments in cryptocurrencies, the accounting under the International Financial Reporting Standards and the Bank Accounts Directive is explored. The analyses confirm that long-term holdings must be classified as intangible assets. Building on this, the prudential implications for credit institutions are assessed and, in particular, how banks must treat cryptocurrencies under the Capital Requirements Regulation. Because Bitcoin & Co qualify as intangibles under the applicable accounting rules, European institutions must fully deduct their holdings from the Common Equity Tier 1 capital. This outcome is compared with the recent consultation of the Basel Committee on Banking Supervision on crypto exposure.
Concise Review of Classical Guitar Modelling Technologies

Analytical modeling and numerical simulation of multiphysics coupled systems is an exciting research area, even when it comes to intrinsically linear or linearized formulations, as is usually the case with coupled vibroacoustic problems. The combined effect of many localized geometrical miss-modeling with significant uncertainty in mechanical characterization of some organic materials yields large discrepancies in the natural frequencies and mode shapes obtained. The main goal of this work is to compare two basic approaches for the modeling of stringed musical instruments in the frequency domain: simplified lumped-parameter analytic modeling, considering only the most influential degrees of freedom, and discretized finite element modal analysis. Thus, the emphasis is on a review of some key references in this field, including previous work by the authors, which may shed light on some of the most relevant issues surrounding this problem.
Fluid Composition and its Evolution in the Belka Pahar Wollastonite Skarn, India: Clues from Skarn Mineral Chemistry with Special Focus on Garnet Chemistry

In this study we use major and trace element chemistry of hydrothermal minerals from the world-class Belka Pahar wollastonite skarn deposit in western India to deduce the sequence of mineral transformation and to characterize the changes in the composition of the fluid with the progress of the fluid-rock interaction during skarn formation. In addition, principal component analyses on large dataset of grandite garnet is used to characterize the major substitutions responsible for the incorporation of different trace elements in hydrothermal skarn garnet. Two major rock types have been considered for detailed study: weakly altered skarn rocks that largely retain the metamorphic mineral assemblage comprising pyroxene, garnet, wollastonite, quartz, and calcite, and severely-altered skarn rocks containing newly grown hydrothermal garnet, scheelite, vesuvianite, fluorite, and sphalerite. Two types of garnet are recognized in the Belka Pahar skarn rocks-texturally earlier metamorphic garnet (Grt1) and texturally late oscillatory-zoned hydrothermal Grt2, which is further sub-divided into three generations Grt2a, Grt2b and Grt2c comprising core, mantle, and rim regions. The Grt1 (And27–47) and Grt2a (And50–60) yield high Y-concentration and high HREE/LREE ratio which is in stark contrast to the late Grt2b (And40-50) and Grt2c (And27–40), which are characterized by high F, low Y-concentration, and HREE-depleted REE pattern. The chemistry of different generations of garnet suggest a shift in pH condition of the fluid from neutral to acidic to less acidic conditions. The crystallization of F-rich garnet, vesuvianite, and fluorite indicate an increase in F-concentration in the later stage fluid. Increase of W concentration in Grt2b and co-precipitation of scheelite and fluorite also suggests its possible link with scheelite precipitation. The high grossular component in the Grt2b and Grt2c suggest transport of Al in the hydrothermal fluid as F-complexes. Results of this work are in contrast with the general perception that andradite formation is an indicator of tungsten mineralization. Composition of the fluid extrapolated from the scheelite composition agrees with the LREE enriched pattern exhibited by all skarn minerals. Principal Component Analysis done on a compiled garnet dataset show that the garnet trace element composition can be a useful indicator of fluid composition and can be used as an indicator of associated ore type.