Reviewerly/exHarmony huggingface dataset:
Research on Artificial Intelligence-based English Writing Blended Teaching Mode

Abstract In this artificial intelligence-based English writing blended teaching model is in the stage of research and innovation, this article uses a university English major as the experimental object, and launched a 19-week teaching experiment. The combination of advantages improves the traditional English writing teaching mode and establishes an artificial intelligence English writing mixed teaching mode. The results of the study show that this model can improve students’ English writing level while improving students’ English writing level, and can also increase students’ interest in English writing.
LS-DST: Long and Sparse Dialogue State Tracking with Smart History Collector in Insurance Marketing

Different from traditional task-oriented and open-domain dialogue systems, insurance agents aim to engage customers for helping them satisfy specific demands and emotional companionship. As a result, customer-to-agent dialogues are usually very long, and many turns of them are pure chit-chat without any useful marketing clues. This brings challenges to dialogue state tracking task in insurance marketing. To deal with these long and sparse dialogues, we propose a new dialogue state tracking architecture containing three components: dialogue encoder, Smart History Collector (SHC) and dialogue state classifier. SHC, a deliberately designed memory network, effectively selects relevant dialogue history via slot-attention, and then updates dialogue history memory. With SHC, our model is able to keep track of the vital information and filter out pure chit-chat. Experimental results demonstrate that our proposed LS-DST significantly outperforms the state-of-the-art baselines on real insurance dialogue dataset.
Construction method of data security protection system of power information system based on big data technology

Construction method of data security protection system of power information system based on big data technology is designed in this paper. The proposed research work combines the introduction of energy storage devices and static reactive power compensators to effectively control the chaotic oscillation of the power system and restore the system from a chaotic state to a stable operating state. At this time, the system state evolves from the original strange attractor to a fixed point in the system. For the voltage auxiliary line of the low-voltage three-phase electric energy meter, it is necessary to perform the crimping treatment from the upper side, hence, this research work conduct the modelling of security protection with the fuzzy clustering model of the data analysis. The experimental results have also proven the efficiency.
Distributed Frequent Subgraph Mining Using Gaston and MapReduce

This paper addresses this issue and devises a new method for frequent subgraph mining in order to retrieve the valuable information from the database that captured the attention of the users. This paper proposes the recurrent-Gaston (R-Gaston) algorithm for the frequent subgraph mining process by enhancing the existing Gaston algorithm. Moreover, the method uses support measures based on the frequency and page duration parameters in order to define the support for the proposed R-Gaston algorithm. The simulation of the proposed R-Gaston is carried out using the weblog and the MSNBC databases. The proposed R-Gaston has attained values of number of structures mined and the execution time as 184, and 1282ms for the MSNBC database, with 60 and 75ms for the weblog database, respectively.
An algorithm of moving pieces to become black alternation with white based on dimension reduction

Abstract Moving pieces to become black alternation with white (MPBBAW)is a game in which black and white pieces are continuously arranged by moving. This article traces the problem of MPBBAW and points out the possibility and necessity of computer calculation and commercial application of the problem. Using the non- recursive algorithm based on dimensionality reduction, this article deals with the problem of n-order MPBBAW. The special-order problem is solved and resubstituted. Thus, the computer processing and rule exploration of n-order and n-order MPBBAW are realised. As an auxiliary card, C++ is used to provide 20 black and 20 white pieces as an example. This article lays a foundation for the computer calculation and commercial application of MPBBAW.
Image Pattern Recognition Algorithm Based on Improved Genetic Algorithm

Abstract Image recognition is an important part of artificial intelligence. In recent years, the technology has been developed rapidly and has been widely used in various fields. However, the existing image recognition technology is still not perfect, especially in the complex environment; the recognition accuracy still needs to be improved. To solve this problem, this paper proposes an image pattern recognition algorithm based on improved genetic algorithm. There are many algorithms for image recognition, but the mainstream algorithm is genetic algorithm. Genetic algorithm also shows its unique advantages in the field of image recognition. Aiming at the problem that the accuracy of existing image pattern recognition is not enough, this paper optimizes and improves the traditional genetic algorithm of image recognition. By improving the calculation method and genetic steps, the adaptability of genetic algorithm in the field of image recognition is improved. In order to further verify the actual effect of this algorithm, the corresponding comparative experiments are carried out. The experimental results show that the accuracy of the traditional genetic algorithm is 73.7%, and the improved genetic algorithm is 98.3%. The analysis shows that the improved genetic algorithm can better meet the actual needs of image pattern recognition. According to the characteristics of image recognition, the genetic algorithm can improve the accuracy and robustness of image recognition while ensuring the image quality.
Analysis of Various Optimization Techniques in Machine Learning

Optimization model the goal of solving a task in the paramount way providing the best results and it implies a best view point for solving query or we can also define optimization as a way of probing an another solution with the maximum cost efficient and utmost practicable performance under the required constraints, by increasing the preferred factor and reducing the unprefferable ones. This increase provides the utmost result or inference without any obstacles or complications. In this paper, various optimization techniques with respect to image processing are studied, where image processing deals with certain tasks and they are Image segmentation, image compression, image enhancement, image restoration (in painting).This paper provides basic knowledge of some optimization techniques.
Mixture of Robust Experts (MoRE): A Flexible Defense Against Multiple Perturbations.

To tackle the susceptibility of deep neural networks to adversarial examples, the adversarial training has been proposed which provides a notion of security through an inner maximization problem presenting the first-order adversaries embedded within the outer minimization of the training loss. To generalize the adversarial robustness over different perturbation types, the adversarial training method has been augmented with the improved inner maximization presenting a union of multiple perturbations e.g., various $\ell_p$ norm-bounded perturbations. However, the improved inner maximization only enjoys limited flexibility in terms of the allowable perturbation types. In this work, through a gating mechanism, we assemble a set of expert networks, each one either adversarially trained to deal with a particular perturbation type or normally trained for boosting accuracy on clean data. The gating module assigns weights dynamically to each expert to achieve superior accuracy under various data types e.g., adversarial examples, adverse weather perturbations, and clean input. In order to deal with the obfuscated gradients issue, the training of the gating module is conducted together with fine-tuning of the last fully connected layers of expert networks through adversarial training approach. Using extensive experiments, we show that our Mixture of Robust Experts (MoRE) approach enables flexible integration of a broad range of robust experts with superior performance.
Sparse adversarial attack based on ℓq-norm for fooling the face anti-spoofing neural networks

Neural networks are vulnerable to various adversarial perturbations added to the input. Highly sparse adversarial perturbations are difficult to identify, which is especially dangerous to network security. Previous research has shown that ℓ0-norm has good sparsity but is challenging to solve. We use ℓq-norm to approach ℓ0-norm and propose a new white-box algorithm to generate adversarial examples aiming at minimizing ℓq distance of the original image. Meanwhile, we extend the adversarial attack to facial anti-spoofing task in the field of face recognition security. This extension enables us to generate sparse and unobservable facial attack perturbation. To increase the diversity of the data set, we make a new data set of real and fake facial images containing images produced by various latest spoofing methods. Extensive experiments show that our proposed method can effectively generate a sparse perturbation and successfully mislead the classifier in multi-classification tasks and facial anti-spoofing tasks.
Implementation of Energy Efficient Fog based Health Monitoring and Emergency Admission Prediction System Using IoT

With rapid development in Information Communication Technology (ICT), Wearable Sensor Networks with Internet of Things (WSN-IoT) has produced several improvements in the smart world environment. One of the main research challenges in Wearable Sensor is energy, since all the sensor nodes operation depends on battery power consumption. Hence a new middleware has to be introduced between Wearable Sensor nodes and Cloud to reduce latency and Power Consumption problems. Overcrowding in hospital premise, detecting priority of hospital admission for patients, managing and monitoring health status of the patient constantly are daily problems in any health care system. Even though IoT based wearable sensors monitor health status of patients regularly and provide intent treatment in critical stage, but there is some block hole in that such as latency, energy issues and unawareness of medical execution plans and policies to preserve them from sudden attacks such as Heart attack. The proposed work is to implement energy efficient FoG based IoT network to monitor patients’ health conditions from chronic diseases and highlights utility of Deep Learning model for analyzing the health condition of patients and predicting Emergency readmission cases well in advance. This model is also compare with existing machine learning algorithms such as Gradient boosted, Decision tree, Random forest and Logistic regression to achieve more accuracy. This paper introduces preemptive interval scheduling algorithm with predictive analysis for constant monitoring of status for critical patients. By means of comparative analysis done in this work energy efficiency has been achieved prominently.
Visual Analysis of Deep Q-network

In recent years, deep reinforcement learning (DRL) models are enjoying great interest as their success in a variety of challenging tasks.Deep Q-Network (DQN) is a widely used deep reinforcement learning model, which trains an intelligent agent that executes optimal actions while interacting with an environment.This model is well known for its ability to surpass skilled human players across many Atari 2600 games.Although DQN has achieved excellent performance in practice, there lacks a clear understanding of why the model works.In this paper, we present a visual analytics system for understanding deep Q-network in a non-blind matter.Based on the stored data generated from the training and testing process, four coordinated views are designed to expose the internal execution mechanism of DQN from different perspectives.We report the system performance and demonstrate its effectiveness through two case studies.By using our system, users can learn the relationship between states and Q-values, the function of convolutional layers, the strategies learned by DQN and the rationality of decisions made by the agent.
Challenges Women in Software Engineering Leadership Roles Face: A Qualitative Study

Software engineering is not only about technical solutions. To a large extent, it is also concerned with organizational issues, project management, and human behavior. There are serious gender issues that can severely limit the participation of women in science and engineering careers. It is claimed that women lead differently than men and are more collaboration-oriented, communicative, and less aggressive than their male counterparts. However, when talking with women in technology companies' leadership roles, a list of problems women face grows fast. We invite women in software engineering management roles to answer the questions from an empathy map canvas. We used thematic analysis for coding the answers and group the codes into themes. From the analysis, we identified seven themes that support us to list the main challenges they face in their careers.
Investigating the Dynamic Relationship Among JKSE, S&amp;P 500, Cryptocurrencies and Gold Price After Covid-19 Outbreak

This study aims to examine the dynamic relationship between the JKSE, S&P 500, gold prices, and bitcoin prices after WHO declared Covid-19 a global pandemic.The data used is daily data from March to November 2020 which follows trading days in the Indonesian capital market.Furthermore, this research uses VAR modelling to see how the impact of the Covid-19 pandemic on the relationship between the JCI, S&P 500, gold prices and bitcoin prices.The results showed that in the short term the S&P 500 has a positive and significant effect on JKSE, but in the long run it has no significant positive effect, in the long run the gold price has a negative and significant effect on JKSE and vice versa has no effect in the short term, both in the long term and in the short-term bitcoin has a negative and significant effect on JKSE.This research also shows that apart from gold, bitcoin has also become a safe haven for investors.
PROSPECTS FOR DEVELOPMENT OF HIGHER EDUCATION IN RUSSIA

This article discusses the prospects for development of higher education in the Russian Federation. It has been revealed that the role and importance of intergovernmental agreements and contracts with higher schools are increasing. The Bologna Charter put forward competitiveness in education through a set of international ratings. It has been determined that the transfer of competition to the education sphere is ambiguously estimated by the subjects of educational activity. It has been proved that the challenges of globalization motivate differentiation of higher schools in terms of competitiveness criteria. It has been established that higher school in the era of significant challenges is designated to be a center for development of technologies and personnel, a real locomotive for industries and regions, should promote development of intellectual environment resistant against crisis trends in global world. It has been determined that in the ideal case, a modern university implies job training on the basis of standard or individual specification of trends and profiles, mutual scientific studies and developments, as well as their implementation on request.
Improved Quantum Algorithms for the k-XOR Problem

The k-XOR problem can be generically formulated as the following: given many n-bit strings generated uniformly at random, find k distinct of them which XOR to zero. This generalizes collision search (two equal elements) to a k-tuple of inputs. This problem has become ubiquitous in cryptanalytic algorithms, including variants in which the XOR operation is replaced by a modular addition (k-SUM) or other non-commutative operations (e.g., the composition of permutations). The case where a single solution exists on average is of special importance. At EUROCRYPT 2020, Naya-Plasencia and Schrottenloher defined a class of quantum merging algorithms for the k-XOR problem, obtained by combining quantum search. They represented these algorithms by a set of merging trees and obtained the best ones through linear optimization of their parameters. In this paper, we give a simplified representation of merging trees that makes their analysis easier. We give better quantum algorithms for the Single-solution k-XOR problem by relaxing one of the previous constraints, and making use of quantum walks. Our algorithms subsume or improve over all previous quantum algorithms for Single-solution k-XOR. For example, we give an algorithm for 4-XOR (or 4-SUM) in quantum time $$\widetilde{\mathcal {O}}(2^{7n/24})$$ .
Round-Optimal Honest-Majority MPC in Minicrypt and with Everlasting Security

We study the round complexity of secure multiparty computation (MPC) in the challenging model where full security, including guaranteed output delivery, should be achieved at the presence of an active rushing adversary who corrupts up to half of parties. It is known that 2 rounds are insufficient in this model (Gennaro et al. Crypto 2002), and that 3 round protocols can achieve computational security under public-key assumptions (Gordon et al. Crypto 2015; Ananth et al. Crypto 2018; and Badrinarayanan et al. Asiacrypt 2020). However, despite much effort, it is unknown whether public-key assumptions are inherently needed for such protocols, and whether one can achieve similar results with security against computationally-unbounded adversaries. In this paper, we use Minicrypt-type assumptions to realize 3-round MPC with full and active security. Our protocols come in two flavors: for a small (logarithmic) number of parties n, we achieve an optimal resiliency threshold of $$t\le \lfloor (n-1)/2\rfloor $$ , and for a large (polynomial) number of parties we achieve an almost-optimal resiliency threshold of $$t\le 0.5n(1-\epsilon )$$ for an arbitrarily small constant $$\epsilon > 0$$ . Both protocols can be based on sub-exponentially hard injective one-way functions in the plain model. If the parties have an access to a collision resistance hash function, we can derive statistical everlasting security for every NC1 functionality, i.e., the protocol is secure against adversaries that are computationally bounded during the execution of the protocol and become computationally unlimited after the protocol execution. As a secondary contribution, we show that in the strong honest-majority setting ( $$t<n/3$$ ), every NC1 functionality can be computed in 3 rounds with everlasting security and complexity polynomial in n based on one-way functions. Previously, such a result was only known based on collision-resistance hash function.
Self-supervised adversarial learning for cross-modal retrieval

Cross-modal retrieval aims at enabling flexible retrieval across different modalities. The core of cross-modal retrieval is to learn projections for different modalities and make instances in the learned common subspace comparable to each other. Self-supervised learning automatically creates a supervision signal by transformation of input data and learns semantic features by training to predict the artificial labels. In this paper, we proposed a novel method named Self-Supervised Adversarial Learning (SSAL) for Cross-Modal Retrieval, which deploys self-supervised learning and adversarial learning to seek an effective common subspace. A feature projector tries to generate modality-invariant representations in the common subspace that can confuse an adversarial discriminator consists of two classifiers. One of the classifiers aims to predict rotation angle from image representations, while the other classifier tries to discriminate between different modalities from the learned embeddings. By confusing the self-supervised adversarial model, feature projector filters out the abundant high-level visual semantics and learns image embeddings that are better aligned with text modality in the common subspace. Through the joint exploitation of the above, an effective common subspace is learned, in which representations of different modlities are aligned better and common information of different modalities is well preserved. Comprehensive experimental results on three widely-used benchmark datasets show that the proposed method is superior in cross-modal retrieval and significantly outperforms the existing cross-modal retrieval methods.
Digital Preservation, Accessibility, and Use of Electronic Theses and Dissertations in University Libraries in Nigeria

This study examined the concepts and aspects of digitization; digital preservation; digital resources; and access to, use of, and challenges posed by electronic theses and dissertations (ETDs) in university libraries in six geopolitical zones of Nigeria. It adopted a documentary research approach to highlight a critical examination of the various aspects of digitization (to provide electronic information services) as practiced by various university libraries and some library schools in Nigeria. Print theses and dissertations (T/Ds) are digitized by document scanning or direct text input (alphanumeric characters) into computer systems. Digital resources include e-books, e-journals, ETDs, and other forms of documents in electronic formats. ETDs are preserved through digitization, archiving, and creation of websites and databases as well as through floating institutional digital repositories (IDRs). ETD access is facilitated by tools such as automated library catalogs and classification schemes, online public access catalog, indexes, abstracts, bibliographies, and mobile devices such as smartphones and personal computers. ETDs are capable of meeting research and publication needs of teaching, information service delivery, and knowledge sharing. Challenges to digital preservation, access, and use of ETDs include inadequate information and communications technology (ICT) facilities, low ICT proficiency, and erratic power supply. Thus, this study recommended that management teams of universities in Nigeria commit adequate funding to procure ICT facilities, thus providing automatic standby generating plants and increasing incentives for library and digital preservation staff. Collaboration among stakeholders such as university libraries and the National Library of Nigeria should be encouraged to create a national union catalog of T/Ds in Nigerian universities and thus enable a platform for a national database. Access to IDRs will further strengthen worldwide university networking. This study will thus add value to the existing studies on ICT application in university libraries and e-resources in university libraries in Nigeria.
Single image dehazing based on haze density estimation in different color spaces

In this paper, an efficient method for single image dehazing is proposed based on haze density estimation. We provide two forms of haze density estimation in different color spaces, which are called scene-based haze density estimation in HSV color space and pixel-based haze density estimation in RGB color space. The attenuation model of pixel-level transmission is established based on the two haze density estimations by an exponential function. Guided filtering is applied to smooth the transmission map and maintain the local edges. Global atmospheric light is obtained adaptively by smoothed transmission. A series of experiments on different types of hazy images are implemented, and the results reveal that the proposed method can obtain high-quality haze-free images along with abundant details, high color fidelity, and few halo artifacts.