Reviewerly/exHarmony huggingface dataset:
Evaluating Sentiments in Social Media Comments on Tax Transformation in India using Deep Learning

The Goods and Services Tax (GST) was implemented by the government of India to have one tax for the entire country. Millions of taxpayers commented on their experience of the GST e-governance system on the Twitter platform, which was collated for the study from June 2017 to May 2020. This paper proposes a comprehensive approach for finding the variation of attention weights for key words (related to broad categories belonging to GST) present in the tweets over different quarters of the three-year period along with month-wise sentiment prediction for every quarter using Bi-directional LSTM model with attention. The contribution of key words, whose attention weights exceed two sigma thresholds, towards the net positive and negative sentiments of tweets is found to be significant in the study.
Reinforcement Learning for Dialogue Generation: A Systematic Literature Review

With the rapid advancement in personal assistants such as Siri, Alexa, and Cortana among many others, dialogue systems have become a very prominent issue in recent years. Open-ended dialogue systems function best in languages with a large volume of text and speech data.. Conversational agents in constrained resource languages are still not quite as developed due to the difficulty of data sparsity, which is one of several challenges to establishing robust and effective dialogue systems. To get a great result, traditional RL techniques take thousands of dialogues. To carry out this SLR, 28 research papers were carefully chosen based on predefined selection criteria. In this study, we presented a taxonomy of reinforcement learning considering its components, goals, and basis and discuss the reinforcement learning framework which may provide researchers with understandings of the dialogue generating aspect. The purpose of this study is to identify and discuss current research on dialogue generation, as well as to choose valuable methodologies and requirements for future study. The findings as well as the research questions are addressed. Finally, challenges and issues have been given to give research with possible future paths in the domain of dialogue generation.
Use of Homomorphic Transform in Encryption

Data security is an important aspect for datasets of every size and type. Data security refers to provide a secure environment to datasets, web-based datasets, and preventing unauthorized access to the data. The key data security technique is encryption, where the digital data is encrypted. As result, the unauthorized person or hacker gets unreadable data. The computing process generates data that is encrypted by the key and can be decrypted with the correct key only. In this research, an approach is proposed of using transforms to provide a better cryptography process to secure data even during communication. The result of the proposed method indicates that in any condition, the ciphertext will always be the same for each input, but the encryption key will change each time, which makes this encryption complex to break.
Human Resource Performance Management Evaluation Based on Big Data Mining

Considering the low efficiency, reliability and stability of traditional methods in evaluating human resource performance, research on human resource performance management evaluation based on big data mining is proposed. Big data mining algorithms are used to analyze various influencing factors of human resource performance, calculate the correlation coefficient between human resource performance correlation factors, and extract human resource performance characteristics by selecting key influencing factors. After screening the results, the analytic hierarchy process determines the weights of human resource performance evaluation indicators, combined with the construction of human resource performance management evaluation, and realizes the management evaluation of human resource performance. The experimental results show that the human resource performance management evaluation method based on big data mining can not only improve the efficiency of human resource performance evaluation but also has higher reliability and stability.
Opportunities and challenges of educational technology in the post epidemic Era

The outbreak of COVID-19 has exerted a great impact on the educational system of countries all over the world. In the post epidemic era, educational technology researchers need further research on how to change education and teaching and continue to give full play to the advantages of online education. By studying the literature, this study combs the research of educational organizations and scholars on the reform of the educational system in the post epidemic era from three aspects: the new trend of educational development, educational and teaching reform, and the exploration of international education. On this basis, it summarizes the three directions that educational technology scholars need to strive for, that is, to improve the information literacy of primary and secondary school teachers; Promote the inclusive use of high-quality educational resources; Committed to improving the teaching quality of mixed teaching.
Attention Model Enhancement Network for Breast Tumor Classification

Breast tumor classification remains a challenging task due to its inter-class ambiguity and intra-class variability. Existing deep learning-based methods typically adopt convolutional neural networks and complex nonlinear projections to extract global features from entire images. However, these methods usually neglect some local discriminative features that may be crucial in improving the classification accuracy. To tackle this problem, we propose a novel method named Attention Model Enhancement Network (AMEN), which is formulated in a multi-branch fashion with the feature extraction submodular, classification submodular, and pixel-wise attention submodular. Specifically, the feature extraction part in AMEN can generate the pixel-wise attention map, while the classification submodular is adopted to initially classify the instances. To obtain more local discriminative features, the sample image in the next branch is enhanced by the pixel-wise attention map. Furthermore, two kinds of boost strategies are adopted to fuse classification results on different branches for better performance. Experiments conducted on three benchmark datasets demonstrate the superiority of the proposed method under various scenarios.
Identifying Duplicate Police Reports

Several crimes occur every day, and the first step in investigating these crimes begins with a police report. Victims report the criminal facts, which in turn must be detailed and contain accurate information about the incident or crime (e.g., factual, accurate, clear, concise, complete, and timely). In addition, the bulletin helps to safeguard the police operation itself, showing where the series of investigative operations that police agencies have been carrying out began. In cities with high crime rates, it is unfeasible to require the police to read and analyze all reported crime narratives. However, it would be helpful if employees could identify reports with similar modus operandi. Priority legal document retrieval is an information retrieval task used to retrieve past case documents related to specific cases and guide the police on how to act. Given a police report, the main objective of this work is to determine the most similar or duplicate police report. Another method is to encode the narrative as an embedded vector. In this article, we experimented with different pre-trained representations at the sentence level. We found the one that most effectively captures the semantic attributes of police report vocabulary and recognizes repeated reports. We are also investigating whether the summarized sentences identify duplicate police reports. Finally, we compare the effectiveness of the duplicated police report with the available sentence incorporation model trained in a large corpus. Our goal is to evaluate the performance of these embedding models (the one trained with our corpus and the pre-trained) to capture duplicate narratives.
Statistical Estimation of Quantization for Probability Distributions: Best Equivariant Estimator of Principal Points

Quantization gives a discrete approximation (with a finite set of points called quantizer) for a probability distribution. When the approximation is optimal with respect to a loss function, it is called optimal quantization (and its set of points is called an optimal quantizer), which has been studied and applied in various areas. Especially in statistics, an optimal quantizer under a quadratic loss function (optimal quantizer of order 2) has been widely investigated and is often called a set of principal points (or simply, principal points) for a probability distribution. In practice, however, the values of the parameters of the probability distribution are sometimes unknown, and hence we have to estimate principal points based on a random sample. A common method for estimating principal points is using principal points of the empirical distribution obtained by a random sample, which can be viewed as a nonparametric estimator of principal points. Several papers discussed statistical parametric estimation of principal points based on maximum likelihood estimators of the parameters. In this paper, a class of equivariant estimators, which includes previous parametric estimators of principal points is considered, and the best equivariant estimator of principal points is derived. It turns out that, under some condition, the best equivariant estimator coincides with a previously obtained parametric estimator. However, it is also shown that, for some probability distributions not satisfying the condition, the best equivariant estimator may not be equivalent to previous estimators.
Attribute-Assisted Heterogeneous Graph Representation

The previous heterogeneous graph representation methods mainly focus on preserving the complex interactions and rich semantics into node representation. As a matter of fact, diverse types of nodes in heterogeneous graph are assisted with different attributes, providing valuable side information for depicting the characteristics of nodes. Integrating attribution information is also desired for heterogeneous graph representation in a real-world application. Fortunately, heterogeneous graph neural networks naturally provide an alternative way to achieve this, meanwhile, have powerful representation ability. In this chapter, we introduce three attribute-assisted heterogeneous graph representation models including heterogeneous graph attention network (HAN), heterogeneous graph propagation network (HPN), and heterogeneous graph structure learning (HGSL), which simultaneously utilize both complex structural information and rich attribute information to learn node representation.
An efficient drift detection approach using data entropy in business processes

Business processes evolve due to adaption to variable environmental conditions. It is vital for organization managers to discover process changes in order to prevent performance issues in organizational processes. These changes may occur suddenly, gradually, periodically, or incrementally. This paper proposes an algorithm for detecting concept drift in event logs for sudden drifts, based on the entropy of trace variants in the execution of processes. In the proposed approach, two sliding window approach is leveraged to extract features of trace variants is given intervals. By counting the number and variety of traces in the reference and detection windows, two feature vectors are created for each window. Comparing two feature vectors that are obtained from two sliding windows by the entropy of feature vectors and SymGtest distance function of trace variants, possible drifts are detected. Experiments on synthetic databases show the accuracy of the method and its superiority to state-of-the-art methods.
Building Action Sets in a Deep Reinforcement Learner

In many policy-learning applications, the agent may execute a set of actions at each decision stage. Choosing among an exponential number of alternatives poses a computational challenge, and even representing actions naturally expressed as sets can be a tricky design problem. Building upon prior approaches that employ deep neural networks and iterative construction of action sets, we introduce a reward-shaping approach to apportion reward to each atomic action based on its marginal contribution within an action set, thereby providing useful feedback for learning to build these sets. We demonstrate our method in two environments where action spaces are combinatorial. Experiments reveal that our method significantly accelerates and stabilizes policy learning with combinatorial actions.
A Distance Estimation Method to Railway Crossing Using Warning Signs

A mobility scooter has come to be used to expand the range of mobility for the elderly. On the other hand, accidents involving mobility scooters have become serious problems. For example, if a mobility scooter stops inside a railway crossing due to battery exhaustion, it is very dangerous because accidental contact with a train may happen. Measuring the distance to a railway crossing during driving is helpful to avoid entrance to a railway crossing without enough battery. In this paper, we propose a method for predicting the distance to a railroad crossing based on the railway crossing warning signs in the video from a camera installed in front of the mobility scooter. In experiments, we evaluate the proposed method using images taken at various positions in relation to the railway crossing and show that the proposed method achieves higher accuracy than the distance estimation using a depth sensor.
Quantum Stabilizer Codes and Beyond

In this chapter, a well-studied class of quantum codes is described, namely stabilizer codes and related quantum codes. The basic concept of stabilizer codes was already introduced in Chapter 8. Here the stabilizer codes are described in a more rigorous way. After rigorous introduction of stabilizer codes (Section 9.1), their properties and the encoded operations are introduced in Section 9.2. Furthermore, stabilizer codes are described by using the finite geometry interpretation in Section 9.3. The standard form of stabilizer codes is introduced in Section 9.4, which is further used to provide efficient encoder and decoder implementations in Section 9.5. Section 9.6 is devoted to nonbinary stabilizer codes. Next, subsystem codes are described in Section 9.7, followed by the topological codes in Section 9.8. Section 9.9 is related to surface codes. Entanglement-assisted codes are described in Section 9.10. For a better understanding of this material, a set of problems is provided at the end of the chapter.
Choice-Based One-Time Pad Approach Using Hybrid Cellular Automata

Cloud computing is a service-based technology which is accessed through the Web. Due to this reason, security and robustness are the important task in a public cloud. However, the present authentication mechanism does not give adequate protection in cloud technologies. So, this article tries to propose a choice- based one-time pad (OTP) encryption method using hybrid cellular automata for the open cloud. This proposed work assures confusion, diffusion, and avalanche requirements. It is identified that by consolidating checksum and hybrid cellular automata technique, a strong final key in the cloud could be produced. The executed results confirm that the proposed technique prevents chosen plaintext and brute force attacks. It has sufficient key space along with accessing time also improved in comparison with earlier methods. The theoretical analysis certified that the proposed hybrid cellular automata-based one-time pad key generation solution provides better security.
A Hybrid Spatiotemporal Attack in Continuous LBS Queries

In recent years, location-based services (LB-S) enjoy a rapidly increasing popularity among the vari-ous mobile applications. As a result, how to protect users&#x0027; location privacy has become an urgent issue. To alleviate this issue, the dummy location selection (DLS) algorithm based on the k-anonymity criterion has been studied in many existing works, and it can provide protection against the adversary with the query probability information. However, such a method losses its effectiveness when facing the hybrid Retrospect attack proposed in this paper. The proposed attacking method is designed to capture the spatial continuity between adjacent locations along with the temporal information in continuous queries of the whole movement trajectory. To demonstrate the effectiveness of the proposed attacking method, two real-life datasets are evaluated in comparison with the state-of-the-art algorithms.
Deep hash Based on Asymmetric Learning and Center Contrast

Benefiting from the storage and retrieval efficiency, learning to hash has been widely used in large-scale multimedia retrieval. Deep learning to hash has attracted the attention of many scholars because it can better extract image features while achieving end-to-end encoding. In recent years, many scholars have focused their research on hash-centered methods that can learn to use global features. Based on the hash center method, this paper uses contrast loss and asymmetric learning methods to enable convolutional neural networks to further learn global features. The experimental part shows that the method proposed in this paper can achieve good results.
Unsupervised De-redundancy and Multi-step Fusion for Continuous Chinese Sign Language Recognition

Deep multi-cue framework is studied widely for continuous sign language recognition (CSLR), however, the influence of the particularity of frame redundancy in a video and different multi-cue combination styles at fusion is rarely explored for this framework. The redundancy particularity and unnatural combination reduce the description and representation capacity of sign language visual grammar while using deep multi-cue network. In this paper, we propose a new deep multi-cue framework for addressing frame redundancy and valid multi-cue fusion. The framework consists of redundancy elimination module (REM) and multi-step fusion module (MFM). The REM eliminates redundancy based on k-means algorithm and aims at dropping lots of similar frames caused by the particularity of video. The MFM complies with part integrity constraint at early fusion and cues equivalence constraint at late fusion, further, builds spatial-temporal correlations between different cues. The part integrity constraint means that facial feature which is an entirety is made up of eye gaze, mouth shape and so on, and two hands should be combined together as an entirety. The cues equivalence constraint means that different cues have the same effect for conveying information by sign language. The MFM devotes different cues to keeping individuality and collaboration. To validate the effectiveness, our framework is evaluated on Chinese Sign Language dataset (CSL) including two strategies of data partition (Split I and Split II). Experiments show that our framework is effective and achieves new state-of-the-art performance on Split I and Split II.
The use of artificial neural networks and big data infrastructure for predictive analytics in solar energy

Abstract Renewable energy appears to be the solution to both the continuously growing energy demand, and pollution from fossil-based fuels. Recent advances in big data means that crucial areas of the energy supply chain are of interest to the use of advanced analytics. Solar energy is one of the most important renewable energy sources; however, it is stochastic, leading to production volatility and making it difficult to dispatch. The European Commission provides the legal framework and guidelines for increasing the adoption of renewable technologies in the European Union (EU). Meanwhile, the research community must provide solutions for increasing the predictability of solar energy: successful integration depends on how well solar energy production is predicted. Working under the Cross-Industry Standard Process for Data Mining, using real word operational data, this research focuses on providing a foundation of the analytics capabilities needed for reducing, or even removing, the disadvantages of solar energy, demonstrating that a world-class predicative tool can be obtained. Using weather and production data from photovoltaic cells installed in Romania, as a case study, coupled with the powerful artificial neural networks (ANN) architecture, results in a benchmark prediction performance. Currently, there is no research addressing photovoltaic energy production prediction by integrating the impact of artificial intelligence and big data.
THE FORMATION OF LEADERSHIP OF CADET AT MILITARY ACADEMY

The article discusses the features of the formation and development of leadership qualities in cadets studying at Military Academy. Referring to the literature are explained the basic concepts of leadership including military leadership, the differences between a leader and manager, the need to form leadership qualities in officer and a set of these qualities. The article higlihts on the required conditions, pedagogical and organizational, the usage of which will make the process of formation and development of leadership qualities among the cadet more effective.
A First Look at Data

For data scientists, the most important use of statistics will be in making sense of data. Therefore, in this first chapter we immediately start by examining, describing, and visualizing data. We will use a dataset called face-data.csv throughout this chapter; this dataset, as well as all the other datasets we use throughout this book, is described in more detail in the preface. The dataset can be downloaded at http://www.nth-iteration.com/statistics-for-data-scientist . In this first chapter we will discuss techniques that help visualize and describe available data. We will use and introduce R, a free and publicly available statistical software package that we will use to handle our calculations and graphics. You can download R at https://www.r-project.org .
Redefining Members

A member in a derived class can redefine a member in its base class. This can be done for all kinds of inherited members, but it is most often used to give instance methods new implementations. To give a method a new implementation, the method is redefined in the child class with the same signature as it has in the base class. The signature includes the name, parameters, and return type of the method.
Image Semantic Segmentation Based on the GAN Auxiliary Network

To enhance the performance of existing deep neural networks in semantic segmentation while preserving efficiency at the same time, a semantic segmentation network with the help of GAN (Generative Adversarial Networks) is proposed. The method consists of a generator and a discriminator. The segmentation results obtained from the generator are encoded and then fed into the discriminator to obtain the pixel-wise uncertainty values. Such uncertainty values are taken as weights for the calculation of CEGU (Cross-Entropy with GAN Uncertainty) to help the optimization of the generator. The discriminator is removed after training. Experiment results show that the mean IoU (Intersection over Union) scores of the segmentation results grow by 4.7% and 3.2% respectively on ResNet-50 and ResNet18, after using the GAN auxiliary method along with the CEGU. It shows that such a GAN auxiliary network can significantly improve the performance of basic end-to-end methods with various backbones on the semantic segmentation task, without introducing extra computation cost in the test phase.