Reviewerly/exHarmony huggingface dataset:
Corporate Deviance Accounts

Corporations occasionally find themselves mired in scandals that threaten their reputation, profitability, and even survival (Fisse and Braithwaite 1988; Goldstraw-White 2012; Gottschalk and Benson 2020; Piazza and Jourdan 2018). For example, cartels (Goncharov and Peter 2019), tax evasion (Balakrishnan et al. 2019; Dyreng et al. 2019; Guenther et al. 2019), misleading disclosure to investors (Jennings 2019), or other forms of misrepresentation in accounting (Albrecht et al. 2001; Qiu and Slezak 2019) can cause financial crime scandals. There is obviously a violation of ethics and lack of corporate social responsibility (Davidson et al. 2019), and scandals are often revealed after whistleblowing (Gao et al. 2015; Gao and Zhang 2019).
Towards Plan Transformations for Real-World Mobile Fetch and Place

In this paper, we present an approach and an implemented framework for applying plan transformations to real-world mobile manipulation plans, in order to specialize them to the specific situation at hand. The framework can improve execution cost and achieve better performance by autonomously transforming robot's behavior at runtime. To demonstrate the feasibility of our approach, we apply three example transformations to the plan of a PR2 robot performing simple table setting and cleaning tasks in the real world. Based on a large amount of experiments in a fast plan projection simulator, we make conclusions on improved execution performance.
Using Convex Hull for Fast and Accurate Ellipse Detection

We present a novel method for fast and accurate ellipse detection based on an efficient arc grouping strategy. We first extract edges from the input image, and then obtain smooth arcs by recognizing sharp turns and inflexion points. To speed up ellipse generation, we group arcs by three intuitive yet more efficient rules, followed by a validation and a more distinctive cluster scheme to further improve the accuracy. Our approach achieves promising results on both synthetic and three real-world datasets.
Adaptive Flight Accident Prediction Method Based on Chaos Theory

To improve the prediction effectiveness of aviation accidents, a prediction model based on chaos was proposed. Firstly, the aviation accident data was preprocessed and then judged by the 0-1 chaotic test; time delay and embedding dimension was determined by mutual information method and false nearest neighbor method respectively. In order to perform phase space reconstruction; the two order Volterra adaptive prediction model was built, adjusting the model parameters in real time NLMS algorithm. For chaotic time series prediction, the results show that the Volterra adaptive prediction is better than the grey model combined with time series forecasting model, the maximum relative error is less than 2%.
A Suspected Noise Filtering Method Using k NN for Robust Classification

The correctness and efficiency of data mining results is dependent on data quality. Thus, improving the quality of data holds considerable research significance. This work presents a high quality data selection method known as suspected data noise filtering. Firstly, we propose a data quality evaluation measure named the NN-kNN measure. Then, a suspected noise filtering algorithm based on NN-kNN is proposed. Experiments are carried out to verify the proposed quality evaluation measure and noise filtering algorithm. The experimental results show that NN- k NN can evaluate data quality well. High quality samples selected by the proposed algorithm can generate higher classification accuracy and robust classification results.
Software Quality Evaluation of Developed Learner’s Information System

This study aimed to evaluate LIS used by SDO I Pangasinan for automating records management via LIS. The respondents of the study were the trained and only LIS users from the SDO I of Pangasinan. A descriptive method of research was used in the form of a questionnaire to gather data. Frequency and Percentage were used to analyzed the acquired data and ISO 9126 was used as the criteria for system software evaluation. It found out that LIS Users were generally young in age and service, Teacher I and Teacher II employees yet educated and LIS trained.  LIS was found of good quality in terms of functionality, reliability, usability, efficiency, maintainability, and portability based on ISO 9126. 90% of the users expressed that the main problem of the system was internet lags in opening the System. The study recommended that; (1) older and more experienced government employees may be given the chance to use the system (2) additional system training be conducted (3) the teachers should upgrade or get good ISP to utilize the system in full potential. Over-all, the study recommends the LIS to be utilized by other school’s division for automated record management of each learners.
Project Management and Agile Technology in Environmental Science and Sustainable Development in the “University – Employer – Region” System

In this article, the authors present the outcomes of three years of their scientific work on the formulation of a research hypothesis, the formation of project research groups, and the presentation of the outcome to groups of “customers”: employers and/or regions. An interdisciplinary approach was applied in this work, which allowed to integrate environmental, economic, and social research methods. The objectives of the study were to determine the range of the relevant research topics in environmental science and sustainable development and formation of cases; to perform the personal and collective work in the project group; to form the skill of working with topic experts and documents in students; and to verify the work and present to the customer. The youth modeling of international and national processes and events, project laboratories, and cases obtained in the course of the study were presented in this work.
Edge Features Enhanced Graph Attention Network for Relation Extraction

Dependency trees of sentences contain much structural information that is useful for capturing long-range relations between words in the text. In order to distill the useless information, the pruning strategy is introduced into the dependency tree for preprocessing. However, most hard-pruning strategies for selecting relevant partial dependency structures are too rough and have poor generalization performance. In this work, we propose an extension of the graph attention network for relation extraction task, which makes use of the whole dependency tree and its edge features. The graph attention layer in our model can implicitly prune the neighbor nodes of each node by assigning different weights according to the content. The edge feature information makes the pruning strategy trainable and non-discrete. Our model can be viewed as a soft-pruning approach strategy that automatically learns the relationship between different nodes in the full dependency tree. The results on various datasets show that our model utilizes the structural information of the dependency tree better and gets the state-of-the-art results.
Micro-content Shortlisting Using Interactive AI Method

The real-time detection of relevant information and then shortlisting them on the user interfaces (UI) is a real challenge. In this paper, we explain the use of Artificial Intelligence (AI) and other methods for instantaneous identification of information on the UI (web browser) and also a system of injecting actionable buttons in an overlay on the website for shortlisting. We also cover the patterns of the data, and it's dynamic validation in order to shortlist them in one place as a universal list. Also making them accessible across other websites with a consistent experience.
Eureka! or, How I learned to stop worrying and love the survey

Two decades ago, a personally-addressed ten-page survey printed on salmon-colored stock appeared in my campus mailbox. An accompanying letter explained that the sender was a student collecting data for a graduate-level thesis and included contact information for the thesis advisor … in marketing. It was the one and only ‘Consumer Durable Goods Study’ I would ever receive as an applied linguist.
Identity-Based Proxy Signing Key Insulated Signature in the Standard Model

Abstract The first signer arranges for an agent to generate a proxy private key using an identity based proxy signature (IBPS) scheme. The agent outputs the proxy signature for the first signer can generate the signature for the original signer using the proxy private key. Because the potential problem of information security must be taken into account with the development of network technology, the proxy private key needed to be protected. We propose an identity-based proxy signing key-insulated proxy signature (IBPSKIPS) scheme to prevent the proxy private key from being exposed.
A Medium-Length History of Digital Cryptography

Having taken a look at the basics of digital cryptography in the previous chapter, we'll now move on to some specifics of the topic. We'll take a gander at technology, which is both historically significant and still relevant in the field today. We'll also cover some important related standards organizations and unlock several concepts crucial in the world of cryptography.
Face Recognition Based on Joint Sparse Representation of Multiple Features for Public Safety

Abstract A face recognition method based on joint sparse representation of multiple features is proposed in this paper. First, principle component analysis (PCA), kernel PCA (KPCA), and non-negative matrix factorization (NMF) are used to extract feature vectors of face images. The three features could provide complementary descriptions for face images. Then, in the classification stage, joint sparse representation is employed to classify the three features thus considering their correlations. Finally, the total reconstruction errors of the three features on different kinds of training classes are calculated to determine the label of test sample. Experiments are conducted on AR and Yale-B databases to validate the effectiveness of the proposed method.
Learning Rule Embeddings over Knowledge Graphs

E-Commerce platforms such as Alibaba and Amazon make people's life more convenient and play an important role in people's life. One critical issue for these platforms is entity alignment over item knowledge graph aiming at detecting if two items belong to the same product. In knowledge graph, rule learning methods and embedding-based methods are either inefficient or unexplainable. In this paper, we combine these two methods together and propose an embedding-based rule learning method that regard alignment rules as embeddings and learn these embeddings in the model training process. The experimental results on the real world E-Commerce dataset indicate that our proposed method is more effective and can reach better performance than other related methods.
Feature based approach for Efficient Crowd Density Estimation

Presently there is major interest in visual surveillance systems for crowd analysis. For preventing the problems caused by large crowd, proper control and management is necessary. In this paper a novel individual feature is introduced and used in combination with the holistic features, to describe crowd density. Various databases are analyzed to check the validity of the feature extraction process. In order to cluster the crowd frames according to congestion degree Support Vector Machine and Artificial Neural Network are used as classifiers. Results obtained shows that the proposed feature performs well in classifying real-world crowd scenes.
Knowing Opposing Arguments in Persuasive Essays Using Random Forest Classifier

Argumentation mining is a relatively new field of research in the perspective of computational linguistics. It can be used to improve the quality of arguments in persuasive essays by detecting whether in a persuasive essay there are opposing arguments or not. This is because the importance of opposing arguments in persuasive essays that can improve the quality of arguments, precision and author's claims. This research adapted the research conducted by Stab and Gurevych who used the SVM classification method with an accuracy 75,6% and macro F1-score 0,734. While in this research used the Random Forest method and get accuracy of 85,125% and macro F1-score of 0,841 by using three features, namely unigram, production rules, and adversative transitions.
Multi-agent Planing Based on Causal Graph

Abstract Multi-agent planning is a kind of difficult but not fully studied planning problem. In the cooperative assumption, previous approaches either find a globally consistent plan by the backtrack-based solution, or make the local plans first, and then merge them. However, local planning between agents is inconsistent and conflicting in most case, in this case finding a globally consistent plan is time-consuming and costly. In this paper, we propose a novel approach to get causal-links graph from causal constraints between agents in solving multi-agent planning problems to reduce the effort of repeatedly searching agents’ consistent local plans. Theoretical analysis show that our approach can efficiently solve multi-agent planning problems with tightly degree of coupling level.
A Fast and Practical CNN Method for Artful Image Regeneration

Although artists’ actions in photo retouching appear to be highly nonlinear in nature and very difficult to characterize analytically, we find that the net effects of interactively editing a mundane image to a desired appearance can be modeled, in most cases, by a parametric monotonically non-decreasing global tone mapping function in the luminance axis and by a global affine transform in the chrominance plane. This allows us to greatly simplify the existing CNN methods for mimicking the artists in photo retouching, and design a new artful image regeneration network (AIRNet). The objective of AIRNet is to learn the image-dependent parameters of the luminance tone mapping function and the affine chrominance transform, rather than learning the end-to-end pixel level mapping as in the standard practice of current CNN methods for image restoration and enhancement. The proposed new approach reduces the complexity of the neural network by two orders of magnitude, and as a side benefit, it also improves the robustness and the generation capability at the inference stage.
Computational Parallel of K-Nearest Neighbor on Page Blocks Classification Dataset

K-Nearest Neighbor (KNN) is considered as one of the simplest machine learning algorithms. While the implementation is quite simple, KNN is actually computationally expensive that makes it take a lot of time when it tries to predict. KNN has been known to be a lazy learning machine learning method that means that this method doesn't generalize the data, instead it has to memorize the training data, even when testing. This paper aims to optimize the KNN classifier to solve page blocks classification by making the algorithm parallel. The part of the KNN algorithm that is changed to become parallel is the outer part where the task for each test data is divided according to the number of processors. In this work, we use parallel KNN to classify page blocks. Page blocks are any blocks of a page layout that are detected by using a segmentation technique, the KNN is trained to classify whether a block is a vertical line, picture, text, horizontal line or graphic. The experiment shows that the KNN classifier obtains an accuracy of 93.51% and by using parallel KNN, a speedup of 4.64 times faster and an efficiency of 57.96% can be obtained by using 8 processors and an increasing number of grids up to 6040 while it obtains the same accuracy as serial.
Improving the Capacity of Data Hiding by Modifying the Interpolation of Audio Samples

In information technology, security has played a crucial role in protecting data. One of the methods that can be used is by hiding the data in a cover file, such as audio. However, the use of this carrier has several limitations, namely the capacity of the secret message and the quality of the stego data, which should not be easily recognized at the time of delivery. Therefore, in this research, we focus on overcoming these problems by exploring the modified high-order Lagrange interpolation to the audio samples equation. It is to produce a new signal as a space to embed relatively big confidential data. Furthermore, the Reduced Difference Expansion and smoothing algorithms are considered to maintain the quality of the stego audio. The evaluation is carried out on ten different sizes of payloads and 15 audio cover data. The results show that the use of the interpolation method can increase capacity.
Generative Networks for Adversarial Examples with Weighted Perturbations

In adversarial deep learning, adversarial examples are used to attack the target models, such as deep neural networks (DNNs). The adversarial examples are always constructed by adding vicious perturbations to the original images, and the added perturbations are not easily perceived by human. In order to achieve high attack success rates of adversarial sample, existing attack strategies always increase the magnitudes of global perturbations. However, as the magnitudes are increasing, the attacks will be easily perceived by human. To address this problem, a new approach is proposed to generate adversarial examples with weighted perturbations and abbreviates as WP-AdvGAN. The weights distribution to perturbations are determined by the sensitivity of the target model to the corresponding local region in the adversarial examples. Local region with high sensitivity have greater impacts on the decision of the target model, so the perturbation weights for these regions will be increased in the adversarial sample, vice versa. Experiments performed on MNIST dataset demonstrate that the adversarial examples generated by the proposed WP-AdvGAN are similar to the original images and have high attack success rates under both white-box and black-box attack settings.
Category-Level Adversarial Network for Cross-Domain Sentiment Classification

Cross-domain sentiment classification utilizes useful information in the source domain to improve the sentiment classification accuracy in the target domain which has few or no labeled data. Most existing methods based on single domain classifier only consider the global alignment without taking category-level alignment into consideration, which can lead to the mismatch of category-level features and reduce classification accuracy. To slove the above problem, we propose the Category-level Adversarial Network (CAN). On the basis of single domain classifier, CAN adds K category-wise domain classifiers which can achieve fine-grained alignment of different data distributions by combining the label information and document representations. Specifically, we obtain document representations by introducing transferable attention network which mirrors the hierarchical structure of documents and transfers attentions across domains. Experiments results demonstrate that CAN model outperforms state-of-the-art methods on the Amazon and Airline datasets.
Parallelizing Polarization Plate Design for Automating Quantum Key Distribution Device based on EDU-QCRYI

Quantum cryptography learning is now more important as the threats toward secure communication system using quantum computing are increasing. EDU-QCRY is one of the quantum cryptography demonstration devices which implements BB84 protocol. EDU-QCRY is still run manually that make it has limited data processing capacity, large operating time, and low-level accuracy. In this study, automation is done by parallelizing the process of selecting bases and states on the polarization rotator and integrating an Arduino-based controller module using demultiplexing techniques. EDU-QCRYI automation changes the process to be more efficient and effective because the system works automatically by a controller module based on qubit data that is inputted by the sender and the receiver gets qubit data automatically. EDU-QCRYI automation is expected to make demonstration of secure communication using Quantum cryptography more reliable for implementation.
Developing smartphone-based laboratory manual as a learning media

Abstract The purpose of this study was to develop a smartphone based laboratory manual as learning media for biology learning. This research was conducted using the ADDIE model (Analysis, design, development, implementation, and evaluation). The media feasibility test obtained an assessment of 90.28%, material experts 87.75%, biology teachers 91.37% and 88.46% students with very good interpretations. The increase in psychomotor ability test obtained a gain value of 0.49, which means there is an increase in psychomotor abilities of students with a moderate category. The conclusion obtained is that the smartphone-based laboratory manual has been successfully developed and is suitable for use in learning.
Optimization Design of Drill Console for ZDY650 Type All Hydraulic Drilling Rig

In order to further improve customer satisfaction and market competitiveness of products, After a lot of market research and scheme discussion, Xi'an Research Institute of China Coal Technology and Engineering Group Corp set up a special project team to carry out the ZDY650 drill console optimization and promotion. The internal structure of the console has been rationally laid out, and the integrated adapter plate assembly has been added. Wire drawing panel and front, rear, left and right shields on the console were designed to enhance safety protection and improve appearance quality. Key hydraulic components such as anti-seismic pressure gauge, multi-way reversing valve, hydraulic pump, and hydraulic motor and manual reversing valve group were optimized, and the operation performance of the console was dramatically improved.
Increase the Speed of Search Index in the Duplicate Text Detection Systems

The work is devoted to the organization of the search index of the duplicate text detection systems Author.NET. The paper considers the structure of the search index. The search index can be divided into several groups of files - terms, documents, TF*IDF index, signatures, shingles. In the article the ways of organizing the search index are considered - index storage in RAM, compression of index file, tree index, index table of contents. It is given a proposal for organizing a search index for duplicate text detection system. During the experiments, it was found that the most profitable option is to use compressed index files stored on the SSD.
Optimization of JavaScript Large-Scale Urban Simulations

In computer animation, applications compute and render frames at fast rates to illustrate the objects’ motions using the animation loop. Applications that require calculations for every object on a per-frame basis, might face a compute-intensive task that is difficult to scale. We study a real-world JavaScript simulation tool that visualizes urban areas and presents the movement of the whole area’s population. We focus on the performance bottleneck(s) of this design and present the challenges that appear while scaling the population size to enable the simulation of large regions/cities. We propose a parallel design that is based on the producer-consumer paradigm with dynamic scheduling message passing and an object-caching strategy on display objects. We implement our prototype, evaluate it against the baseline tool and analyze its impact.
Research on sentiment analysis of short text based on Attention

Abstract In order to classify the short essays, a circulating neural network is used to classify the texts by natural language processing technology. Recurrent neural network has some advantages in processing text sequence with memory function. In this paper, BiLSTM algorithm is used to extract text data features, and self-attention mechanism is introduced to improve the fitting ability of the model. The accuracy of this model algorithm on text emotion classification task is 78.1%, which is improved compared with LSTM and CNN neural network model.