Reviewerly/exHarmony huggingface dataset:
Merging Epistemic States and Manipulation

In this work we study the problem of manipulation in the framework of merging complex epistemic states. We adopt the techniques concerning representation and impossibility in belief merging of complex epistemic states proposed by Mata Díaz and Pino Pérez in 2017. We introduce here the notion of belief lifting, aiming to capture the preferences of an agent over formulas. This allows us to define a general notion of manipulability. We prove that, provided some rational properties, a merging operator is either manipulable, with respect to any well-behaved belief lifting, or it admits an Arrovian dictator. We also study the behaviour of some concrete belief merging operators, showing that some of them are manipulable. Moreover, we prove that strategy-proofness cannot be characterized in terms of Arrovian dictators.
Target unbiased meta-learning for graph classification

Abstract Even though numerous works focus on the few-shot learning issue by combining meta-learning, there are still limits to traditional graph classification problems. The antecedent algorithms directly extract features from the samples, and do not take into account the preference of the trained model to the previously “seen” targets. In order to overcome the aforementioned issues, an effective strategy with training an unbiased meta-learning algorithm was developed in this paper, which sorted out problems of target preference and few-shot under the meta-learning paradigm. First, the interactive attention extraction module as a supplement to feature extraction was employed, which improved the separability of feature vectors, reduced the preference of the model for a certain target, and remarkably improved the generalization ability of the model on the new task. Second, the graph neural network was used to fully mine the relationship between samples to constitute graph structures and complete image classification tasks at a node level, which greatly enhanced the accuracy of classification. A series of experimental studies were conducted to validate the proposed methodology, where the few-shot and semisupervised learning problem has been effectively solved. It also proved that our model has better accuracy than traditional classification methods on real-world datasets.
Challenges in Geographically Distributed Information System Development: A Case Study

Geographically distributed information system development (ISD) projects are more and more common, especially among organisations operating in global markets. Distributed ISD yields potential competitive advantages by developing new products near the target markets, utilizing global labour markets, and exposing the organisation to innovations, ideas and new paradigms. However, distributed ISD also presents challenges and problems which organisations must take into consideration. The pivotal challenge is usually communication. People working on the same project in different locations find it difficult to communicate due to lack of formal and informal face-to-face communication, different working cultures and languages, and time difference. In this study, we set out to investigate what challenges may rise in geographically distributed ISD, and how these challenges and problems of geographical distribution could be mitigated.
Evaluation of Indoor Localisation and Heart Rate Evolution

The number of older adults and their proportion within the general population are expected to increase in the upcoming decades. Decision makers have turned to technology in order to provide cost-mitigating solutions. Assisstive systems have been developed for many of the challenges that can be addressed through technology. However, we find most of them to have singular focus, using proprietary equipment and communication protocols which makes them difficult to integrate into a comprehensive system. Our work was carried out in the development phase of a cyber-physical platform that enables seamless integration of third party vendor devices and applications into a configurable, extensible and cost-effective technological platform for elderly care. The present paper targets the integration of commercial off the shelf components into a real-time indoor localization system, which allows monitoring the older adult’s level of activity and detecting potentially dangerous situations automatically. We present the initial results of our comparative evaluation on the accuracy of localisation and heart rate monitoring of persons within an indoor setting. We briefly present the integrated cyber-physical system developed as part of the project. We use intelligent luminaires that act as location beacons and detail the results of an experiment where we evaluated the performance of two different luminaire types. While our previous experiments have shown that room-level localisation of a moving subject was possible using custom-developed luminaires, we extended our evaluation to cover a cost-effective, commercially available alternative. Real-time location data was combined with heart rate information recorded using a commercially available smartwatch in order to obtain a more complete picture of the monitored person’s level of activity. Our initial results showed that existing technologies can be configured and integrated into a more complex platform, which remains customizable according to each end user’s needs.
Information Extraction of Domain-Specific Business Documents with Limited Data

Information extraction is a key corner-stone in the digitization of office data which requires the conversion of unstructured to structured data. However, in the actual application to business cases, there is a big deadlock to adapt common extraction systems to domain-specific documents due to the limitation of preparation of training data. To overcome this issue, we introduce a model, which employs pre-trained language models with a customized CNN layer for domain adaptation. The model is validated on three Japanese domain-specific and two benchmark machine reading comprehension data sets (SQuADs). Experimental results confirm that our model achieves promising results which are applicable for actual business scenarios.
The Code Generation Method Based on Gated Attention and InterAction-LSTM

Code generation is an important research field of software engineering, aiming to reduce development costs and improve program quality. Nowadays, more and more researchers intend to implement code generation by natural language understanding. In this paper, we propose a generation method to convert natural language descriptions to the program code based on deep learning. We use an encoder-decoder model with gated attention mechanism. Here, the decoder is an InterAction-LSTM. The gated attention combines the previous decoding cell state with source representations to improve the limitation of invariant source representations. The decoder makes the information interact each other before putting them into the gate of the LSTM. The code generation is verified on two datasets, Conala and Django. Compared with known models, our model outperforms the baselines both in Accuracy and Bleu.
Attribute-based encryption for named data networking

We compare and discuss the applicability and trade-offs of different attribute-based encryption (ABE) schemes to the possible use-cases of content-centric networking requiring end-to-end encryption of data with fine-grained access control, where the nature of content producers and consumers may vary, as well as the required expressivity of policies. We also report on the choice and implementation of an ABE scheme, as well as the overheads associated with its use.
Real-Time Facemask Detection and Analytics

The COVID-19 pandemic has emphasized the need for preventive measures in any pandemic situation. As we are now well aware, wearing a mask while stepping out in public places is imperative. In this context, our current work aims to build an end-to-end application-based system that scans people’s faces to determine whether they are wearing a face mask or not, or if they are wearing it improperly. It is a real-time system that provides feedback instantly. At the same time, different types of data obtained from the triple classification system are stored in a simple database to further visualize the same. The system we have created utilizes Single Shot Detector (SSD), a deep learning architecture, to extract a face from a given video frame, and MobileNetV2, another deep learning architecture, to classify it into one of the three categories mentioned above. Those without or improperly wearing a mask, are warned by the system. This work also makes use of QR (Quick Response) codes to identify the people in the video feed. Dataframes from Pandas in Python are used to organize the acquired data into a structured format which is then exported to Tableau. From this, we have visualized and observed the data and from the inferences made, meaningful insights have been arrived at, which can then be used to make informed decisions.
Spontaneous Symmetry Breaking in Data Visualization

Data visualization tools should create low-dimensional representations of data that emphasize structure and suppress noise. However, such non-linear amplifications of structural differences can have side effects like spurious clustering in t-SNE []. We present a more general class of spurious structure, namely broken symmetry, defined as visualizations that lack symmetry present in the underlying data. We develop a simple workflow for detection of broken symmetry and give examples of spontaneous symmetry breaking in t-SNE and other well-known algorithms such as GPLVM and kPCA. Our extensive, quantitative study shows that these algorithms frequently break symmetry, thereby highlighting new shortcomings of current visualization tools.
New symmetric 2-(176,50,14) designs

In this paper, we construct two new symmetric designs with parameters 2-(176,50,14) as designs invariant under certain subgroups of the full automorphism group of the Higman design. One is self-dual and has the full automorphism group of order 11520 and the other one is not self-dual and has the full automorphism group of order 2520.
Study on forearm swing recognition algorithms to drive the underwater power‐assisted device of frogman

Abstract The frogmen have a great demand for the underwater power‐assisted device (UPaD) because of their poor working conditions and complex missions. Although the wearable power‐assisted device on land has been diversified, the development of UPaDs is relatively slow. Meanwhile, the most of current small frogman transport equipment is needed to be manipulated by the frogmen with their hands with the aim of changing their moving speed or posture, which increases the difficulty of frogmen executing their tasks. The major contribution of this paper is to develop a method that frees the hands and realizes power assisting at the same time for frogmen, which can be applied to drive the UPaDs of frogmen. Two posture recognition algorithms based on the Human–Computer Interface have been promoted. The algorithms can recognize the forearm swings of the wearer of UPaD, calculate the frequency of the wearer's forearm swing, and adjust the moving speed of the wearer's UPaD according to the wearer's forearm swing frequency. Moreover, we evaluate the performance of two algorithms recognizing forearm swings underwater from four different subjects. Meanwhile, one of the two algorithms is improved and evaluated by experiments from the previous four subjects. The results of accuracy of two algorithms recognizing the wearer's forearm swing are more than 90%. The performance of the speed regulating system of UPaD based on the designed two algorithms is also acceptable.
‘The Spanish of the Internet’: Is That a Thing?

This chapter first describes certain innovations found in computer mediated communication in Spanish at the levels of discourse and morphosyntax, such as the creation of new discursive traditions (clickbait headlines) and the innovative patterns displayed by a number of morphemes (-i) and lexemes (fuerte, ojalá). It then offers a qualitative analysis of these phenomena, working with data mostly taken from Twitter, and compares them to similar uses described for English (such as the Because X construction). The chapter ends with an explanation of the high frequency of these uses in computer mediated communication in terms of indexicality.
Topics and their Evolution in the research field of Teaching Chinese as A Foreign Language : A bibliometrics analysis based on VOSviewer

Teaching Chinese as A Foreign Language (TCFL) has increasingly become an important cause in China’s international communication and a hot research area in language teaching and linguistic studies. To identify main research topics and reveal how these topics evolved since 2000, we conducted a bibliometrics analysis based on the CNKI literature database. VOSviewer, a software tool for constructing and visualizing bibliometric networks, was employed to illustrate the distribution and evolution maps in this field.
Message Drives Image: A Coverless Image Steganography Framework Using Multi-Domain Image Translation

The coverless steganography methods based on the GAN satisfy the need of large image set which is a shortcoming for traditional coverless methods. But most of GAN based methods have either low hiding capacity or weak robustness. In this paper, to solve these problems we design a coverless image steganography framework based on multi-domain image translation called MDI. The target label used to translation is built a relationship with secret message. This framework includes a generator and a multi-label classifier. Generator is used to translate cover image into stego image according to target label. Classifier is adopted to extract label in the image and obtain secret message. Moreover, common types of image attacks are considered in advance to improve the robustness of classifier. Finally, our experiments and discussion show our method has more robustness and higher hiding capacity than state of art coverless image steganography methods.
Word-Level Textual Adversarial Attack in the Embedding Space

Many studies have revealed the vulnerability of deep neural networks (DNNs) in the face of adversarial attacks. By adding a small perturbation to the input, adversarial attacks could fool many advanced models for computer vision, speech recognition and natural language processing tasks, posing severe security threats to DNNs. In this paper, we proposed a gradient-based word-level attack method in the embedding space to attack text classification models. This method computes the significance of each word and chooses the optimal word for substitution after word embedding; the generated adversarial texts have little semantic changes but could successfully fool classification DNNs. Through extensive experiments, we confirmed that the generated adversarial texts could achieve a success rate approaching 100% with a very low word substitution rate in attacking the WordCNN and LSTM models on three datasets. By human evaluation, the adversarial texts evaded human notice which implied little semantic changes were made. Experiments on different models also confirmed the transferability of the adversarial texts. Finally, we adopted adversarial training, and this improved the models' generalization capacity and robustness.
A Study on Optimization of Emergency Logistics Path in Complex Waters Based on Improved Dijkstra Algorithm

Emergency logistics path analysis is a practical research problem. Aiming at the problem that the traditional logistics distribution algorithm can not deal with the complex water area well, which leads to the long time and low accuracy of the algorithm, an improved Dijkstra algorithm is proposed to optimize the emergency logistics path of complex water area. Taking the emergency logistics path analysis problem as the combination optimization problem, the objective function is established from three aspects: safety factor, time factor and economic factor, and the emergency logistics model of complex waters is established by giving the weight coefficient of the objective function. On this basis, the Dijkstra algorithm and analysis a Dijkstr are described a shortcomings of the algorithm and improved it, which only needs to traverse the nodes in the table in order and consider the road interruption factor. Complete the optimization of emergency logistics path in complex waters. The experimental results show that the proposed algorithm takes a short time, the fitness of weight coefficient design in the construction of emergency logistics model is high, and the algorithm has a high accuracy of calculation, up to 90. It shows that the algorithm has good application performance.
S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification

Open set classification (OSC) tackles the problem of determining whether the data are in-class or out-of-class during inference, when only provided with a set of in-class examples at training time. Traditional OSC methods usually train discriminative or generative models with the owned in-class data, and then utilize the pre-trained models to classify test data directly. However, these methods always suffer from the embedding confusion problem, i.e., partial out-of-class instances are mixed with in-class ones of similar semantics, making it difficult to classify. To solve this problem, we unify semi-supervised learning to develop a novel OSC algorithm, S2OSC, which incorporates out-of-class instances filtering and model re-training in a transductive manner. In detail, given a pool of newly coming test data, S2OSC firstly filters the mostly distinct out-of-class instances using the pre-trained model, and annotates super-class for them. Then, S2OSC trains a holistic classification model by combing in-class and out-of-class labeled data with the remaining unlabeled test data in a semi-supervised paradigm. Furthermore, considering that data are usually in the streaming form in real applications, we extend S2OSC into an incremental update framework (I-S2OSC), and adopt a knowledge memory regularization to mitigate the catastrophic forgetting problem in incremental update. Despite the simplicity of proposed models, the experimental results show that S2OSC achieves state-of-the-art performance across a variety of OSC tasks, including 85.4% of F1 on CIFAR-10 with only 300 pseudo-labels. We also demonstrate how S2OSC can be expanded to incremental OSC setting effectively with streaming data.
Scale aware remote sensing image enhancement using rolling guidance

Enhancement of remotely sensed images is a challenging problem, since the enhanced image has to have an improved contrast and edge information while preserving the original radiance values as much as possible. In this paper, a scale aware enhancement method based on rolling guidance is proposed for remotely sensed images. For each scale, a guidance image is defined and the approximation image is provided by an iterative joint filtering of the approximation and guidance images. Then the extracted details are amplified through an adaptive scheme and added to the final level approximation layer to provide the resulting enhanced image. A comparative study between the proposed methods with classical edge preserving filters and traditional methods have been carried out by using several criteria. The proposed methods have an average of 12% improvement for contrast gain (CG) metric and 81% improvement for enhancement measurement (EME) metric compared to the closest comparison method.
CC-BRRT: A Path Planning Algorithm Based on Central Circle Sampling Bidirectional RRT

Path planning through a bidirectional fast extended random tree algorithm cannot converge quickly, which does not meet the requirements of mobile robot path planning. To address this problem, an improved, central circle sampling bidirectional RRT (CC-BRRT) algorithm is proposed in this paper. The algorithm searches for the next sampling point by a central circle sampling strategy to reduce the numbers of searching nodes, and reduces the randomness by a target biasing strategy to speed up the convergence of the algorithm. For the obtained path, a sextic spline interpolation method is used to generate a smooth and executable path. Finally, experiments on mobile robot path planning are conducted both in simple with fewer obstacles and complex with more obstacles scenarios. The results show that the proposed CC-BRRT algorithm is superior to several other algorithms, with substantially fewer nodes sampled and a good smoothness and feasibility of the planned path.
Assessment of the Clarity of Bank of Russia Monetary Policy Communication by Neural Network Approach

Inflation targeting requires clear and transparent central bank’s communication. Analysts and market participants understand it as a broad list of information disclosed by the central bank. The general public understands it rather as the ability of a central bank to speak and explain its decisions in a plain language. In recent decades, monetary authorities in many countries have made significant progress in this direction. However, there has been no research on the quality of communication for the Bank of Russia. This paper aims to create a tool for automated evaluation of the readability of the Bank of Russia’s monetary policy communication, taking into account the available experience of linguistic and textual analysis, including machine learning methods, as well as to provide recommendations for its improvement. This can contribute to improving the effectiveness of the Bank of Russia communication on monetary policy, which is vital for its credibility, anchoring inflation expectations, and predictability of the regulator’s decisions.
Deep Learning Based Video Super-Resolution and its Application in Video Conferences

As a remote communication method, remote video scene is widely used in some occasions such as telecommuting, telelearning, and teleconsultation. However, the remote video scene requires a large network bandwidth for image information transmission, resulting in the insufficient real-time performance. In this paper, applying the super-resolution image restoration method to a remote video scene only requires 1/16 of the original network bandwidth, but a good visual effect of image reconstruction can be obtained. The Enhanced Information Multi-Distillation Block (EIMDB) and the Pixel-Level Information Distillation Block (PIDB) are proposed, which can improve the super-resolution effect of the image with a small amount of calculation. Finally, a novel real-time remote video communication super-resolution network (RVCSRN) is proposed which achieves a good balance between speed and restoration effect, and can effectively improve the visual effect of the remote video scene. In addition, since the pictures processed in the remote video scene are different from those processed by the general super-resolution method, these pictures have a compression loss, so a remote video scene dataset (RVSet) is created to obtain a better super-resolution effect.
Information Systems Of School Financial Management With Digital Signature Recognition Using MobileNet Algorithm

School financial management is needed for various fields, such as budgeting activities for academic, organizational, and administrative funds. At the moment, the method of budgeting funds uses a website platform. This study proposes an Android-based School Financial Management Information System using the introduction of Digital Signature to improve authentication. Each user only performs a digital signature on an android application. Digital signature results are sent to the server for classification using transfer learning method for feature extraction and Support Vector Classifier (SVC) for classification. The results showed that the school financial management information system used Digital Signature with an accuracy of 0.96 using the transfer learning method and SVC. This system has been used in real terms by Surabaya education authorities.