Reviewerly/exHarmony huggingface dataset:
Multi‐feedback Pairwise Ranking via Adversarial Training for Recommender

Personalized recommendation systems predict potential demand by analyzing user preferences. Generally, user feedback information is inferred from implicit feedback or explicit feedback. Nevertheless, feedback can be contaminated by user's mis-operations or malicious operations, and may thus lead to incorrect results. We propose a novel Multi-feedback pairwise ranking method via Adversarial training (AT-MPR) for recommender to enhance the robustness and overall performance in the event of rating pollution. The MPR method extends Bayesian personalized ranking (BPR) to cover three types of feedback: positive, negative, and unobserved. It obtains user preferences in a probabilistic way through multiple feedbacks at different levels. To reduce the impact of feedback noise, we train an MPR objective function using minimax adversarial training. Experiments on two datasets show that the AT-MPR model achieves satisfactory performance and outperforms the state-of-the-art implicit feedback collaborative ranking models in two evaluation metrics.
Fuzzy Graph Modelling of Anonymous Networks

Anonymous networks have enabled secure and anonymous communication between the users and service providers while maintaining their anonymity and privacy. The hidden services in the networks are dynamic and continuously change their domains and service features to maintain anonymity and prevent fingerprinting. This makes modelling of such networks a challenging task. Further, modelling with crisp graphs is not suitable as they cannot capture the dynamic nature of the anonymous networks. In this work, we model the anonymous networks using fuzzy graphs and provide a methodology to simulate and analyse an anonymous network. We consider the case studies of two popular anonymous communication networks: TOR and Freenet, and show how the two networks can be analyzed using our proposed fuzzy representation.
Energy-aware scheduling of malleable HPC applications using a Particle Swarm optimised greedy algorithm

The scheduling of parallel tasks is a topic that has received a lot of attention in recent years, in particular, due to the development of larger HPC clusters. It is regarded as an interesting problem because when combined with performant hardware, it ensures fast and efficient computing. However, it comes with a cost. The growing number of HPC clusters entails a greater global energy consumption which has a clear negative environmental impact. A green solution is thus required to find a compromise between energy-saving and high-performance computing within those clusters. In this paper, we evaluate the use of malleable jobs and idle servers powering off as a way to reduce both jobs mean stretch time and servers average power consumption. Malleable jobs have the particularity that the number of allocated servers can be changed during runtime. We present an energy-aware greedy algorithm with Particle Swarm Optimised parameters as a possible solution to schedule malleable jobs. An in-depth evaluation of the approach is then outlined using results from a simulator that was developed to handle malleable jobs. The results show that the use of malleable tasks can lead to an improved performance in terms of power consumption. We believe that our results open the door for further investigations on using malleable jobs models coupled with the energy-saving aspect.
A Stacking Ensemble Model for Prediction of Multi-type Tweet Engagements

The RecSys Challenge 2020 is a competition with a task of predicting four types of user engagements on Twitter: Like, Reply, Retweet and Retweet with comment. In this paper, we describe Team Wantedly’s approach to this challenge, which won the third place. We found that the targets are highly correlated and it is important to use every engagement to predict the other engagements. Therefore, we choose to stack LightGBM models to use this co-occurrences effectively in the large dataset. Our final scores are as follows: 1.5266 (Retweet PR-AUC), 30.06 (Retweet RCE), 0.1918 (Reply PR-AUC), 20.44 (Reply RCE), 0.7716 (Like PR-AUC), 24.76 (Like RCE), 0.0724 (Retweet with comment PR-AUC), 14.86 (Reply RCE). Our code is available at https://github.com/wantedly/recsys2020-challenge.
An Attribute-Based Break-Glass Access Control Framework for Medical Emergencies

IoT-enabled medical services are nowadays gaining the major ground in modern lifestyle management strategies due to the increasing trends of advancements in the design and development of healthcare devices. A growing number of necessities in creating connected environments for effective treatment of a chronic disorder are further adding the objectivity in modern healthcare services. The IoT environments in healthcare paradigm consist of mainly the data acquisition system that captures the data from patient body and data aggregators or the data commuters that transform the physical layer data into the cloud layer data for analysis. This data is made accessible to medical professionals for further analysis and disease prediction from the cloud space. Data in the cloud storage needs to be protected by various encryption schemes from the unauthorized accesses. It is critically important to offer the data and device services timely during emergency situations in healthcare scenarios. In this view, authors designed a lightweight access control framework that enables the users to access encrypted data and devices in two modes: attribute oriented access and emergency break-glass access. Normally, a policy-based, multilayered authentication technique is used in encrypting data and a medical professional who satisfies the policy guidelines can decrypt the data through set of attributes. Whereas, during emergency situations the need of break-glass access arises so as to bypass the access policies and stringent security walls in order to timely access the data by rescue workers. The proposed framework is lightweight and fast since there are minimal calculations which are required at device and storage level thus by forming low computational and latency complexities. The proposed model is proved secure against the standard security models, and the efficiency is demonstrated through numerous experimentations.
Towards Specific Software Engineering Practices for Early-Stage Startups

Abstract In this position paper, our goal is to argue the need for specific software development practices to early-stage startups. In order to reach this goal, we discuss the consequences of innovative and market-driven contexts, which are two of the key elements when describing software startups. We also argue that these practices could be applied to innovative initiatives within established companies since they share similar characteristics and challenges as those from startups.
Multi-AUV Target Recognition Method Based on GAN-meta Learning

The presence of adverse factors such as turbid water quality and target occlusion prevented the acquisition of valid target characterization data. Due to the variable shape of the target, the accuracy of recognition for untrained new targets is low. In view of the above problems, this paper proposes a Multi-AUV target recognition method based on GAN-meta learning. VGG-19 network is used for feature extraction of target images, and the WGAN network is used to make up for missing target information. Based on the meta-learning theory, the parameters of the feature extraction process are trained by using stochastic gradient descent to improve the algorithm's ability to recognize new targets. Ensure that the GAN-meta learning model has a strong generalization ability. Simulation experiments are conducted on four underwater targets (underwater reconnaissance equipment, submarine, frogman, and torpedo) in the SUN dataset, and the results demonstrate that the proposed model achieves better performance.
Design of a Marine Environment Monitoring System Based on the Internet of Things

Chi, H.T.; Du, Y., and Brett, P.M., 2020. Design of a marine environment monitoring system based on the Internet of Things. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 256–260. Coconut Creek (Florida), ISSN 0749-0208.A good marine environment is the basis for the development and utilization of marine resources. Marine environment monitoring is an important part of marine environment protection. Therefore, the research on the marine environment monitoring technologies is of great significance. In order to realize intelligent monitoring of the marine environment, combining with the sensor technology, 4G communication technology and Zigbee communication technology, this study designed a marine environment monitoring system based on the Internet of Things (IoT). The system is composed of three parts: the information management subsystem, the data collection subsystem and the monitoring terminal subsystem; the structural composition and main implementation technologies of each subsystem are elaborated and the functions of each module in the system are tested by experiments. The test results show that, the IoT-based marine environment monitoring system can realize the functions of collecting, receiving, sending, analyzing, processing, querying and releasing marine environment data. This study provides a technical support for China's marine environment monitoring, and has important theoretical reference value for the research of intelligent marine environment monitoring in China.
Splitting models for multivariate count data

We investigate the class of splitting distributions as the composition of a singular multivariate distribution and a univariate distribution. It will be shown that most common parametric count distributions (multinomial, negative multinomial, multivariate hypergeometric, multivariate negative hypergeometric, …) can be written as splitting distributions with separate parameters for both components, thus facilitating their interpretation, inference, the study of their probabilistic characteristics and their extensions to regression models. We highlight many probabilistic properties deriving from the compound aspect of splitting distributions and their underlying algebraic properties. Parameter inference and model selection are thus reduced to two separate problems, preserving time and space complexity of the base models. Based on this principle, we introduce several new distributions. In the case of multinomial splitting distributions, conditional independence and asymptotic normality properties for estimators are obtained. Mixtures of splitting regression models are used on a mango tree dataset in order to analyze the patchiness.
Media Discourse About the Pandemic Novel Coronavirus (COVID-19) in East Asia: The Case of China and Japan

Irrespective of the nations and media, from 20 January 2020 to the date, the term â€˜coronavirusâ€™ is uttered and or written most frequently Recent emergence of t
Precise Cerebrovascular Segmentation

Analyzing cerebrovascular changes using Time-of-Flight Magnetic Resonance Angiography (ToF–MRA) images can detect the presence of serious diseases and track their progress, e.g., hypertension. Such analysis requires accurate segmentation of the vasculature from the surroundings, which motivated us to propose a fully automated cerebral vasculature segmentation approach based on extracting both prior and current appearance features that capture the appearance of macro and micro-vessels. The appearance prior is modeled with a novel translation and rotation invariant Markov-Gibbs Random Field (MGRF) of voxel intensities with pairwise interaction analytically identified from a set of training data sets, while the current appearance is represented with a marginal probability distribution of voxel intensities by using a Linear Combination of Discrete Gaussians (LCDG) whose parameters are estimated by a modified Expectation-Maximization (EM) algorithm. The proposed approach was validated on 190 data sets using three metrics, which revealed high accuracy compared to existing approaches.
Circular Security Is Complete for KDM Security

Circular security is the most elementary form of key-dependent message (KDM) security, which allows us to securely encrypt only a copy of secret key bits. In this work, we show that circular security is complete for KDM security in the sense that an encryption scheme satisfying this security notion can be transformed into one satisfying KDM security with respect to all functions computable by a-priori bounded-size circuits (bounded-KDM security). This result holds in the presence of any number of keys and in any of secret-key/public-key and CPA/CCA settings. Such a completeness result was previously shown by Applebaum (EUROCRYPT 2011) for KDM security with respect to projection functions (projection-KDM security) that allows us to securely encrypt both a copy and a negation of secret key bits. Besides amplifying the strength of KDM security, our transformation in fact can start from an encryption scheme satisfying circular security against CPA attacks and results in one satisfying bounded-KDM security against CCA attacks. This result improves the recent result by Kitagawa and Matsuda (TCC 2019) showing a CPA-to-CCA transformation for KDM secure public-key encryption schemes.
An image inpainting method for object removal based on difference degree constraint

Abstract In the inpainting method for object removal, SSD (Sum of Squared Differences) is commonly used to measure the degree of similarity between the exemplar patch and the target patch, which has a very important impact on the restoration results. Although the matching rule is relatively simple, it is likely to lead to the occurrence of mismatch error. Even worse, the error may be accumulated along with the process continues. Finally some unexpected objects may be introduced into the target region, making the result unable to meet the requirements of visual consistency. In view of these problems, we propose an inpainting method for object removal based on difference degree constraint. Firstly, we define the MSD (Mean of Squared Differences) and use it to measure the degree of differences between corresponding pixels at known positions in the target patch and the exemplar patch. Secondly, we define the SMD (Square of Mean Differences) and use it to measure the degree of differences between the pixels at known positions in the target patch and the pixels at unknown positions in the exemplar patch. Thirdly, based on MSD and SMD, we define a new matching rule and use it to find the most similar exemplar patch in the source region. Finally, we use the exemplar patch to restore the target patch. Experimental results show that the proposed method can effectively prevent the occurrence of mismatch error and improve the restoration effect.
KazNLP: A Pipeline for Automated Processing of Texts Written in Kazakh Language

We present the current results of our ongoing work on develop-ing tools and algorithms for processing Kazakh language in the framework of KazNLP project. The project is motivated by the need in accessible, easy to use, cross-platform, and well-documented automated text processing tools for Kazakh, particularly user generated text, which includes transliteration, code switching, and other artifacts of language-specific raw data that needs pre-processing. Thus, apart from a basic tokenization-tagging-parsing pipeline, and downstream applications such as named entity recognition and spell checking, KazNLP offers pre-processing tools such as text normalization and language identification. All of the KazNLP tools are released under the Creative Commons license. Since the detailed description of the methods and algorithms that were used in KazNLP are published or to be published in various venues, reference to which is given in the corresponding sections, this work provides just an overview of the tools and their performance level.
Cutting through the Jungle: Disambiguating Model-based Traceability Terminology

Traceability, a classic requirements engineering topic, is increasingly used in the context of model-based engineering. However, researchers and practitioners lack a concise terminology to discuss aspects of requirements traceability in situations in which engineers heavily rely on models and model-based engineering. While others have previously surveyed the domain, no one has so far provided a clear, unambiguous set of terms that can be used to discuss traceability in such a context. We therefore set out to cut a path through the jungle of terminology for model-based traceability, ground it in established terminology from requirements engineering, and derive an unambiguous set of relevant terms. We also map the terminology used in existing primary and secondary studies to our taxonomy to show differences and commonalities. The contribution of this paper is thus a terminology for model-based traceability that allows requirements engineers and engineers working with models to unambiguously discuss their joint traceability efforts.
Global Co-Occurrence Feature and Local Spatial Feature Learning for Skeleton-Based Action Recognition

Recent progress on skeleton-based action recognition has been substantial, benefiting mostly from the explosive development of Graph Convolutional Networks (GCN). However, prevailing GCN-based methods may not effectively capture the global co-occurrence features among joints and the local spatial structure features composed of adjacent bones. They also ignore the effect of channels unrelated to action recognition on model performance. Accordingly, to address these issues, we propose a Global Co-occurrence feature and Local Spatial feature learning model (GCLS) consisting of two branches. The first branch, based on the Vertex Attention Mechanism branch (VAM-branch), captures the global co-occurrence feature of actions effectively; the second, based on the Cross-kernel Feature Fusion branch (CFF-branch), extracts local spatial structure features composed of adjacent bones and restrains the channels unrelated to action recognition. Extensive experiments on two large-scale datasets, NTU-RGB+D and Kinetics, demonstrate that GCLS achieves the best performance when compared to the mainstream approaches.
School Media Education During the COVID–19 Pandemic: Limitations and New Opportunities

This paper focuses on the media use for distance learning in Ukraine during the quarantine caused by the COVID-19 pandemic.The study found out that during the COVID-19 pandemic the media have a powerful and contradictory influence on education, becoming a leading factor in socialization, social learning, and a means and source of formal and non-formal education.Theoretical methods were used in the work: analysis, generalization of scientific sources and normative documents.To generalize the experience of media educational activities of teachers, the survey was used (830 respondents).Its results were interpreted using methods of analysis, specification and classification.The teachers reported using media-related tasks, such as presentations, watching movies, visiting virtual museums, taking photos and making educational videos, reading popular science literature, comparing fiction and screenplays, comparing textual information from different sources, creating pages of literary characters in social networks, comics, mental maps, news reports, writing fictionalized biographies and annotations, preparing crossword puzzles after watching educational films, sharing life experience, personal life events, drawing after listening to audio and watching videos.The criteria for the responsible use of media by teachers in distance learning are following: preferential use of established professional platforms, or communication groups, verifying the sources, collaboration with colleagues and parents, adjusting information to meet individual needs.
Development of a multilingual digital signage system using a directional volumetric display and language identification

In a previous study, we have developed a directional volumetric display that handles multiple directional images. To realize a multilingual digital signage system, we implement a language identification function on the directional volumetric display. The language identification (English, Spanish or French) is performed using a convolutional neural network. The discrimination accuracy of the three languages is 91.9%. Combining the language identification function with the directional volumetric display, an image can be directed toward a specific speaker. The result is a multilingual digital signage system that adapts the image presentation to the spoken language.
Generative Adversarial Networks for Bitcoin Data Augmentation

In Bitcoin entity classification, results are strongly conditioned by the ground-truth dataset, especially when applying supervised machine learning approaches. However, these ground-truth datasets are frequently affected by significant class imbalance as generally they contain much more information regarding legal services (Exchange, Gambling), than regarding services that may be related to illicit activities (Mixer, Service). Class imbalance increases the complexity of applying machine learning techniques and reduces the quality of classification results, especially for underrepresented, but critical classes.In this paper, we propose to address this problem by using Generative Adversarial Networks (GANs) for Bitcoin data augmentation as GANs recently have shown promising results in the domain of image classification. However, there is no “one-fits-all” GAN solution that works for every scenario. In fact, setting GAN training parameters is non-trivial and heavily affects the quality of the generated synthetic data. We therefore evaluate how GAN parameters such as the optimization function, the size of the dataset and the chosen batch size affect GAN implementation for one underrepresented entity class (Mining Pool) and demonstrate how a “good” GAN configuration can be obtained that achieves high similarity between synthetically generated and real Bitcoin address data. To the best of our knowledge, this is the first study presenting GANs as a valid tool for generating synthetic address data for data augmentation in Bitcoin entity classification.
Policy Gradient based Quantum Approximate Optimization Algorithm

The quantum approximate optimization algorithm (QAOA), as a hybrid quantum/classical algorithm, has received much interest recently. QAOA can also be viewed as a variational ansatz for quantum control. However, its direct application to emergent quantum technology encounters additional physical constraints: (i) the states of the quantum system are not observable; (ii) obtaining the derivatives of the objective function can be computationally expensive or even inaccessible in experiments, and (iii) the values of the objective function may be sensitive to various sources of uncertainty, as is the case for noisy intermediate-scale quantum (NISQ) devices. Taking such constraints into account, we show that policy-gradient-based reinforcement learning (RL) algorithms are well suited for optimizing the variational parameters of QAOA in a noise-robust fashion, opening up the way for developing RL techniques for continuous quantum control. This is advantageous to help mitigate and monitor the potentially unknown sources of errors in modern quantum simulators. We analyze the performance of the algorithm for quantum state transfer problems in single- and multi-qubit systems, subject to various sources of noise such as error terms in the Hamiltonian, or quantum uncertainty in the measurement process. We show that, in noisy setups, it is capable of outperforming state-of-the-art existing optimization algorithms.