Reviewerly/exHarmony huggingface dataset:
K-FFRaaS: A Generic Model for Financial Forensic Readiness as a Service in Korea

While Korean financial companies are currently providing electronic financial services by establishing the high-level information technology and security system in accordance with the Electronic Financial Supervision Regulations (EFSR), they are rarely equipped with digital forensic readiness (DFR) to maximize the capability to collect critical digital evidence (DE). So, there is a limit to identifying the root cause of financial incidents and securing admissible DE. In this paper, there, the authors present Financial Forensic Readiness as a Service in Korea (K-FFRaaS), as DFR of financial companies to secure the admissible DE. Based on ISO/IEC 27043:2015 international standard, K-FFRaaS consists of 3 processes groups, namely: Planning processes group, Implementation processes group, and Assessment processes group. Planning processes group is the processes group to prepare the organization to be forensically ready before potential incidents happen. Implementation processes group is the stage to carry out the processes defined in the planning group. Assessment processes group consists of activities that evaluate whether the result of the implementation process group is consistent with the objective of K-FFRaaS. The contribution of this research is to present that the financial company can adopt the systematic management procedure for mitigating the causes of incidents, store admissible DE, and present scientific evidence to a court of law through K-FFRaaS.
EasyCloud: Multi-clouds made easy

Interoperability between different cloud platforms is a critical requirement for letting users to smoothly switch between different cloud providers and combine their services. However, the lack of standard interfaces to access these cloud platforms may result in the vendor lock-in situation, whereby users are locked into a specific cloud provider. In this paper, we present EasyCloud, a toolkit able to effectively support the creation and usage of Multi-cloud Systems (MSs) by providing interoperability, platform independence, effective resource provisioning, and ease of use. We describe its architecture and implementation, and experimentally assess the performance of EasyCloud, and compare it to existing alternative MS toolkits that are representative of the state-of-the-art. Our results clearly show that EasyCloud is highly scalable, quite efficient, and outperforms the other alternative toolkits.
Nonsynaptic Backpropagation Learning of Interval-valued Long-term Cognitive Networks

This paper elaborates on the modeling and simulation of complex systems involving uncertainty. More explicitly, we are interested in situations in which experts hesitate about the exact values of variables when designing the model. Such situations can be modeled using Interval-valued Long-term Cognitive Networks (IVLTCNs). In this model, the activation values and the weights between neural concepts are expressed as interval grey numbers. Unlike other grey cognitive networks, our model neither imposes restrictions on the weights nor performs a whitenization process. The second contribution of this paper is a nonsynaptic grey backpropagation algorithm, which allows adjusting the learnable parameters of IVLTCNs under uncertainty conditions. Moreover, this learning algorithm does not alter the linear knowledge representations provided by domain experts during the modeling phase.
A Meta-Learning Approach for Automated Hyperparameter Tuning in Evolving Data Streams

Most machine learning algorithms rely on a set of hyperparameters, which are tuned either by domain experts or using automated methods. In the context of data streams, the complexity of this task is compounded by the need for tuning every time a concept drift occurs. The problem of automated tuning may lead to classification performance improvements. Current methods optimise the hyperparameters from scratch every time concept drift is detected. Additionally, meta-learning techniques developed for static machine learning tasks allow pre-built knowledge to adapt faster to new tasks. We propose a meta-learning approach for automated tuning in evolving data streams, specifically for tuning parameters in Adaptive Random Forest (ARF) and parameters for several drift detectors. We compare our approach against non-adaptive methods on both synthetic and real-world datasets. Empirical results show that our approach makes a compromises between predictive performance and resource consumption for ARF, and between true positives and false positives for drift detectors.
Virtual Reality in Sexual Harassment Prevention

Sexual harassment (SH) training is a major public health priority, and available programs show limited efficacy. We present a new application for virtual agents in virtual reality (VR), which has the potential to deliver SH training that imitates real world scenarios and is yet safe. We conducted a proof-of-concept study to examine whether a VR simulation of a job interview during which the interviewer, a semi-automated virtual agent, sexually harasses the interviewee is an effective practice tool for women, and could serve as the basis for developing skills for effective response. Five females (24-25 years old) participated in this VR scenario and were then interviewed about their reactions to the VR scenario and the virtual agent. Four main themes emerged: paralysis and fear, uncertainty how to respond, resurfacing of previous SH experiences, and VR as an effective training tool for preventing SH. Findings suggest that contemporary virtual agents in VR can induce the sense of being harassed, similar to real humans. VR offers a safe exposure for SH and may serve as a learning platform to empower women in considering and practicing more effective responses to future SH experiences.
A Hierarchy of Deep Reinforcement Learning Agents for Decision Making in Blockchain Nodes

Distributed P2P network applications are increasingly growing and it is expected to gain more attention in the future for the wide range of attractive features it offers. One of the popular applications of P2P networks is the blockchain technology and associated applications such as cryptocurrencies. The blockchain technology was developed to operate on a distributed environment of nodes without the need for a central authority. One of the challenging tasks facing applications that operate on P2P networks is the collective decision-making process between the distributed nodes for reaching a consensus between the peer nodes. In this paper, a framework is proposed to handle the problem of collective decision making between distributed peer nodes. The framework introduces the usage of distributed intelligent agents which will be trained by deep reinforcement learning. Three models have been developed and evaluated using randomly generated data. The best achieved result by the proposed framework was obtained by using Model 3, the overall accuracy of the model was 82.2%.
Stream Distributed Coded Computing

The emerging large-scale and data-hungry algorithms require the computations to be delegated from a central server to several worker nodes. One major challenge in the distributed computations is to tackle delays and failures caused by the stragglers. To address this challenge, introducing efficient amount of redundant computations via distributed coded computation has received significant attention. Recent approaches in this area have mainly focused on introducing minimum computational redundancies to tolerate certain number of stragglers. To the best of our knowledge, the current literature lacks a unified end-to-end design in a heterogeneous setting where the workers can vary in their computation and communication capabilities. The contribution of this paper is to devise a novel framework for joint scheduling-coding, in a setting where the workers and the arrival of stream computational jobs are based on stochastic models. In our initial joint scheme, we propose a systematic framework that illustrates how to select a set of workers and how to split the computational load among the selected workers based on their differences in order to minimize the average in-order job execution delay. Through simulations, we demonstrate that the performance of our framework is dramatically better than the performance of naive method that splits the computational load uniformly among the workers, and it is close to the ideal performance.
BeepTrace for COVID-19 Pandemic: A Demo

Given the heavy economic losses and threats to human health caused by COVID-19 worldwide, as well as the privacy concerns from existing contact tracing technologies, we developed a novel contact tracing mobile application BeepTrace based on blockchain, targeted at mitigating the pandemic and easing the privacy concerns of contact tracing. The app features dual-mode, where the passive mode uses GPS, and the active mode with Bluetooth Low Energy (BLE) technology to solve contact information respectively. Thanks to blockchain technology, it solves third-party trust concerns and opens up anonymous data sharing for COVID-19 protection and beyond by adopting BeepTrace approach as an open platform of health/disease tracing data. At present, we have released the alpha version of the app with the preliminary realization of relevant functions. We believe this application will have significant implications for the global response to the pandemic of and beyond COVID-19.
Current Advances on Deep Learning-based Human Action Recognition from Videos: a Survey

Human action recognition (HAR) from RGB videos is essential and challenging in the computer vision field due to its wide range of real-world applications in fields of human behaviour analysis, human-computer interactions, robotics and surveillance etc. Since the breakthrough and fast development of deep learning technology, the performance of HAR based on deep neural networks has been significantly improved in this decade. In this survey, we discuss the growing use of deep learning for HAR, such as representative two-stream and 3D CNNs, and particularly highlight most recent success achieved by using attention and transformers. We will provide our perspective on the new trend of designing innovative deep learning methods. In addition, we also present popular HAR datasets developed in recent years and benchmark accuracy achieved by current advancement in deep learning. This draws research attention to the challenges of HAR by identifying performance gaps when applying the deep learning methods on large HAR datasets. Further, this survey sheds light on the development of new methods and facilitates qualitative comparison with state of the art.
Nature-Inspired Algorithms for Image Enhancement

The paper presents a comparative analysis of nature inspired Artificial Bee Colony and Particle Swarm Optimization algorithms for the enhancement of images. The paper briefly reviews the swarm-based algorithms. It describes the dynamics of ABC and Particle Swarm Optimization algorithm and mapping for image enhancement. The key purpose of Image processing is to extract essential features from images. During an image processing operation, the input given is an image, and its output is an enhanced high-quality image based on some characteristics/features related to that Image. The mapping of the ABC and PSO algorithms for image enhancement was implemented using the Matlab image processing toolbox. For both algorithms ABC and PSO, the parameters of Contrast Enhancement alpha was set in the range of 0-255 and beta was set in the range of 1-109. Finally, the Comparative Analysis of ABC and PSO Algorithms was done using the metrics of Entropy, MSE and PSNR values for the Images. It is found that ABC yields a better performance as compared to PSO.
Event structures for the reversible early internal π-calculus

The π-calculus is a widely used process calculus, which models communications between processes and allows the passing of communication links. Various operational semantics of the π-calculus have been proposed, which can be classified according to whether transitions are unlabelled (so-called reductions) or labelled. With labelled transitions, we can distinguish early and late semantics. The early version allows a process to receive names it already knows from the environment, while the late semantics and reduction semantics do not. All existing reversible versions of the π-calculus use reduction or late semantics, despite the early semantics of the (forward-only) π-calculus being more widely used than the late. We introduce two reversible forms of the internal π-calculus; these are the first to use early semantics. The internal π-calculus is a subset of the π-calculus where every link sent by an output is private, yielding greater symmetry between inputs and outputs. One of the new reversible calculi uses static reversibility, where performing an action does not change the structure of the process, and the other uses dynamic reversibility, where performing an action moves it to a separate history. We show an operational correspondence between the two calculi. For the static calculus we define denotational event structure semantics, which generate an event structure inductively on the structure on the process. For the dynamic calculus we define operational event structure semantics, which generate an event structure based on a labelled asynchronous transition system. We describe a correspondence between the resulting event structures.
Doki Doki: A Modular Wearable for Social Interaction in the COVID Era and Beyond

Doki Doki [fig 1] is a responsive, emotive wearable created as speculative research. It exists to reimagine how we might approach our usual attitudes and rituals of social interaction via nonverbal communication in the midst of the ongoing COVID-19 global pandemic. The garment takes physiological and environmental data, and visualises it as cues for use during social or other encounters. The artefact is modular, incorporating a plug and play circuit board for sustainability. This allows for sensors and actuators to be exchanged or reused, and code updated, depending on requirements. The development and usage of the device contributes to the discussion of how and why we might use data driven wearable technology to aid us in social interaction during the pandemic and beyond.
Shared Neural Item Representations for Completely Cold Start Problem

Neural networks have become popular recently in recommendation systems to extract user and item representations. Most previous works follow a two-branch setting, where user and item networks learn user and item representations in the first and second branches, respectively. In the item cold-start problem, where the usage patterns of the items do not exist, the user network uses ID/interaction vector as the input and the item network uses the item side information (content) as the input. In this paper, we will show that by using this structure, two representations are learned for each item in the training set; one is the output of the item network and the other one is hidden inside the user network and is used for learning user representations. Learning two representations makes training slower and optimization more difficult. We propose to unify the two representations and only use the one generated by the item network. Also, we will show how attention mechanisms fit in our setting and how they can improve the quality of the representations. Our results on public and real-world datasets show that our approach converges faster, achieves higher recall in fewer iterations, and is more robust to the changes in the number of training samples compared to the previous works.
Improving Distant Supervised Relation Extraction using shortest dependency path

Abstract Distant supervised relation extraction is the problem of classifying the semantic relation between a pair of entities in a given sentence, where data is created by aligning unlabelled text with knowledge base automatically. Prior researches indicated that most of the sentences in the distant supervised relation extraction setting are very long and benefit from the attention over words. We note that the shortest dependency path between the entity pair in the syntax analysis tree of a sentence can help word based attention. In this paper, we propose a noval distant supervised neural relation extraction method, which makes use of the shortest dependency path to supervise the learning of word attention. Through extensive experiments on benchmark datasets, we demonstrate effectiveness of our model.
Deep Successive Subspace Learning for Data Clustering

Deep clustering combines embedding and clustering together to obtain an optimal low dimensional embedding sub- space (aka latent subspace) for clustering, which can be more effective compared to conventional clustering approaches such as k-means. Typical deep clustering methods employ autoencoder (AE) and obtain their optimal latent space through minimizing data reconstruction loss which has no substantial connection with the clustering performance. In contrast, in this paper we propose a novel AE-based clustering scheme Deep Successive Subspace Learning (DSSL) which simultaneously minimizes weighted reconstruction and clustering losses of data points, where weights are defined based on similarity between latent representation of data points and cluster centers. DSSL obtains its optimal latent space through K (i.e. number of clusters) successive training runs where each run corresponds to an individual cluster. At each run, DSSL focuses on reconstruction and clustering of those data points that are more likely to belong to the corresponding cluster; hence, implicitly training those network parameters that have more influence on that cluster. Experimental results on benchmark datasets demonstrate that the proposed DSSL method can significantly outperform state-of-the-art clustering approaches.
Sea Wave Detection System Using Web-Based Naive Bayes Algorithm

The tidal wave was a staggering fiasco with substantial setbacks and harm to structures in the influenced region because of the monstrous rapid sea waves that cleared the land. Indonesia is a country that is truly powerless against torrent assaults on the grounds that it comprises of thousands of islands encompassed by seas and situated in regions where dynamic structural plates meet. The reason for a wave itself can be a quake that is focused under the ocean, volcanic emissions submerged, avalanches submerged, or a meteor sway adrift. Based on the above problems, we need a system capable of detecting ocean waves and providing early warnings to the public. The system will be designed using the method naïve Bayes to provide early warning to fishers and coastal communities based on the parameters of the speed and height of sea waves connected to the Internet of things. Tests using naïve Bayes have excellent accuracy performance between 98% to 100%.
A Frequent Itemset Mining Method Based on Local Differential Privacy

As an important means of data analysis, frequent itemset mining is widely used in the field of big data. In recent years, local differential privacy has become a representative privacy protection technology in the field of frequent itemset mining due to its good mathematical theory, which has attracted the continuous attention of researchers. The existing frequent itemset mining methods based on local differential privacy have problems with insufficient data availability. Aiming at the existing binary coding-based perturbation method that causes large matching errors, an improved data perturbation method is proposed to enhance the availability of mining results while protecting data privacy. To solve the large privacy budget of existing methods, the hidden Markov model is introduced to avoid accessing a huge quantity of itemset. Thus, the candidate set can be quickly generated, which improves the efficiency of the algorithm. Experimental results show that the proposed method has a lower privacy budget and higher data accuracy.
Spectral Clustering Joint Deep Embedding Learning by Autoencoder

Spectral clustering has become one of the most popular clustering methods due to its superior performance compared to the traditional clustering methods. However, the performance of spectral clustering would be limited by complex data, such as a huge number of samples and high dimensionality. To address this problem, some existing methods apply deep learning to learn the lower-dimensional representations, spectral clustering is then applied to the representations. Different from the existing methods that separate the two stages of feature representation learning and spectral clustering, in this paper, we propose Spectral Clustering Joint Deep Embedding (SCJDE), a method that simultaneously learns the feature representations and the spectral embedding of spectral clustering via a deep autoencoder. Moreover, a sparsity constraint is imposed to generate better spectral embedding for spectral clustering. Finally, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means is performed on the spectral embedding to obtain clustering result. The proposed method can learn a good spectral embedding for spectral clustering by deep learning to obtain better clustering results. The experimental results on both synthetic and real-world datasets demonstrate the effectiveness of the proposed method.
Selective Learning from External Data for CT Image Segmentation

Learning from external data is an effective and efficient way of training deep networks, which can substantially alleviate the burden on collecting training data and annotations. It is of great significance in improving the performance of CT image segmentation tasks, where collecting a large amount of voxel-wise annotations is expensive or even impractical. In this paper, we propose a generic selective learning method to maximize the performance gains of harnessing external data in CT image segmentation. The key idea is to learn a weight for each external data such that ‘good’ data can have large weights and thus contribute more to the training loss, thereby implicitly encouraging the network to mine more valuable knowledge from informative external data while suppressing to memorize irrelevant patterns from ‘useless’ or even ‘harmful’ data. Particularly, we formulate our idea as a constrained non-linear programming problem, solved by an iterative solution that alternatively conducts weights estimating and network updating. Extensive experiments on abdominal multi-organ CT segmentation datasets show the efficacy and performance gains of our method against existing methods. The code is publicly available (Released at https://github.com/YouyiSong/Codes-for-Selective-Learning).
To Batch or Not to Batch? Comparing Batching and Curriculum Learning Strategies across Tasks and Datasets

Many natural language processing architectures are greatly affected by seemingly small design decisions, such as batching and curriculum learning (how the training data are ordered during training). In order to better understand the impact of these decisions, we present a systematic analysis of different curriculum learning strategies and different batching strategies. We consider multiple datasets for three tasks: text classification, sentence and phrase similarity, and part-of-speech tagging. Our experiments demonstrate that certain curriculum learning and batching decisions do increase performance substantially for some tasks.