Reviewerly/exHarmony huggingface dataset:
Big Data Processing at the Edge with Data Skew Aware Resource Allocation

With the increasing number of connected devices and the generation of a large amount of data, efficient methods are required to deal with the complexities of big data processing, especially within edge computing infrastructures. Processing batch data on the cloud is often too costly. However, edge servers can be used as alternative resources. Various factors influence Cloud-Edge resource provisioning cause a challenging issue, one of which, data skewness is the focus of this paper. Some data blocks require more processing resources. For example, when processing a log file, the number of repetitions of a target URL varies in different parts of the file which impacts the required processing resources. Existing resource allocation methods ignore the data skew feature in big data processing and apply the same policies to different parts of the input data. In this paper, we propose a data skew aware approach for allocating each data block to the edge or the cloud server for processing. We reduce the cost of data processing on the cloud by categorizing the data blocks based on their significance (i.e., their impact on the processing output). Then, we assign the blocks that are less significant and require less processing power to the Edge and the rest to the cloud. Our experimental results confirm that our provisioning approach improves the processing cost by up to 35% compared to the state-of-the-art.
Grover on KATAN: Quantum Resource Estimation

This article presents the cost analysis of mounting Grover's key search attack on the family of KATAN block cipher.Several designs of the reversible quantum circuit of KATAN are proposed.Owing to the National Insitute of Standards and Technology's (NIST) proposal for postquantum cryptography standardization, the circuits are designed focusing on minimizing the overall depth.We observe that the reversible quantum circuits designed using and gates and T -depth one Toffoli gate give more shallow circuits.Grover oracle for KATAN is designed based on the reversible circuits, which are used further to mount Grover's key search attack on KATAN.The designs are implemented using the software framework ProjectQ, which provides a resource estimation tool to perform an appropriate cost analysis in an automated way.While estimating the resources, NIST's depth restrictions are also respected.
MC-OCR Challenge: Mobile-Captured Image Document Recognition for Vietnamese Receipts

The paper describes the organisation of the "Mobile Captured Receipt Recognition Challenge" (MC-OCR) task at the RIVF conference 2021 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> on recognizing the fine-grained information in Vietnamese receipts captured using mobile devices. The task is organized as a multi-tasking model on a dataset containing 2,436 Vietnamese receipts. The participants were challenged to build a model that is capable of (1) predicting receipt’s quality based on readable information, and (2) recognizing textual information of four required information (i.e., "SELLER", "SELLER ADDRESS", "TIMESTAMP", and "TOTAL COST") in the receipts. MC-OCR challenge happened in one month and top winners of each task will present their solutions at RIVF 2021. Participants were competing on CodaLab.Org from 05 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> December 2020 to 23 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> January 2021. All participants with valid submitted results were encouraged to submit their papers. Within one month, the challenge has attracted 105 participants and recorded about 1,285 submission entries.
Ambiguity in user stories: A systematic literature review

Ambiguity in user stories is a problem that has received little research attention. Due to the absence of review studies, it is not known how and to what extent this problem, which impacts the effectiveness of user stories in supporting systems development, has been solved. We review the studies that investigate or develop solutions for problems related to ambiguity in user stories. We investigate how these problems manifest themselves, what their causes and consequences are, what solutions have been proposed and what evidence of their effectiveness has been presented. Based on the insights we obtain from this review, we identify research gaps and suggest opportunities for future research. We followed Systematic Literature Review guidelines to review problems investigated, solutions proposed, and validation/evaluation methods used. We classified the reviewed studies according to the four linguistic levels of ambiguity (i.e., lexical, syntactic, semantic, pragmatic) proposed by Berry and Kamsties to obtain insights from patterns that we observe in the classification of problems and solutions. A total of 36 studies published in 2001–2020 investigated ambiguity in user stories. Based on four patterns we discern, we identify three research gaps. First, we need more research on human behaviors and cognitive factors causing ambiguity. Second, ambiguity is seldom studied as a problem of a set of related user stories, like a theme or epic in Scrum. Third, there is a lack of holistic solution approaches that consider ambiguity at multiple linguistic levels. Ambiguity in user stories is a known problem. However, a comprehensive solution for addressing ambiguity in a set of related user stories as it manifests itself at different linguistic levels as a cognitive problem is lacking.
Long-term power forecasting using FRNN and PCA models for calculating output parameters in solar photovoltaic generation

This paper evaluated a 1.4 kW grid-connected photovoltaic system (GCPV) using two neural network models based on experimental data for one year. The novelty of this study is to propose and compare full recurrent neural network (FRNN), and principal component analysis (PCA) models based on entire year experimental data, considering limited research conducted to predict GCPV behaviour using the two methods. The system data was collected for 12 months secondly and hourly data with 50400 samples daily. The GCPV evaluates using specific yield, energy cost, capacity factor, payback period, current, voltage, power, and efficiency. The predicted GCPV current and power using FRNN and PCA were evaluated and compared with measured values to validate results. However, the results indicated that FRNN is better in simulating the experimental results curve compared with PCA. The measured and predicted data are compared and evaluated. It is found that the GCPV is suitable and promising for the study area in terms of technical and economic evaluation with a 3.24-4.82 kWh/kWp-day yield, 21.7% capacity factor, 0.045 USD/kWh cost of energy, and 11.17 years payback period.
Interpersonal Affective Touch in a Virtual World: Feeling the Social Presence of Others to Overcome Loneliness

Humans are by nature social beings tuned to communicate and interact from the very beginning of their lives. The sense of touch represents the most direct and intimate channel of communication and a powerful means of connection between the self and the others. In our digital age, the development and diffusion of internet-based technologies and virtual environments offer new opportunities of communication overcoming physical distance. It however, happens that social interactions are often mediated, and the tactile aspects of communication are overlooked, thus diminishing the feeling of social presence, which may contribute to an increased sense of social disconnection and loneliness. The current manuscript aims to review the extant literature about the socio-affective dimension of touch and current advancements in interactive virtual environments in order to provide a new perspective on multisensory virtual communication. Specifically, we suggest that interpersonal affective touch might critically impact virtual social exchanges, promoting a sense of co-presence and social connection between individuals, possibly overcoming feelings of sensory loneliness. This topic of investigation will be of crucial relevance from a theoretical perspective aiming to understand how we integrate multisensory signals in processing and making sense of interpersonal exchanges, this is important in both typical and atypical populations. Moreover, it will pave the way to promising applications by exploring the possibility to use technical innovations to communicate more interactively in the case of people who suffer from social isolation and disconnection from others.
Research contributions and challenges in DLT-based cryptocurrency regulation: a systematic mapping study

The purpose of this paper is to identify research that has been carried out about cryptocurrency regulation contributions and the current challenges that need to be addressed in future studies. The methodology used to conduct this research and report the findings was systematic mapping. We use this methodology to search, identify, and select all relevant primary studies on cryptocurrency regulation. The findings reveal that the key cryptocurrency regulation research topics are distributed governance, central bank digital currency, monetary policy, cryptocurrency adoption, security, regulation, cryptocurrency market, cybercrime economy and money laundering. The research proposals for cryptocurrency regulation comprise tools, protocols, methods, models, frameworks and, knowledge. The cryptocurrency regulatory challenges are cryptocurrency adoption, central bank digital currency regulation, accounting for cryptocurrencies and risk for cryptocurrencies. This systematic mapping provides an overview of the solutions proposed to regulate cryptocurrency as well as the current research challenges.
Text mining and visual analytics in research: Exploring the innovative tools

The aim of the study is to present an advanced overview and potential application of the innovative tools/software's/methods used for data visualization, text mining, scientific mapping, and bibliometric analysis. Text mining and data visualization has been a topic of research for several years for academic researchers and practitioners. With the advancement in technology and innovation in the data analysis techniques, there are many online and offline software tools available for text mining and visualisation. The purpose of this study is to present an advanced overview of latest, sophisticated, and innovative tools available for this purpose. The unique characteristic about this study is that it provides an overview with examples of the five most adopted software tools such as VOSviewer, Biblioshiny, Gephi, HistCite and CiteSpace in social science research. This study will contribute to the academic literature and will help the researchers and practitioners to apply these tools in future research to present their findings in a more scientific manner.
Blockchain-Based Transparent Integrity Auditing and Encrypted Deduplication for Cloud Storage

In this paper, we introduce a concept of transparent integrity auditing and propose a concrete scheme based on the blockchain, which goes one step beyond existing public auditing schemes, since the auditing does not rely on third-party auditors while freeing users from heavy communication costs on auditing the data integrity. Then we construct a secure transparent deduplication scheme based on the blockchain that supports deduplication over encrypted data and enables users to attest the deduplication pattern on the cloud server. Such a scheme allows users to directly benefit from data deduplication and protects data content against anyone who does not own the data. Finally, we integrate the proposed transparent integrity auditing scheme and transparent deduplication scheme into one system, dubbed BLIND. We evaluate BLIND from security and efficiency, which demonstrates that BLIND achieves a strong security guarantee with high efficiency.
Preserving integrity in online social networks

The recent progress and ongoing problems of keeping online platforms and their users safe from malicious activities.
Investigating the Efficient Use of Word Embedding with Neural-Topic Models for Interpretable Topics from Short Texts

With the rapid proliferation of social networking sites (SNS), automatic topic extraction from various text messages posted on SNS are becoming an important source of information for understanding current social trends or needs. Latent Dirichlet Allocation (LDA), a probabilistic generative model, is one of the popular topic models in the area of Natural Language Processing (NLP) and has been widely used in information retrieval, topic extraction, and document analysis. Unlike long texts from formal documents, messages on SNS are generally short. Traditional topic models such as LDA or pLSA (probabilistic latent semantic analysis) suffer performance degradation for short-text analysis due to a lack of word co-occurrence information in each short text. To cope with this problem, various techniques are evolving for interpretable topic modeling for short texts, pretrained word embedding with an external corpus combined with topic models is one of them. Due to recent developments of deep neural networks (DNN) and deep generative models, neural-topic models (NTM) are emerging to achieve flexibility and high performance in topic modeling. However, there are very few research works on neural-topic models with pretrained word embedding for generating high-quality topics from short texts. In this work, in addition to pretrained word embedding, a fine-tuning stage with an original corpus is proposed for training neural-topic models in order to generate semantically coherent, corpus-specific topics. An extensive study with eight neural-topic models has been completed to check the effectiveness of additional fine-tuning and pretrained word embedding in generating interpretable topics by simulation experiments with several benchmark datasets. The extracted topics are evaluated by different metrics of topic coherence and topic diversity. We have also studied the performance of the models in classification and clustering tasks. Our study concludes that though auxiliary word embedding with a large external corpus improves the topic coherency of short texts, an additional fine-tuning stage is needed for generating more corpus-specific topics from short-text data.
Marker-free surgical navigation of rod bending using a stereo neural network and augmented reality in spinal fusion

The instrumentation of spinal fusion surgeries includes pedicle screw placement and rod implantation. While several surgical navigation approaches have been proposed for pedicle screw placement, less attention has been devoted towards the guidance of patient-specific adaptation of the rod implant. We propose a marker-free and intuitive Augmented Reality (AR) approach to navigate the bending process required for rod implantation. A stereo neural network is trained from the stereo video streams of the Microsoft HoloLens in an end-to-end fashion to determine the location of corresponding pedicle screw heads. From the digitized screw head positions, the optimal rod shape is calculated, translated into a set of bending parameters, and used for guiding the surgeon with a novel navigation approach. In the AR-based navigation, the surgeon is guided step-by-step in the use of the surgical tools to achieve an optimal result. We have evaluated the performance of our method on human cadavers against two benchmark methods, namely conventional freehand bending and marker-based bending navigation in terms of bending time and rebending maneuvers. We achieved an average bending time of 231s with 0.6 rebending maneuvers per rod compared to 476s (3.5 rebendings) and 348s (1.1 rebendings) obtained by our freehand and marker-based benchmarks, respectively.
ECCNAS: Efficient Crowd Counting Neural Architecture Search

Recent solutions to crowd counting problems have already achieved promising performance across various benchmarks. However, applying these approaches to real-world applications is still challenging, because they are computation intensive and lack the flexibility to meet various resource budgets. In this article, we propose an efficient crowd counting neural architecture search (ECCNAS) framework to search efficient crowd counting network structures, which can fill this research gap. A novel search from pre-trained strategy enables our cross-task NAS to explore the significantly large and flexible search space with less search time and get more proper network structures. Moreover, our well-designed search space can intrinsically provide candidate neural network structures with high performance and efficiency. In order to search network structures according to hardwares with different computational performance, we develop a novel latency cost estimation algorithm in our ECCNAS. Experiments show our searched models get an excellent trade-off between computational complexity and accuracy and have the potential to deploy in practical scenarios with various resource budgets. We reduce the computational cost, in terms of multiply-and-accumulate (MACs), by up to 96% with comparable accuracy. And we further designed experiments to validate the efficiency and the stability improvement of our proposed search from pre-trained strategy.
Bath-Induced Correlations Enhance Thermometry Precision at Low Temperatures

We study the role of bath-induced correlations in temperature estimation of cold bosonic baths. Our protocol includes multiple probes, that are not interacting, nor are they initially correlated to each other. They interact with a bosonic sample and reach a nonthermal steady state, which is measured to estimate the temperature of the sample. It is well known that in the steady state such noninteracting probes may get correlated to each other and even entangled. Nonetheless, the impact of these correlations in metrology has not been deeply investigated yet. Here, we examine their role for thermometry of cold bosonic gases and show that, although being classical, bath-induced correlations can lead to significant enhancement of precision for thermometry. The improvement is especially important at low temperatures, where attaining high precision thermometry is particularly demanding. The proposed thermometry scheme does not require any precise dynamical control of the probes and tuning the parameters and is robust to noise in initial preparation, as it is built upon the steady state generated by the natural dissipative dynamics of the system. Therefore, our results put forward new possibilities in thermometry at low temperatures, of relevance, for instance, in cold gases and Bose-Einstein condensates.
Modeling implicit bias with fuzzy cognitive maps

This paper presents a Fuzzy Cognitive Map model to quantify implicit bias in structured datasets where features can be numeric or discrete. In our proposal, problem features are mapped to neural concepts that are initially activated by experts when running what-if simulations, whereas weights connecting the neural concepts represent absolute correlation/association patterns between features. In addition, we introduce a new reasoning mechanism equipped with a normalization-like transfer function that prevents neurons from saturating. Another advantage of this new reasoning mechanism is that it can easily be controlled by regulating nonlinearity when updating neurons’ activation values in each iteration. Finally, we study the convergence of our model and derive analytical conditions concerning the existence and unicity of fixed-point attractors.
Auto-CASH: A meta-learning embedding approach for autonomous classification algorithm selection

With years of development, machine learning algorithms have excellent performance in some tasks of data analysis and data mining. To apply machine learning to new tasks, suitable algorithm and hyperparameters selection techniques, which is known as Combined Algorithm Selection and Hyperparameter optimization problem, are in demand. In the field of data analysis, how to automate the algorithm selection process has become a hot research topic in recent years. Most of the existing approaches are developed under the background of Automated Machine Learning with high time or space complexity. To alleviate the issue, an approach extracts and learns from prior experience based on meta-learning theory named Auto-CASH is proposed in this paper. One of the major drawbacks of existing meta-learning methods is that they rely too much on human expertise to extract and filter knowledge that guides subsequent training. Auto-CASH can automatically select features of tasks by introducing a reinforcement learning strategy. Thus Auto-CASH becomes less dependent on human expertise. Besides, two pruning strategies when processing Hyperparameter Optimization to improve efficiency are firstly proposed. Extensive experiments on classification tasks are conducted and results demonstrate that Auto-CASH outperforms state-of-the-art CASH approaches and popular AutoML systems with less time cost.
JoT-GAN: A Framework for Jointly Training GAN and Person Re-Identification Model

To cope with the problem caused by inadequate training data, many person re-identification (re-id) methods exploit generative adversarial networks (GAN) for data augmentation, where the training of GAN is typically independent of that of the re-id model. The coupling relation between them that probably brings in a performance gain of re-id is thus ignored. In this work, we propose a general framework, namely JoT-GAN, to jointly train GAN and the re-id model. It can simultaneously achieve the optima of both the generator and the re-id model, where the training is guided by each other through a discriminator. The re-id model is boosted for two reasons: (1) the adversarial training encourages it to fool the discriminator, and (2) the generated samples augment the training data. Extensive results on benchmark datasets show that for the re-id model trained with the identification loss as well as the triplet loss, the proposed joint training framework outperforms existing methods with separate training and achieves state-of-the-art re-id performance.
Enhanced Virtualization-Based Dynamic Bin-Packing Optimized Energy Management Solution for Heterogeneous Clouds

Cloud computing provides unprecedented advantages of using computing resources with very less efforts and cost. The energy utilization in cloud data centers has forced the cloud service providers to raise the expense of using its services and has increased the carbon footprints in the environment. Many static bin-packing algorithms exist which can reduce energy by some percentage, but with new era of digitization, advanced and dynamic techniques are required which can serve heterogeneous users and random users’ requests. Thus, in this paper, two new dynamic best-fit decreasing-based bin-packing algorithms are proposed wherein the first technique is for service providers and focuses on increasing server utilization and the second approach acts as a switcher to harness best results among all algorithms. Both techniques deliberately achieve high performance in terms of total energy consumption, resource utilization, and makespan along with serving continuous and varying requests from customers. The simulations are performed using Java. The results exhibited that DEE-BFD can escalate resource utilization by 96% and EM switcher can reduce total energy consumption by 49% and reduce makespan by 56%.