Reviewerly/exHarmony huggingface dataset:
High Resolution Fingerprint Retrieval Based on Pore Indexing and Graph Comparison

Fingerprint retrieval aims to identify a query fingerprint image in a large database using indexing algorithms. Because of the abundant level 3 pore features within high-resolution fingerprint images, pore-based fingerprint retrieval algorithms have been rapidly developed. These retrieval algorithms, however, suffer from severe calculation-consuming problems with the pores increasing. This paper proposes a pore-based fingerprint retrieval method for high-resolution fingerprint images. The proposed method consists of two main steps. 1) In the pore indexing step, an indexing space is constructed using the binary codes of pores in enrolled images. Then, a designed graph-based searching algorithm searches the nearest neighbors of pores from the query image to construct one-to-many correspondences. 2) In the refinement step, the one-to-many correspondences are refined by a random walker-based graph comparison algorithm to remove the false correspondences. The remained nearest neighbors are used to calculate the similarities between the query image and the enrolled images. The proposed method is evaluated on two databases, showing that our method achieves better retrieval accuracies with a higher speed than the existing pore-based retrieval algorithms.
MM-trafficEvent: An Interactive Incident Retrieval System for First-view Travel-log Data

Dashcam video has become popular recently due to the safety of both individuals and communities. While an individual can have undeniable evidence for legal and insurance, communities can benefit from sharing these dashcam videos for further traffic education and criminal investigation. Moreover, relying on recent computer vision and AI development, few companies have launched the so-called AI dashcam that can alert drivers to near-risk accidents (e.g.., following distance detection, forward collision warning), forwarding to improving driver’s safety. However, even though dashcam videos create a driver’s travel log (i.e., traveling diary), little research focuses on creating a valuable and friendly tool to find any incident or event with few described sketches by users. Besides, most incident detection models have been built using a traditional supervised learning approach (i.e., collecting and labeling data for a new incident class). That prevents the quick and customized development of a new incident class. Inspired from these observations, we introduce an interactive incident detection and retrieval system for first-view travel-log data, namely MM-trafficEvent, that can (1) online defined-incident detection, (2) offline fine-grained incident retrieval for both defined and undefined incidents, (3) offline automatically new incident class creating using user’s queries. Moreover, the system gives promising results when being evaluated on several public datasets.
Risk Analysis of Textile Industry Foreign Investment Based on Deep Learning

With the decline of China's economic growth rate and the uproar of antiglobalization, the textile industry, one of the business cards of China's globalization, is facing a huge impact. When the economic model is undergoing transformation, it is more important to prevent enterprises from falling into financial distress. So, the financial risk early warning is one of the important means to prevent enterprises from falling into financial distress. Aiming at the risk analysis of the textile industry's foreign investment, this paper proposes an analysis method based on deep learning. This method combines residual network (ResNet) and long short-term memory (LSTM) risk prediction model. This method first establishes a risk indicator system for the textile industry and then uses ResNet to complete deep feature extraction, which are further used for LSTM training and testing. The performance of the proposed method is tested based on part of the measured data, and the results show the effectiveness of the proposed method.
Distributed GAN: Toward a Faster Reinforcement-Learning-Based Architecture Search

In the existing reinforcement learning (RL)-based neural architecture search (NAS) methods for a generative adversarial network (GAN), both the generator and the discriminator architecture are usually treated as the search objects. In this article, we take a different perspective to propose an approach by treating the generator as the search objective and the discriminator as the judge to evaluate the performance of the generator architecture. Consequently, we can convert this NAS problem to a GAN-style problem, similar to using a controller to generate sequential data via reinforcement learning in a sequence GAN, except that the controller in our methods generates serialized data information of architecture. Furthermore, we adopt an RL-based distributed search method to update the controller parameters <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\theta$</tex-math></inline-formula> . Generally, the reward value is calculated after the whole architecture searched, but as another novelty in this article, we employ the reward shaping method to judge the intermediate reward and assign it to every cell in the architecture to encourage the diversity and the integrity of all cells. The main contribution of this article is to provide a novel performance estimation mechanism, which could speed up the efficiency of architecture search, and improve the searching results with specific supplementary strategies. Crucially, this estimation mechanism can be applied to most RL-based NAS methods for the GAN. The experiments demonstrate that our methods achieve satisfactory results against our design objectives.
Conflicts between Likelihood and Knowledge Distillation in Task Incremental Learning for 3D Object Detection

In autonomous driving scenarios, edge cases require perception algorithms, like 3D object detection, to incrementally learn new data during a long term. To achieve it, previous methods seek help from knowledge distillation and recursively transfer knowledge from old models to new models. However, conflicts exist between the likelihood term and the distillation regularizer on both old and new knowledge. In this paper, we discuss the drawback of knowledge distillation in the task-incremental-learning scenario for 3D object detection and propose a New-Task-Aware Biased Sampling and Knowledge-Distillation-Aware Detection Loss to solve the conflicts. Based on the KITTI dataset, we thoroughly evaluate our proposed method from the aspects of both forward and backward transfer in the task incremental-learning scenario. A great margin of improvement on the whole task sequence (5.6 mAP) demonstrates the effectiveness of our proposed method.
Few-Shot learning for frame-Wise phoneme recognition: Adaptation of matching networks

Recently, the topic of Few-Shot Learning (FSL) is emerging as a radical direction in machine learning, well established with a variety of paradigms and network realizations for image recognition. However, FSL is yet to emerge in speech recognition and allied topics. In this paper, we adapt an FSL paradigm ‘matching networks' to the problem of speech recognition, in a first of its kind attempt, to different tasks such as multi-speaker small-to-medium vocabulary word recognition, monolingual and cross-lingual phoneme recognition tasks under mel-spectrogram and single-frame feature representations. The key to FSL is the ability to use extremely small number of training utterances, e.g. as low as 1 exemplar / class, as a N-way K-shot learning problem for large N and very small K (e.g. K=1 to 10). We show a remarkably high performance for each of the different speech recognition tasks considered here with matching networks, consistently requiring only very few ‘shots' of exemplars/class, even while surpassing the performance of a direct application of KDE (kernel density estimation) without the matching network's embedding. This adaptation sets the basis for applying the matching network framework to continuous speech recognition and cross-lingual ASR with extremely low training requirements in the target test language.
Inverse kinematics for full-body self representation in VR-based cognitive rehabilitation

Being self-represented through an avatar increases embodiment and the feeling of presence in virtual reality. Nevertheless, currently users in VR are typically represented only by their hands, as not enough tracking data is available for full body self-representation. In our use case of VR-based diagnostics and cognitive rehabilitation of stroke patients, we aim for full body self-representation in order to increase therapeutic effectivity of the treatment. To solve this problem, Inverse Kinematics (IK) can be used for pose estimation, where no tracking data is available. IK allows to minimize the use of hardware and efforts of patients and clinical staff and, at the same time, provides a full-body representation based only on positions of the VR-HMD and users hands as input. In some use cases tracking data from additional, visual full body tracking sensors can be used to estimate the position of the lower body joints. In this study, we evaluate existing IK-based pose estimators; find that VRIK from Final IK is the most suitable approach for the given use case; integrate VRIK in our VR-rehabilitation system; adapt VRIK to meet the use case requirements and conduct subjective tests to validate a significantly increased notion of embodiment and presence through full-body over hands-only self-representation.
Application of Deep Learning for Image Sequence Classification

Understanding human behavior from videos is a difficult challenge in computer vision. Computers are already pretty competent at recognizing objects in static photos, and such technology is frequently utilized today, but doing so in moving images is far more difficult. Identifying human behavior in films is a key step in creating smarter, gentler robots. Identifying the human activity in motion neural network plays a crucial role. In this study, we proposed a convolutional neural network to identify human activities in video. This algorithm helps in many sectors like anti-terrorists, anti-crime securities, and as well as surveillance, life logging and assistance.
Cross Network Representation Matching with Outliers

Research has revealed the effectiveness of network representation techniques in handling diverse downstream machine learning tasks upon graph structured data. However, most network representation methods only seek to learn information in a single network, which fails to learn knowledge across different networks. Moreover, outliers in real-world networks pose great challenges to match distribution shift of learned embeddings. In this paper, we propose a novel joint learning framework, called CrossOSR, to learn network-invariant embeddings across different networks in the presence of outliers in the source network. To learn outlier-aware representations, a modified graph convolutional network (GCN) layer is designed to indicate the potential outliers. To learn more fine-grained information between different domains, a subdomain matching is adopted to align the shift distribution of learned vectors. To learn robust network representations, the learned indicator is utilized to smooth the noise effect from source domain to target domain. Extensive experimental results on three real-world datasets in the node classification task show that the proposed framework yields state-of-the-art cross network representation matching performance with outliers in the source network.
Syntax in End-to-End Natural Language Processing

This tutorial surveys the latest technical progress of syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks, in which semantic role labeling (SRL) and machine translation (MT) are the representative NLP tasks that have always been beneficial from informative syntactic clues since a long time ago, though the advance from end-to-end deep learning models shows new results.In this tutorial, we will first introduce the background and the latest progress of syntactic parsing and SRL/NMT.Then, we will summarize the key evidence about the syntactic impacts over these two concerning tasks, and explore the behind reasons from both computational and linguistic background.
Semantic Interoperability

Interoperability, on the semantic level, deals with shared understanding of data, between IoT artifacts. Positioned on top of the syntactic layer, semantic interoperability facilitates solutions to problems that arise after the data is in a common format, with syntax understood by all participants. Provisioning of compatibility between not directly compatible data structures, representations, conventions and standards, falls strictly under the responsibility of semantic methods.This chapter introduces the INTER-IoT perspective on data semantics and summarizes it’s achievements in the field of semantic interoperability. Being a generic and far-reaching solution, the syntactic compatibility challenge is also mentioned, as it is a necessary precondition to comprehensive data interoperability suite.
Capacity Scaling Augmented With Unreliable Machine Learning Predictions

Modern data centers suffer from immense power consumption. As a result, data center operators have heavily invested in capacity scaling solutions, which dynamically deactivate servers if the demand is low and activate them again when the workload increases. We analyze a continuoustime model for capacity scaling, where the goal is to minimize the weighted sum of flow-time, switching cost, and power consumption in an online fashion. We propose a novel algorithm, called the Adaptive Balanced Capacity Scaling (ABCS) algorithm, that has access to black-box machine learning predictions. ABCS aims to adapt to the predictions and is also robust against unpredictable surges in the workload. In particular, we prove that the ABCS algorithm is (1 + ")-competitive if the predictions are accurate, and yet, it has a uniformly bounded competitive ratio even if the predictions are completely inaccurate.
Improved beetle swarm optimization algorithm for energy efficient virtual machine consolidation on cloud environment

Abstract In cloud computing, virtual machine (VM) consolidation and migration are significant schemes to enhance the energy efficiency and costs of the cloud environment. The process of VM consolidation is to execute a more number of tasks with less power consumption. However, due to unreliable physical resources, energy consumption is increased. So, to solve this issue, improved beetle swarm optimization (IBSO) algorithm based on energy‐aware VM consolidation is presented in this article. IBSO algorithm joins together beetle swarm optimization (BSO) and particle swarm optimization (PSO). The proposed algorithm is defined with the effective representation of solution and multi‐objective functions such as power consumption, resource wastage, and SLA violation. Results of the work are depicted that the proposed approach outperformed different previous methods in terms of different evaluation measures.
Tries-Based Parallel Solutions for Generating Perfect Crosswords Grids

A general crossword grid generation is considered an NP-complete problem and theoretically it could be a good candidate to be used by cryptography algorithms. In this article, we propose a new algorithm for generating perfect crosswords grids (with no black boxes) that relies on using tries data structures, which are very important for reducing the time for finding the solutions, and offers good opportunity for parallelisation, too. The algorithm uses a special tries representation and it is very efficient, but through parallelisation the performance is improved to a level that allows the solution to be obtained extremely fast. The experiments were conducted using a dictionary of almost 700,000 words, and the solutions were obtained using the parallelised version with an execution time in the order of minutes. We demonstrate here that finding a perfect crossword grid could be solved faster than has been estimated before, if we use tries as supporting data structures together with parallelisation. Still, if the size of the dictionary is increased by a lot (e.g., considering a set of dictionaries for different languages—not only for one), or through a generalisation to a 3D space or multidimensional spaces, then the problem still could be investigated for a possible usage in cryptography.
ICT Adoption and Stock Market Development: Empirical Evidence Using a Panel of African Countries

The aim of this study was to examine the impact of adopting information and communication technologies (ICT) on the development of African stock exchanges. The study examined a panel of 11 African stock exchanges for the period 2008–2017 and employed the generalised method of moments (GMM) to estimate the results. The results of the study documented that ICT adoption had a positive impact on stock market development in African countries. Firstly, it was found that the stock market traded volume and mobile–telephone user variables were positively related. Secondly, a positive relationship was also proven between the stock market traded volume and the broadband user variable. Thirdly, a positive relationship was documented between the stock market capitalisation variable and the fixed telephone user variable. Fourthly, the research findings confirmed a positive relationship between the stock market turnover ratio and the fixed telephone user variable. The findings of this study imply that policymakers should be more resolute when formulating ICT policies. ICT adoption can spur stock market development which in turn can propel economic growth, resulting in the economic prosperity of the African countries. Moreover, ICT adoption could enhance the integration of African stock exchanges, further buttressing the drive towards the common market areas in various regions.
Aspects of Mineral Exploration Thinking

Editor’s note: The aim of the Geology and Mining series is to introduce early career professionals and students to various aspects of mineral exploration, development, and mining in order to share the experiences and insight of each author on the myriad of topics involved with the mineral industry and the ways in which geoscientists contribute to each. Abstract Successful exploration requires an understanding of ore deposit models, the experience to recognize ore guides in an outcrop, nonlinear thinking, and some intuition. Models, using porphyry Cu deposits as examples, combine magmatic and hydrothermal processes; however, process and the results of process are different. Models provide important understanding of process but are not ore guides and do not drive discoveries; models function as rules that inhibit prediscovery exploration thinking. Results of the genetic process are recorded in descriptive models that do not reflect the considerable geologic variations existing between the hundreds of known porphyry Cu deposits. Discoveries and discovery cycles are driven by nonlinear thinking about ore guides visible in outcrop, not by genetic or descriptive models. Reality in an outcrop typically departs from generalized models. Reinterpretations that lead to drilling prospects rejected by previous exploration groups is what makes many discoveries. Increasingly, field-portable instruments for mineral and chemical analyses will add efficiencies. The most important product of early exploration work is the geologic map, defined here as a decision-making document. Mapping of ore guides in any ore-forming system invariably leads to sampling of outcrops where high grading can help geologists rig the odds in their favor. However, the objective is a highly profitable mine, not just a high-grade sample. That means the mineralization must be sufficiently continuous to build the inventory of recoverable metal required for a profitable mine, regardless of grade. High grade gets you interested, but continuity gets the mine. The principal intangible in any discovery is intuition, often described as nothing more or less than recognition, and it invariably involves experience. Perhaps the only tangible expression of intuition is displayed by individuals or teams that are unwilling to abandon a complex prospect, a behavior often described in case histories as tenacity.
Design of the University Mobile Educational Environment

The article addresses issues related to the description of the aspects of the instructional design of a mobile educational environment of a modern higher education institution. The authors give the definitions of the mobile educational environment, describe its functional features, and also consider the requirements for its instructional design and development. The article presents the clustering of digital services for the full implementation of a mobile educational environment (services for planning and supporting educational activities, services for organizing and supporting educational activities, communication and feedback services, general information services). The results of the experimental research to identify the attitude of students, teachers and administrative workers to the functional significance of mobile technologies and their application in the educational process are presented.
Model for Credit Card Fraud Detection using Machine Learning Algorithm

Master Card organizations can recognize fake transaction so clients do not pay for things that they didn’t buy. Such issues may be handled with Machine Learning. This undertaking means to outline the demonstration of an informational collection utilizing AI with Credit Card Fraud Detection. This Fraud Detection issue incorporates displaying previous credit card transaction with information of the ones that ended up being extortion. Our model is then used to check if other purchase/order is fraudulent. The aim is, to distinguish maximum of deceitful transactions and limiting the erroneous misrepresentation arrangements. In this cycle, we have zeroed in on dissecting and pre-handling informational collections just as the sending of numerous inconsistency location calculations, for example, SVM, logistic regression, KNN and random forest calculation on the PCA changed Credit Card transaction information.