Reviewerly/exHarmony huggingface dataset:
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning

Face hallucination is a domain-specific super-resolution problem that aims to generate a high-resolution (HR) face image from a low-resolution (LR) input. In contrast to the existing patch-wise super-resolution models that divide a face image into regular patches and independently apply LR to HR mapping to each patch, we implement deep reinforcement learning and develop a novel attention-aware face hallucination (Attention-FH) framework, which recurrently learns to attend a sequence of patches and performs facial part enhancement by fully exploiting the global interdependency of the image. Specifically, our proposed framework incorporates two components: a recurrent policy network for dynamically specifying a new attended region at each time step based on the status of the super-resolved image and the past attended region sequence, and a local enhancement network for selected patch hallucination and global state updating. The Attention-FH model jointly learns the recurrent policy network and local enhancement network through maximizing a long-term reward that reflects the hallucination result with respect to the whole HR image. Extensive experiments demonstrate that our Attention-FH significantly outperforms the state-of-the-art methods on in-the-wild face images with large pose and illumination variations.
Detecting "Pump &amp; Dump Schemes" on Cryptocurrency Market Using An Improved Apriori Algorithm

With the popularity of Bitcoin, a cryptocurrency market emerged. However, because of insufficient supervision, the market attracts scams, for example, pump and dump (P&D) scheme, a famous fraudulent behavior in stock markets, has been found rampant in the market. To help deal with this issue, as a preliminary study, this paper proposes an improved apriori algorithm to detect user groups which may involve in P&D schemes. The validity of the algorithm is verified by using the leaked transaction history of Mt. Gox Bitcoin exchange. Furthermore, by exploring some of the detected user groups, many abnormal trading behaviors in the exchange found. These findings provide new insights into the behavior of users in the cryptocurrency market, thus leading to meaningful implications for policymakers, investors, and managers dealing with the cryptocurrency market.
An Approach to Developing Adaptive Electronic Educational Course

Modern information and telecommunication technologies allow not only to provide information required for training students in electronic form, they promote changes in educational process. The electronic information educational environment provides the necessary mechanism for implementing student's individual trajectories of training. Depending on his or her learning progress the process can vary from basic to intensive, with changes in volumes of information provided. One of the approaches to developing an adaptive electronic educational course is described in this paper. The tree of concepts mastered by students is considered as a basis for knowledge component organization. The tree of concepts will allow not only to organize theoretical material of the course in the form of the separate blocks of educational information intended for studying but also to determine a necessary and sufficient set of questions for testing students.
Artificial Intelligence Tools for Smart Tourism Development

The recent development and use of information technologies in tourism lead to innovations that have revolutionised and automated almost every phase of tourist journey. In order to respond to these changes, the smart tourism concept emerged, providing real-time solutions, advanced analytics and enhancing tourist experience. The volume of created data and the need of real-time interactions and results challenge the tourism sector and open the door for the use of artificial intelligence (AI). However, there has been a lack of academic research on AI and its connection to smart tourism development so far. Therefore, the aim of the paper is to review artificial intelligence tools used in tourism and to identify its role in smart tourism development. The article focuses on best practice examples and case studies that examine the use of AI phenomena in tourism.
Media competencies of university professors and students. Comparison of levels in Spain, Portugal, Brazil and Venezuela / Las competencias mediáticas de profesores y estudiantes universitarios. Comparación de niveles en España, Portugal, Brasil y Venezuela

Media competencies are a set of skills that every individual should possess in order to be able to consume and produce media and digital and information products in a critical and analytical way. This exploratory and comparative work analyses the level of media competence among 1,676 university students and 524 professors in Brazil, Spain, Portugal and Venezuela. One of the main results shows that the level of knowledge of technology and interaction — which is linked to digital competencies — does not depend solely on age, thus contradicting theories of digital natives and migrants. Our study also found that the general level of media competence is no better than medium to low when considering language, technology, interaction, production and dissemination, ideology and values, and aesthetics. These results point to the need to develop transversal actions for instructing both university professors and students in media competencies to face an ecosystem dominated by fake news and disinformation, as well as public policies directed at improving these skills among citizens at large.
Survey on the Application of BlockChain to IoT

A network composed of lightweight devices such as IoT has problems due to limited resources such as lack of storage space and low computing performance. These issues pose significant challenges in the application of robust security technologies, which reduces network security performance. The blockchain with strong security is a suitable technology to solve IoT problems with weak security. As a result, various research is being carried out to increase security, lightness, and efficiency of the IoT network by applying blockchain to IoT. This paper introduces the trend of research to apply blockchain to IoT.
Personalization in text information retrieval: A survey

Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual users and user groups by taking account of additional information about users besides their queries. In the past two decades or so, PIR has received extensive attention in both academia and industry. This article surveys the literature of personalization in text retrieval, following a framework for aspects or factors that can be used for personalization. The framework consists of additional information about users that can be explicitly obtained by asking users for their preferences, or implicitly inferred from users' search behaviors. Users' characteristics and contextual factors such as tasks, time, location, etc., can be helpful for personalization. This article also addresses various issues including when to personalize, the evaluation of PIR, privacy, usability, etc. Based on the extensive review, challenges are discussed and directions for future effort are suggested.
A hybrid multiple feature construction approach for classification using Genetic Programming

The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter’s fitness function and wrapper’s fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance.
Combining clustering and active learning for the detection and learning of new image classes

Discriminative classification models often assume all classes are available at the training phase. As such models do not have a strategy to learn new concepts from available unlabeled instances, they usually work poorly when unknown classes emerge from future data to be classified. To address the appearance of new classes, some authors have developed approaches to transfer knowledge from known to unknown classes. Our study provides a more flexible approach to learn new (visual) classes that emerge over time. The key idea is materialized by an iterative classifier that combines Support Vector Machines with clustering via an optimization algorithm. An entropy and density-based selection strategy explores label uncertainty and high-density regions from unlabeled data to be classified. Selected instances from new classes are submitted to get labels and then used to improve the model. The proposed image classifier is consistently better than approaches that select instances randomly or from clusters. We also show that features obtained via Deep Learning methods improve results when compared with shallow features, but only using our selection strategy. Our approach requires fewer iterations to learn new classes, thereby significantly saving labeling costs.
Machine learning, practically speaking

To apply machine learning, labs needn't have years of computational expertise, but they do need a cautious mind-set.
Improved non-maximum suppression for object detection using harmony search algorithm

Non-maximum suppression (NMS) plays a key role in many modern object detectors. It is responsible to remove detection boxes that cover the same object. NMS greedily selects the detection box with maximum score; other detection boxes are suppressed when the degree of overlap between these detection boxes and the selected box exceeds a predefined threshold. Such a strategy easily retain some false positives, and it limits the ability of NMS to perceive nearby objects in cluttered scenes. This paper proposes an effective method combining harmony search algorithm and NMS to alleviate this problem. This method regards the task of NMS as a combination optimization problem. It seeks final detection boxes under the guidance of an objective function. NMS is applied to each harmony to remove imprecise detection boxes, and the remaining boxes are used to calculate the fitness value. The remaining detection boxes in a harmony with highest fitness value are chosen as the final detection results. The standard Pattern Analysis, Statistical Modeling and Computational Learning Visual Object Classes dataset and the Microsoft Common Objects in Context dataset are used in all of the experiments. The proposed method is applied to two popular detection networks, namely Faster Region-based Convolutional Neural Networks and Region-based Fully Convolutional Networks. The experimental results show that the proposed method improves the average precision of these two detection networks. Moreover, the location performance and average recall of these two detectors are also improved.
Fog Computing as an Enabler for Immersive Media: Service Scenarios and Research Opportunities

Immersive media services, such as augmented reality and virtual reality (AR/VR), <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup><i>a</i></sup></xref> 360-degree video, and free-viewpoint video (FVV), are popular today. They require massive data storage, ultrahigh computing power, and ultralow latency. It is hard to fulfill these requirements simultaneously in a conventional communication system using a cloud/centralized radio access network (C-RAN). Specifically, due to centralized processing in such a system, the end-to-end latency, as well as the burden on the fronthaul network, are expected to be high. Fog computing-based radio access networks (F-RAN), in contrast, have been widely considered as an enabler for immersive media. Our contribution in this paper is threefold: First, we propose various service scenarios reflecting the characteristics of immersive media. Second, we identify the technologies that are required to support the proposed service scenarios under F-RAN and discuss how they can support the proposed scenarios efficiently. Third, we discuss possible research opportunities. <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>a</sup></label> A list of acronyms can be found in the Appendix. </fn>
A systematic mapping study of API usability evaluation methods

An Application Programming Interface (API) provides a programmatic interface to a software component that is often offered publicly and may be used by programmers who are not the API’s original designers. APIs play a key role in software reuse. By reusing high quality components and services, developers can increase their productivity and avoid costly defects. The usability of an API is a qualitative characteristic that evaluates how easy it is to use an API. Recent years have seen a considerable increase in research efforts aiming at evaluating the usability of APIs. An API usability evaluation can identify problem areas and provide recommendations for improving the API. In this systematic mapping study, we focus on 47 primary studies to identify the aim and the method of the API usability studies. We investigate which API usability factors are evaluated, at which phases of API development is the usability of API evaluated and what are the current limitations and open issues in API usability evaluation. We believe that the results of this literature review would be useful for both researchers and industry practitioners interested in investigating the usability of API and new API usability evaluation methods.
Mammogram Image Enhancement Using Entropy and CLAHE Based Intuitionistic Fuzzy Method

Mortality rate because of breast cancer diminishes to a large extent if the categorization of breast lesions as malignant or benign is done properly. But this process is quite complicated owing to erroneous detection of noise pixels as false positives. It can be reduced by proper enhancement of the features of the mammogram giving an indication of cancer. In this paper, contrast limited adaptive histogram equalization (CLAHE) and entropy-based intuitionistic fuzzy method are anticipated for improving the contrast of digital mammogram images. To validate the efficacy of the proposed algorithm over type II fuzzy set-based techniques, subjective, quantitative and visual evaluation is done on publicly available MIAS database. Experimental results prove that the proposed technique gives better visual quality. It provides high values of subjective and quantitative metrics compared to several states of art algorithms.
Edge cloud resource expansion and shrinkage based on workload for minimizing the cost

The joint use of cloud and edge has been paid more and more attention by enterprises. Enterprises use the cloud’s utility computing and elasticity to placement resources flexibly under the adaptive load. However, there are still many challenges in how to minimize edge cloud costs and ensure data integrity in the resource expansion and shrinkage of edge cloud. This paper studies the resource management problem based on load balance in edge and cloud environments. The novelty is that we consider the resource granularity of the cloud service provider and the data loss problem that resource shrinkage may bring. Firstly, the resource expansion and shrinkage model based on service cost is proposed to reduce the cost of edge cloud clusters under the condition of satisfying the cluster load. Secondly, due to the problem of unbalanced cluster load caused by resource expansion and the data reliability caused by resource shrinkage, a data migration model is proposed aiming at the data loss problem that resource shrinkage may bring. Finally, the proposed algorithms are evaluated with extensive comparisons among the benchmarks and sensitivity tests to various environment factors.
A scalable real-time tracking and monitoring architecture for logistics and transport in RoRo terminals

Supply chain management and complex logistics compel constant monitoring and handling of even more growing shipment chains. Therefore, it prompts the necessity to track and trace goods, for ensuring the control and management of the different logistics operations. In port terminals, tracking and tracing cargo is primitive for smooth and flexible services. Technologies such as RFID, GPS, RTLS, etc. are widely used for monitoring cargo flow in the logistics chain. In light of this, this paper proposes a novel architecture based on auto-ID technologies like QRcode, Barcode, Magnetic card ID to support real-time tracking of vehicles in a RoRo terminal and provide their stay time as well as processing time in each service. The modeled system aims to solve dynamic logistics process management problems, monitor rolling cargo, identify similar behavior as well as increase traffic flow and reduce check-in time. Design/approach: The proposed architecture provides a real-time tracking capability. It tracks a vehicle in each area of the terminal through a portal that identify it using a barcode, a QRcode or a magnetic ID card. The system performs the process logic checking/reasoning and provides process knowledge support. Originality: The proposed architecture is a novel approach which leverages the logistics performance in RoRo terminals and facilitates the tracking of vehicles and provides a real-time knowledge support on processes for those who handle logistics operations.
Enabling Energy-Efficient Unsupervised Monocular Depth Estimation on ARMv7-Based Platforms

This work deals with the implementation of energy-efficient monocular depth estimation using a low-cost CPU for low-power embedded systems. It first describes the PyD-Net depth estimation network, which consists of a lightweight CNN able to approach state-of-the-art accuracy with ultra-low resource usage. Then it proposes an accuracy-driven complexity reduction strategy based on a hardware-friendly fixed-point quantization. Finally, it introduces the low-level optimization enabling effective use of integer neural kernels. The objective is threefold: (i) prove the efficiency of the new quantization flow on a depth estimation network, that is, the capability to retaining the accuracy reached by floating-point arithmetic using 16- and 8-bit integers, (ii) demonstrate the portability of the quantized model into a general-purpose 32-bit RISC architecture of the ARM Cortex family, (iii) quantify the accuracy-energy tradeoff of unsupervised monocular estimation to establish its use in the embedded domain. The experiments have been run on a Raspberry PI board powered by a Broadcom BCM2837 chipset. A parametric analysis conducted over the KITTI date-set shows marginal accuracy loss with 16-bit (8-bit) integers and energy savings up to 6.55× (9.23×) w.r.t. floating-point. Compared to high-end CPU and GPU the proposed solution improves scalability.
Stroke Extraction for Offline Handwritten Mathematical Expression Recognition

Offline handwritten mathematical expression recognition is often considered much harder than its online counterpart due to the absence of temporal information. In order to take advantage of the more mature methods for online recognition and save resources, an oversegmentation approach is proposed to recover strokes from textual bitmap images automatically. The proposed algorithm first breaks down the skeleton of a binarized image into junctions and segments, then segments are merged to form strokes, finally stroke order is normalized by using recursive projection and topological sort. Good offline accuracy was obtained in combination with ordinary online recognizers, which are not specially designed for extracted strokes. Given a ready-made state-of-the-art online handwritten mathematical expression recognizer, the proposed procedure correctly recognized 58.22%, 65.65%, and 65.22% of the offline formulas rendered from the datasets of the Competitions on Recognition of Online Handwritten Mathematical Expressions(CROHME) in 2014, 2016, and 2019 respectively. Furthermore, given a trainable online recognition system, retraining it with extracted strokes resulted in an offline recognizer with the same level of accuracy. On the other hand, the speed of the entire pipeline was fast enough to facilitate on-device recognition on mobile phones with limited resources. To conclude, stroke extraction provides an attractive way to build optical character recognition software.
Blockchain Enabled IoT Edge Computing

Number of internet connected devices is increasing as IoT is getting more prevalent. Volume of data collected by IoT sensors is very high and requires considerable resources for data processing like analytics. Edge processing enables getting the sensor data processed closer to the source. It's not always possible and/or economical to set up a resource intense infrastructure at the edge. This paper proposes use of blockchain based decentralized application to enable IoT edge processing. With this setup, it is possible for a resource owner to join the ecosystem and to lend the compute resources as needed. IoT devices can offload some of the edge computation to the resource owner nodes as and when required. This paper explains architectural components and overall solution for implementation. Based on the usecase of video analytics at edge, experimental setup is implemented for the IoT edge solution using hyperledger sawtooth blockchain.
Multi-agent behavioral control system using deep reinforcement learning

Deep reinforcement learning (DRL) has emerged as the dominant approach to achieving successive advancements in the creation of human-wise agents. By leveraging neural networks as decision-making controllers, DRL supplements traditional reinforcement methods to address the curse of dimensionality in complicated tasks. However, agents in complicated environments are likely to get stuck in sub-optimal solutions. In such cases, the agent inadvertently turns into a "zombie" owing to its short-term vision and harmful behaviors. In this study, we use human learning strategies to adjust agent behaviors in high-dimensional environments. Therefore, the agent behaves predictably and succeeds in attaining its designated goal. In summary, the contribution of this study is two-fold. First, we introduce a lightweight workflow that enables a nonexpert to preserve a certain level of safety in AI systems. Specifically, the workflow involves a novel concept of a target map and a multi-agent behavioral control system named Multi-Policy Control System (MPCS). MPCS successfully controls agent behaviors in real time without involving the burden of human feedback. Second, we develop a multi-agent game named Tank Battle that provides a configurable environment to examine agent behaviors and human-agent interactions in DRL. Finally, simulation results show that agents guided by MPCS outperform agents that do not use MPCS with respect to the mean of total rewards and human-like behaviors in complicated environments such as Seaquest and Tank Battle.