Reviewerly/exHarmony huggingface dataset:
Technical performance analysis of the smart solar photovoltaic blinds based on the solar tracking methods considering the climate factors

To reduce greenhouse gas emissions, many countries have decided to build nearly-zero-energy buildings (nZEBs) with renewable energy sources. Among the various products that can be applied to buildings to produce clean energy, the demand for the solar photovoltaic blinds (SPB), an emerging technology for building integrated photovoltaic (BIPV) system applications that can simultaneously block the incoming sunlight and generate electricity, is increasing. Thus, to maximize the technical performance of SPB (the solar radiation reaching the PV panel), it is important to develop a method that can accurately track the sun's position considering the various climate factors. Therefore, this study aims to conduct a technical performance analysis of SPB based on the solar tracking method considering the climate factors. Towards this end, this study conducted an experiment in South Korea using the prototype model of smart SPB. The results demonstrate that the technical performance of smart SPB can be improved up to 24.8% by using the optimal solar tracking method considering the climate factors. This study can help in further developing a hybrid solar tracking method for maximizing the technical performance of smart SPB considering the climate factors.
N-ary decomposition for multi-class classification

A common way of solving a multi-class classification problem is to decompose it into a collection of simpler two-class problems. One major disadvantage is that with such a binary decomposition scheme it may be difficult to represent subtle between-class differences in many-class classification problems due to limited choices of binary-value partitions. To overcome this challenge, we propose a new decomposition method called N-ary decomposition that decomposes the original multi-class problem into a set of simpler multi-class subproblems. We theoretically show that the proposed N-ary decomposition could be unified into the framework of error correcting output codes and give the generalization error bound of an N-ary decomposition for multi-class classification. Extensive experimental results demonstrate the state-of-the-art performance of our approach.
Position control of a quadcopter drone using evolutionary algorithms-based self-tuning for first-order Takagi–Sugeno–Kang fuzzy logic autopilots

Trajectory tracking control of a quadcopter drone is a challenging work due to highly-nonlinear dynamics of the system, coupled with uncertainties in the flight environment (e.g. unpredictable wind gusts, measurement noise, modelling errors, etc). This paper addresses the aforementioned research challenges by proposing evolutionary algorithms-based self-tuning for first-order Takagi–Sugeno–Kang-type fuzzy logic controller (FLC). We consider three major state-of-the-art optimisation algorithms, namely, Genetic Algorithm, Particle Swarm Optimisation, and Artificial Bee Colony to facilitate automatic tuning. The effectiveness of the proposed control schemes is tested and compared under several different flight conditions, such as, constant, varying step and sine functions. The results show that the ABC-FLC outperforms the GA-FLC and PSO-FLC in terms of minimising the settling time in the absence of overshoots.
Contextual Awareness in Human-Advanced-Vehicle Systems: A Survey

Autonomous vehicles are becoming a reality in places with advanced infrastructure to support their operations. In crowded places, harsh environments, missions that require these vehicles to be aware of the context in which they are operating, and situations requiring continuous coordination with humans such as in disaster relief, advanced-vehicle systems (AVSs) need to be better contextually aware. The vast literature referring to “context-aware systems” is still sparse, focusing on very limited forms of contextual awareness. It requires a structured approach to bring it together to truly realize contextual awareness in AVSs. This paper uses a human-AVSs (HAVSs) lens to polarize the literature in a coherent form suitable for designing distributed HAVSs. We group the relevant literature into two categories: contextual-awareness related to the vehicle infrastructure itself that enables AVSs to operate, and contextual-awareness related to HAVSs. The former category focuses on the communication backbone for AVSs including ad-hoc networks, services, wireless communication, radio systems, and the cyber security and privacy challenges that arise in these contexts. The latter category covers recommender systems, which are used to coordinate the actions that sit at the interface of the human and AVSs, human-machine interaction issues, and the activity recognition systems as the enabling technology for recommender systems to operate autonomously. The structured analysis of the literature has identified a number of open research questions and opportunities for further research in this area.
Korean-Vietnamese Neural Machine Translation System With Korean Morphological Analysis and Word Sense Disambiguation

Although deep neural networks have recently led to great achievements in machine translation (MT), various challenges are still encountered during the development of Korean-Vietnamese MT systems. Because Korean is a morphologically rich language and Vietnamese is an analytic language, neither have clear word boundaries. The high rate of homographs in Korean causes word ambiguities, which causes problems in neural MT (NMT). In addition, as a low-resource language pair, there is no freely available, adequate Korean-Vietnamese parallel corpus that can be used to train translation models. In this paper, we manually established a lexical semantic network for the special characteristics of Korean as a knowledge base that was used for developing our Korean morphological analysis and word-sense disambiguation system: UTagger. We also constructed a large Korean-Vietnamese parallel corpus, in which we applied the state-of-the-art Vietnamese word segmentation method RDRsegmenter to Vietnamese texts and UTagger to Korean texts. Finally, we built a bi-directional Korean-Vietnamese NMT system based on the attention-based encoder-decoder architecture. The experimental results indicated that UTagger and RDRsegmenter could significantly improve the performance of the Korean-Vietnamese NMT system, achieving remarkable results by 27.79 BLEU points and 58.77 TER points in Korean-to-Vietnamese direction and 25.44 BLEU points and 58.72 TER points in the reverse direction.
A Comprehensive Feature Comparison Study of Open-Source Container Orchestration Frameworks

(1) Background: Container orchestration frameworks provide support for management ofcomplex distributed applications. Different frameworks have emerged only recently, and they havebeen in constant evolution as new features are being introduced. This reality makes it difficult forpractitioners and researchers to maintain a clear view of the technology space. (2) Methods: wepresent a descriptive feature comparison study of the three most prominent orchestrationframeworks: Docker Swarm, Kubernetes, and Mesos, which can be combined with Marathon,Aurora or DC/OS. This study aims at (i) identifying the common and unique features of allframeworks, (ii) comparing these frameworks qualitatively ánd quantitatively with respect togenericity in terms of supported features, and (iii) investigating the maturity and stability of theframeworks as well as the pioneering nature of each framework by studying the historical evolutionof the frameworks on GitHub. (3) Results: (i) we have identified 124 common features and 54 uniquefeatures that we divided into a taxonomy of 9 functional aspects and 27 functional sub-aspects. (ii)Kubernetes supports the highest number of accumulated common and unique features for all 9functional aspects; however, no evidence has been found for significant differences in genericitywith Docker Swarm and DC/OS. (iii) Very little feature deprecations have been found and 15 out of27 sub-aspects have been identified as mature and stable. These are pioneered in descending orderby Kubernetes, Mesos, and Marathon. (4) Conclusion: there is a broad and mature foundation thatunderpins all container orchestration frameworks. Likely areas for further evolution and innovationinclude system support for improved cluster security and container security, performance isolationof GPU, disk and network resources, and network plugin architectures.
Translating Tourist Requirements into Mobile AR Application Engineering Through QFD

Augmented reality (AR) has moved into the spotlight of technological developments to enhance tourist experiences, presenting a need to develop meaningful AR applications. However, few studies so far have focused on requirements for a user-centric AR application design. The study aims to propose a method on translating psychological and behavioral indicators of users into relevant technical design elements for the development of mobile AR tourism applications in the context of urban heritage tourism. The research was conducted in three phases to generate a quality function deployment (QFD) model based on interviews, focus groups and questionnaires of international tourists and industry professionals. Key categories, content requirements, function requirements, and user resistance were defined for the identification of requirements. The outcomes of the study outline tourist requirements based on behavioral and psychological indicators and propose a method for translating them into technical design elements for tourist mobile AR applications.
Image denoising with conditional generative adversarial networks (CGAN) in low dose chest images

Recently, low-dose medical imaging attracts a significant interest owing to the harmfulness of ionized radiations including X-rays. However, when the radiation dose is reduced during the medical image acquisition process, a significant quantum noise is commonly generated. The purpose of this study is to develop a deep-learning-based image-denoising method for low-dose chest imaging, which is a commonly performed medical imaging for diagnosis. Conditional generative adversarial networks (CGANs) were used in the development of the denoising algorithm. In order to train the deep-learning model, we used the SPIE American-Association-of-Physicists-in-Medicine lung-CT-challenge, and Lung-Image-Database-Consortium and Image-Database-Resource-Initiative databases. The obtained image demonstrated that the proposed method achieved an excellent image quality by removing the noise component. Compared with conventional denoising algorithms such as the total-variation (TV) minimization and non-local means (NLM), the proposed method exhibited a superior quality of the obtained images. Losses of image information, detrimental in medical diagnoses, occurred in the medical images obtained using conventional denoising algorithms. Unlike the conventional denoising algorithms, the proposed algorithm restored the corrupted image resolution owing to image noise. The quantitative evaluation through structure similarity index measure (SSIM) demonstrated the superiority of the proposed method over the conventional methods. The SSIM of the proposed method was improved by 1.5 and 2.5 times, compared to those of the NLM and TV methods, respectively. Therefore, we developed a denoising algorithm for medical imaging with CGAN, which is one of the latest deep-learning structures, for low-dose chest images. The proposed denoising method is expected to contribute to the improvement of image quality and reduction of the patient dose.
Blockchain-Based Certificate Transparency and Revocation Transparency

Traditional X.509 public key infrastructures (PKIs) depend on certification authorities (CAs) to sign certificates, used in SSL/TLS to authenticate web servers and establish secure channels. However, recent security incidents indicate that CAs may (be compromised to) sign fraudulent certificates. In this paper, we propose blockchain-based certificate transparency and revocation transparency. Our scheme is compatible with X.509 PKIs but significantly reinforces the security guarantees of a certificate. The CA-signed certificates and their revocation status information of an SSL/TLS web server are published by the subject (i.e., the web server) as a transaction, and miners of the community append it to the global certificate blockchain after verifying the transaction and mining a block. The certificate blockchain acts as append-only public logs to monitor CAs' certificate signing and revocation operations, and an SSL/TLS web server is granted with the cooperative control on its certificates to balance the absolute authority of CAs in traditional PKIs. We implement the prototype system with Firefox and Nginx, and the experimental results show that it introduces reasonable overheads.
Adaptive hysteresis thresholding segmentation technique for localizing the breast masses in the curve stitching domain

Massive work by distinguished researchers in the domain of breast segmentation has been proposed. However, no significant solution reduces the limitations of the false positive rate of cancerous cells in the breast body for probing the abnormalities of particular features. This problem is challenging in its nature and essential to be solved. It is needed to reach the optimal measurements of the breast parenchyma, the breast patchy regions of the mammogram, or the breast registration for searching of precise oddities.In this work, we propose a novel approach for observing the abnormal breast cells identification with high sensitivity. A cancer tumor often produces a specific protein in the blood that serves as a marker for the cancer cells. These cells break off from the cancer and move into the blood stream. However, presence of pectoral muscle in breast mammogram highly affects the detection process of breast tumor. A novel aspect of the proposed method is that the curve stitching technique is developed for removing of pectoral muscle. Following this, an adaptive hysteresis thresholding is used for segmentation. This hybrid technique is used for segmenting a breast region of digital mammogram with suppression of pectoral muscle.The proposed method attains a highest sensitivity rate of 96.6% for the MIAS dataset and 96.4% for the DDSM dataset as compared to existing methods.The main idea behind this is to improve the threshold based segmentation techniques to create an adaptive threshold and apposite templates, in order to conserve tumor salient features about suspicious regions to classify benign and malignant mass in mammogram. First, a spline based curve fitting is applied on edges of the breast parenchyma and fill the region with a very low intensity value and map on original image to preserve the original intensity of breast region free of pectoral muscle. The results of the experiments show that the proposed segmentation technique is efficient when tested on MIAS and DDSM dataset.
Optimal Contract-Based Mechanisms for Online Data Trading Markets

In the age of emerging applications, such as Internet of Things (IoT), big data, and data mining, our life becomes more convenient through customized services that utilize a huge amount of personal data generated and collected by various IoT devices. To fully exploit the data value as well as enhance the data utilization, more and more data are being traded in online data markets. While enjoying the benefit from data trading, data sellers are also suffering from severe risk of privacy leakage. In this paper, our objective is to maximize data seller's received utility via balancing the tradeoff between data trading benefit and data privacy cost. To achieve this, contract theory is utilized to design optimal contract trading mechanisms for both complete and incomplete information markets. From our thorough theoretical analysis, comprehensive simulations, and real-data experiments, the effectiveness of our proposed optimal contract mechanisms can be validated, i.e., the maximum utility can be obtained at the seller side, the individual rationality and incentive compatibility can be guaranteed at the buyer side, and the advantages of our mechanism over the single contract mechanisms can be confirmed.
A Generic Technique for Sketches to Adapt to Different Counting Ranges

Sketch is a compact data structure for network measurements. To achieve fast speed, it needs to be held in the on-chip memory (SRAM), which is very small. To enable the sketch fit into the on-chip memory, the product of counter size and number of counters must be below a certain limit. If we use small counters, e.g., 8 bits, some counters will overflow. If we use large counters, e.g., 16 bits per counter, the total number of counters will be small, each counter will be shared by more flows, leading to poor accuracy. To address this issue, we propose a generic technique: self-adaptive counters (SA Counter). When the value of the counter is small, it works as a normal counter. When the value of the counter is large, we increment it using a predefined probability, so as to represent a large value. Moreover, in SA Counter, the probability decreases when the value increases. This technique can significantly improve the accuracy of sketches. To verify the effectiveness of SA Counter, we apply SA Counter to three typical sketches, and conduct extensive experiments on one real dataset and one synthetic dataset. Experimental results show that, compared with the state-of-the-art, sketches using SA Counter improve the accuracy by up to 13.6 times.
Analysis and research on mental health of college students based on cognitive computing

In recent years, according to the survey, college students have frequent psychological problems, anxiety, depression, inferiority, interpersonal sensitivity and other psychological problems, and even more, even the idea of suicide. It has a very serious negative impact on the family and society. Children are the flowers of the motherland, and college students are the important cornerstone of the country's future development. Therefore, the mental health problems of college students are particularly important. Based on the research of cognitive computing, this paper combines the research data to analyze the influencing factors of mental health, analyzes the shortcomings of mental health education, and proposes corresponding ideas and solutions.
Toward Knowledge as a Service Over Networks: A Deep Learning Model Communication Paradigm

The advent of artificial intelligence and Internet of Things has led to the seamless transition turning the big data into the big knowledge. The deep learning models, which assimilate knowledge from large-scale data, can be regarded as an alternative but promising modality of knowledge for artificial intelligence services. Yet, the compression, storage, and communication of the deep learning models towards better knowledge services, especially over networks, pose a set of challenging problems on both industrial and academic realms. This paper presents the deep learning model communication paradigm based on multiple model compression, which greatly exploits the redundancy among multiple deep learning models in different application scenarios. We analyze the potential and demonstrate the promise of the compression strategy for deep learning model communication through a set of experiments. Moreover, the interoperability in deep learning model communication, which is enabled based on the standardization of compact deep learning model representation, is also discussed and envisioned.
A new product anti‐counterfeiting blockchain using a truly decentralized dynamic consensus protocol

Summary The growth of counterfeit goods has plagued the international community for decades. Nowadays, the battle against counterfeiting remains a significant challenge. Most of the current anti‐counterfeiting systems are centralized. Motivated by the evolution of blockchain technology, we propose (Block‐Supply), a decentralized anti‐counterfeiting supply chain that exploits NFC and blockchain technologies. This paper also proposes a new truly decentralized consensus protocol that, unlike most of the existing protocols, does not require PoW and randomly employs a different set of different size of validators each time a new block is proposed. Our protocol utilizes a game theoretical model to analyze the risk likelihood of the block's proposing nodes. This risk likelihood is used to determine the number of validators involved in the consensus process. Additionally, the game model enforces the honest consensus nodes' behavior by rewarding honest players and penalizing dishonest ones. Our protocol utilizes a novel, decentralized, dynamic mapping between the nodes that participate in the consensus process. This mapping ensures that the interaction between these nodes is executed anonymously and blindly. This way of mapping withstands many attacks that require knowing the identities of the participating nodes in advance, such as DDoS, Bribery, and Eclipse attacks.
Automatic Analysis of Endoscopic Images for Polyps Detection and Segmentation

Polyps of the gastrointestinal tract are avowed as a risk factor for cancer. Early polyps detection and removal can reduce the probability of cancer. Quality of endoscopic examination strongly depends on the physician experience and other subjective factors. Automatic classification allows to increase effectiveness of gastroscopy. The present paper discusses the polyp segmentation task and its solution using automatic two-stage classification. The first stage is binary classification (presence / absence of polyp) based on the global features taken from the initial endoscopic images. The second stage involves the use of convolutional neural networks for segmentation. The result of the method is a pathology map with polyps marked by color. Evaluations on standard public databases show that the proposed method has specificity - 0.82 and sensitivity - 0.93.
Deep learning opens new horizons in personalized medicine (Review)

Although the idea of the personalization of patient care dates back to the time of Hippocrates, recent advances in diagnostic medical imaging and molecular medicine are gradually transforming healthcare services, by offering information and diagnostic tools enabling individualized patient management. Facilitating personalized / precision medicine requires taking into account multiple heterogenous parameters, such as sociodemographics, gene variability, environmental and lifestyle factors. Therefore, one of the most critical challenges in personalized medicine is the need to transform large, multi-modal data into decision support tools, capable of bridging the translational gap to the clinical setting. Towards these challenges, deep learning (DL) provides a novel approach, which enables obtaining or developing high-accuracy, multi-modal predictive models, that allow the implementation of the personalized medicine vision in the near future. DL is a highly effective strategy in addressing these challenges, with DL-based models leading to unprecedented results, matching or even improving state-of-the-art prediction/detection rates based on both intuitive and non-intuitive disease descriptors. These results hold promise for significant socio-economic benefits from the application of DL personalized medicine.