Reviewerly/exHarmony huggingface dataset:
Models and hierarchical methodologies for evaluating solar energy availability under different sky conditions toward enhancing concentrating solar collectors use: Texas as a case study

Abstract The precise estimation of solar radiation data is substantial in the long-term evaluation for the techno-economic performance of solar energy conversion systems (e.g., concentrated solar thermal collectors and photovoltaic plants) for each site around the world, particularly, direct normal irradiance which is utilized commonly in designing solar concentrated collectors. However, the lack of direct normal irradiance data comparing to global and diffuse horizontal irradiance data and the high cost of measurement equipment represent significant challenges for exploiting and managing solar energy. Consequently, this study was performed to develop two hierarchical methodologies by using various models, empirical correlations and regression equations to estimate hourly solar irradiance data for various worldwide locations (using new correlation coefficients) and different sky conditions (using cloud cover range). Additionally, the preliminary assessment for the potential of solar energy in the selected region was carried out by developing a comprehensive analysis for the solar irradiance data and the clearness index to make a proper decision for the capability of utilizing solar energy technologies. A case study for the San Antonio region in Texas was selected to demonstrate the accuracy of the proposed methodologies for estimating hourly direct normal irradiance and monthly average hourly direct normal irradiance data at this region. The estimated data show a good accuracy comparing with measured solar data by using locally adjusted coefficients and different statistical indicators. Furthermore, the obtained results show that the selected region is unequivocally amenable to harnessing solar energy as the prime source of energy by utilizing concentrating and non-concentrating solar energy systems.
Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning

Competent multi-lane cruising requires using lane changes and within-lane maneuvers to achieve good speed and maintain safety. This paper proposes a design for autonomous multi-lane cruising by combining a hierarchical reinforcement learning framework with a novel state-action space abstraction. While the proposed solution follows the classical hierarchy of behavior decision, motion planning and control, it introduces a key intermediate abstraction within the motion planner to discretize the state-action space according to high level behavioral decisions. We argue that this design allows principled modular extension of motion planning, in contrast to using either monolithic behavior cloning or a large set of handwritten rules. Moreover, we demonstrate that our state-action space abstraction allows transferring of the trained models without retraining from a simulated environment with virtually no dynamics to one with significantly more realistic dynamics. Together, these results suggest that our proposed hierarchical architecture is a promising way to allow reinforcement learning to be applied to complex multi-lane cruising in the real world.
A Secure Fragile Video Watermarking Algorithm for Content Authentication Based on Arnold Cat Map

This paper presents a fragile watermarking algorithm in spatial domain to authenticate integrity of the video digital content. The watermark is a binary image and it has been replicated so that has the same size with frame size of the video. To increase security, before embedding, the watermark is encrypted by XOR-ing it with a random image. The random image is generated by using a chaos map, i.e Arnold Cat Map. The encrypted watermark is embedded by modifying pixel values of video frames. Some attacks has been done to the watermarked video. Experiment results show that the algorithm can detect and localize the modified region of video frames very well.
A High capacity Double-Layer Gray Code Based Security Scheme for Secure Data Embedding

With the recent amelioration in digital communication and cloud storage, security remains a concern. Even though cryptographic algorithms have been readily used, they have been proved to be vulnerable to brute force and side channels attacks. Hence an additional security layer must be integrated to overcome the suspicion introduced by cryptography which is only concerned with the secrecy of the transmitted data. Adjacently, steganography tackles the secrecy of communication in the first place and hence a hybrid double layer security scheme that integrates both systems safeguards the data in terms of secrecy and imperceptibility. In this paper, we propose a double layer blind reversible security scheme which deploys Blowfish encryption as a cryptographic layer followed by a least significant bit image steganographic layer which spreads the encrypted secret data required to be transmitted over the pixels of an image cover medium. Our scheme also spreads the data again over the color channels of the pixels selected for embedding according to a gray code sequence. In order to judge the performance of our proposed scheme, the values of peak signal to noise ratio, mean squared error and structural similarity index have been computed and compared to other least significant bit schemes in the literature. The proposed scheme has been proved superior in terms of capacity, imperceptibility and computational complexity.
Society 5.0: Optimization of Socio-Technical System in Poverty Reduction

Abstract The purpose of this study is to design a digital platform design as one solution to solving poverty problems that exist in the community with various causes. Poverty is a complex problem caused by multidimensionality. The cause of poverty is not only a problem of economic inequality but also caused by social problems, education, and lack of access to communicate or communication. The methodology used in this research is Sociotechnical System (STS), STS analysis used six attributes, namely goals, people, buildings/infrastructure, technology, culture, and processes/procedures. This approach (STS) analyzes approaches based on social problems and technical problems. The final results of this study created a digital platform design as a public space to capture community participation in order to care and want to share with poor communities in the form of entrepreneurship training groups (UKM). This technology becomes a tool to help in educating society, not a rival of the community in accordance with the principles of society 5.0.
TCAIOSC: Application Code Conversion

Traditional mobile application development requires going through multiple development cycles in order for an application to work on different platforms. In cross-platform development, the application goes through only one development cycle to be deployed in multiple environments. Various cross-platform methodologies were explored to make it easy for developers to deploy their apps; one of which is the Trans-Compiler methodology. This paper discusses advances to a cross-platform solution, TCAIOSC, which converts Android projects to iOS, inquiring into further aspects of Android-to-iOS conversion.
SlimGuard

Attacks on the heap are an increasingly severe threat. State-of-the-art secure dynamic memory allocators can offer protection, however their memory footprint is high, making them suboptimal in many situations. We introduce Slim-Guard, a secure allocator whose design is driven by memory efficiency. Among other features, SlimGuard uses an efficient fine-grain size classes indexing mechanism and implements a novel dynamic canary scheme. It offers a low memory overhead due its size classes optimized for canary usage, its on-demand metadata allocation, and the combination of randomized allocations and over-provisioning into a single memory efficient security feature. SlimGuard protects against widespread heap-related attacks such as overflows, over-reads, double/invalid free, and use-after-free. Evaluation over a wide range of applications shows that it offers a significant reduction in memory consumption compared to the state-of-the-art secure allocator (up to 2x in macro-benchmarks), while offering similar or better security guarantees and good performance.
SEConomy: A Framework for the Economic Assessment of Cybersecurity

Cybersecurity concerns are one of the significant side effects of an increasingly interconnected world, which inevitably put economic factors into perspective, either directly or indirectly. In this context, it is imperative to understand the significant dependencies between complex and distributed systems (e.g., supply-chain), as well as security and safety risks associated with each actor. This paper proposes SEConomy, a strictly step-based framework to measure economic impact of cybersecurity activities in a distributed ecosystem with several actors. Through the mapping of actors, responsibilities, inter-dependencies, and risks, it is possible to develop specific economic models, which can provide in a combined manner an accurate picture of cybersecurity economic impacts.
Explaining Class-of-Service Oriented Network Traffic Classification with Superfeatures

Recent studies have demonstrated that machine learning can be useful for application-oriented network traffic classification. However, a network operator may not be able to infer the application of a traffic flow due to the frequent appearance of new applications or due to privacy and other constraints set by regulatory bodies. In this work, we consider traffic flow classification based on the class of service (CoS), using delay sensitivity as an example in this preliminary study. Our focus is on direct CoS classification without first inferring the application. Our experiments with real-world encrypted TCP flows show that this direct approach can be substantially more accurate than a two-step approach that first classifies the flows based on their applications. However, without invoking application labels, the direct approach is more opaque than the two-step approach. Therefore, to provide human understandable interpretation of the trained learning model, we further propose an explanation framework that utilizes groups of superfeatures defined using domain knowledge and their Shapley values in a cooperative game that mimics the learning model. Our experimental results further demonstrate that this explanation framework is consistent and provides important insights into the classification results.
GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data Distributions

Despite the remarkable success of generative adversarial networks, their performance seems less impressive for diverse training sets, requiring learning of discontinuous mapping functions.Though multi-mode prior or multigenerator models have been proposed to alleviate this problem, such approaches may fail depending on the empirically chosen initial mode components.In contrast to such bottom-up approaches, we present GAN-Tree, which follows a hierarchical divisive strategy to address such discontinuous multi-modal data.Devoid of any assumption on the number of modes, GAN-Tree utilizes a novel modesplitting algorithm to effectively split the parent mode to semantically cohesive children modes, facilitating unsupervised clustering.Further, it also enables incremental addition of new data modes to an already trained GAN-Tree, by updating only a single branch of the tree structure.As compared to prior approaches, the proposed framework offers a higher degree of flexibility in choosing a large variety of mutually exclusive and exhaustive tree nodes called GAN-Set.Extensive experiments on synthetic and natural image datasets including ImageNet demonstrate the superiority of GAN-Tree against the prior state-of-the-art.* equal contribution 1.0 0.75 0.50 0.25 0.0 Transformation function X → Z Z → X Approx.fun.
Cross-Domain Scene Text Detection via Pixel and Image-Level Adaptation

Building a robust text detector suitable for different kinds of scene images is a challenging task because the performance of the text detector will be degraded due to the domain shift between different scenes. In this paper, we propose a cross-domain scene text detection method, consisting of pixel-level domain adaptation component (PDA) and image-level domain adaptation component (IDA), to enhance the robustness of scene text detection cross domains. We implement the PDA and IDA by a Gradient Reverse Layer (GRL) and a domain classifier that can distinguish the input scene comes from the source domain or target domain. Besides, to encourage the model to extract domain-invariant features, we introduce the adversarial training to the GRL block. Thus the detector trained with source data can produce more generalized results on the target domain. Experimental results on two pairs of datasets, including MTWI2018 and MSRA-TD500, ICDAR2015 and ICDAR2013, indicate that our proposed method is effective in cross-domain scene text detection and outperforms the methods without domain adaptation.
FaQuAD: Reading Comprehension Dataset in the Domain of Brazilian Higher Education

Academic secretaries and faculty members of higher education institutions face a common problem: the abundance of questions sent by academics whose answers are found in available institutional documents. The official documents produced by Brazilian public universities are vast and disperse, which discourage students to further search for answers in such sources. In order to lessen this problem, we present FaQuAD: a novel machine reading comprehension dataset in the domain of Brazilian higher education institutions. FaQuAD follows the format of SQuAD (Stanford Question Answering Dataset) [Rajpurkar et al.2016]. It comprises 900 questions about 249 reading passages(paragraphs), which were taken from 18 official documents of a computer science college from a Brazilian federal university and 21 Wikipedia articles related to Brazilian higher education system. As far as we know, this is the first Portuguese reading comprehension dataset in this format. We trained a state-of-the-art model on this dataset, which is based on the Bi-Directional Attention Flow model [Seo et al. 2016]. We report on several ablation tests to assess different aspects of both the model and the dataset. For instance, we report learning curves to assess the amount of training data, the use of different levels of pre-trained models, and the use of more than one correct answer for each question.
Assembling the Case

This study examines caseworkers' and citizens' interactions when assembling resource development applications for citizens with serious health and personal issues. As with other types of welfare schemes, the application serves as a mechanism of both support and control. From our study, we illustrate how an increased reliance on data is transforming the citizen-caseworker interaction in social welfare. We characterize this transformation as 'dataRication': a phenomenon where the increased reliance on data for decision-support across contexts of data production makes it challenging for individual citizens to contest or correct data-born accounts of their situation. Our contribution is two-fold: Rirst, we empirically characterize the citizen-caseworker interaction in the application process. Second, we discuss how citizens' private resourcing complements the formal application process and provides them with strategies to give authority to their case and exercise personal autonomy. The private resourcing practices we observed show how integrating supplementary accounts from citizens into the systems that caseworkers rely on could make citizens' experiences and social context legible. This in turn has policy and technology design implications as public services increasingly introduce data-driven modes of case management.
Technologies in Health Care Domain

Blockchain and Internet of Thing (IoT) has been widely used to impart constructive and smart healthcare services to the community. Blockchain technology has also invaded complete facet of information communications technology (ICT) and it has been growing and growing in today's fast paced world. Use of Blockchain Technology will increase the certainty and efficiency of electronic healthcare records (EHRs). The data mining approaches and machine learning algorithms assist in predicting the chronic disorders in patient thereby helping in the identification of the patient's treatment. This article provides a survey of the state of art in this domain. The key features, as well as the challenges of the various approaches, have been analyzed. A novel methodology to overcome the existing limitations has been proposed for future work and research. This proposed framework will make the data aggregations much easier to save and for decision-making. The function of alerting to caretakers and to inform hospital in critical condition will make this system new and unique.
Framework Architecture for Securing IoT Using Blockchain, Smart Contract and Software Defined Network Technologies

The botnet problem of launching Distributed Denial of Service (DDoS) attacks on other networks mainly arises from the rapid growth in the number of insecure Internet of Things (IoT) devices distributed across these networks. The focus of this work is to defend such an IoT network and its associated resources from attacks, and to prevent such networks from becoming a part of botnet launching DDoS attacks on other networks and resources. To achieve these objectives, this research emphases designing of a botnet prevention model for Internet of Things using emerging technologies such as Blockchain, Smart Contract, and Software Defined Networking (SDN). Blockchain is decentralized structure which fits with the decentralized nature of IoT. For securing IoT network, the proposed solution presented in this research based on building above the IoT network a Blockchain network and on top of it to use Smart Contracts that embedded the SDN rules.
Why Japan’s Digital Transformation Is Inevitable

This chapter examines the digital transformation of Japanese business in terms of key technologies, the barriers to their expansion in Japan, and the drivers moving them forward. After clarifying definitions and discussing key technologies, the chapter discusses six sources of pressure toward digital business transformation in Japan. These pressures include overseas partners, fintech, the drop in Japanese population, generational change, and the preference for food grown in Japan. The sixth of these is a sense of innovation deeply baked into the upcoming generation that will likely impact all areas of life and business in Japan. The new generation is impatient with conventional services and feels the need for transformation as available labor decreases and aging increases demands of individuals on society. There is an additional, even though temporary, source of pressure as well – the 2020 Olympics. The sectors that are most likely to feel the impact of new digital technologies are finance, retail, manufacturing, services, agriculture, and elder care. The big question that looms in front of Japan, as with other countries, is how Japanese business and society will provide the human resources and investments needed to bring transformation to fruition.
Blockchain Based Fine-Grained and Scalable Access Control for IoT Security and Privacy

In this paper, we focuses on an access control issue in the Internet of Things (IoT). Generally, we firstly propose a decentralized IoT system based on blockchain. Then we establish a secure fine-grained access control strategies for users, devices, data, and implement the strategies with smart contract. To trigger the smart contract, we design different transactions. Finally, we use the multi-index table struct for the access right's establishment, and store the access right into Key-Value database to improve the scalability of the decentralized IoT system. In addition, to improve the security of the system we also store the access records on the blockchain and database.
Documenting recurring concerns and patterns in large-scale agile development

The introduction of agile methods at scale entails unique concerns such as inter-team coordination, dependencies to other organizational units, or distribution of work without a defined architecture. Compared to the rich body of agile software development literature describing typical challenges and best practices, recurring concerns and patterns in large-scale agile development are not yet documented extensively. We aim to fill this gap by presenting a pattern language for large-scale agile software development as part of our larger research initiative in close collaboration with 10 companies. The structure and practical relevance of the proposed language were evaluated by 14 interviews. In this paper, we showcase our pattern language by presenting four patterns.
Human Support Robot as Research Platform of Domestic Mobile Manipulator

The Human Support Robot (HSR) has been used in Domestic Standard Platform League (DSPL) of RoboCup@Home since RoboCup Nagoya in 2017. Currently, the number of HSR users is expanding to 44 sites in 12 countries worldwide (as of 30th March, 2019). In this paper, we explain the design concept of HSR, and examples of recent activities of the developers community. We hope that it would contribute to RoboCup and researchers.
Research on Interturn Short Circuit Fault Identification Method of PMSM based on Deep Learning

Based on neural network deep learning, the fault diagnosis of permanent magnet synchronous motor was studied.By creating different sample labels for neural network deep learning training, diagnosis of interturn short circuit fault cause;By comparing the accuracy of different neural networks, the deep learning neural network has the highest accuracy in PMSM fault diagnosis.Compared with the traditional motor fault diagnosis method, the deep learning method of genetic neural network can solve the problems of large knowledge base, low fault search efficiency, effectively shorten the fault diagnosis time and maintain the stable operation of the PMSM.
Improved Related-Tweakey Rectangle Attacks on Reduced-Round Deoxys-BC-384 and Deoxys-I-256-128

Deoxys-BC is the core internal tweakable block cipher of the authenticated encryption schemes Deoxys-I and Deoxys-II. Deoxys-II is one of the six schemes in the final portfolio of the CAESAR competition, while Deoxys-I is a 3rd round candidate. By well studying the new method proposed by Cid et al. at ToSC 2017 and BDT technique proposed by Wang and Peyrin at ToSC 2019, we find a new 11-round related-tweakey boomerang distinguisher of Deoxys-BC-384 with probability of $$2^{-118.4}$$ , and give a related-tweakey rectangle attack on 13-round Deoxys-BC-384 with a data complexity of $$2^{125.2}$$ and time complexity of $$2^{186.7}$$ , and then apply it to analyze 13-round Deoxys-I-256-128 in this paper. This is the first time that an attack on 13-round Deoxys-I-256-128 is given, while the previous attack on this version only reaches 12 rounds.
The Audible Artefact

This paper introduces two ongoing projects where audio augmented reality is implemented as a means of engaging museum and gallery visitors with audio archive material and associated objects, artworks and artefacts. It outlines some of the issues surrounding the presentation and engagement with sound based material within the context of the cultural institution, discusses some previous and related work on approaches to the cultural application of audio augmented reality, and describes the research approach and methodology currently engaged with in developing an increased understanding in this area. Additionally, it discusses the project within the context of related cultural and sound studies literature, presents some initial conclusions as a result of a practice-based approach, and outlines the next steps for the project.