Reviewerly/exHarmony huggingface dataset:
Building the internet of energy infrastructure: The Distributed Ledger Technologies approach

The drawbacks of the current centralized energy market render it necessary that a decentralized architecture - consisting of entities such as microgrids, utility companies, financial institutions, and consumers- is developed. Distributed Ledger Technologies (DLT) provide an immutable, tamper-proof and secure way of automating and recording complex transactions. They can be used in conjunction with smart energy meters to create collaborative economy business models in energy, thus, energy production and consumption measurements can be authenticated in a secure way and the optimal balance between supply, demand and their pricing can be achieved. Moreover, the decentralized structure of blockchain enables business processes to become automated via smart contracts and decentralized applications, contributing to a viable energy market. This study attempts to technically evaluate different startup approaches in the Decentralized Energy Internet domain, review their current progress in engineering, security, regulatory and societal aspects, and examine their future challenges and the potential of using a Unified and Adaptable Model.
ParaML: A Polyvalent Multicore Accelerator for Machine Learning

In recent years, machine learning (ML) techniques are proven to be powerful tools in various emerging applications. Traditionally, ML techniques are processed on general-purpose CPUs and GPUs, but their energy efficiencies are limited due to their excessive support for flexibility. As an efficient alternative to CPUs/GPUs, hardware accelerators are still limited as they often accommodate only a single ML technique (family). However, different problems may require different ML techniques, which implies that such accelerators may achieve poor learning accuracy or even be ineffective. In this paper, we present a polyvalent accelerator architecture integrated with multiple processing cores, called ParaML, which accommodates ten representative ML techniques, including k-means, k-nearest neighbors (k-NN), naive Bayes (NB), support vector machine (SVM), linear regression (LR), classification tree (CT), deep neural network (DNN), learning vector quantization (LVQ), parzen window (PW), and principal component analysis (PCA). Benefited from our thorough analysis on computational primitives and locality properties of different ML techniques, the single-core ParaML can perform up to 1056 GOP/s (e.g., additions and multiplications) in an area of 3.51 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and consumes 596 mW only, estimated by ICC and PrimeTime PX with postsynthesis netlist, respectively. Compared with the NVIDIA K20M GPU (28-nm process), the single-core ParaML (65-nm process) is 1.21× faster, and can reduce the energy by 137.93×. We also compare the single-core ParaML with other accelerators. Compared with PRINS, single-core ParaML achieves 72.09× and 2.57× energy benefit for k-NN and k-means, respectively, and speeds up each query in k-NN by 44.76×. Compared with EIE, the single-core ParaML achieves 5.02× speedup and 4.97× energy benefit with 11.62× less area when evaluating with dense DNN. Compared with TPU, the single-core ParaML achieves 2.45× better power efficiency (5647 Gop/W versus 2300 Gop/W) with 321.36× less area. Compared to the single-core version, the 8-core ParaML will further improve the speedup up to 3.98× with an area of 13.44 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and a power of 2036 mW.
OpenFlow Based Information Flow Control Considering Route Switching Cost

There are various information flows in the network because of the diverse ways in which companies and individuals communicate. Therefore, OpenFlow is attracting much attention since it can flexibly deal with various types of information flow requirements through centralized management of devices. However, whenever is necessary to change routes due to a network failure or a topology change, there is a delay in the service as processes, such as the transmission of the commands and switch processing time. In this paper, we describe the design a method to control information flow effectively based on various network switch costs such as route switching time in OpenFlow.
Research on the incremental learning SVM algorithm based on the improved generalized KKT condition

Abstract In order to adapt to the classification of the large-scale data and the dynamic data, this paper proposes an incremental learning strategy of SVM called GGKKT–ISVM algorithm based on the generalized KKT condition. The algorithm sets the generalized extension factors by the samples distribution density in order to make the useful samples become new support vectors, and it trains a new classifier. Then this algorithm modifies the classifier secondly, and it can not only keep the historical classification information, also can make full use of the new samples’ information, and structure the classifier that has stronger generalization ability. The experimental results show that the algorithm has a good classification effect.
Automatic Human Pose Annotation for Loose-fitting Clothes

This paper proposes a framework for automatically annotating the keypoints of a human body in images for learning 2D pose estimation models. While considerable contributions in the community provide us a huge number of pose-annotated images, all of them mainly focus on people wearing common clothes, which are relatively easy to annotate the body keypoints. This paper, on the other hand, focuses on annotating people wearing loose-fitting clothes that occlude many body keypoints. In order to automatically and correctly annotate these people, we divert the 3D coordinates of the keypoints observed without loose-fitting clothes, which can be captured by a motion capture system (MoCap). These 3D keypoints are projected to an image where the body pose under loose-fitting clothes is similar to the one captured by the MoCap. Pose similarity between bodies with and without loose-fitting clothes is evaluated with 3D geometric configurations of MoCap markers that are visible even with loose-fitting clothes. Experimental results validate the effectiveness of our proposed framework for human pose estimation.
Decentralized fuzzy linguistic control of multiple robotic manipulators with guaranteed global stability

Abstract A decentralized adaptive control based on human linguistic is investigated to learn human behaviors for multiple robotic manipulators. Many experts’ words or sentences can be transferred into the control actions by employing membership functions in robot systems, which can be synthesized fuzzy controller by employing reasoning mechanism. For the unknown model dynamical robot manipulators, one adjustable parameter that relates to the approximation accuracy of fuzzy logic systems is introduced at first, which be utilized to deal with the unknown dynamics of robot manipulators. Switching fuzzy adaptive controller is designed to overcome the limitation of logic structure that the number of adaptive laws only focus on fuzzy rules in conventional fuzzy logic systems. Another advantage of this design method is that the control with human linguistic extend the semi-global stability to global stability. Finally, effectiveness of the developed control design scheme has been shown in simulation example.
Edge Detection in JPEG Grayscale Images

Over the last four decades, hundreds of methods for edge detection in digital images have been published. Each new edge detector was tested on a smaller or larger number of images. Sometimes the researchers have used the images, encoded in lossy JPEG format to test their proposed algorithm. The goal of this paper is to show whether lossy image compression can affect the quality of edge detection. The results presented in this article show that lossy image compression can impair the efficiency of edge detection by up to six percent.
Development and Evaluation of Gamified Multimodal System to Improve Experience Value of Floor Wiping

It is difficult to keep motivation for daily house cleaning as people must repeat the same task monotonically. Floor wiping is a task to wipe the entire floor, but it is usually difficult to complete the task due to such obstacles as furniture. Therefore, the purpose of this study is to provide a system that supports a complete and enjoyable floor wiping by multimodal feedbacks including tactile and auditory feedbacks. We developed a system composed of floor wiper, optical motion capture system for reconstruction of the location of the wiper, tactile feedback device, Bluetooth surround sound headphone, and PC. In addition, we implemented “rhythm game” to increase enjoyment, and 3D surround sound and vibration to present unwiped area. As a result of the evaluation experiment of our developed system, cleaning became fun with our system. However, the improvement of feedbacks as well as the accuracy of the wiper’s location restoration remain as future work.
Measuring the impact of blockchain on healthcare applications

Blockchain is a technology with potential for making ground breaking steps in addressing social, economic and healthcare challenges. The global information technology scene is being overcrowded with blockchain applications with special focus on the vast healthcare market [12]. The value of information related to healthcare creates a clear path for applying blockchain as a solution for some of the challenges in the healthcare sector, in particular with the goal of creating a fair and transparent way for sharing information and patient data. It is however a fact that while blockchain technology introduces additional complexity to the implementation healthcare software, the benefit the technology actually brings still remains unclear and difficult to evaluate. This vision paper demonstrates our research focus on providing a body of knowledge and tools to help evaluate this impact of blockchain on eHealth applications. In particular, we identify that such a research effort has to explicitly consider cost of addressing challenges inherent to the eHealth domain like integration of disparate software systems (hospitals, research institutions, government agencies, health insurance and pharmaceutical companies, etc.), the potential introduction of cryptocurrencies in healthcare systems, degree of patient service improvement, transparency and compliance to laws and regulations, and others. The more traditional influencing factors, like cost of development and running, licenses for using third-party software services, and the ones inherent to blockchain like cost of computation and energy will also have to be taken into consideration in the metrics model.
Interactive Edutainment: A Technologically Enhanced Theme Park

Heraklion Christmas Castle is a joint effort between ICS-FORTH and the Municipality of Heraklion-Crete (Municipal Public Service Enterprise of Heraklion), trying to pioneer and be innovative in the presentation of Christmas customs and ideas by creating a festive neighborhood governed by interactive technology. The approach followed by this research work was to employ interactive systems to give a feeling of Christmas to children and adults through the combination of education and entertainment. This work presents the innovative systems designed and developed for the Christmas Castle to augment and enhance the festive spirit through multimodal interaction techniques, such as virtual environments, kinesthetic interaction, physical object identification and serious games. All the systems were designed and integrated within art artefacts (special constructions) that match the Christmas look-and-feel and provide a user-centric design.
Classification of Rheumatoid Arthritis Based on Image Processing Technique

Arthritis is a disabling and agonizing disease. The rapid growth of biomedical image processing techniques assists the doctor in diagnosis and treatment of the disease. In Rheumatoid Arthritis as the disease progresses, it results in reducing physical activity level of the patient. The method presented in this paper is a completely automated framework to detect and quantify joint space width. This system detects severe stage of RA that are contaminated by disease to the degree that the joint space is no longer noticeable in the X-ray image. In proposed work RA is classified in three stages Normal (Non-RA), Abnormal (RA) and Severe stage RA. Joint location accuracy achieved is 92%. 60 images were tested, Out of 60 Test images 20 images are Normal, 22 images are abnormal i.e. RA affected and 18 images are severe. SVM classifier with Radial basis function kernel is efficient compared to FFNN and k-NN as Non-RA i.e. Normal patient classification accuracy is 95%, RA classification accuracy is 70%, Severe stage classification accuracy of RA is 100%.
Evaluating the Usability of Pervasive Conversational User Interfaces for Virtual Mentoring

To improve the academic and professional achievement of underrepresented minorities in computing, a newfound interest in innovative mentoring practices has captivated STEM education researchers. Studies suggest that virtual mentoring conversational agents can be leveraged across multiple platforms to provide supplemental mentorship, offsetting the lack of access to in-person mentorship in disadvantaged communities. A within-subjects mixed-method experiment was carried out to assess the usability of a mentoring conversational agent. Mobile interfaces (Twitter and SMS) were compared to each other and against a web-based embodied conversational agent (ECA). Results suggest that mobile interfaces are more usable than the web-based ECA. The findings from this study help to identify areas for improvement in virtual learning alternatives and other potential applications for pervasive conversational interfaces.
Silicon Proven $1.8~\mu\text{m}\times\,9.2\,\,\mu\text{m}$ 65-nm Digital Bit Generator for Hardware Security Applications

Many circuit/gate level countermeasures require randomness to provide immunity against power analysis attacks. These applications usually require up to 64 bit sequences at different locations in the design in parallel. While these sequences are not required to be truly random, they must be independent. In this brief, we present, for the first time, results of silicon proven compact bit generator (cBG). The measurements prove that cBGs are able to amplify random fluctuations in the supply voltage to successfully produce the required bit sequences. The results show that less than 0.00014 random bits (out of 4 bits) are lost because of the negligible dependence between neighbouring generators. Additionally, the generators produce high enough entropy (minimum of 0.9656 over 10 Mbits) so they can be used as distributed generators for gate level countermeasures. The generators were tested with clock frequencies up to 100 MHz and the measured energy per bit of a single generator is 45 pJ@100 MHz.
Physician-Level Aggregated Classifier for Genetic Muscle Disorders

Muscle histopathology is the one of the most important diagnostic methods in the diagnosis of muscle diseases [1]. Nevertheless, it is a highly specialized field and only a limited number of experts are available in the world. Not surprisingly, significant number of cases are undiagnosed in underserved areas. We therefore intended to establish a computer-aided diagnostic system which should be helpful in this domain. This study aims to develop a multi-class classifier with deep learning for computer-aided muscle histopathological diagnosis. We chose five genetic muscle disease categories (dystrophinopathy, limb-girdle muscular dystrophy 2A (LGMD2A), limb-girdle muscular dystrophy 2B (LGMD2B), Ullrich congenital muscular dystrophy (UCMD), and Fukuyama-type congenital muscular dystrophy (FCMD)) as targets and aimed to distinguish between these diseases. We developed a new classifier, which we call the aggregated classifier, that improves the classification accuracy of existing classifiers to deal with histopathological images. The classifier achieved better classification accuracy than not only the existing classifier but also eight physicians who have been trained for muscle histopathology for variable duration. This result suggests that causative genes may possibly be precisely predicted only by hematoxylin-eosin (H&E) stained images without genetic testing.
A Verifiable Fully Homomorphic Encryption Scheme

With development of cloud computing, how to keep privacy and compute outsourcing data effectively at the same time is highly significant in practice. Homomorphic encryption is a common method to support ciphertext calculation, but most schemes do not provide fully homomorphic properties. Some fully homomorphic encryption schemes feature complicated design, high computational complexity and no practicability. Some cloud service providers are not trustable and return incorrect computational results due to resource saving or other malicious behaviors. Therefore, this paper proposes a verifiable fully homomorphic encryption scheme VFHES. VFHES implements fully homomorphic encryption based on the principle of the matrix computing principle and matrix blinding technology and supports to verify correctness of the computational results. Security analysis proves that VFHES is privacy-safe and verifiable. The performance analysis and experimental results show that VFHES is practicable and effective.
New Perspectives for People with Special Needs According the Development of New Generation ICT Technologies

The paper considers the new possibilities that Fourth industrial revolution brings to everyday life and inclusion in the world of work for people with disabilities and the elderly (persons with special needs). Technological responses to the challenges of this involvement have been in the market for some time now. However, they are currently very limited to mostly static functions, so their application, though useful and necessary, is relatively limited. Cloud, M2M, IoT and especially 5G networks bring a new dimension and open new perspectives for developing applications tailored to the needs of such people. The paper provides an analysis of linkages between applications for people with special needs with upcoming technological possibilities and the perspective of such applications in the new ICT environment.
The Framework for Blockchain Innovation and the Impact on Digital Economic Transformation

Blockchain technology has become an epidemic and significant decision that organizations may make in the next few years, as integrated business solution enabling institutions to integrate business functions, operations, and processes in a decentralized distributed ledger technology. This technology will transform the business world and economy in solving the limitations created by centralization and system inefficiency. Accordingly, with the highly demanding and complexity of growing economies such as Gulf Cooperation Council GCC countries, the need for a typical solution technology is a game changer. The result of this will lead GCC to a solid base of the economy. Blockchain technology can be applicable in many different fields such as: banking, education, health care, finance, government, trade, etc. This article will propose a conceptual framework for the acceptance of Blockchain technology and innovation in the GCC, particularly in Saudi Arabia. Also, more research can be conducted in the future as the system might be integrated in these countries.
Interactive Web 3D Contents Development Framework Based on Linked Data for Japanese History Education

This paper treats Japanese history education as one of the activities of the center called ICER (Innovation Center for Educational Resources), Kyushu University. In this activity, one of the key challenges is the development of educational materials that attract students to Japanese history. As a first step, a database will be built that contains the Japanese history knowledge for the educational material development. So, the authors have already been building a database based on Linked Data. The other research agenda is to provide e-learning materials themselves using the database that attract students to Japanese history. The use of recent ICT (Information & Communication Technology) like 3D graphics enables e-learning materials to attract the students. In this paper, the authors propose interactive web 3D contents development framework based on Linked Data for Japanese history education.
Privacy-Preserving Encryption-Domain Video Retrieval over the Cloud via Block Transformations of Key Frames

Thanks to the popularity of mobile devices and convenience of cloud computing environment, more and more people are getting use to store their captured video in the cloud; however, there's no guarantee that the stored information is not accessible to the cloud computing server, thus leading to the concern of privacy. A naïve approach to encrypt all videos before uploading them to the cloud does protect the privacy, but at the expense of seriously affecting the retrieval performance as every encrypted video should be downloaded, decrypted, and compared with the query request. We proposed to perform the video retrieval directly in the encrypted domain, i.e., in the cloud while only the desired ones will be retrieved and decrypted.
Proposal and Evaluation of AR-Based Microscopic Brain Surgery Support System

In this study, we describe an augmented reality (AR) support system that superimposes a three-dimensional computer graphics (3DCG) brain model image on an intraoperative microscopic image using simultaneous localization and mapping (SLAM). First, we extract regions such as tumor and blood vessel from computed tomography (CT) images or magnetic resonance images (MRI) of real patients, where data is stored in the digital imaging and communications in medicine (DICOM) format. Then, the entire brain and identified regions are converted into three-dimensional computer graphics (3DCG) model with a standard triangulated language (STL) format. The motion of the camera is estimated from the surgical video using SLAM. Based on the movement, the 3DCG brain model can be moved according to the movement of the real brain. Finally, we verified the estimation accuracy of the camera movement using SLAM.
Towards Emotional Intelligence in Social Robots Designed for Children

Social robots are robots designed to interact and communicate directly with humans, following traditional social norms. However, many of these current robots operate in discrete settings with predefined expectations for specific social interactions. In order for these machines to operate in the real world, they must be capable of understanding the multiple factors that contribute to human-human interaction. One such factor is emotional intelligence. Emotional intelligence allows one to consider the emotional state of another in order to motivate, plan, and achieve one's desires. One common method of analyzing the emotional state of an individual involves analyzing the emotion displayed on their face. Several artificial intelligence (AI) systems have been developed to conduct this task. These systems are often classifiers trained using a variety of machine learning techniques which require large amounts of training data. As such, they are susceptible to biases that may appear during performance analyses due to disproportions existing in training datasets. Children, in particular, are often less represented in the primary datasets of annotated faces used for training such emotion classification systems. This work seeks to first analyze the extent of these performance differences in commercial systems, then to present new computational techniques that work to mitigate some of the effects of minimal representation in datasets, and to finally present a social robot which utilizes an improved emotional AI to interact with children in various scenarios where emotional intelligence is key to successful human-robot interaction.