Reviewerly/exHarmony huggingface dataset:
Radio–Image Transformer: Bridging Radio Modulation Classification and ImageNet Classification

Radio modulation classification is widely used in the field of wireless communication. In this paper, in order to realize radio modulation classification with the help of the existing ImageNet classification models, we propose a radio–image transformer which extracts the instantaneous amplitude, instantaneous phase and instantaneous frequency from the received radio complex baseband signals, then converts the signals into images by the proposed signal rearrangement method or convolution mapping method. We finally use the existing ImageNet classification network models to classify the modulation type of the signal. The experimental results show that the proposed signal rearrangement method and convolution mapping method are superior to the methods using constellation diagrams and time–frequency images, which shows their performance advantages. In addition, by comparing the results of the seven ImageNet classification network models, it can be seen that, except for the relatively poor performance of the architecture MNASNet1_0, the modulation classification performance obtained by the other six network architectures is similar, indicating that the proposed methods do not have high requirements for the architecture of the selected ImageNet classification network models. Moreover, the experimental results show that our method has good classification performance for signal datasets with different sampling rates, Orthogonal Frequency Division Multiplexing (OFDM) signals and real measured signals.
Towards Semantically Guided Traceability

In many regulated domains, traceability is established across diverse artifacts such as requirements, design, code, test cases, and hazards - either manually or with the help of supporting tools, and the resulting trace links are used to support activities such as impact analysis, compliance verification, and safety inspections. Automated tracing techniques need to leverage the semantics of underlying artifacts in order to establish more accurate trace links and to provide explanations of links that have been created in either a manual or automated fashion. To support this, we propose an automated technique which leverages source code, project artifacts and an external domain corpus to generate a domain-specific concept model. We then use the generated concept model to improve traceability results and to provide explanations of the results. Our approach overcomes existing problems with deep-learning traceability algorithms, as it does not require a training set of existing trace links. Finally, as an initial proof-of-concept, we apply our semantically-guided approach to the Dronology project, and show that it improves over other tracing techniques that do not use a concept model.
Minimizing convex quadratics with variable precision conjugate gradients

Summary We investigate the method of conjugate gradients, exploiting inaccurate matrix‐vector products, for the solution of convex quadratic optimization problems. Theoretical performance bounds are derived, and the necessary quantities occurring in the theoretical bounds estimated, leading to a practical algorithm. Numerical experiments suggest that this approach has significant potential, including in the steadily more important context of multiprecision computations.
BPU: A Blockchain Processing Unit for Accelerated Smart Contract Execution

Modern blockchains use smart contracts to implement automatic and decentralized programs, which are the foundations of Decentralized Applications (DApp). The poor performance on general purpose computers has become the bottleneck that limits the blockchain and smart contracts from being widely used. In this paper, we present BPU, a high-performance modularized blockchain processing unit. BPU aims at bringing performance and flexibility to the blockchain and DApp processing. Our design achieves significant speedup compared against the software implementation on an Intel CPU.
INCA: INterruptible CNN Accelerator for Multi-tasking in Embedded Robots

In recent years, Convolutional Neural Network (CNN) has been widely used in robotics, which has dramatically improved the perception and decision-making ability of robots. A series of CNN accelerators have been designed to implement energy-efficient CNN on embedded systems. However, despite the high energy efficiency on CNN accelerators, it is difficult for robotics developers to use it. Since the various functions on the robot are usually implemented independently by different developers, simultaneous access to the CNN accelerator by these multiple independent processes will result in hardware resources conflicts.To handle the above problem, we propose an INterruptible CNN Accelerator (INCA) to enable multi-tasking on CNN accelerators. In INCA, we propose a Virtual-Instruction-based interrupt method (VI method) to support multi-task on CNN accelerators. Based on INCA, we deploy the Distributed Simultaneously Localization and Mapping (DSLAM) on an embedded FPGA platform. We use CNN to implement two key components in DSLAM, Feature-point Extraction (FE) and Place Recognition (PR), so that they can both be accelerated on the same CNN accelerator. Experimental results show that, compared to the layer-by-layer interrupt method, our VI method reduces the interrupt respond latency to 1%.
AGILE AND SECURE SOFTWARE DEVELOPMENT: AN UNFINISHED STORY

Given the widespread adoption of agile methods and the rising number of software vulnerabilities, we analyze the literature with an interest in the effect of security practices on software development agility.We propose a novel taxonomy to systematize the body of knowledge around secure agile development and then organize and summarize the selected research using the new taxonomy.At a high-level we create two categories, Phase Focused and Phase Independent.The Phase Focused category is then subdivided along the traditional SDLC phases.The Phase Independent category spans all phases of the SDLC or is phase independent.We conclude that, although there is a significant body of literature on the topic, the story is unfinished.There is further investigation needed to ensure agility as secure development practices are adopted and in regard to empirical evaluations of the proposed agile and secure software development integration approaches.
Data Mining with Associated Methods to Predict Consumer Purchasing Patterns

Technology is developed and utilized as an honest computer in order that it can provide useful information.With the aim of developing and meeting business objectives, the utilization of sales transaction data in minimarket GP is processed into information or knowledge as a recommendation to ascertain the possible value of purchased simultaneously.This processing uses data mining.Database buildup in computerized systems is justified by getting added value from this data set.Data mining can predicts trends and therefore the nature of business behavior which is extremely useful to support important deciding.The algorithm wont to form the association rules during this study is CT-Pro.CT-Pro algorithm may be a development of FP-Growth.The difference is within the second step where FP-Growth creates the FP-Tree arrangement while CT-Pro makes the Compressed FP-Tree (CFP-Tree) arrangement.The CT-Pro algorithm process by analyzing employing a tree system where the things most frequently purchased become root and other items will follow the basis.The CFP-Tree process will provide levels for every transaction and facilitate mining results.CT-Pro algorithm implementation with CFP-Tree arrangement applied to data mining systems is in a position to research sales data for 3 months, namely January 2020 -March 2020 with a complete data record of 1.303 and 320 sales transactions at minimarket GP become information or knowledge.The results of this study are the relationship between the tendency of products that are bought together based on categories in a kind of percentage to be used as a recommendation in structuring the position of items that are mutually frequent in certain categories.
Weakly-supervised learning for catheter segmentation in 3D frustum ultrasound

Accurate and efficient catheter segmentation in 3D ultrasound (US) is essential for ultrasound-guided cardiac interventions. State-of-the-art segmentation algorithms, based on convolutional neural networks (CNNs), suffer from high computational cost and large 3D data size for GPU implementation, which are far from satisfactory for real-time applications. In this paper, we propose a novel approach for efficient catheter segmentation in 3D US. Instead of using Cartesian US, our approach performs catheter segmentation in Frustum US (i.e., the US data before scan conversion). Compared to Cartesian US, Frustum US has a much smaller volume size, therefore the catheter can be segmented more efficiently in Frustum US. However, annotating the irregular and deformed Frustum images is challenging, and it is laborious to obtain the voxel-level annotation. To address this, we propose a weakly supervised learning framework, which requires only bounding-box annotations. The labels of the voxels are generated by incorporating class activation maps with line filtering, which are iteratively updated during the training cycles. Our experimental results show that, compared to Cartesian US, the catheter can be segmented much more efficiently in Frustum US (i.e., 0.25 s per volume) with better accuracy. Extensive experiments also validate the effectiveness of the proposed weakly supervised learning method.
Factors Influencing Adoption of Cryptocurrency-Based Transaction from an Islamic Perspective

This paper presents a user study of “perception of the cryptocurrency-based transaction fromthe Islamic views”. The motivation lies with the fact that some users of cryptocurrency-based transactionraised concern on the nature of transactions with Bitcoin. Specifically, some argued that Bitcoin can beeasily used for illegal purposes. Therefore, “Technological Acceptance Model” was adopted andquantitative research methodology was utilized, to formulate and test some hypothesis that will lead to anestablishment of a model. Sample of 306 participants was used in the study. The result of the hypothesistesting indicates that “Behavioral Intention to Use Cryptocurrency from the Islamic perspective” isinfluenced directly by Shari’ah Compliance, Perceived Ease of Use, Emotionality, Perceived Usefulness,and Financial Concern. As evident from the analysis, Emotionality is influenced directly by Financialconcern and Shari’ah Compliance. Whereas, Behavioral Intention is influenced indirectly by FinancialConcern. The sample is general and does not specify a specific group of study. This study hascontributed to understanding the Islamic issues behind the implementation of Cryptocurrency. This studyadopted
Particle Swarm Optimization for Predicting the Development Effort of Software Projects

Software project planning includes as one of its main activities software development effort prediction (SDEP). Effort (measured in person-hours) is useful to budget and bidding the projects. It corresponds to one of the variables most predicted, actually, hundreds of studies on SDEP have been published. Therefore, we propose the application of the Particle Swarm Optimization (PSO) metaheuristic for optimizing the parameters of statistical regression equations (SRE) applied to SDEP. Our proposal incorporates two elements in PSO: the selection of the SDEP model, and the automatic adjustment of its parameters. The prediction accuracy of the SRE optimized through PSO (PSO-SRE) was compared to that of a SRE model. These models were trained and tested using eight data sets of new and enhancement software projects obtained from an international public repository of projects. Results based on statistically significance showed that the PSO-SRE was better than the SRE in six data sets at 99% of confidence, in one data set at 95%, and statistically equal than SRE in the remaining data set. We can conclude that the PSO can be used for optimizing SDEP equations taking into account the type of development, development platform, and programming language type of the projects.
Can a Chatbot Increase the Motivation to Provide Personal Health Information?

In healthcare settings, questionnaires are used to collect information from a patient. A standard method for this are paper-based questionnaires, but they are often complex to understand or long and frustrating to fill. To increase motivation, we developed a chatbot-based system Ana that asks questions that are normally asked using paper forms or in face-to-face encounters. Ana has been developed for the specific use case of collecting the music biography in the context of music therapy. In this paper, we compare user motivation, relevance of answers and time needed to answer the questions depending on the data entry method (i.e. app Ana versus paper-based questionnaire). A randomised trial was performed with 26 students of music therapy. The results show that the chatbot is more motivating and answers are given faster than on paper. No differences in answer relevance could be determined between the two means. We conclude that a chatbot could become an additional data entry method for collecting personal health information.
Blockchain and Collaborative Intelligence based next generation Smart Toll Application

The confluence of Internet of Things(IoT) , Blockchain(BC) and Artificial Intelligence(AI) acts as a key accelerator for enabling Machine Economy. To be ready for future businesses these technologies needs to be adapted by extending the IoT capabilities to Economy of Things (EoT) capabilities. In this paper we focus on one such implementation experience for Smart Toll Transaction application in the domain of mobility. Our paper showcases a possible solution by leveraging negotiations, decision making, distributed learning capabilities at the devices level using AI-enabled Multi-Agent Systems and the real-time smart contracts between the Cars and Tolls using Blockchain. This solution also showcases the monetization of real time data coming from various IoT devices which are part of vehicles and infrastructure. While blockchain secures the privacy of the participants it also acts as an economic transactional layer and governance layer between the devices in the network.
THE SHORTENING AND THE AUTOMATION OF PAYMENTS: THE POTENTIALITY OF SMART CONTRACT IN THE AECO SECTOR

The research aims to implement Blockchain in Smart Contract in the design phase to reduce the financial exposition of SMEs. The adoption of Smart Contract, streamlining and accelerating the payments, shows up as a potential revolution of the traditional contractual framework. However, due to the increasing fragmentation and project complexity, the AECO sector is accused of slowness in accommodating and propagating new technologies. The Smart Contract can embed a Blockchain protocol that executes the predefined and agreed clauses by the contracting parties automatically and autonomously. It is a smooth and predictable data-driven process in which, at the after each clause, the payment is automatically issued, ensuring the shortening of payments. In the construction sector, the transaction delay represents a relevant problem that is responsible for disputes and economic disadvantages for the SMEs. Therefore, the paper investigates the process of digitalization starting from Smart Contract. The research is applied to the design phase developed in a BIM environment, pointing out both the advantages and the limitations, especially in the actual legislation. The Smart Contract features allow the entire supply chain to be protected against insolvencies due to late payments, improving process efficiency, and reducing payout times. From the results obtained in this research, the approach could be applied to a real case study and extended to the overall construction process.
Conditioning Factors in the Integration of Technology in the Teaching of Portuguese Non-Native Language: A Post-COVID 19 Reflection for the Current Training of Teachers

This presentation is the result of a mixed research and part of a PHD investigation, aimed to understand whether in the process of integrating technology in teaching Portuguese as a Non-Native Language (PNNL), external conditions such as age, time and teacher experience justify the low use of digital resources in language teaching, even though we live in the digital era currently modulated by the COVID-19 pandemic. It was also sought to understand whether training (initial and continuous) endows the teacher with skills to integrate technology in teaching. Data were obtained from 101 PNNL teachers with experience in Portugal and abroad. The results showed that training has no influence on the use of digital resources in education; age and experience influence low utilization due to lack of confidence and teacher’s attitudes; and, regarding time, it was noticed that these digital resources could be facilitating elements in time management. It was concluded that we should focus heavily on CALL training for PNNL teaching, in particular due to the standardization that occurred in distance learning, caused by COVID-19, in order to provide teachers with digital literacy, thus enhancing greater time management, greater confidence and a more adjusted attitude to socio-constructivism.
Automatic generation of minimum dataset and quality indicators from data collected routinely by the clinical information system in an intensive care unit

Quality indicators (QIs) are being increasingly used in medicine to compare and improve the quality of care delivered. The feasibility of data collection is an important prerequisite for QIs. Information technology can improve efforts to measure processes and outcomes. In intensive care units (ICU), QIs can be automatically measured by exploiting data from clinical information systems (CIS). To describe the development and application of a tool to automatically generate a minimum dataset (MDS) and a set of ICU quality metrics from CIS data. We used the definitions for MDS and QIs proposed by the Spanish Society of Critical Care Medicine and Coronary Units. Our tool uses an extraction, transform, and load process implemented with Python to extract data stored in various tables in the CIS database and create a new associative database. This new database is uploaded to Qlik Sense, which constructs the MDS and calculates the QIs by applying the required metrics. The tool was tested using data from patients attended in a 30-bed polyvalent ICU during a six-year period. We describe the definitions and metrics, and we report the MDS and QI measurements obtained through the analysis of 4546 admissions. The results show that our ICU’s performance on the QIs analyzed meets the standards proposed by our national scientific society. This is the first step toward using a tool to automatically obtain a set of actionable QIs to monitor and improve the quality of care in ICUs, eliminating the need for professionals to enter data manually, thus saving time and ensuring data quality.
Secure Learning Management System Based on User Behavior

The COVID-19 outbreak is an international problem and has affected people and students all over the world. When lockdowns were imposed internationally, learning management systems began to be used more than in the previous period. These systems have been used also for traditional forms of learning and not only for online learning. This pandemic has highlighted the need for online learning systems in the educational environment, but it is very important for these systems to be secure and to verify the authenticity of the students when they access a course or evaluation questions. In this period, everything is moving towards the digital world, with students that are connected from a distance to online systems. All activities in the educational environment will soon be performed digitally on learning management systems, which includes also the evaluation process of the students. In this paper, we propose a secure learning management system that uses the student’s behavior to identify if they are an authentic student or not. This system can support the teacher’s activities in the learning process and verify the authenticity of the students logged on to the system. This paper is aimed at learning management system developers, who can use the proposed algorithms in their developed platforms, and also at teachers, who should understand the importance of the identification of students on these platforms.
A Novel Deep CNN Method Based on Aesthetic Rule for User Preferential Images Recommendation

ABSTRACTHow to quickly and effectively search for users’ preferential images among numerous image information resources has become an important problem to be solved in the field of image recommendation. The traditional method improves the performance of personalized image search, but the descriptive information or label setting for the image is subjective to the user. In this paper, we propose an aesthetic rule-oriented deep CNN (convolutional neural network) method for user preferential images recommendation. The image selected by the user is used to represent the user’s preference, and then the deep CNN is adopted to extract the image features to fully capture the inherent diversity of the image. Meanwhile, the image aesthetics rule calculation method is also used to calculate the aesthetic characteristics of the image to ensure that the user’s aesthetic characteristics are satisfied. Experiments show that this proposed algorithm is an effective recommendation method for aesthetic preference.
Neural Text-to-Speech with a Modeling-by-Generation Excitation Vocoder

This paper proposes a modeling-by-generation (MbG) excitation vocoder for a neural text-to-speech (TTS) system. Recently proposed neural excitation vocoders can realize qualified waveform generation by combining a vocal tract filter with a WaveNet-based glottal excitation generator. However, when these vocoders are used in a TTS system, the quality of synthesized speech is often degraded owing to a mismatch between training and synthesis steps. Specifically, the vocoder is separately trained from an acoustic model front-end. Therefore, estimation errors of the acoustic model are inevitably boosted throughout the synthesis process of the vocoder back-end. To address this problem, we propose to incorporate an MbG structure into the vocoder's training process. In the proposed method, the excitation signal is extracted by the acoustic model's generated spectral parameters, and the neural vocoder is then optimized not only to learn the target excitation's distribution but also to compensate for the estimation errors occurring from the acoustic model. Furthermore, as the generated spectral parameters are shared in the training and synthesis steps, their mismatch conditions can be reduced effectively. The experimental results verify that the proposed system provides high-quality synthetic speech by achieving a mean opinion score of 4.57 within the TTS framework.